diff --git a/.github/workflows/pr.yaml b/.github/workflows/pr.yaml index 6c1724f7c..0cd0daad9 100644 --- a/.github/workflows/pr.yaml +++ b/.github/workflows/pr.yaml @@ -4,7 +4,7 @@ env: GALAXY_FORK: galaxyproject GALAXY_BRANCH: release_21.09 MAX_CHUNKS: 4 - MAX_FILE_SIZE: 1M + MAX_FILE_SIZE: 20M concurrency: # group runs by PR, but keep runs on master separate # because we do not want to cancel toolshed uploads @@ -191,7 +191,7 @@ jobs: - name: lintr run: | library(lintr) - linters <- with_defaults(line_length_linter = NULL, cyclocomp_linter = NULL, object_usage_linter = NULL) + linters <- linters_with_defaults(line_length_linter = NULL, cyclocomp_linter = NULL, object_usage_linter = NULL) con <- file("repository_list.txt", "r") status <- 0 while (TRUE) { @@ -199,7 +199,7 @@ jobs: if (length(repo) == 0) { break } - lnt <- lint_dir(repo, relative_path=T, linters=linters) + lnt <- lint_dir(repo, relative_path=T, linters=linters, exclusions=list("tools/SRS_tools/comparison_div.r"=c(153, 160))) if (length(lnt) > 0) { status <- 1 for (l in lnt) { diff --git a/tools/SRS_tools/.shed.yml b/tools/SRS_tools/.shed.yml new file mode 100644 index 000000000..4d0c3a48b --- /dev/null +++ b/tools/SRS_tools/.shed.yml @@ -0,0 +1,14 @@ +categories: + - Ecology +owner: ecology +remote_repository_url: https://github.com/Marie59/Sentinel_2A/SRS_tools +long_description: | + Compute biodiveristy indicators from Sentinel 2 remote sensing data +type: unrestricted +auto_tool_repositories: + name_template: "{{ tool_id }}" + description_template: "Wrapper for spatial remote sensing tool: {{ tool_name }}." +suite: + name: "Sentinel_2A" + description: "A suite of indicators for remote sensing data" + type: unrestricted diff --git a/tools/SRS_tools/Lib_preprocess_S2.r b/tools/SRS_tools/Lib_preprocess_S2.r new file mode 100644 index 000000000..2b3074106 --- /dev/null +++ b/tools/SRS_tools/Lib_preprocess_S2.r @@ -0,0 +1,1335 @@ +# == == == == == == == == == == == == == == == == == == == == == == == == == == == +# preprocS2 +# Lib_preprocess_S2.R +# == == == == == == == == == == == == == == == == == == == == == == == == == == == +# PROGRAMMERS: +# Jean-Baptiste FERET +# Copyright 2021/08 Jean-Baptiste FERET +# == == == == == == == == == == == == == == == == == == == == == == == == == == == +# This Library contains functions to preprocess Sentinel-2 images downloaded from +# different data hubs, such as THEIA, PEPS or SCIHUB +# == == == == == == == == == == == == == == == == == == == == == == == == == == == + +#" This function adjusts information from ENVI header +#" +#" @param dsn character. path where to store the stack +#" @param bands list. should include "bandname", and if possible "wavelength" +#" @param sensor character. Name of the sensor used to acquire the image +#" @param stretch boolean. Set TRUE to get 10% stretching at display for reflectance, mentioned in hdr only +#" +#" @return None +#" @importFrom utils read.table +#" @importFrom raster hdr raster +#" @export +adjust_envi_hdr <- function(dsn, bands, sensor = "Unknown", stretch = FALSE) { + + # Edit hdr file to add metadata + hdr <- read_envi_header(get_hdr_name(dsn)) + hdr$`band names` <- bands$bandname + if (length(bands$wavelength) == length(bands$bandname)) { + hdr$wavelength <- bands$wavelength + }else { + hdr$wavelength <- NULL + } + if (stretch == TRUE) { + hdr$`default stretch` <- "0.000000 1000.000000 linear" + } + hdr$`z plot range` <- NULL + hdr$`data ignore value` <- "-Inf" + hdr$`sensor type` <- sensor + write_envi_header(hdr = hdr, hdrpath = get_hdr_name(dsn)) + + # remove unnecessary files + file2remove <- paste(dsn, ".aux.xml", sep = "") + if (file.exists(file2remove)) file.remove(file2remove) + file2remove <- paste(dsn, ".prj", sep = "") + if (file.exists(file2remove)) file.remove(file2remove) + file2remove <- paste(dsn, ".stx", sep = "") + if (file.exists(file2remove)) file.remove(file2remove) + return(invisible()) +} + +#" This function saves reflectance files +#" +#" @param s2sat character. Sentinel-2 mission ("2A" or "2B") +#" @param tile_s2 character. S2 tile name (2 numbers + 3 letters) +#" @param dateacq_s2 double. date of acquisition +#" +#" @return s2mission character. name of the S2 mission (2A or 2B) +#" @importFrom sen2r safe_getMetadata check_scihub_connection s2_list +#" @export +check_s2mission <- function(s2sat, tile_s2, dateacq_s2) { + + # is mission already defined by user? + if (!is.null(s2sat)) { + if (s2sat == "2A") { + s2mission <- "2A" + }else if (s2sat == "2B") { + s2mission <- "2B" + }else { + message("Could not identify if image from Sentinel-2A or -2B") + message("Defining central wavelength of spectral bands based on S2A") + s2mission <- "2A" + } + }else { + message("Could not identify if image from Sentinel-2A or -2B") + message("Defining central wavelength of spectral bands based on S2A") + s2mission <- "2A" + } + return(s2mission) +} + +#" this function aims at computing directory size +#" @param path character. path for directory +#" @param recursive boolean . set T if recursive +#" +#" @return size_files numeric. size in bytes +#" - image stack +#" - path for individual band files corresponding to the stack +#" - path for vector (reprojected if needed) +#" +#" @importFrom raster raster +#" @importFrom tools file_path_sans_ext file_ext +#" @export +dir_size <- function(path, recursive = TRUE) { + stopifnot(is.character(path)) + files <- list.files(path, full.names = TRUE, recursive = recursive) + vect_size <- sapply(files, function(x) file.size(x)) + size_files <- sum(vect_size) + return(size_files) +} + +#" This function reads S2 data from L2A directories downloaded from +#" various data hubs including THEIA, PEPS & SCIHUB (SAFE format & LaSRC) +#" @param path_dir_s2 character. path for S2 directory +#" @param path_vector character. path for vector file +#" @param s2source character. type of directory format (depends on atmospheric correction: SAFE produced from Sen2Cor) +#" @param resolution numeric. buffer applied to vector file (in meters) +#" @param interpolation character. method for resampling. default = "bilinear" +#" @param fre_sre character. SRE or FRE products from THEIA +#" +#" @return listout list. +#" - image stack +#" - path for individual band files corresponding to the stack +#" - path for vector (reprojected if needed) +#" +#" @importFrom raster raster +#" @importFrom tools file_path_sans_ext file_ext +#" @export +extract_from_s2_l2a <- function(path_dir_s2, path_vector = NULL, s2source = "SAFE", + resolution = 10, interpolation = "bilinear", fre_sre = "FRE") { + # Get list of paths corresponding to S2 bands and depending on S2 directory + s2_bands <- get_s2_bands(path_dir_s2 = path_dir_s2, + s2source = s2source, + resolution = resolution, + fre_sre = fre_sre) + + if (length(s2_bands$s2bands_10m) > 0) { + rastmp <- raster::raster(s2_bands$s2bands_10m[[1]]) + } else if (length(s2_bands$s2bands_20m) > 0) { + rastmp <- raster::raster(s2_bands$s2bands_20m[[1]]) + } + # check if vector and raster share the same projection. if not, re-project vector + if (!is.null(path_vector)) { + raster_proj <- raster::projection(rastmp) + path_vector_reproj <- paste(tools::file_path_sans_ext(path_vector), "_reprojected.shp", sep = "") + path_vector <- reproject_shp(path_vector_init = path_vector, + newprojection = raster_proj, + path_vector_reproj = path_vector_reproj) + } + # Extract data corresponding to the vector footprint (if provided) & resample data if needed + if (length(s2_bands$s2bands_10m) > 0) { + stack_10m <- read_s2bands(s2_bands = s2_bands$s2bands_10m, path_vector = path_vector, + resampling = 1, interpolation = interpolation) + } + if (length(s2_bands$s2bands_20m) > 0) { + if (resolution == 10 && s2source != "LaSRC") { + resampling <- 2 + }else { + resampling <- 1 + } + stack_20m <- read_s2bands(s2_bands = s2_bands$s2bands_20m, path_vector = path_vector, + resampling = resampling, interpolation = interpolation) + } + # get full stack including 10m and 20m spatial resolution + if (length(s2_bands$s2bands_10m) > 0 && length(s2_bands$s2bands_20m) > 0) { + diffxstart <- attributes(stack_10m)$dimensions[[1]]$from - attributes(stack_20m)$dimensions[[1]]$from + diffxstop <- attributes(stack_10m)$dimensions[[1]]$to - attributes(stack_20m)$dimensions[[1]]$to + diffystart <- attributes(stack_10m)$dimensions[[2]]$from - attributes(stack_20m)$dimensions[[2]]$from + diffystop <- attributes(stack_10m)$dimensions[[2]]$to - attributes(stack_20m)$dimensions[[2]]$to + if (!diffxstop == 0) { + # size of 20m > size of 10m --> reduce 20m + # size of 10m > size of 20m --> reduce 10m + if (diffxstop > 0) { + stack_10m <- stack_10m[, 1:(dim(stack_10m)[1] - diffxstop), , ] + }else if (diffxstop < 0) { + stack_20m <- stack_20m[, 1:(dim(stack_20m)[1] + diffxstop), , ] + } + } + if (!diffystop == 0) { + if (diffystop > 0) { + stack_10m <- stack_10m[, , 1:(dim(stack_10m)[2] - diffystop), ] + }else if (diffystop < 0) { + stack_20m <- stack_20m[, , 1:(dim(stack_20m)[2] + diffystop), ] + } + } + if (!diffxstart == 0) { + if (diffxstart > 0) { + stack_20m <- stack_20m[, (1 + diffxstart):dim(stack_20m)[1], , ] + }else if (diffxstart < 0) { + stack_10m <- stack_10m[, (1 - diffxstart):dim(stack_10m)[1], , ] + } + } + if (!diffystart == 0) { + if (diffystart > 0) { + stack_20m <- stack_20m[, , (1 + diffystart):dim(stack_20m)[2], ] + }else if (diffystart < 0) { + stack_10m <- stack_10m[, , (1 - diffystart):dim(stack_10m)[2], ] + } + } + # reorder bands with increasing wavelength + s2bands <- c("B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B11", "B12", "Cloud") + namebands <- c(names(s2_bands$s2bands_10m), names(s2_bands$s2bands_20m)) + reorder_bands <- match(s2bands, namebands) + namebands <- namebands[reorder_bands] + listfiles <- c(stack_10m$attr, stack_20m$attr)[reorder_bands] + + # adjust size to initial vector footprint without buffer + # --> buffer is needed in order to ensure that extraction following + # footprint of vector matches for images of different spatial resolution + # get bounding box corresponding to footprint of image or image subset + bb_xycoords <- get_bb(path_raster = listfiles[1], + path_vector = path_vector, buffer = 0) + + # prepare reading data for extent defined by bounding box + nxoff <- bb_xycoords$UL$col + nyoff <- bb_xycoords$UL$row + nxsize <- bb_xycoords$UR$col - bb_xycoords$UL$col + 1 + nysize <- bb_xycoords$LR$row - bb_xycoords$UR$row + 1 + nbufxsize <- nxsize + nbufysize <- nysize + s2_stack <- stars::read_stars(listfiles, along = "band", + RasterIO = list(nXOff = nxoff, nYOff = nyoff, + nXSize = nxsize, nYSize = nysize, + nBufXSize = nbufxsize, nBufYSize = nbufysize, + resample = "nearest_neighbour"), proxy = TRUE) + + + names(s2_stack$attr) <- namebands + }else if (length(s2_bands$s2bands_10m) > 0) { + s2_stack <- stack_10m + namebands <- names(s2_bands$s2bands_10m) + names(s2_stack$attr) <- namebands + }else if (length(s2_bands$s2bands_20m) > 0) { + s2_stack <- stack_20m + namebands <- names(s2_bands$s2bands_20m) + names(s2_stack$attr) <- namebands + } + + listout <- list("s2_stack" = s2_stack, "s2_bands" = s2_bands, "path_vector" = path_vector, + "namebands" = namebands) + return(listout) +} + +#" This function gets coordinates of a bounding box defined by a vector (optional) and a raster +#" +#" @param path_raster character. path for raster file +#" @param path_vector character. path for vector file +#" @param buffer numeric. buffer applied to vector file (in meters) +#" +#" @return bb_xycoords list. Coordinates (in pixels) of the upper/lower right/left corners of bounding box +#" @export +get_bb <- function(path_raster, path_vector = NULL, buffer = 0) { + + if (!is.null(path_vector)) { + # get bounding box with a 50m buffer in order to allow for interpolation + bb_xycoords <- get_bb_from_vector(path_raster = path_raster, + path_vector = path_vector, + buffer = buffer) + }else if (is.null(path_vector)) { + bb_xycoords <- get_bb_from_fullimage(path_raster) + } + return(bb_xycoords) +} + +#" This function gets extreme coordinates of a bounding box corresponding to a full image +#" +#" @param path_raster character. path for raster file +#" +#" @return bb_xycoords list. Coordinates (in pixels) of the upper/lower right/left corners of bounding box +#" @importFrom raster raster +#" @export +get_bb_from_fullimage <- function(path_raster) { + # get raster coordinates corresponding to Full image + rasterobj <- raster::raster(path_raster) + bb_xycoords <- list() + bb_xycoords[["UL"]] <- data.frame("row" = 1, "col" = 1) + bb_xycoords[["UR"]] <- data.frame("row" = 1, "col" = dim(rasterobj)[2]) + bb_xycoords[["LL"]] <- data.frame("row" = dim(rasterobj)[1], "col" = 1) + bb_xycoords[["LR"]] <- data.frame("row" = dim(rasterobj)[1], "col" = dim(rasterobj)[2]) + return(bb_xycoords) +} + +#" This gets bounding box corresponding to a vector from a raster (UL, UR, LL, LR corners) +#" +#" @param path_raster character. path for raster file +#" @param path_vector character. path for vector file +#" @param buffer numeric. buffer applied to vector file (in meters) +#" +#" @return bb_xycoords list. Coordinates (in pixels) of the upper/lower right/left corners of bounding box +#" @importFrom sf st_read st_bbox st_crop +#" @importFrom rgeos gbuffer bbox2SP +#" @importFrom sp SpatialPoints bbox +#" @importFrom raster projection extract extent raster +#" @importFrom methods as +#" @export +get_bb_from_vector <- function(path_raster, path_vector, buffer = 0) { + + data_raster <- raster::raster(path_raster) + # extract BB coordinates from vector + bb_vector <- rgeos::gbuffer(spgeom = as(sf::st_read(dsn = path_vector, quiet = TRUE), "Spatial"), + width = buffer, byid = TRUE) + # extract BB coordinates from raster + bb_raster <- rgeos::bbox2SP(bbox = bbox(data_raster)) + # compute intersection + intersect <- rgeos::gIntersection(bb_vector, bb_raster) + bbext <- raster::extent(intersect) + xmin <- bbext[1] + xmax <- bbext[2] + ymin <- bbext[3] + ymax <- bbext[4] + # get coordinates of bounding box corresponding to vector + corners <- list() + corners[["UR"]] <- sp::SpatialPoints(coords = cbind(xmax, ymax)) + corners[["LR"]] <- sp::SpatialPoints(coords = cbind(xmax, ymin)) + corners[["UL"]] <- sp::SpatialPoints(coords = cbind(xmin, ymax)) + corners[["LL"]] <- sp::SpatialPoints(coords = cbind(xmin, ymin)) + raster::projection(corners[["UL"]]) <- raster::projection(corners[["UR"]]) <- + raster::projection(corners[["LL"]]) <- raster::projection(corners[["LR"]]) <- + raster::projection(sf::st_read(dsn = path_vector, quiet = TRUE)) + # get coordinates for corners of bounding box + bb_xycoords <- list() + for (corner in names(corners)) { + ex_df <- as.data.frame(raster::extract(data_raster, corners[[corner]], cellnumbers = TRUE)) + colrow <- ind2sub(data_raster, ex_df$cell) + bb_xycoords[[corner]] <- data.frame("row" = colrow$row, "col" = colrow$col) + } + return(bb_xycoords) +} + +#" get hdr name from image file name, assuming it is BIL format +#" +#" @param impath path of the image +#" +#" @return corresponding hdr +#" @import tools +#" @export +get_hdr_name <- function(impath) { + if (tools::file_ext(impath) == "") { + impathhdr <- paste(impath, ".hdr", sep = "") + }else if (tools::file_ext(impath) == "bil") { + impathhdr <- gsub(".bil", ".hdr", impath) + }else if (tools::file_ext(impath) == "zip") { + impathhdr <- gsub(".zip", ".hdr", impath) + }else { + impathhdr <- paste(tools::file_path_sans_ext(impath), ".hdr", sep = "") + } + + if (!file.exists(impathhdr)) { + message("WARNING : COULD NOT FIND hdr FILE") + print(impathhdr) + message("Process may stop") + } + return(impathhdr) +} + +#" This function returns path for the spectral bands to be used +#" +#" @param path_dir_s2 character. Path for the directory containing S2 data. either L2A .SAFE S2 file or THEIA directory +#" @param s2source character. defines if data comes from SciHub as SAFE directory, from THEIA or from LaSRC +#" @param resolution numeric. spatial resolution of the final image: 10m or 20m +#" @param fre_sre character. SRE or FRE products from THEIA +#" +#" @return listbands list. contains path for spectral bands corresponding to 10m and 20m resolution +#" @export +get_s2_bands <- function(path_dir_s2, s2source = "SAFE", resolution = 10, fre_sre = "FRE") { + + if (s2source == "SAFE" || s2source == "Sen2Cor") { + listbands <- get_s2_bands_from_sen2cor(path_dir_s2 = path_dir_s2, resolution = resolution) + }else if (s2source == "THEIA") { + listbands <- get_s2_bands_from_theia(path_dir_s2 = path_dir_s2, resolution = resolution, + fre_sre = fre_sre) + }else if (s2source == "LaSRC") { + listbands <- get_s2_bands_from_lasrc(path_dir_s2 = path_dir_s2, resolution = resolution) + }else { + message("The data source (Atmospheric correction) for Sentinel-2 image is unknown") + message("Please provide S2 images from one of the following data sources:") + message("- LaSRC (atmospheric correction: LaSRC)") + message("- THEIA (atmospheric correction: MAJA)") + message("- SAFE (atmospheric correction: Sen2Cor)") + s2bands_10m <- s2bands_20m <- granule <- mtdfile <- metadata_msi <- metadata_lasrc <- NULL + listbands <- list("s2bands_10m" = s2bands_10m, "s2bands_20m" = s2bands_20m, "GRANULE" = granule, + "metadata" = mtdfile, "metadata_MSI" = metadata_msi, + "metadata_lasrc" = metadata_lasrc) + } + return(listbands) +} + +#" This function returns path for the spectral bands in SAFE / sen2Cor directory +#" +#" @param path_dir_s2 character. Path for the SAFE directory containing S2 data +#" @param resolution numeric. spatial resolution of the final image: 10m or 20m +#" +#" @return listbands list. contains path for spectral bands corresponding to 10m and 20m resolution, as well name of as granule +#" @export +get_s2_bands_from_sen2cor <- function(path_dir_s2, resolution = 10) { + # build path for all bands + if (resolution == 10) { + b10m <- c("B02", "B03", "B04", "B08") + b20m <- c("B05", "B06", "B07", "B8A", "B11", "B12") + }else { + b10m <- c() + b20m <- c("B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B11", "B12") + } + # get granule directory & path for corresponding metadata XML file + granule <- list.dirs(list.dirs(path_dir_s2, recursive = FALSE)[grep(pattern = "GRANULE", + x = list.dirs(path_dir_s2, recursive = FALSE))], recursive = FALSE) + mtdfile <- file.path(granule, "MTD_TL.xml") + if (file.exists(file.path(path_dir_s2, "MTD_MSIL2A.xml"))) { + mtd_msi_file <- file.path(path_dir_s2, "MTD_MSIL2A.xml") + } else { + mtd_msi_file <- NULL + } + + # Define path for bands + s2bands_20m_dir <- file.path(granule, "IMG_DATA", "R20m") + s2bands_10m_dir <- file.path(granule, "IMG_DATA", "R10m") + s2bands_10m <- s2bands_20m <- list() + for (band in b20m) { + s2bands_20m[[band]] <- file.path(s2bands_20m_dir, list.files(s2bands_20m_dir, pattern = band)) + } + for (band in b10m) { + s2bands_10m[[band]] <- file.path(s2bands_10m_dir, list.files(s2bands_10m_dir, pattern = band)) + } + # get cloud mask + cloud <- "MSK_CLDPRB_20m" + cloud_20m_dir <- file.path(granule, "QI_DATA") + s2bands_20m[["Cloud"]] <- file.path(cloud_20m_dir, list.files(cloud_20m_dir, pattern = cloud)) + listbands <- list("s2bands_10m" = s2bands_10m, + "s2bands_20m" = s2bands_20m, + "GRANULE" = granule, + "metadata" = mtdfile, + "metadata_MSI" = mtd_msi_file, + "metadata_lasrc" = NULL) + return(listbands) +} + +#" This function returns path for the spectral bands in LaSRC directory +#" +#" @param path_dir_s2 character. Path for the SAFE directory containing S2 data +#" @param resolution numeric. spatial resolution of the final image: 10m or 20m +#" +#" @return listbands list. contains path for spectral bands corresponding to 10m and 20m resolution, as well name of as granule +#" @importFrom stringr str_subset +#" @export +get_s2_bands_from_lasrc <- function(path_dir_s2, resolution = 10) { + + # get granule directory & path for corresponding metadata XML file + granule <- path_dir_s2 + mtdfile <- file.path(granule, "MTD_TL.xml") + if (file.exists(file.path(path_dir_s2, "MTD_MSIL1C.xml"))) { + mtd_msi_file <- file.path(path_dir_s2, "MTD_MSIL1C.xml") + } else { + mtd_msi_file <- NULL + } + + # build path for all bands + b10m <- c("band2", "band3", "band4", "band5", "band6", "band7", "band8", "band8a", "band11", "band12") + b10m_standard <- c("B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B11", "B12") + # Define path for bands + s2bands_10m <- s2bands_20m <- list() + for (i in 1:seq_along(b10m)) { + s2bands_10m[[b10m_standard[i]]] <- file.path(path_dir_s2, + list.files(path_dir_s2, + pattern = paste(b10m[i], ".tif", sep = ""))) + } + + # get metadata file containing offset + mtd_lasrc <- str_subset(list.files(path_dir_s2, pattern = "S2"), ".xml$") + if (file.exists(file.path(path_dir_s2, mtd_lasrc))) { + metadata_lasrc <- file.path(path_dir_s2, mtd_lasrc) + } else { + metadata_lasrc <- NULL + } + # get cloud mask + cloud <- "CLM" + s2bands_10m[["Cloud"]] <- file.path(path_dir_s2, list.files(path_dir_s2, pattern = cloud)) + listbands <- list("s2bands_10m" = s2bands_10m, + "s2bands_20m" = s2bands_20m, + "GRANULE" = granule, + "metadata" = mtdfile, + "metadata_MSI" = mtd_msi_file, + "metadata_lasrc" = metadata_lasrc) + return(listbands) +} + +#" This function returns path for the spectral bands in THEIA directory +#" +#" @param path_dir_s2 character. Path for the SAFE directory containing S2 data +#" @param resolution numeric. spatial resolution of the final image: 10m or 20m +#" @param fre_sre character. SRE or FRE products from THEIA +#" +#" @return listbands list. contains path for spectral bands corresponding to 10m and 20m resolution, as well name of as granule +#" @export +get_s2_bands_from_theia <- function(path_dir_s2, resolution = 10, fre_sre = "FRE") { + + # build path for all bands + if (resolution == 10) { + b10m <- c("B02", "B03", "B04", "B08") + b20m <- c("B05", "B06", "B07", "B8A", "B11", "B12") + } else { + b10m <- c() + b20m <- c("B02", "B03", "B04", "B05", "B06", "B07", "B08", "B8A", "B11", "B12") + } + + # get path_tile_s2 directory & path for corresponding metadata XML file + path_tile_s2 <- list.dirs(path_dir_s2, recursive = FALSE) + files_tile_s2 <- list.files(path_tile_s2, recursive = FALSE) + mtdfile <- file.path(path_tile_s2, files_tile_s2[grep(pattern = "MTD_ALL.xml", x = files_tile_s2)]) + + # Define path for bands + s2bands_10m_dir <- s2bands_20m_dir <- path_tile_s2 + s2bands_10m <- s2bands_20m <- list() + for (band in b20m) { + band_20m_pattern <- paste0(gsub("0", "", band), ".tif") # for THEAI band 2 is "B2" ("B02" for SAFE) + list_files_20m <- list.files(s2bands_20m_dir, pattern = band_20m_pattern) + s2bands_20m[[band]] <- file.path(s2bands_20m_dir, list_files_20m)[grep(pattern = fre_sre, + x = file.path(s2bands_20m_dir, list_files_20m))] + } + for (band in b10m) { + band_10m_pattern <- paste0(gsub("0", "", band), ".tif") # for THEAI band 2 is "B2" ("B02" for SAFE) + list_files_10m <- list.files(s2bands_10m_dir, pattern = band_10m_pattern) + s2bands_10m[[band]] <- file.path(s2bands_10m_dir, list_files_10m)[grep(pattern = fre_sre, + x = file.path(s2bands_10m_dir, list_files_10m))] + } + + # get cloud mask 10m + cloud_10m <- "CLM_R1" + cloud_10m_dir <- file.path(path_tile_s2, "MASKS") + s2bands_10m[["Cloud"]] <- file.path(cloud_10m_dir, list.files(cloud_10m_dir, pattern = cloud_10m)) + + # get cloud mask 20m + cloud_20m <- "CLM_R2" + cloud_20m_dir <- file.path(path_tile_s2, "MASKS") + s2bands_20m[["Cloud"]] <- file.path(cloud_20m_dir, list.files(cloud_20m_dir, pattern = cloud_20m)) + + # return list bands + listbands <- list("s2bands_10m" = s2bands_10m, + "s2bands_20m" = s2bands_20m, + "path_tile_s2" = path_tile_s2, + "metadata" = mtdfile) + return(listbands) +} + +#" This function check S2 data level: +#" - L2A: already atmospherically corrected +#" - L1C: requires atmospheric corrections with sen2cor +#" +#" @param prodname character. original name for the S2 image +#" +#" @return s2level character. S2 level: L1C or L2A +#" @export +get_s2_level <- function(prodname) { + prodname <- basename(prodname) + if (length(grep(pattern = "L1C_", x = prodname)) == 1) { + s2level <- "L1C" + } else if (length(grep(pattern = "L2A_", x = prodname)) == 1) { + s2level <- "L2A" + } + return(s2level) +} + +#" This function gets tile from S2 image +#" +#" @param prodname character. original name for the S2 image +#" +#" @return tilename character +#" @importFrom tools file_path_sans_ext +#" @export +get_tile <- function(prodname) { + prodname <- basename(prodname) + tilename <- tools::file_path_sans_ext(gsub("_.*", "", gsub(".*_T", "", prodname))) + return(tilename) +} + +#" This function gets acquisition date from S2 image +#" +#" @param prodname character. original name for the S2 image +#" +#" @return dateacq character +#" @export +get_date <- function(prodname) { + prodname <- basename(prodname) + dateacq <- as.Date(gsub("T.*", "", gsub(".*_20", "20", prodname)), format = "%Y%m%d") + return(dateacq) +} + +#" download S2 L1C data from Copernicus hub or Google cloud +#" +#" @param list_safe safe object. produced with sen2r::s2_list +#" @param l1c_path character. path for storage of L1C image +#" @param path_vector path for a vector file +#" @param time_interval dates. time interval for S2 query +#" @param googlecloud boolean. set to TRUE if google cloud SDK is installed and +#" @param forcegoogle boolean. set to TRUE if only google requested +#" sen2r configured as an alternative hub for S2 download +#" +#" @return prodname character. S2 Product name +#" @importFrom sen2r safe_is_online s2_list s2_download s2_order check_gcloud +#" @export +get_s2_l1c_image <- function(list_safe, l1c_path, path_vector, time_interval, + googlecloud = FALSE, forcegoogle = FALSE) { + # Check if available from Copernicus hub first + copernicus_avail <- sen2r::safe_is_online(list_safe) + # if available: download + prodname <- attr(list_safe, which = "name") + if (file.exists(file.path(l1c_path, prodname))) { + message("L1C file already downloaded") + message(file.path(l1c_path, prodname)) + } else { + if (copernicus_avail == TRUE && forcegoogle == FALSE) { + sen2r::s2_download(list_safe, outdir = l1c_path) + } else if (copernicus_avail == FALSE || forcegoogle == TRUE) { + # if not available and googlecloud==TRUE + if (googlecloud == TRUE) { + # check if google cloud SDK available from this computer + ggc <- sen2r::check_gcloud() + if (ggc == TRUE) { + message("downloading from Google cloud") + list_safe_ggc <- sen2r::s2_list(spatial_extent = sf::st_read(dsn = path_vector), + time_interval = time_interval, + server = "gcloud") + prodname <- attr(list_safe_ggc, which = "name") + if (file.exists(file.path(l1c_path, prodname))) { + message("L1C file already downloaded") + message(file.path(l1c_path, prodname)) + } else { + sen2r::s2_download(list_safe_ggc, outdir = l1c_path) + # check if QI_DATA exists in DATASTRIP, and create it if not the case + datastrip_path <- file.path(l1c_path, prodname, "DATASTRIP") + dsdir <- list.dirs(datastrip_path, recursive = FALSE) + if (length(match(list.dirs(dsdir, recursive = FALSE, full.names = FALSE), "QI_DATA")) == 0) { + dir.create(file.path(dsdir, "QI_DATA")) + } + } + } else if (ggc == FALSE) { + message("googlecloud set to TRUE but missing") + message("Please install Google cloud SDK") + message("https://cloud.google.com/sdk/docs/install") + message("and/or set configuration of sen2r following instructions") + message("https://www.r-bloggers.com/2021/06/downloading-sentinel-2-archives-from-google-cloud-with-sen2r/") + } + } + } + if (copernicus_avail == FALSE && googlecloud == FALSE) { + message("S2 image in Long Term Archive (LTA)") + message("Ordering image from LTA") + message("This may take 1 day, please run your script later") + orders2 <- sen2r::s2_order(list_safe) + message("An alternative is possible with Google cloud SDK") + message("https://cloud.google.com/sdk/docs/install") + message("and/or set configuration of sen2r following instructions") + message("https://www.r-bloggers.com/2021/06/downloading-sentinel-2-archives-from-google-cloud-with-sen2r/") + } + } + return(prodname) +} + +#" download S2 L2A data from Copernicus hub or convert L1C to L2A +#" +#" @param l2a_path character. path for storage of L2A image +#" @param spatial_extent path for a vector file +#" @param dateacq character. date of acquisition +#" @param deletel1c Boolean. set TRUE to delete L1C images +#" @param Sen2Cor Boolean. set TRUE to automatically perform atmospheric corrections using sen2Cor +#" @param googlecloud boolean. set to TRUE if google cloud SDK is installed and +#" sen2r configured as an alternative hub for S2 download +#" +#" @return pathl2a character. Path for L2A image +#" @importFrom sen2r s2_list s2_download +#" @importFrom R.utils getAbsolutePath + +#" @export +get_s2_l2a_image <- function(l2a_path, spatial_extent, dateacq, + deletel1c = FALSE, sen2cor = TRUE, + googlecloud = FALSE) { + + # Needs to be updated: define path for L1c data + l1c_path <- l2a_path + # define time interval + time_interval <- as.Date(c(dateacq, dateacq)) + # get list S2 products corresponding to study area and date of interest using sen2r package + if (googlecloud == TRUE) { + server <- c("scihub", "gcloud") + } else if (googlecloud == FALSE) { + server <- "scihub" + } + list_safe <- sen2r::s2_list(spatial_extent = sf::st_read(dsn = spatial_extent), + time_interval = time_interval, + server = server, availability = "check") + # download products + sen2r::s2_download(list_safe, outdir = l2a_path) + # name all products + prodname <- attr(list_safe, which = "name") + prodfullpath <- file.path(l2a_path, prodname) + if (sen2cor == TRUE) { + for (imgname in prodname) { + s2level <- get_s2_level(imgname) + if (s2level == "L1C") { + datepattern <- gsub(pattern = "-", replacement = "", x = dateacq) + pathl2a <- s2_from_l1c_to_l2a(prodname = imgname, l1c_path = l2a_path, l2a_path = l2a_path, + datepattern = datepattern, tmp_path = NULL) + if (deletel1c == TRUE) { + unlink(x = R.utils::getAbsolutePath(file.path(l1c_path, prodname)), + recursive = TRUE, force = TRUE) + # delete from full path and add atmospherically corrected + whichimg <- grep(x = prodfullpath, pattern = imgname) + dateacq <- get_date(imgname) + tilename <- get_tile(imgname) + pathl2a <- list.files(path = l2a_path, pattern = tilename, full.names = TRUE) + pathl2a <- pathl2a[grep(x = pathl2a, pattern = dateacq)] + pathl2a <- pathl2a[grep(x = basename(pathl2a), pattern = "L2A")] + prodfullpath[whichimg] <- pathl2a + } + } + } + } + + return(prodfullpath) +} + +#" convert image coordinates from index to X-Y +#" +#" @param Raster image raster object +#" @param image_index coordinates corresponding to the raster +ind2sub <- function(data_raster, image_index) { + c <- ((image_index - 1) %% data_raster@ncols) + 1 + r <- floor((image_index - 1) / data_raster@ncols) + 1 + my_list <- list("col" = c, "row" = r) + return(my_list) +} + +#" mosaicing a set of rasters +#" +#" @param list_rasters character. list of paths corresponding to rasters to mosaic +#" @param dst_mosaic character. path and name of mosaic produced +#" @param stretch boolean. Set TRUE to get 10% stretching at display for reflectance, mentioned in hdr only +#" +#" @return None +#" @importFrom gdalUtils mosaic_rasters +#" @importFrom raster hdr raster +#" @export +mosaic_rasters <- function(list_rasters, dst_mosaic, stretch = FALSE) { + + # produce mosaic + gdalUtils::mosaic_rasters(gdalfile = list_rasters, dst_dataset = dst_mosaic, + separate = FALSE, of = "Ehdr", verbose = TRUE) + + # convert hdr to ENVI format + raster::hdr(raster(dst_mosaic), format = "ENVI") + # add info to hdr based on initial rasters + hdr_init <- read_envi_header(get_hdr_name(list_rasters[1])) + hdr <- read_envi_header(get_hdr_name(dst_mosaic)) + hdr$`band names` <- hdr_init$`band names` + hdr$wavelength <- hdr_init$wavelength + if (stretch == TRUE) { + hdr$`default stretch` <- "0.000000 1000.000000 linear" + } + hdr$`z plot range` <- NULL + hdr$`data ignore value` <- "-Inf" + hdr$`sensor type` <- hdr_init$`sensor type` + hdr$`coordinate system string` <- read.table(paste(file_path_sans_ext(dst_mosaic), ".prj", sep = "")) + write_envi_header(hdr = hdr, hdrpath = get_hdr_name(dst_mosaic)) + return(invisible()) +} + +#" Reads ENVI hdr file +#" +#" @param hdrpath Path of the hdr file +#" +#" @return list of the content of the hdr file +#" @export +read_envi_header <- function(hdrpath) { + if (!grepl(".hdr$", hdrpath)) { + stop("File extension should be .hdr") + } + hdr <- readLines(hdrpath) + ## check ENVI at beginning of file + if (!grepl("ENVI", hdr[1])) { + stop("Not an ENVI header (ENVI keyword missing)") + } else { + hdr <- hdr [-1] + } + ## remove curly braces and put multi-line key-value-pairs into one line + hdr <- gsub("\\{([^}]*)\\}", "\\1", hdr) + l <- grep("\\{", hdr) + r <- grep("\\}", hdr) + + if (length(l) != length(r)) { + stop("Error matching curly braces in header (differing numbers).") + } + + if (any(r <= l)) { + stop("Mismatch of curly braces in header.") + } + + hdr[l] <- sub("\\{", "", hdr[l]) + hdr[r] <- sub("\\}", "", hdr[r]) + + for (i in rev(seq_along(l))) { + hdr <- c( + hdr [seq_len(l [i] - 1)], + paste(hdr [l [i]:r [i]], collapse = "\n"), + hdr [-seq_len(r [i])] + ) + } + + ## split key = value constructs into list with keys as names + hdr <- sapply(hdr, split_line, "=", USE.NAMES = FALSE) + names(hdr) <- tolower(names(hdr)) + + ## process numeric values + tmp <- names(hdr) %in% c( + "samples", "lines", "bands", "header offset", "data type", + "byte order", "default bands", "data ignore value", + "wavelength", "fwhm", "data gain values" + ) + hdr [tmp] <- lapply(hdr [tmp], function(x) { + as.numeric(unlist(strsplit(x, ", "))) + }) + + return(hdr) +} + +#" This function reads a list of files corresponding to S2 bands +#" S2 bands are expected to have uniform spatial resolution and footprint +#" @param s2_bands list. list of S2 bands obtained from get_s2_bands +#" @param path_vector path for a vector file +#" @param resampling numeric. resampling factor (default = 1, set to resampling = 2 to convert 20m into 10m resolution) +#" @param interpolation character. method for resampling. default = "bilinear" +#" +#" @return stack_s2 list. contains stack of S2 bands +#" +#" @importFrom stars read_stars +#" @importFrom sf st_bbox st_read st_crop +#" @export + +read_s2bands <- function(s2_bands, path_vector = NULL, + resampling = 1, interpolation = "bilinear") { + # get bounding box corresponding to footprint of image or image subset + bb_xycoords <- get_bb(path_raster = s2_bands[[1]], + path_vector = path_vector, buffer = 50) + + # prepare reading data for extent defined by bounding box + nxoff <- bb_xycoords$UL$col + nyoff <- bb_xycoords$UL$row + nxsize <- bb_xycoords$UR$col - bb_xycoords$UL$col + 1 + nysize <- bb_xycoords$LR$row - bb_xycoords$UR$row + 1 + nbufxsize <- resampling * nxsize + nbufysize <- resampling * nysize + if (resampling == 1) { + interpolation <- "nearest_neighbour" + } + # write interpolated individual bands in temp directory + tmpdir <- tempdir() + tmpfile <- list() + for (band in names(s2_bands)) { + stack_s2_tmp <- stars::read_stars(s2_bands[[band]], along = "band", + RasterIO = list(nXOff = nxoff, nYOff = nyoff, + nXSize = nxsize, nYSize = nysize, + nBufXSize = nbufxsize, nBufYSize = nbufysize, + resample = interpolation), proxy = FALSE) + if (!is.null(path_vector)) { + stack_s2_tmp <- sf::st_crop(x = stack_s2_tmp, y = st_bbox(st_read(dsn = path_vector, quiet = TRUE))) + } + tmpfile[[band]] <- file.path(tmpdir, tools::file_path_sans_ext(basename(s2_bands[[band]]))) + if (band == "Cloud") { + stars::write_stars(obj = stack_s2_tmp, dsn = tmpfile[[band]], + driver = "ENVI", type = "Byte", overwrite = TRUE) + } else { + stars::write_stars(obj = stack_s2_tmp, dsn = tmpfile[[band]], + driver = "ENVI", type = "Int16", overwrite = TRUE) + } + gc() + } + + stack_s2 <- stars::read_stars(tmpfile, along = "band", proxy = TRUE) + return(stack_s2) +} + +#" This function reads a raster stack, and gets footprint as pixel coordinates or vector file as input +#" @param path_raster character. path for raster file +#" @param path_vector character. path for vector file +#" @param bbpix list. coordinates of pixels corresponding to a bounding box +#" +#" @return starsobj stars object corresponding to raster or raster subset +#" +#" @importFrom stars read_stars +#" @importFrom sf st_bbox st_read st_crop +#" @export +read_raster <- function(path_raster, path_vector = NULL, bbpix = NULL) { + # get bounding box corresponding to footprint of image or image subset + if (is.null(bbpix)) { + bb_xycoords <- get_bb(path_raster = path_raster, + path_vector = path_vector, buffer = 0) + } else { + bb_xycoords <- bbpix + } + # prepare reading data for extent defined by bounding box + nxoff <- bb_xycoords$UL$col + nyoff <- bb_xycoords$UL$row + nxsize <- bb_xycoords$UR$col - bb_xycoords$UL$col + 1 + nysize <- bb_xycoords$LR$row - bb_xycoords$UR$row + 1 + nbufxsize <- nxsize + nbufysize <- nysize + starsobj <- stars::read_stars(path_raster, along = "band", + RasterIO = list(nXOff = nxoff, nYOff = nyoff, + nXSize = nxsize, nYSize = nysize, + nBufXSize = nbufxsize, nBufYSize = nbufysize), + proxy = FALSE) + return(starsobj) +} + +#" This function reprojects a shapefile and saves reprojected shapefile +#" +#" @param path_vector_init character. path for a shapefile to be reprojected +#" @param newprojection character. projection to be applied to path_vector_init +#" @param path_vector_reproj character. path for the reprojected shapefile +#" +#" @return path_vector character. path of the shapefile +#" - path_vector_init if the vector did not need reprojection +#" - path_vector_reproj if the vector needed reprojection +#" +#" @importFrom rgdal readOGR writeOGR +#" @importFrom sp spTransform +#" @importFrom raster projection +#" @export +reproject_shp <- function(path_vector_init, newprojection, path_vector_reproj) { + + dir_vector_init <- dirname(path_vector_init) + # shapefile extension + fileext <- file_ext(basename(path_vector_init)) + if (fileext == "shp") { + name_vector_init <- file_path_sans_ext(basename(path_vector_init)) + vector_init_ogr <- rgdal::readOGR(dir_vector_init, name_vector_init, verbose = FALSE) + } else if (fileext == "kml") { + vector_init_ogr <- rgdal::readOGR(path_vector_init, verbose = FALSE) + } + vector_init_proj <- raster::projection(vector_init_ogr) + + if (!vector_init_proj == newprojection) { + dir_vector_reproj <- dirname(path_vector_reproj) + name_vector_reproj <- file_path_sans_ext(basename(path_vector_reproj)) + vector_reproj <- sp::spTransform(vector_init_ogr, newprojection) + rgdal::writeOGR(obj = vector_reproj, dsn = dir_vector_reproj, layer = name_vector_reproj, + driver = "ESRI Shapefile", overwrite_layer = TRUE) + path_vector <- path_vector_reproj + } else { + path_vector <- path_vector_init + } + return(path_vector) +} + + +#" perform atmospheric corrections to convert L1C to L2A data with Sen2cor +#" +#" @param prodname character. produced with sen2r::s2_list +#" @param l1c_path character. path of directory where L1C image is stored +#" @param l2a_path character. path of directory where L2A image is stored +#" @param datepattern character. pattern corresponding to date of acquisition to identify L2A directory +#" @param tmp_path character. path of temporary directory where L2A image is stored +#" sen2r configured as an alternative hub for S2 download +#" +#" @return pathl2a character. S2 Product name +#" @importFrom sen2r safe_is_online s2_list s2_download s2_order +#" @importFrom R.utils getAbsolutePath +#" +#" @export +s2_from_l1c_to_l2a <- function(prodname, l1c_path, l2a_path, datepattern, tmp_path = NULL) { + + # define path for tmp directory + if (is.null(tmp_path)) { + tmp_path <- tempdir(check = TRUE) + } + tmp_prodlist <- prodname + # perform Sen2Cor atmospheric corrections + binpath <- sen2r::load_binpaths() + # 2- open a command prompt and directly run sen2cor with following command line + cmd <- paste(binpath$sen2cor, + "--output_dir", R.utils::getAbsolutePath(l2a_path), + R.utils::getAbsolutePath(file.path(l1c_path, prodname)), sep = " ") + system(cmd) + pathl2a <- list.files(path = l2a_path, pattern = datepattern, full.names = TRUE) + + return(pathl2a) +} + +#" This function saves cloud masks. +#" "cloudMask_Binary" is default binary mask with 0 where clouds are detected and 1 for clean pixels +#" "cloudMask_RAW" is the original cloud layer produced by atmospheric correction algorithm +#" --> may be useful to refine cloud mask +#" +#" @param s2_stars list. stars object containing raster data. Can be produced with function extract_from_s2_l2a +#" @param cloud_path character. +#" @param s2source character. +#" @param footprint character. path for vector file defining footprint of interest in the image +#" @param saveraw boolean. should the original cloud mask layer be saved? +#" @param maxchunk numeric. Size of individual chunks to be written (in Mb) +#" +#" @return list of cloudmasks (binary mask, and raw mask if required) +#" @importFrom sf st_read +#" @importFrom stars write_stars +#" @importFrom raster raster +#" @export +save_cloud_s2 <- function(s2_stars, cloud_path, s2source = "SAFE", + footprint = NULL, saveraw = FALSE, maxchunk = 256) { + + whichcloud <- which(names(s2_stars$attr) == "Cloud") + # Save cloud mask + if (saveraw == TRUE) { + cloudraw <- file.path(cloud_path, "CloudMask_RAW") + obj <- stars::read_stars(s2_stars$attr[whichcloud], proxy = TRUE) + sizeobj <- dim(obj)[1] * dim(obj)[2] / (1024**2) + nbchunks <- ceiling(sizeobj / maxchunk) + stars::write_stars(obj, + dsn = cloudraw, + driver = "ENVI", + type = "Byte", + chunk_size = c(dim(obj)[1], dim(obj)[2] / nbchunks), + progress = TRUE) + } else { + cloudraw <- NULL + } + # Save cloud mask as in biodivMapR (0 = clouds, 1 = pixel ok) + cloudmask <- stars::read_stars(s2_stars$attr[whichcloud], proxy = FALSE) + if (s2source == "SAFE" || s2source == "THEIA") { + cloudy <- which(cloudmask[[1]] > 0) + sunny <- which(cloudmask[[1]] == 0) + } else if (s2source == "LaSRC") { + cloudy <- which(is.na(cloudmask[[1]])) + sunny <- which(cloudmask[[1]] == 1) + } + + cloudmask[[1]][cloudy] <- 0 + cloudmask[[1]][sunny] <- 1 + cloudbin <- file.path(cloud_path, "CloudMask_Binary") + stars::write_stars(cloudmask, dsn = cloudbin, driver = "ENVI", type = "Byte", overwrite = TRUE) + cloudmasks <- list("BinaryMask" = cloudbin, "RawMask" = cloudraw) + # delete temporary file + file.remove(s2_stars$attr[whichcloud]) + if (file.exists(paste(s2_stars$attr[whichcloud], ".hdr", sep = ""))) file.remove(paste(s2_stars$attr[whichcloud], ".hdr", sep = "")) + gc() + return(cloudmasks) +} + +#" This function saves reflectance files +#" +#" @param s2_stars list. stars object containing raster data. Can be produced with function extract_from_s2_l2a +#" @param refl_path character. path for reflectance file to be stored +#" @param format character. file format for reflectance data +#" @param datatype character. data type (integer, float, 16bits, 32bits...) +#" @param s2sat character. Sentinel-2 mission ("2A" or "2B") +#" @param tile_s2 character. S2 tile name (2 numbers + 3 letters) +#" @param dateacq_s2 double. date of acquisition +#" @param MTD character. path for metadata file +#" @param MTD_MSI character. path for metadata MSI file +#" @param mtd_lasrc character. path for metadata LaSRC file +#" @param maxchunk numeric. Size of individual chunks to be written (in Mb) +#" +#" @return None +#" @importFrom stars write_stars st_apply +#" @importFrom XML xml +#" @export +save_reflectance_s2 <- function(s2_stars, refl_path, format = "ENVI", datatype = "Int16", + s2sat = NULL, tile_s2 = NULL, dateacq_s2 = NULL, + mtd = NULL, mtd_msi = NULL, mtd_lasrc = NULL, + maxchunk = 256) { + # identify if S2A or S2B, if possible + s2mission <- check_s2mission(s2sat = s2sat, tile_s2 = tile_s2, dateacq_s2 = dateacq_s2) + + # define central wavelength corresponding to each spectral band + if (s2mission == "2A") { + wl_s2 <- list("B02" = 496.6, "B03" = 560.0, "B04" = 664.5, + "B05" = 703.9, "B06" = 740.2, "B07" = 782.5, "B08" = 835.1, + "B8A" = 864.8, "B11" = 1613.7, "B12" = 2202.4) + } else if (s2mission == "2B") { + wl_s2 <- list("B02" = 492.1, "B03" = 559.0, "B04" = 665.0, + "B05" = 703.8, "B06" = 739.1, "B07" = 779.7, "B08" = 833.0, + "B8A" = 864.0, "B11" = 1610.4, "B12" = 2185.7) + } + if (s2mission == "2A") { + sensor <- "Sentinel_2A" + } else if (s2mission == "2B") { + sensor <- "Sentinel_2B" + } + + # apply offset when necessary + listbands_bis <- c("B2", "B3", "B4", "B5", "B6", "B7", "B8", "B8A", "B11", "B12") + if (!is.null(mtd_msi) && is.null(mtd_lasrc)) { + # read XML file containing info about geometry of acquisition + s2xml <- XML::xmlToList(mtd_msi) + xml_offset <- s2xml$General_Info$Product_Image_Characteristics$BOA_ADD_offset_VALUES_LIST + bands <- lapply(s2xml$General_Info$Product_Image_Characteristics$Spectral_Information_List, "[[", 4) + if (!is.null(xml_offset) && !is.null(bands)) { + bandid <- lapply(bands, "[[", 1) + bandname <- lapply(bands, "[[", 2) + offset <- data.frame("bandname" = unlist(bandname), + "bandid" = unlist(bandid), + "offset" = unlist(lapply(xml_offset, "[[", 1))) + selbands <- match(listbands_bis, offset$bandname) + offset <- offset[selbands, ] + boa_quantval <- as.numeric(s2xml$General_Info$Product_Image_Characteristics$QUANTIFICATION_VALUES_LIST$BOA_QUANTIFICATION_VALUE[1]) + } else { + offset <- data.frame("bandname" = listbands_bis, + "bandid" = c(1, 2, 3, 4, 5, 6, 7, 8, 11, 12), + "offset" = 0) + boa_quantval <- 10000 + } + } else if (!is.null(mtd_lasrc)) { + # read XML file containing info about geometry of acquisition + s2xml <- XML::xmlToList(mtd_lasrc) + attributes_lasrc <- s2xml$bands[[14]]$.attrs + attributes_lasrc_df <- data.frame(attributes_lasrc) + if (match("add_offset", rownames(attributes_lasrc_df)) > 0 && match("scale_factor", rownames(attributes_lasrc_df)) > 0) { + xml_offset <- as.numeric(attributes_lasrc[["add_offset"]]) + boa_quantval <- 1 / as.numeric(attributes_lasrc[["scale_factor"]]) + offset <- data.frame("bandname" = listbands_bis, + "bandid" = c(1, 2, 3, 4, 5, 6, 7, 8, 11, 12), + "offset" = xml_offset) + } else { + offset <- data.frame("bandname" = listbands_bis, + "bandid" = c(1, 2, 3, 4, 5, 6, 7, 8, 11, 12), + "offset" = 0) + boa_quantval <- 10000 + } + } else { + offset <- data.frame("bandname" = listbands_bis, + "bandid" = c(1, 2, 3, 4, 5, 6, 7, 8, 11, 12), + "offset" = 0) + boa_quantval <- 10000 + } + + # identify where spectral bands are in the stars object + stars_spectral <- list() + starsnames <- names(s2_stars$attr) + stars_spectral$bandname <- starsnames[which(!starsnames == "Cloud")] + stars_spectral$wavelength <- wl_s2[stars_spectral$bandname] + + sortedwl <- names(wl_s2) + reorder <- match(sortedwl, stars_spectral$bandname) + elim <- which(is.na(reorder)) + if (length(elim) > 0) { + reorder <- reorder[-elim] + } + pathr <- s2_stars$attr[reorder] + + names(pathr) <- NULL + s2_stars2 <- stars::read_stars(pathr, along = "band", proxy = TRUE) + stars_spectral$bandname <- stars_spectral$bandname[reorder] + stars_spectral$wavelength <- stars_spectral$wavelength[reorder] + + uniqueoffset <- as.numeric(unique(offset$offset)) + if (length(uniqueoffset) > 1) { + message("Warning: BOA offset differs between bands.") + message("offset will not be applied to the final S2 reflectance raster") + message("check metadata file to identify the offset applied on each band") + print(mtd_msi) + } else { + message("applying offset to reflectance data") + if (is.null(mtd_lasrc) || uniqueoffset == 0) { + offsets2 <- function(x) (round(x + uniqueoffset) * (10000 / boa_quantval)) + s2_stars2 <- stars::st_apply(X = s2_stars2, MARGIN = "band", FUN = offsets2) + } else { + offsets2 <- function(x) (round(10000 * ((x + uniqueoffset * boa_quantval) / boa_quantval))) + s2_stars2 <- stars::st_apply(X = s2_stars2, MARGIN = "band", FUN = offsets2) + } + } + write_stack_s2(stars_s2 = s2_stars2, stars_spectral = stars_spectral, refl_path = refl_path, + format = format, datatype = datatype, sensor = sensor, maxchunk = maxchunk) + # save metadata file as well if available + if (!is.null(mtd)) { + if (file.exists(mtd)) { + file.copy(from = mtd, to = file.path(dirname(refl_path), basename(mtd)), overwrite = TRUE) + } + } + # save metadata file as well if available + if (!is.null(mtd_msi)) { + if (file.exists(mtd_msi)) { + file.copy(from = mtd_msi, to = file.path(dirname(refl_path), basename(mtd_msi)), overwrite = TRUE) + } + } + # save LaSRC metadata file as well if available + if (!is.null(mtd_lasrc)) { + if (file.exists(mtd_lasrc)) { + file.copy(from = mtd_lasrc, to = file.path(dirname(refl_path), basename(mtd_lasrc)), overwrite = TRUE) + } + } + # delete temporary file + for (pathtemp in pathr) { + file.remove(pathtemp) + if (file.exists(paste(pathtemp, ".hdr", sep = ""))) file.remove(paste(pathtemp, ".hdr", sep = "")) + } + gc() + return(invisible()) +} + +#" ENVI functions +#" +#" based on https://github.com/cran/hyperSpec/blob/master/R/read.ENVI.R +#" added wavelength, fwhm, ... to header reading +#" Title +#" +#" @param x character. +#" @param separator character +#" @param trim_blank boolean. +#" +#" @return list. +#" @export +split_line <- function(x, separator, trim_blank = TRUE) { + tmp <- regexpr(separator, x) + key <- substr(x, 1, tmp - 1) + value <- substr(x, tmp + 1, nchar(x)) + if (trim_blank) { + blank_pattern <- "^[[:blank:]]*([^[:blank:]]+.*[^[:blank:]]+)[[:blank:]]*$" + key <- sub(blank_pattern, "\\1", key) + value <- sub(blank_pattern, "\\1", value) + } + value <- as.list(value) + names(value) <- key + return(value) +} + +#" save raster footprint as vector file +#" +#" @param path_raster character. path for a raster file +#" @param path_vector character. path for a vector file +#" @param driver character. driver for vector +#" +#" @return None +#" @importFrom raster raster extent projection +#" @importFrom sf st_as_sf st_write +#" @export +vectorize_raster_extent <- function(path_raster, path_vector, driver = "ESRI Shapefile") { + rast <- raster(path_raster) + e <- extent(rast) + # coerce to a SpatialPolygons object + p <- as(e, "SpatialPolygons") + projection(p) <- projection(rast) + p <- sf::st_as_sf(p) + sf::st_write(obj = p, path_vector, driver = driver) # create to a shapefile + return(invisible()) +} + +#" writes ENVI hdr file +#" +#" @param hdr content to be written +#" @param hdrpath Path of the hdr file +#" +#" @return None +#" @importFrom stringr str_count +#" @export +write_envi_header <- function(hdr, hdrpath) { + h <- lapply(hdr, function(x) { + if (length(x) > 1 || (is.character(x) && stringr::str_count(x, "\\w+") > 1)) { + x <- paste0("{", paste(x, collapse = ", "), "}") + } + # convert last numerics + x <- as.character(x) + }) + writeLines(c("ENVI", paste(names(hdr), h, sep = " = ")), con = hdrpath) + return(invisible()) +} + +#" This function writes a raster Stack object into a ENVI raster file +#" +#" @param stackobj list. raster stack +#" @param stackpath character. path where to store the stack +#" @param bands list. should include "bandname", and if possible "wavelength" +#" @param datatype character. should be INT2S or FLT4S for example +#" @param sensor character. Name of the sensor used to acquire the image +#" @param stretch boolean. Set TRUE to get 10% stretching at display for reflectance, mentioned in hdr only +#" +#" @return None +#" @importFrom utils read.table +#" @export +write_rasterstack_envi <- function(stackobj, stackpath, bands, datatype = "INT2S", + sensor = "Unknown", stretch = FALSE) { + + r <- raster::writeRaster(x = stackobj, filename = stackpath, format = "Ehdr", overwrite = TRUE, datatype = datatype) + raster::hdr(r, format = "ENVI") + # Edit hdr file to add metadata + hdr <- read_envi_header(get_hdr_name(stackpath)) + hdr$`band names` <- bands$bandname + if (length(bands$wavelength) == length(bands$bandname)) { + hdr$wavelength <- bands$wavelength + } else { + hdr$wavelength <- NULL + } + if (stretch == TRUE) { + hdr$`default stretch` <- "0.000000 1000.000000 linear" + } + hdr$`z plot range` <- NULL + hdr$`data ignore value` <- "-Inf" + hdr$`coordinate system string` <- read.table(paste(stackpath, ".prj", sep = "")) + proj <- strsplit(x = strsplit(x = projection(stackobj), split = " ")[[1]][1], split = "=")[[1]][2] + zone <- strsplit(x = strsplit(x = projection(stackobj), split = " ")[[1]][2], split = "=")[[1]][2] + datum <- strsplit(x = strsplit(x = projection(stackobj), split = " ")[[1]][3], split = "=")[[1]][2] + oldproj <- hdr$`map info` + newproj <- gsub(pattern = "projection", replacement = proj, x = oldproj) + newproj <- paste(newproj, zone, datum, sep = ", ") + hdr$`map info` <- newproj + hdr$`sensor type` <- sensor + write_envi_header(hdr = hdr, hdrpath = get_hdr_name(stackpath)) + + # remove unnecessary files + file2remove <- paste(stackpath, ".aux.xml", sep = "") + file.remove(file2remove) + file2remove <- paste(stackpath, ".prj", sep = "") + file.remove(file2remove) + file2remove <- paste(stackpath, ".stx", sep = "") + file.remove(file2remove) + return(invisible()) +} + + +#" This function writes a stars object into a raster file +#" +#" @param stars_s2 list. stars object containing raster data. Can be produced with function Crop_n_resample_S2 +#" @param stars_spectral list. band name to be saved in the stack and spectral bands corresponding to the image +#" @param refl_path character. path where to store the image +#" @param format character. default = ENVI BSQ. otherwise use gdal drivers +#" @param datatype character. should be Int16 or Float64 for example +#" @param sensor character. Name of the sensor used to acquire the image +#" @param maxchunk numeric. Size of individual chunks to be written (in Mb) +#" +#" @return None +#" @export +write_stack_s2 <- function(stars_s2, stars_spectral, refl_path, format = "ENVI", + datatype = "Int16", sensor = "Unknown", maxchunk = 256) { + + # write raster file from proxy using chunks + sizeobj <- 2 * dim(stars_s2)[1] * dim(stars_s2)[2] * dim(stars_s2)[3] / (1024**2) + nbchunks <- ceiling(sizeobj / maxchunk) + stars::write_stars(obj = stars_s2, + dsn = refl_path, + driver = format, + type = datatype, + chunk_size = c(dim(stars_s2)[1], ceiling(dim(stars_s2)[2] / nbchunks)), + progress = TRUE) + + if (format == "ENVI") { + adjust_envi_hdr(dsn = refl_path, bands = stars_spectral, + sensor = sensor, stretch = TRUE) + } + return(invisible()) +} diff --git a/tools/SRS_tools/alpha_beta.r b/tools/SRS_tools/alpha_beta.r new file mode 100644 index 000000000..c2e433327 --- /dev/null +++ b/tools/SRS_tools/alpha_beta.r @@ -0,0 +1,148 @@ +#Rscript + +########################################### +## Mapping alpha and beta diversity ## +########################################### + +#####Packages : stars +# utils +# biodivmapr +# raster +# sf +# mapview +# leafpop +# RColorBrewer +# labdsv +# rgdal +# ggplot2 +# gridExtra +remotes::install_github("jbferet/biodivMapR") +#####Load arguments + +args <- commandArgs(trailingOnly = TRUE) + +#####Import the S2 data + +if (length(args) < 1) { + stop("This tool needs at least 1 argument") +}else { + data_raster <- args[1] + rasterheader <- args[2] + data <- args[3] + # type of PCA: + # PCA: no rescaling of the data + # SPCA: rescaling of the data + typepca <- as.character(args[4]) + alpha <- as.logical(args[5]) + beta <- as.logical(args[6]) + funct <- as.logical(args[7]) + all <- as.logical(args[8]) + source(args[9]) +} + +################################################################################ +## DEFINE PARAMETERS FOR DATASET TO BE PROCESSED ## +################################################################################ +if (data_raster == "") { + #Create a directory where to unzip your folder of data + dir.create("data_dir") + unzip(data, exdir = "data_dir") + # Path to raster + data_raster <- list.files("data_dir/results/Reflectance", pattern = "_Refl") + input_image_file <- file.path("data_dir/results/Reflectance", data_raster[1]) + input_header_file <- file.path("data_dir/results/Reflectance", data_raster[2]) + +} else { + input_image_file <- file.path(getwd(), data_raster, fsep = "/") + input_header_file <- file.path(getwd(), rasterheader, fsep = "/") +} + +################################################################################ +## PROCESS IMAGE ## +################################################################################ +# 1- Filter data in order to discard non vegetated / shaded / cloudy pixels + +print("PERFORM PCA ON RASTER") +pca_output <- biodivMapR::perform_PCA(Input_Image_File = input_image_file, Input_Mask_File = input_mask_file, + Output_Dir = output_dir, TypePCA = typepca, FilterPCA = filterpca, nbCPU = nbcpu, MaxRAM = maxram) + +pca_files <- pca_output$PCA_Files +pix_per_partition <- pca_output$Pix_Per_Partition +nb_partitions <- pca_output$nb_partitions +# path for the updated mask +input_mask_file <- pca_output$MaskPath + +# 3- Select principal components from the PCA raster +# Select components from the PCA/SPCA/MNF raster +sel_compo <- c("1\n", "2\n", "3\n", "4\n", "5\n", "6\n", "7\n", "8") +image_name <- tools::file_path_sans_ext(basename(input_image_file)) +output_dir_full <- file.path(output_dir, image_name, typepca, "PCA") + +write.table(sel_compo, paste0(output_dir_full, "/Selected_Components.txt")) +sel_pc <- file.path(output_dir_full, "Selected_Components.txt") + +################################################################################ +## MAP ALPHA AND BETA DIVERSITY ## +################################################################################ +print("MAP SPECTRAL SPECIES") + +kmeans_info <- biodivMapR::map_spectral_species(Input_Image_File = input_image_file, Output_Dir = output_dir, PCA_Files = pca_files, Input_Mask_File = input_mask_file, Pix_Per_Partition = pix_per_partition, nb_partitions = nb_partitions, nbCPU = nbcpu, MaxRAM = maxram, nbclusters = nbclusters, TypePCA = typepca) + +if (alpha == TRUE || beta == TRUE || all == TRUE) { +## alpha + print("MAP ALPHA DIVERSITY") + index_alpha <- c("Shannon") + alpha_div <- biodivMapR::map_alpha_div(Input_Image_File = input_image_file, Output_Dir = output_dir, TypePCA = typepca, window_size = window_size, nbCPU = nbcpu, MaxRAM = maxram, Index_Alpha = index_alpha, nbclusters = nbclusters) + + alpha_zip <- file.path(output_dir, image_name, typepca, "ALPHA", "Shannon_10_Fullres.zip") + alpha_path <- file.path(output_dir, image_name, typepca, "ALPHA") + unzip(alpha_zip, exdir = alpha_path) + alpha_path <- file.path(output_dir, image_name, typepca, "ALPHA", "Shannon_10_Fullres") + alpha_raster <- raster::raster(alpha_path) + get_alpha <- convert_raster(alpha_raster) + + if (alpha == TRUE || all == TRUE) { + colnames(get_alpha) <- c("Alpha", "longitude", "latitude") + plot_indices(get_alpha, titre = "Alpha") + + write.table(get_alpha, file = "alpha.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE) +} + if (beta == TRUE || all == TRUE) { +## beta + print("MAP BETA DIVERSITY") + beta_div <- biodivMapR::map_beta_div(Input_Image_File = input_image_file, Output_Dir = output_dir, TypePCA = typepca, window_size = window_size, nb_partitions = nb_partitions, nbCPU = nbcpu, MaxRAM = maxram, nbclusters = nbclusters) + + beta_path <- file.path(output_dir, image_name, typepca, "BETA", "BetaDiversity_BCdiss_PCO_10") + beta_raster <- raster::raster(beta_path) + get_beta <- convert_raster(beta_raster) + + colnames(get_beta) <- c("Beta", "longitude", "latitude") + plot_indices(get_beta, titre = "Beta") + + write.table(get_beta, file = "beta.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE) + } +} + + +################################################################################ +## COMPUTE ALPHA AND BETA DIVERSITY FROM FIELD PLOTS ## +################################################################################ +## read selected features from dimensionality reduction +selected_features <- read.table(sel_pc)[[1]] +## path for selected components + +if (funct == TRUE || all == TRUE) { + mapper <- biodivMapR::map_functional_div(Original_Image_File = input_image_file, Functional_File = pca_files, Selected_Features = selected_features, Output_Dir = output_dir, window_size = window_size, nbCPU = nbcpu, MaxRAM = maxram, TypePCA = typepca) + + funct_zip <- file.path(output_dir, image_name, typepca, "FUNCTIONAL", "FunctionalDiversity_Map_MeanFilter_Fullres.zip") + funct_path <- file.path(output_dir, image_name, typepca, "FUNCTIONAL") + unzip(funct_zip, exdir = funct_path) + funct_path <- file.path(output_dir, image_name, typepca, "FUNCTIONAL", "FunctionalDiversity_Map_MeanFilter_Fullres") + funct_raster <- raster::raster(funct_path) + get_funct <- convert_raster(funct_raster) + + colnames(get_funct) <- c("Functionnal", "longitude", "latitude") + plot_indices(get_funct, titre = "Functionnal") + + write.table(get_funct, file = "Functionnal.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE) +} diff --git a/tools/SRS_tools/alpha_beta.xml b/tools/SRS_tools/alpha_beta.xml new file mode 100644 index 000000000..42ed1968d --- /dev/null +++ b/tools/SRS_tools/alpha_beta.xml @@ -0,0 +1,185 @@ + + from remote sensing data + + macro.xml + + + r-matrixStats + r-rastervis + r-future + r-future.apply + r-ggplot2 + r-xfun + r-mapview + r-leafpop + r-rcolorbrewer + r-labdsv + r-gridextra + r-data.table + r-ecodist + r-fields + r-geometry + r-matlab + r-snow + r-vegan + r-remotes + r-emstreer + r-mmand + r-dissutils + r-filesstrings + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + type == 'alpha' or type == 'all' + + + type == 'beta' or type == 'all' + + + type == 'funct' or type == 'all' + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tools/SRS_tools/biodiv_indices_global.r b/tools/SRS_tools/biodiv_indices_global.r new file mode 100644 index 000000000..f739bb8ca --- /dev/null +++ b/tools/SRS_tools/biodiv_indices_global.r @@ -0,0 +1,112 @@ +#Rscript + +########################################### +## Mapping biodiversity indicators ## +########################################### + +#####Packages : raster, +# rgdal, +# sp, +# rasterdiv, +# ggplot2, + +#####Load arguments + +args <- commandArgs(trailingOnly = TRUE) + +#####Import the S2 data + +if (length(args) < 1) { + stop("This tool needs at least 1 argument") +}else { + data_raster <- args[1] + data_header <- args[2] + data <- args[3] + alpha <- as.numeric(args[4]) + source(args[5]) + +} + +######################################################################## +## COMPUTE BIODIVERSITY INDICES ## +######################################################################## + +if (data_raster == "") { + #Create a directory where to unzip your folder of data + dir.create("data_dir") + unzip(data, exdir = "data_dir") + # Path to raster + data_raster <- list.files("data_dir/results/Reflectance", pattern = "_Refl") + data_raster <- file.path("data_dir/results/Reflectance", data_raster[1]) +} + +# Read raster +copndvi <- raster::raster(data_raster) + +copndvilr <- raster::reclassify(copndvi, cbind(252, 255, NA), right = TRUE) + +#Resample using raster::aggregate and a linear factor of 10 +copndvilr <- raster::aggregate(copndvilr, fact = 20) +#Set float numbers as integers to further speed up the calculation +storage.mode(copndvilr[]) <- "integer" + +# Convert raster to SpatialPointsDataFrame +r_pts <- convert_raster(copndvilr) + +#Shannon's Diversity +sha <- rasterdiv::Shannon(copndvilr, window = 9, np = 1) +sha_df <- data.frame(raster::rasterToPoints(sha, spatial = TRUE)) +sha_name <- "Shannon" +r_pts[, sha_name] <- sha_df[, 1] + +#Renyi's Index +ren <- rasterdiv::Renyi(copndvilr, window = 9, alpha = alpha, np = 1) +ren_df <- data.frame(raster::rasterToPoints(ren[[1]])) +ren_name <- "Renyi" +r_pts[, ren_name] <- ren_df[, 3] + +#Berger-Parker's Index +ber <- rasterdiv::BergerParker(copndvilr, window = 9, np = 1) +ber_df <- data.frame(raster::rasterToPoints(ber, spatial = TRUE)) +ber_name <- "Berger-Parker" +r_pts[, ber_name] <- ber_df[, 1] + +#Pielou's Evenness +pie <- rasterdiv::Pielou(copndvilr, window = 9, np = 1) +pie_df <- data.frame(raster::rasterToPoints(pie)) +if (length(pie_df[, 1]) == length(r_pts[, 1])) { + pie_name <- "Pielou" + r_pts[, pie_name] <- pie_df[, 1] +} + +#Hill's numbers +hil <- rasterdiv::Hill(copndvilr, window = 9, alpha = alpha, np = 1) +hil_df <- data.frame(raster::rasterToPoints(hil[[1]])) +hil_name <- "Hill" +r_pts[, hil_name] <- hil_df[, 3] + +#Parametric Rao's quadratic entropy with alpha ranging from 1 to 5 +prao <- rasterdiv::paRao(copndvilr, window = 9, alpha = alpha, dist_m = "euclidean", np = 1) +prao_df <- data.frame(raster::rasterToPoints(prao$window.9[[1]])) +prao_name <- "Prao" +r_pts[, prao_name] <- prao_df[, 3] + +#Cumulative Residual Entropy +cre <- rasterdiv::CRE(copndvilr, window = 9, np = 1) +cre_df <- data.frame(raster::rasterToPoints(cre)) +if (length(cre_df[, 1]) == length(r_pts[, 1])) { + cre_name <- "CRE" + r_pts[, cre_name] <- cre_df[, 1] +} + +if (length(cre_df[, 1]) == length(r_pts[, 1]) || length(pie_df[, 1]) == length(r_pts[, 1])) { +list_indice <- list("Shannon", "Renyi", "Berger-Parker", "Pielou", "Hill", "Prao", "CRE") +} else { +list_indice <- list("Shannon", "Renyi", "Berger-Parker", "Hill", "Prao") +} +## Plotting all the graph and writing a tabular +for (indice in list_indice) { + plot_indices(data = r_pts, titre = indice) +} + +write.table(r_pts, file = "BiodivIndex.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE) diff --git a/tools/SRS_tools/biodiv_indices_global.xml b/tools/SRS_tools/biodiv_indices_global.xml new file mode 100644 index 000000000..385aeb9a5 --- /dev/null +++ b/tools/SRS_tools/biodiv_indices_global.xml @@ -0,0 +1,121 @@ + + from remote sensing data + + macro.xml + + + r-raster + r-rgdal + r-sp + r-rasterdiv + r-ggplot2 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tools/SRS_tools/comparison_div.r b/tools/SRS_tools/comparison_div.r new file mode 100644 index 000000000..603a33cd3 --- /dev/null +++ b/tools/SRS_tools/comparison_div.r @@ -0,0 +1,191 @@ +#Rscript + +########################################### +## Mapping alpha and beta diversity ## +########################################### + +#####Packages : stars +# utils +# biodivmapr +# raster +# sf +# mapview +# leafpop +# RColorBrewer +# labdsv +# rgdal +# ggplot2 +# gridExtra +remotes::install_github("jbferet/biodivMapR") +#####Load arguments + +args <- commandArgs(trailingOnly = TRUE) + +#####Import the S2 data + +if (length(args) < 1) { + stop("This tool needs at least 1 argument") +}else { + data_raster <- args[1] + rasterheader <- args[2] + data <- args[3] + plots_zip <- args[4] + choice <- as.character(args[5]) + source(args[6]) + # type of PCA: + # PCA: no rescaling of the data + # SPCA: rescaling of the data + typepca <- as.character(args[7]) +} + +################################################################################ +## DEFINE PARAMETERS FOR DATASET TO BE PROCESSED ## +################################################################################ +if (data_raster == "") { + #Create a directory where to unzip your folder of data + dir.create("data_dir") + unzip(data, exdir = "data_dir") + # Path to raster + data_raster <- list.files("data_dir/results/Reflectance", pattern = "_Refl") + input_image_file <- file.path("data_dir/results/Reflectance", data_raster[1]) + input_header_file <- file.path("data_dir/results/Reflectance", data_raster[2]) + +} else { + input_image_file <- file.path(getwd(), data_raster, fsep = "/") + input_header_file <- file.path(getwd(), rasterheader, fsep = "/") +} + +################################################################################ +## PROCESS IMAGE ## +################################################################################ +# 1- Filter data in order to discard non vegetated / shaded / cloudy pixels + +print("PERFORM PCA ON RASTER") +pca_output <- biodivMapR::perform_PCA(Input_Image_File = input_image_file, Input_Mask_File = input_mask_file, + Output_Dir = output_dir, TypePCA = typepca, FilterPCA = filterpca, nbCPU = nbcpu, MaxRAM = maxram) + +pca_files <- pca_output$PCA_Files +pix_per_partition <- pca_output$Pix_Per_Partition +nb_partitions <- pca_output$nb_partitions +# path for the updated mask +input_mask_file <- pca_output$MaskPath + +# 3- Select principal components from the PCA raster +# Select components from the PCA/SPCA/MNF raster +sel_compo <- c("1\n", "2\n", "3\n", "4\n", "5\n", "6\n", "7\n", "8") +image_name <- tools::file_path_sans_ext(basename(input_image_file)) +output_dir_full <- file.path(output_dir, image_name, typepca, "PCA") + +write.table(sel_compo, paste0(output_dir_full, "/Selected_Components.txt")) +sel_pc <- file.path(output_dir_full, "Selected_Components.txt") + + +################################################################################ +## MAP ALPHA AND BETA DIVERSITY ## +################################################################################ +print("MAP SPECTRAL SPECIES") + +kmeans_info <- biodivMapR::map_spectral_species(Input_Image_File = input_image_file, Output_Dir = output_dir, PCA_Files = pca_files, Input_Mask_File = input_mask_file, Pix_Per_Partition = pix_per_partition, nb_partitions = nb_partitions, nbCPU = nbcpu, MaxRAM = maxram, nbclusters = nbclusters, TypePCA = typepca) + +################################################################################ +## COMPUTE ALPHA AND BETA DIVERSITY FROM FIELD PLOTS ## +################################################################################ +## read selected features from dimensionality reduction +selected_features <- read.table(sel_pc)[[1]] +## path for selected components + +# location of the directory where shapefiles used for validation are saved +dir.create("VectorDir") +unzip(plots_zip, exdir = "VectorDir") + +# list vector data +path_vector <- biodivMapR::list_shp("VectorDir") +name_vector <- tools::file_path_sans_ext(basename(path_vector)) + +# location of the spectral species raster needed for validation +path_spectralspecies <- kmeans_info$SpectralSpecies +# get diversity indicators corresponding to shapefiles (no partitioning of spectral dibversity based on field plots so far...) + +biodiv_indicators <- biodivMapR::diversity_from_plots(Raster_SpectralSpecies = path_spectralspecies, Plots = path_vector, nbclusters = nbclusters, Raster_Functional = pca_files, Selected_Features = selected_features) + +shannon_rs <- c(biodiv_indicators$Shannon)[[1]] +fric <- c(biodiv_indicators$FunctionalDiversity$FRic) +feve <- c(biodiv_indicators$FunctionalDiversity$FEve) +fdiv <- c(biodiv_indicators$FunctionalDiversity$FDiv) +# if no name for plots +biodiv_indicators$Name_Plot <- seq(1, length(biodiv_indicators$Shannon[[1]]), by = 1) + + +#################################################### +# write RS indicators # +#################################################### +# write a table for Shannon index + +# write a table for all spectral diversity indices corresponding to alpha diversity +results <- data.frame(name_vector, biodiv_indicators$Richness, biodiv_indicators$Fisher, + biodiv_indicators$Shannon, biodiv_indicators$Simpson, + biodiv_indicators$FunctionalDiversity$FRic, + biodiv_indicators$FunctionalDiversity$FEve, + biodiv_indicators$FunctionalDiversity$FDiv) + +names(results) <- c("ID_Plot", "Species_Richness", "Fisher", "Shannon", "Simpson", "fric", "feve", "fdiv") +write.table(results, file = "Diversity.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE) + +if (choice == "Y") { +# write a table for Bray Curtis dissimilarity +bc_mean <- biodiv_indicators$BCdiss +bray_curtis <- data.frame(name_vector, bc_mean) +colnames(bray_curtis) <- c("ID_Plot", bray_curtis[, 1]) +write.table(bray_curtis, file = "BrayCurtis.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE) + +#################################################### +# illustrate results +#################################################### +# apply ordination using PCoA (same as done for map_beta_div) + +mat_bc_dist <- as.dist(bc_mean, diag = FALSE, upper = FALSE) +betapco <- labdsv::pco(mat_bc_dist, k = 3) + +# assign a type of vegetation to each plot, assuming that the type of vegetation +# is defined by the name of the shapefile + +nbsamples <- shpname <- c() +for (i in 1:length(path_vector)) { + shp <- path_vector[i] + nbsamples[i] <- length(rgdal::readOGR(shp, verbose = FALSE)) + shpname[i] <- tools::file_path_sans_ext(basename(shp)) +} + +type_vegetation <- c() +for (i in 1: length(nbsamples)) { + for (j in 1:nbsamples[i]) { + type_vegetation <- c(type_vegetation, shpname[i]) + } +} + +#data frame including a selection of alpha diversity metrics and beta diversity expressed as coordinates in the PCoA space +results <- data.frame("vgtype" = type_vegetation, "pco1" = betapco$points[, 1], "pco2" = betapco$points[, 2], "pco3" = betapco$points[, 3], "shannon" = shannon_rs, "fric" = fric, "feve" = feve, "fdiv" = fdiv) + +#plot field data in the PCoA space, with size corresponding to shannon index +g1 <- ggplot2::ggplot(results, ggplot2::aes(x = pco1, y = pco2, color = vgtype, size = shannon)) + ggplot2::geom_point(alpha = 0.6) + ggplot2::scale_color_manual(values = c("#e6140a", "#e6d214", "#e68214", "#145ae6")) + +g2 <- ggplot2::ggplot(results, ggplot2::aes(x = pco1, y = pco3, color = vgtype, size = shannon)) + ggplot2::geom_point(alpha = 0.6) + ggplot2::scale_color_manual(values = c("#e6140a", "#e6d214", "#e68214", "#145ae6")) + +g3 <- ggplot2::ggplot(results, ggplot2::aes(x = pco2, y = pco3, color = vgtype, size = shannon)) + ggplot2::geom_point(alpha = 0.6) + ggplot2::scale_color_manual(values = c("#e6140a", "#e6d214", "#e68214", "#145ae6")) + +#extract legend +get_legend <- function(a_gplot) { + tmp <- ggplot2::ggplot_gtable(ggplot2::ggplot_build(a_gplot)) + leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box") + legend <- tmp$grobs[[leg]] + return(legend) +} + +legend <- get_legend(g3) +gall <- gridExtra::grid.arrange(gridExtra::arrangeGrob(g1 + ggplot2::theme(legend.position = "none"), g2 + ggplot2::theme(legend.position = "none"), g3 + ggplot2::theme(legend.position = "none"), nrow = 1), legend, nrow = 2, heights = c(3, 2)) + + +filename <- ggplot2::ggsave("BetaDiversity_PcoA1_vs_PcoA2_vs_PcoA3.png", gall, scale = 0.65, width = 12, height = 9, units = "in", dpi = 200, limitsize = TRUE) + +filename +} diff --git a/tools/SRS_tools/comparison_div.xml b/tools/SRS_tools/comparison_div.xml new file mode 100644 index 000000000..5c2158b8a --- /dev/null +++ b/tools/SRS_tools/comparison_div.xml @@ -0,0 +1,161 @@ + + with remote sensing data + + macro.xml + + + r-matrixStats + r-future + r-future.apply + r-ggplot2 + r-xfun + r-mapview + r-leafpop + r-rcolorbrewer + r-labdsv + r-gridextra + r-data.table + r-ecodist + r-fields + r-geometry + r-matlab + r-snow + r-vegan + r-remotes + r-emstreer + r-mmand + r-dissutils + r-filesstrings + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + choice == 'Y' + + + + choice =='Y' + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tools/SRS_tools/functions.r b/tools/SRS_tools/functions.r new file mode 100644 index 000000000..09d5d6c13 --- /dev/null +++ b/tools/SRS_tools/functions.r @@ -0,0 +1,79 @@ +#Rscript + +######################### +## Functions ## +######################### + +#####Packages : raster +# sp +# ggplot2 + +####Set paramaters for all tools using BiodivMapR + +# path for the Mask raster corresponding to image to process +# expected to be in ENVI HDR format, 1 band, integer 8bits +# expected values in the raster: 0 = masked, 1 = selected +# set to FALSE if no mask available +input_mask_file <- FALSE + +# relative or absolute path for the Directory where results will be stored +# For each image processed, a subdirectory will be created after its name +output_dir <- "RESULTS" + +# SPATIAL RESOLUTION +# resolution of spatial units for alpha and beta diversity maps (in pixels), relative to original image +# if Res.Map = 10 for images with 10 m spatial resolution, then spatial units will be 10 pixels x 10m = 100m x 100m surfaces +# rule of thumb: spatial units between 0.25 and 4 ha usually match with ground data +# too small window_size results in low number of pixels per spatial unit, hence limited range of variation of diversity in the image +window_size <- 10 + +# PCA FILTERING: Set to TRUE if you want second filtering based on PCA outliers to be processed. Slower +filterpca <- TRUE + +################################################################################ +## DEFINE PARAMETERS FOR METHOD ## +################################################################################ +nbcpu <- 4 +maxram <- 0.5 +nbclusters <- 50 + +################################################################################ +## PROCESS IMAGE ## +################################################################################ +# 1- Filter data in order to discard non vegetated / shaded / cloudy pixels +ndvi_thresh <- 0.5 +blue_thresh <- 500 +nir_thresh <- 1500 +continuum_removal <- TRUE + + + +#### Convert raster to dataframe + +# Convert raster to SpatialPointsDataFrame +convert_raster <- function(data_raster) { +r_pts <- raster::rasterToPoints(data_raster, spatial = TRUE) + +# reproject sp object +geo_prj <- "+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0" +r_pts <- sp::spTransform(r_pts, sp::CRS(geo_prj)) + + +# Assign coordinates to @data slot, display first 6 rows of data.frame +r_pts@data <- data.frame(r_pts@data, longitude = sp::coordinates(r_pts)[, 1], + latitude = sp::coordinates(r_pts)[, 2]) + +return(r_pts@data) +} + + +#### Potting indices + +plot_indices <- function(data, titre) { + graph_indices <- ggplot2::ggplot(data) + + ggplot2::geom_point(ggplot2::aes_string(x = data[, 2], y = data[, 3], color = data[, titre]), shape = "square", size = 2) + ggplot2::scale_colour_gradient(low = "blue", high = "orange", na.value = "grey50") + + ggplot2::xlab("Longitude") + ggplot2::ylab("Latitude") + + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5, hjust = 1), plot.title = ggplot2::element_text(color = "black", size = 12, face = "bold")) + ggplot2::ggtitle(titre) + +ggplot2::ggsave(paste0(titre, ".png"), graph_indices, width = 12, height = 10, units = "cm") +} diff --git a/tools/SRS_tools/indices_spectral.r b/tools/SRS_tools/indices_spectral.r new file mode 100644 index 000000000..0834ce271 --- /dev/null +++ b/tools/SRS_tools/indices_spectral.r @@ -0,0 +1,105 @@ +#Rscript + +########################################### +## Mapping alpha and beta diversity ## +########################################### + +#####Packages : expint, +# pracma, +# R.utils, +# raster, +# sp, +# matrixStats, +# ggplot2, +# expandFunctions, +# stringr, +# XML, +# rgdal, +# stars, +#####Load arguments + +args <- commandArgs(trailingOnly = TRUE) + +#####Import the S2 data + +if (length(args) < 1) { + stop("This tool needs at least 1 argument") +}else { + data_raster <- args[1] + data_header <- args[2] + data <- args[3] + source(args[4]) + source(args[5]) + source(args[6]) + indice_choice <- strsplit(args[7], ",")[[1]] + source(args[8]) + output_raster <- as.character(args[9]) + +} + +######################################################################## +## COMPUTE SPECTRAL INDEX : NDVI ## +######################################################################## + +if (data != "") { + #Create a directory where to unzip your folder of data + dir.create("data_dir") + unzip(data, exdir = "data_dir") + + # Read raster + data_raster <- list.files("data_dir/results/Reflectance", pattern = "_Refl") + data_raster <- file.path("data_dir/results/Reflectance", data_raster[1]) + refl <- raster::brick(data_raster) + refl2 <- raster::raster(data_raster) +} else { + # Read raster + refl <- raster::brick(data_raster) + refl2 <- raster::raster(data_raster) +} +# get raster band name and clean format. Expecting band name and wav +# reflFactor = 10000 when reflectance is coded as INT16 +refl <- raster::aggregate(refl, fact = 10) + +# Convert raster to SpatialPointsDataFrame +refl2 <- raster::aggregate(refl2, fact = 10) +r_pts <- convert_raster(refl2) +table_ind <- r_pts +# create directory for Spectralelength to be documented in image +hdr_refl <- read_envi_header(get_hdr_name(data_raster)) +sensorbands <- hdr_refl$wavelength +# compute a set of spectral indices defined by indexlist from S2 data indices +si_path <- file.path("SpectralIndices") +dir.create(path = si_path, showWarnings = FALSE, recursive = TRUE) +# Save spectral indices + +indices <- lapply(indice_choice, function(x) { + indices_list <- computespectralindices_raster(refl = refl, sensorbands = sensorbands, + sel_indices = x, + reflfactor = 10000, stackout = FALSE) + + index_path <- file.path(si_path, paste(basename(data_raster), "_", x, sep = "")) + spec_indices <- stars::write_stars(stars::st_as_stars(indices_list$spectralindices[[1]]), dsn = index_path, driver = "ENVI", type = "Float32") + + # write band name in HDR + hdr <- read_envi_header(get_hdr_name(index_path)) + hdr$`band names` <- x + write_envi_header(hdr = hdr, hdrpath = get_hdr_name(index_path)) + # Writting the tabular and the plot + r_pts[, x] <- as.data.frame(sapply(spec_indices, c)) + plot_indices(data = r_pts, titre = x) + return(r_pts) +}) + +new_table <- as.data.frame(indices) +new_table <- new_table[, !grepl("longitude", names(new_table))] +new_table <- new_table[, !grepl("latitude", names(new_table))] +new_table <- new_table[, !grepl(basename(data_raster), names(new_table))] + +table_ind <- cbind(table_ind, new_table) +if (length(indice_choice) == 1) { + colnames(table_ind) <- c(basename(data_raster), "longitude", "latitude", indice_choice) +} + +write.table(table_ind, file = "Spec_Index.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE) + +# Get the raster layer of the indice as an output diff --git a/tools/SRS_tools/indices_spectral.xml b/tools/SRS_tools/indices_spectral.xml new file mode 100644 index 000000000..98af04818 --- /dev/null +++ b/tools/SRS_tools/indices_spectral.xml @@ -0,0 +1,263 @@ + + as NDVI from remote sensing data + + macro.xml + + + r-R.utils + r-raster + r-sp + r-rgdal + r-rasterdiv + r-stars + r-stringr + r-pracma + r-expint + r-matrixStats + r-ggplot2 + r-xml + r-zip + r-dplyr + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + choice == 'Y' + + + + + + + + + + + + + + + + diff --git a/tools/SRS_tools/macro.xml b/tools/SRS_tools/macro.xml new file mode 100644 index 000000000..58d01c11e --- /dev/null +++ b/tools/SRS_tools/macro.xml @@ -0,0 +1,95 @@ + + 0.0.0 + + + r-R.utils + r-raster + r-sf + r-sp + r-rgdal + r-rgeos + r-stars + r-stringr + + + + + + + + + + + + @Manual{, + title = {obisindicators: Develop marine biodiversity indicators from OBIS data}, + author = {Ben Ben and Pieter Provoost and Tylar Murray}, + year = {2022}, + note = {https://marinebon.org/obisindicators, + https://github.com/marinebon/obisindicators}, + } + + + + + + + @Manual{, + title = {preprocS2: preprocessing of Sentinel-2 data downloaded from various data hubs / produced from various atmospheric correction methods}, + author = {Jean-Baptiste Feret}, + year = {2022}, + note = {R package version 1.1.3}, + url = {https://gitlab.com/jbferet/preprocS2}, + } + + + + + + + @Manual{, + title = {biodivMapR: biodivMapR: an R package for a- and ß-diversity mapping using remotely-sensed images}, + author = {Jean-Baptiste Feret and Florian {de Boissieu}}, + year = {2022}, + note = {R package version 1.9.4}, + url = {https://github.com/jbferet/biodivMapR}, + } + + + + + + doi:10.1016/j.ecolind.2016.07.039 + doi:10.1101/2021.01.23.427872 + + + + + + @Manual{, + title = {prosail: PROSAIL leaf and canopy radiative transfer model and inversion routines}, + author = {Jean-Baptiste Feret and Florian {de Boissieu}}, + year = {2022}, + note = {R package version 1.1.1}, + url = {https://gitlab.com/jbferet/prosail}, + } + + + + + + doi.org/10.5281/zenodo.5907920 + + + + + doi.org/10.1016/j.rse.2013.09.022 + + + + + topic_0610 + topic_3050 + + + diff --git a/tools/SRS_tools/pca_raster.r b/tools/SRS_tools/pca_raster.r new file mode 100644 index 000000000..4efaef45c --- /dev/null +++ b/tools/SRS_tools/pca_raster.r @@ -0,0 +1,75 @@ +#Rscript + +########################################### +## Getting PCA raster ## +########################################### + +#####Packages : stars +# utils +# biodivmapr +# raster +# sf +# mapview +# leafpop +# RColorBrewer +# labdsv +# rgdal +# ggplot2 +# gridExtra +remotes::install_github("jbferet/biodivMapR") +#####Load arguments + +args <- commandArgs(trailingOnly = TRUE) + +#####Import the S2 data + +if (length(args) < 1) { + stop("This tool needs at least 1 argument") +}else { + data_raster <- args[1] + rasterheader <- args[2] + data <- args[3] + typepca <- as.character(args[4]) + source(args[5]) +} + +################################################################################ +## DEFINE PARAMETERS FOR DATASET TO BE PROCESSED ## +################################################################################ +# expected to be in ENVI HDR + +if (data_raster == "") { + #Create a directory where to unzip your folder of data + dir.create("data_dir") + unzip(data, exdir = "data_dir") + # Path to raster + data_raster <- list.files("data_dir/results/Reflectance", pattern = "_Refl") + input_image_file <- file.path("data_dir/results/Reflectance", data_raster[1]) + input_header_file <- file.path("data_dir/results/Reflectance", data_raster[2]) + +} else { + input_image_file <- file.path(getwd(), data_raster, fsep = "/") + input_header_file <- file.path(getwd(), rasterheader, fsep = "/") +} + +################################################################################ +## PROCESS IMAGE ## +################################################################################ +# 1- Filter data in order to discard non vegetated / shaded / cloudy pixels +print("PERFORM PCA ON RASTER") +pca_output <- biodivMapR::perform_PCA(Input_Image_File = input_image_file, Input_Mask_File = input_mask_file, + Output_Dir = output_dir, TypePCA = typepca, FilterPCA = filterpca, nbCPU = nbcpu, MaxRAM = maxram) + + +pca_path <- file.path(output_dir, basename(data_raster), typepca, "PCA", "OutputPCA_8_PCs") +pca_raster <- raster::raster(pca_path) +get_pca <- convert_raster(pca_raster) + +colnames(get_pca) <- c("PCA", "longitude", "latitude") +plot_indices(get_pca, titre = "PCA") + +write.table(get_pca, file = "PCA.tabular", sep = "\t", dec = ".", na = " ", row.names = FALSE, col.names = TRUE, quote = FALSE) +#### Get the raster layer files +pca_files <- file.path("RESULTS", basename(data_raster), typepca, "PCA") +to_dir_short <- output_dir +file.copy(pca_files, to_dir_short) #copy files from long to short paths diff --git a/tools/SRS_tools/pca_raster.xml b/tools/SRS_tools/pca_raster.xml new file mode 100644 index 000000000..99dd323a9 --- /dev/null +++ b/tools/SRS_tools/pca_raster.xml @@ -0,0 +1,144 @@ + + from remote sensing data + + macro.xml + + + r-matrixStats + r-future + r-future.apply + r-ggplot2 + r-xfun + r-mapview + r-leafpop + r-rcolorbrewer + r-labdsv + r-gridextra + r-data.table + r-ecodist + r-fields + r-geometry + r-matlab + r-snow + r-vegan + r-remotes + r-emstreer + r-mmand + r-dissutils + r-filesstrings + r-zip + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tools/SRS_tools/preprocess_S2.r b/tools/SRS_tools/preprocess_S2.r new file mode 100644 index 000000000..fb16f8a9a --- /dev/null +++ b/tools/SRS_tools/preprocess_S2.r @@ -0,0 +1,120 @@ +#Rscript + +########################################### +## Preprocessing Sentinel 2 data ## +########################################### + +#####Packages : sen2r, +# jqr, +# protolite, +# raster, +# sf, +# rgeos, +# sp, +# raster, +# stars, +# stringr, +# progress, +# rgdal, +# R.utils, +# gdalUtils, +# fasterize, +# XML, +# XML2 + +#####Load arguments + +args <- commandArgs(trailingOnly = TRUE) + +if (length(args) < 1) { + stop("This tool needs at least 1 argument") +}else { + data <- args[1] + source(args[2]) + data_source <- as.character(args[3]) +} + +##____________________________________________________________________## +## Define where data is stored and where to write results ## +##--------------------------------------------------------------------## + +#Create a directory where to unzip your folder of data +dir.create("data_dir") +unzip(data, exdir = "data_dir") + +# Result directory +result_path <- "results" +dir.create(path = result_path, showWarnings = FALSE, recursive = TRUE) + +#Csv file for output useless but needed for linter +write.csv(data_source, "Mission.csv") + +# define raster path +if (data_source == "SAFE") { + path_s2 <- file.path("data_dir", list.files("data_dir", pattern = ".SAFE")) + #To define the level and know if a correction is needed (convert not ready yet) + level_info <- get_s2_level(path_s2) + if (level_info == "L1C") { + stop("! This tool works for data of L2A level and NOT for the L1C level which is currently a work in progress !") + } +}else { + path_s2 <- file.path("data_dir") +} + +##____________________________________________________________________## +## Extract, resample & stack data ## +##--------------------------------------------------------------------## +# define resolution +resolution <- 10 +# define source of data +s2source <- data_source + +s2obj <- extract_from_s2_l2a(path_dir_s2 = path_s2, + path_vector = NULL, + s2source = s2source, + resolution = resolution) + +##____________________________________________________________________## +## Write CLOUD MASK ## +##--------------------------------------------------------------------## + +# directory for cloud mask +cloud_path <- file.path(result_path, "CloudMask") +dir.create(path = cloud_path, showWarnings = FALSE, recursive = TRUE) +# Filename for cloud mask +cloudmasks <- save_cloud_s2(s2_stars = s2obj$s2_stack, + cloud_path = cloud_path, + s2source = s2source, saveraw = TRUE) + +zip_cloud <- file.path("Cloud.zip") +zip::zip(zip_cloud, cloud_path) +##____________________________________________________________________## +## Write REFLECTANCE ## +##--------------------------------------------------------------------## + +# directory for Reflectance +refl_dir <- file.path(result_path, "Reflectance") +dir.create(path = refl_dir, showWarnings = FALSE, recursive = TRUE) + +if (data_source == "SAFE") { + # filename for Reflectance + refl_path <- file.path(refl_dir, paste(basename(s2obj$s2_bands$GRANULE), "_Refl", sep = "")) + + # Save Reflectance file as ENVI image with BIL interleaves + tile_s2 <- substring(strsplit(basename(s2obj$s2_bands$GRANULE), "_")[[1]][2], 2) + dateacq_s2 <- as.Date(substring(strsplit(basename(s2obj$s2_bands$GRANULE), "_")[[1]][4], 1, 8), format = "%Y%m%d") +}else { + # filename for Reflectance + refl_path <- file.path(refl_dir, paste(basename(s2obj$s2_bands$path_tile_s2), "_Refl", sep = "")) + + # Save Reflectance file as ENVI image with BIL interleaves + tile_s2 <- substring(strsplit(basename(s2obj$s2_bands$path_tile_s2), "_")[[1]][2], 2) + dateacq_s2 <- as.Date(substring(strsplit(basename(s2obj$s2_bands$path_tile_s2), "_")[[1]][4], 1, 8), format = "%Y%m%d") +} + +save_data <- save_reflectance_s2(s2_stars = s2obj$s2_stack, refl_path = refl_path, + s2sat = NULL, tile_s2 = tile_s2, dateacq_s2 = dateacq_s2, + format = "ENVI", datatype = "Int16", mtd = s2obj$s2_bands$metadata, mtd_msi = s2obj$s2_bands$metadata_MSI) + +zip_files <- file.path("Refl.zip") +zip::zip(zip_files, refl_dir) diff --git a/tools/SRS_tools/preprocess_S2.xml b/tools/SRS_tools/preprocess_S2.xml new file mode 100644 index 000000000..50f0b77be --- /dev/null +++ b/tools/SRS_tools/preprocess_S2.xml @@ -0,0 +1,98 @@ + + read, crop, resample and write it as a raster stack. + + macro.xml + + + r-jqr + r-protolite + r-sen2r + r-progress + r-gdalutils + r-fasterize + r-xml + r-xml2 + r-zip + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tools/SRS_tools/prosail-master/R/Lib_PROSAIL.R b/tools/SRS_tools/prosail-master/R/Lib_PROSAIL.R new file mode 100644 index 000000000..585a878b5 --- /dev/null +++ b/tools/SRS_tools/prosail-master/R/Lib_PROSAIL.R @@ -0,0 +1,1233 @@ +# ============================================================================= = +# prosail +# Lib_PROSAIL.R +# ============================================================================= = +# PROGRAMMERS: +# Jean-Baptiste FERET +# Florian de BOISSIEU +# copyright 2019 / 11 Jean-Baptiste FERET +# ============================================================================= = +# This Library includes functions dedicated to PROSAIL simulation +# SAIL versions available are 4SAIL and 4SAIL2 +# ============================================================================= = + +#" computes bidirectional reflectance factor based on outputs from PROSAIL and sun position +#" +#" The direct and diffuse light are taken into account as proposed by: +#" Francois et al. (2002) conversion of 400-1100 nm vegetation albedo +#" measurements into total shortwave broadband albedo using a canopy +#" radiative transfer model, Agronomie +#" Es = direct +#" Ed = diffuse +#" +#" @param rdot numeric. Hemispherical-directional reflectance factor in viewing direction +#" @param rsot numeric. Bi-directional reflectance factor +#" @param tts numeric. Solar zenith angle +#" @param specatm_sensor list. direct and diffuse radiation for clear conditions +#" @return brf numeric. Bidirectional reflectance factor +#" @export +compute_brf <- function(rdot, rsot, tts, specatm_sensor) { + + ############################## # + ## direct / diffuse light ## + ############################## # + es <- specatm_sensor$Direct_Light + ed <- specatm_sensor$Diffuse_Light + rd <- pi / 180 + skyl <- 0.847 - 1.61 * sin((90 - tts) * rd) + 1.04 * sin((90 - tts) * rd) * sin((90 - tts) * rd) # diffuse radiation (Francois et al., 2002) + pardiro <- (1 - skyl) * es + pardifo <- skyl * ed + brf <- (rdot * pardifo + rsot * pardiro) / (pardiro + pardifo) + return(brf) +} + +#" Performs PROSAIL simulation based on a set of combinations of input parameters +#" @param spec_sensor list. Includes optical constants required for PROSPECT +#" refractive index, specific absorption coefficients and corresponding spectral bands +#" @param input_prospect list. PROSPECT input variables +#" @param N numeric. Leaf structure parameter +#" @param CHL numeric. Chlorophyll content (microg.cm-2) +#" @param CAR numeric. Carotenoid content (microg.cm-2) +#" @param ANT numeric. Anthocyain content (microg.cm-2) +#" @param BROWN numeric. Brown pigment content (Arbitrary units) +#" @param EWT numeric. Equivalent Water Thickness (g.cm-2) +#" @param LMA numeric. Leaf Mass per Area (g.cm-2) +#" @param PROT numeric. protein content (g.cm-2) +#" @param CBC numeric. NonProtCarbon-based constituent content (g.cm-2) +#" @param alpha numeric. Solid angle for incident light at surface of leaf (simulation of roughness) +#" @param typelidf numeric. Type of leaf inclination distribution function +#" @param lidfa numeric. +#" if typelidf == 1, controls the average leaf slope +#" if typelidf == 2, corresponds to average leaf angle +#" @param lidfb numeric. +#" if typelidf == 1, unused +#" if typelidf == 2, controls the distribution"s bimodality +#" @param lai numeric. Leaf Area Index +#" @param q numeric. Hot Spot parameter +#" @param tts numeric. Sun zeith angle +#" @param tto numeric. Observer zeith angle +#" @param psi numeric. Azimuth Sun / Observer +#" @param rsoil numeric. Soil reflectance +#" @param fraction_brown numeric. Fraction of brown leaf area +#" @param diss numeric. Layer dissociation factor +#" @param cv numeric. vertical crown cover percentage +#" = % ground area covered with crowns as seen from nadir direction +#" @param zeta numeric. Tree shape factor +#" = ratio of crown diameter to crown height +#" @param sailversion character. choose between 4SAIL and 4SAIL2 +#" @param brownvegetation list. Defines optical properties for brown vegetation, if not NULL +#" - WVL, reflectance, Transmittance +#" - Set to NULL if use PROSPECT to generate it +#" +#" @return list. rdot, rsot, rddt, rsdt +#" rdot: hemispherical-directional reflectance factor in viewing direction +#" rsot: bi-directional reflectance factor +#" rsdt: directional-hemispherical reflectance factor for solar incident flux +#" rddt: bi-hemispherical reflectance factor +#" @import prospect +#" @export +pro4sail <- function(spec_sensor, input_prospect = NULL, N = 1.5, CHL = 40.0, + CAR = 8.0, ANT = 0.0, BROWN = 0.0, EWT = 0.01, + LMA = 0.008, PROT = 0.0, CBC = 0.0, alpha = 40.0, + typelidf = 2, lidfa = NULL, lidfb = NULL, lai = NULL, + q = NULL, tts = NULL, tto = NULL, psi = NULL, rsoil = NULL, + fraction_brown = 0.0, diss = 0.0, cv = 1, zeta = 1, + sailversion = "4SAIL", brownvegetation = NULL) { + + ############################ # + # LEAF OPTICAL PROPERTIES ## + ############################ # + if (is.null(input_prospect)) { + input_prospect <- data.frame("CHL" = CHL, "CAR" = CAR, "ANT" = ANT, "BROWN" = BROWN, "EWT" = EWT, + "LMA" = LMA, "PROT" = PROT, "CBC" = CBC, "N" = N, "alpha" = alpha) + } + greenvegetation <- prospect::PROSPECT(SpecPROSPECT = spec_sensor, + N = input_prospect$N[1], + CHL = input_prospect$CHL[1], + CAR = input_prospect$CAR[1], + ANT = input_prospect$ANT[1], + BROWN = input_prospect$BROWN[1], + EWT = input_prospect$EWT[1], + LMA = input_prospect$LMA[1], + PROT = input_prospect$PROT[1], + CBC = input_prospect$CBC[1], + alpha = input_prospect$alpha[1]) + + if (sailversion == "4SAIL2") { + # 4SAIL2 requires one of the following combination of input parameters + # Case #1: valid optical properties for brown vegetation + if (!is.null(brownvegetation)) { + # need to define reflectance and Transmittance for brownvegetation + if (length(grep("reflectance", names(brownvegetation))) == 0 || length(grep("Transmittance", names(brownvegetation))) == 0) { + message("Please define brownvegetation as a list including reflectance and Transmittance") + stop() + } + # check if spectral domain for optical properties of brown vegetation match + # with spectral domain for optical properties of green vegetation + if (length(setdiff(spec_sensor$lambda, brownvegetation$wvl)) > 0) { + message("Please define same spectral domain for brownvegetation and SpecPROSPECT") + stop() + } + if (length(unique(lengths(input_prospect))) == 1) { + if (!unique(lengths(input_prospect)) == 1) { + message("brownvegetation defined along with multiple leaf chemical properties") + message("Only first set of leaf chemical properties will be used to simulate green vegetation") + } + } + # if no leaf optical properties brown vegetation defined + } else if (is.null(brownvegetation)) { + # if all PROSPECT input parameters have the same length + if (length(unique(lengths(input_prospect))) == 1) { + # if all PROSPECT input parameters are unique (no possibility to simulate 2 types of leaf optics) + if (unique(lengths(input_prospect)) == 1) { + # if fraction_brown set to 0, then assign green vegetation optics to brown vegetation optics + if (fraction_brown == 0) { + brownvegetation <- greenvegetation + # else run 4SAIL + } else { + message("4SAIL2 needs two sets of optical properties for green and brown vegetation") + message("Currently one set is defined. will run 4SAIL instead of 4SAIL2") + sailversion <- "4SAIL" + } + # if all PROSPECT parameters have at least 2 elements + } else if (unique(lengths(input_prospect)) >= 2) { + # compute leaf optical properties + brownvegetation <- prospect::PROSPECT(SpecPROSPECT = spec_sensor, + N = input_prospect$N[2], + CHL = input_prospect$CHL[2], + CAR = input_prospect$CAR[2], + ANT = input_prospect$ANT[2], + BROWN = input_prospect$BROWN[2], + EWT = input_prospect$EWT[2], + LMA = input_prospect$LMA[2], + PROT = input_prospect$PROT[2], + CBC = input_prospect$CBC[2], + alpha = input_prospect$alpha[2]) + if (unique(lengths(input_prospect)) > 2) { + message("4SAIL2 needs two sets of optical properties for green and brown vegetation") + message("Currently more than 2 sets are defined. will only use the first 2") + } + } + } + } + } + if (sailversion == "4SAIL") { + if (length(unique(lengths(input_prospect))) == 1) { + if (unique(lengths(input_prospect)) > 1) { + message("4SAIL needs only one set of optical properties") + message("Currently more than one set of leaf chemical constituents is defined.") + message("Will run 4SAIL with the first set of leaf chemical constituents") + } + } + } + + if (sailversion == "4SAIL") { + # run 4SAIL + ref <- foursail(leafoptics = greenvegetation, + typelidf, lidfa, lidfb, lai, q, tts, tto, psi, rsoil) + } else if (sailversion == "4SAIL2") { + # run 4SAIL2 + ref <- foursail2(leafgreen = greenvegetation, leafbrown = brownvegetation, + typelidf, lidfa, lidfb, lai, q, tts, tto, psi, rsoil, + fraction_brown, diss, cv, zeta) + } + return(ref) +} + +#" Performs PROSAIL simulation based on a set of combinations of input parameters +#" @param leafoptics list. Includes leaf optical properties (reflectance and transmittance) +#" and corresponding spectral bands +#" @param typelidf numeric. Type of leaf inclination distribution function +#" @param lidfa numeric. +#" if typelidf == 1, controls the average leaf slope +#" if typelidf == 2, corresponds to average leaf angle +#" @param lidfb numeric. +#" if typelidf == 1, unused +#" if typelidf == 2, controls the distribution"s bimodality +#" @param lai numeric. Leaf Area Index +#" @param q numeric. Hot Spot parameter +#" @param tts numeric. Sun zeith angle +#" @param tto numeric. Observer zeith angle +#" @param psi numeric. Azimuth Sun / Observer +#" @param rsoil numeric. Soil reflectance +#" +#" @return list. rdot, rsot, rddt, rsdt +#" rdot: hemispherical-directional reflectance factor in viewing direction +#" rsot: bi-directional reflectance factor +#" rsdt: directional-hemispherical reflectance factor for solar incident flux +#" rddt: bi-hemispherical reflectance factor +#" @export + +foursail <- function(leafoptics, typelidf = 2, lidfa = NULL, lidfb = NULL, lai = NULL, + q = NULL, tts = NULL, tto = NULL, psi = NULL, rsoil = NULL) { + + ############################## # + # LEAF OPTICAL PROPERTIES ## + ############################## # + rho <- leafoptics$Reflectance + tau <- leafoptics$Transmittance + + # Geometric quantities + rd <- pi / 180 + cts <- cos(rd * tts) + cto <- cos(rd * tto) + ctscto <- cts * cto + tants <- tan(rd * tts) + tanto <- tan(rd * tto) + cospsi <- cos(rd * psi) + dso <- sqrt(tants * tants + tanto * tanto - 2. * tants * tanto * cospsi) + + # Generate leaf angle distribution from average leaf angle (ellipsoidal) or (a, b) parameters + if (typelidf == 1) { + foliar_distrib <- dladgen(lidfa, lidfb) + lidf <- foliar_distrib$lidf + litab <- foliar_distrib$litab + + } else if (typelidf == 2) { + foliar_distrib <- campbell(lidfa) + lidf <- foliar_distrib$lidf + litab <- foliar_distrib$litab + } + + # angular distance, compensation of shadow length + # Calculate geometric factors associated with extinction and scattering + # Initialise sums + ks <- 0 + ko <- 0 + bf <- 0 + sob <- 0 + sof <- 0 + + # Weighted sums over LIDF + na <- length(litab) + for (i in 1:na) { + ttl <- litab[i] # leaf inclination discrete values + ctl <- cos(rd * ttl) + # SAIL volume scattering phase function gives interception and portions to be + # multiplied by rho and tau + resvolscatt <- volscatt(tts, tto, psi, ttl) + chi_s <- resvolscatt$chi_s + chi_o <- resvolscatt$chi_o + frho <- resvolscatt$frho + ftau <- resvolscatt$ftau + + #******************************************************************************** + #* SUITS SYSTEM coEFFICIENTS + #* + #* ks : Extinction coefficient for direct solar flux + #* ko : Extinction coefficient for direct observed flux + #* att : Attenuation coefficient for diffuse flux + #* sigb : Backscattering coefficient of the diffuse downward flux + #* sigf : Forwardscattering coefficient of the diffuse upward flux + #* sf : Scattering coefficient of the direct solar flux for downward diffuse flux + #* sb : Scattering coefficient of the direct solar flux for upward diffuse flux + #* vf : Scattering coefficient of upward diffuse flux in the observed direction + #* vb : Scattering coefficient of downward diffuse flux in the observed direction + #* w : Bidirectional scattering coefficient + #******************************************************************************** + + # Extinction coefficients + ksli <- chi_s / cts + koli <- chi_o / cto + + # Area scattering coefficient fractions + sobli <- frho * pi / ctscto + sofli <- ftau * pi / ctscto + bfli <- ctl * ctl + ks <- ks + ksli * lidf[i] + ko <- ko + koli * lidf[i] + bf <- bf + bfli * lidf[i] + sob <- sob + sobli * lidf[i] + sof <- sof + sofli * lidf[i] + } + + # Geometric factors to be used later with rho and tau + sdb <- 0.5 * (ks + bf) + sdf <- 0.5 * (ks - bf) + dob <- 0.5 * (ko + bf) + dof <- 0.5 * (ko - bf) + ddb <- 0.5 * (1. + bf) + ddf <- 0.5 * (1. - bf) + + # Here rho and tau come in + sigb <- ddb * rho + ddf * tau + sigf <- ddf * rho + ddb * tau + att <- 1 - sigf + m2 <- (att + sigb) * (att - sigb) + m2[which(m2 <= 0)] <- 0 + m <- sqrt(m2) + + sb <- sdb * rho + sdf * tau + sf <- sdf * rho + sdb * tau + vb <- dob * rho + dof * tau + vf <- dof * rho + dob * tau + w <- sob * rho + sof * tau + + # Here the LAI comes in + # Outputs for the case LAI = 0 + if (lai < 0) { + tss <- 1 + too <- 1 + tsstoo <- 1 + rdd <- 0 + tdd <- 1 + rsd <- 0 + tsd <- 0 + rdo <- 0 + tdo <- 0 + rso <- 0 + rsos <- 0 + rsod <- 0 + + rddt <- rsoil + rsdt <- rsoil + rdot <- rsoil + rsodt <- 0 * rsoil + rsost <- rsoil + rsot <- rsoil + } else { + # Other cases (LAI > 0) + e1 <- exp(-m * lai) + e2 <- e1 * e1 + rinf <- (att - m) / sigb + rinf2 <- rinf * rinf + re <- rinf * e1 + denom <- 1. - rinf2 * e2 + + j1ks <- jfunc1(ks, m, lai) + j2ks <- jfunc2(ks, m, lai) + j1ko <- jfunc1(ko, m, lai) + j2ko <- jfunc2(ko, m, lai) + + ps <- (sf + sb * rinf) * j1ks + qs <- (sf * rinf + sb) * j2ks + pv <- (vf + vb * rinf) * j1ko + qv <- (vf * rinf + vb) * j2ko + + rdd <- rinf * (1. - e2) / denom + tdd <- (1. - rinf2) * e1 / denom + tsd <- (ps - re * qs) / denom + rsd <- (qs - re * ps) / denom + tdo <- (pv - re * qv) / denom + rdo <- (qv - re * pv) / denom + + tss <- exp(-ks * lai) + too <- exp(-ko * lai) + z <- jfunc3(ks, ko, lai) + g1 <- (z - j1ks * too) / (ko + m) + g2 <- (z - j1ko * tss) / (ks + m) + + tv1 <- (vf * rinf + vb) * g1 + tv2 <- (vf + vb * rinf) * g2 + t1 <- tv1 * (sf + sb * rinf) + t2 <- tv2 * (sf * rinf + sb) + t3 <- (rdo * qs + tdo * ps) * rinf + + # Multiple scattering contribution to bidirectional canopy reflectance + rsod <- (t1 + t2 - t3) / (1. - rinf2) + + # Treatment of the hotspot-effect + alf <- 1e6 + # Apply correction 2 / (K + k) suggested by F.-M. Breon + if (q > 0) { + alf <- (dso / q) * 2. / (ks + ko) + } + if (alf > 200) { + # inserted H. Bach 1 / 3 / 04 + alf <- 200 + } + if (alf == 0) { + # The pure hotspot - no shadow + tsstoo <- tss + sumint <- (1 - tss) / (ks * lai) + } else { + # Outside the hotspot + fhot <- lai * sqrt(ko * ks) + # Integrate by exponential Simpson method in 20 steps + # the steps are arranged according to equal partitioning + # of the slope of the joint probability function + x1 <- 0 + y1 <- 0 + f1 <- 1 + fint <- (1. - exp(-alf)) * 0.05 + sumint <- 0 + for (i in 1:20) { + if (i < 20) { + x2 <- -log(1. - i * fint) / alf + } else { + x2 <- 1 + } + y2 <- -(ko + ks) * lai * x2 + fhot * (1. - exp(-alf * x2)) / alf + f2 <- exp(y2) + sumint <- sumint + (f2 - f1) * (x2 - x1) / (y2 - y1) + x1 <- x2 + y1 <- y2 + f1 <- f2 + } + tsstoo <- f1 + } + # Bidirectional reflectance + # Single scattering contribution + rsos <- w * lai * sumint + # Total canopy contribution + rso <- rsos + rsod + # Interaction with the soil + dn <- 1. - rsoil * rdd + # rddt: bi-hemispherical reflectance factor + rddt <- rdd + tdd * rsoil * tdd / dn + # rsdt: directional-hemispherical reflectance factor for solar incident flux + rsdt <- rsd + (tsd + tss) * rsoil * tdd / dn + # rdot: hemispherical-directional reflectance factor in viewing direction + rdot <- rdo + tdd * rsoil * (tdo + too) / dn + # rsot: bi-directional reflectance factor + rsodt <- rsod + ((tss + tsd) * tdo + (tsd + tss * rsoil * rdd) * too) * rsoil / dn + rsost <- rsos + tsstoo * rsoil + rsot <- rsost + rsodt + } + my_list <- list("rdot" = rdot, "rsot" = rsot, "rddt" = rddt, "rsdt" = rsdt) + return(my_list) +} + +#" Performs pro4sail2 simulation based on a set of combinations of input parameters +#" @param leafgreen list. includes relfectance and transmittance for vegetation #1 (e.g. green vegetation) +#" @param leafbrown list. includes relfectance and transmittance for vegetation #2 (e.g. brown vegetation) +#" @param typelidf numeric. Type of leaf inclination distribution function +#" @param lidfa numeric. +#" if typelidf == 1, controls the average leaf slope +#" if typelidf == 2, corresponds to average leaf angle +#" @param lidfb numeric. +#" if typelidf == 1, unused +#" if typelidf == 2, controls the distribution"s bimodality +#" @param lai numeric. Leaf Area Index +#" @param hot numeric. Hot Spot parameter = ratio of the correlation length of leaf projections in the horizontal plane and the canopy height (doi:10.1016 / j.rse.2006.12.013) +#" @param tts numeric. Sun zeith angle +#" @param tto numeric. Observer zeith angle +#" @param psi numeric. Azimuth Sun / Observer +#" @param rsoil numeric. Soil reflectance +#" @param fraction_brown numeric. Fraction of brown leaf area +#" @param diss numeric. Layer dissociation factor +#" @param cv numeric. vertical crown cover percentage +#" = % ground area covered with crowns as seen from nadir direction +#" @param zeta numeric. Tree shape factor +#" = ratio of crown diameter to crown height +#" +#" @return list. rdot, rsot, rddt, rsdt +#" rdot: hemispherical-directional reflectance factor in viewing direction +#" rsot: bi-directional reflectance factor +#" rsdt: directional-hemispherical reflectance factor for solar incident flux +#" rddt: bi-hemispherical reflectance factor +#" alfast: canopy absorptance for direct solar incident flux +#" alfadt: canopy absorptance for hemispherical diffuse incident flux +#" @export + +foursail2 <- function(leafgreen, leafbrown, + typelidf = 2, lidfa = NULL, lidfb = NULL, + lai = NULL, hot = NULL, tts = NULL, tto = NULL, psi = NULL, rsoil = NULL, + fraction_brown = 0.5, diss = 0.5, cv = 1, zeta = 1) { + + # This version does not include non-Lambertian soil properties. + # original codes do, and only need to add the following variables as input + rddsoil <- rdosoil <- rsdsoil <- rsosoil <- rsoil + + # Geometric quantities + rd <- pi / 180 + + # Generate leaf angle distribution from average leaf angle (ellipsoidal) or (a, b) parameters + if (typelidf == 1) { + foliar_distrib <- dladgen(lidfa, lidfb) + lidf <- foliar_distrib$lidf + litab <- foliar_distrib$litab + + } else if (typelidf == 2) { + foliar_distrib <- campbell(lidfa) + lidf <- foliar_distrib$lidf + litab <- foliar_distrib$litab + } + + if (lai < 0) { + message("Please define positive LAI value") + rddt <- rsdt <- rdot <- rsost <- rsot <- rsoil + alfast <- alfadt <- 0 * rsoil + } else if (lai == 0) { + tss <- too <- tsstoo <- tdd <- 1.0 + rdd <- rsd <- tsd <- rdo <- tdo <- 0.0 + rso <- rsos <- rsod <- rsodt <- 0.0 + rddt <- rsdt <- rdot <- rsost <- rsot <- rsoil + alfast <- alfadt <- 0 * rsoil + } else if (lai > 0) { + cts <- cos(rd * tts) + cto <- cos(rd * tto) + ctscto <- cts * cto + tants <- tan(rd * tts) + tanto <- tan(rd * tto) + cospsi <- cos(rd * psi) + dso <- sqrt(tants * tants + tanto * tanto - 2.0 * tants * tanto * cospsi) + + # Clumping effects + cs <- co <- 1.0 + if (cv <= 1.0) { + cs <- 1.0 - (1.0 - cv)^(1.0 / cts) + co <- 1.0 - (1.0 - cv)^(1.0 / cto) + } + overlap <- 0.0 + if (zeta > 0.0) { + overlap <- min(cs * (1.0 - co), co * (1.0 - cs)) * exp(-dso / zeta) + } + fcd <- cs * co + overlap + fcs <- (1.0 - cs) * co - overlap + fod <- cs * (1.0 - co) - overlap + fos <- (1.0 - cs) * (1.0 - co) + overlap + fcdc <- 1.0 - (1.0 - fcd)^(0.5 / cts + 0.5 / cto) + + # Part depending on diss, fraction_brown, and leaf optical properties + # First save the input fraction_brown as the old fraction_brown, as the following change is only artificial + # Better define an fraction_brown that is actually used: fb, so that the input is not modified! + + fb <- fraction_brown + # if only green leaves + if (fraction_brown == 0.0) { + fb <- 0.5 + leafbrown$Reflectance <- leafgreen$Reflectance + leafbrown$Transmittance <- leafgreen$Transmittance + } + if (fraction_brown == 1.0) { + fb <- 0.5 + leafgreen$Reflectance <- leafbrown$Reflectance + leafgreen$Transmittance <- leafbrown$Transmittance + } + s <- (1.0 - diss) * fb * (1.0 - fb) + # rho1 && tau1 : green foliage + # rho2 && tau2 : brown foliage (bottom layer) + rho1 <- ((1 - fb - s) * leafgreen$Reflectance + s * leafbrown$Reflectance) / (1 - fb) + tau1 <- ((1 - fb - s) * leafgreen$Transmittance + s * leafbrown$Transmittance) / (1 - fb) + rho2 <- (s * leafgreen$Reflectance + (fb - s) * leafbrown$Reflectance) / fb + tau2 <- (s * leafgreen$Transmittance + (fb - s) * leafbrown$Transmittance) / fb + + # angular distance, compensation of shadow length + # Calculate geometric factors associated with extinction and scattering + # Initialise sums + ks <- ko <- bf <- sob <- sof <- 0 + + # Weighted sums over LIDF + + for (i in 1:seq_along(litab)) { + ttl <- litab[i] + ctl <- cos(rd * ttl) + # SAIL volscatt function gives interception coefficients + # and two portions of the volume scattering phase function to be + # multiplied by rho and tau, respectively + resvolscatt <- volscatt(tts, tto, psi, ttl) + chi_s <- resvolscatt$chi_s + chi_o <- resvolscatt$chi_o + frho <- resvolscatt$frho + ftau <- resvolscatt$ftau + # Extinction coefficients + ksli <- chi_s / cts + koli <- chi_o / cto + # Area scattering coefficient fractions + sobli <- frho * pi / ctscto + sofli <- ftau * pi / ctscto + bfli <- ctl * ctl + ks <- ks + ksli * lidf[i] + ko <- ko + koli * lidf[i] + bf <- bf + bfli * lidf[i] + sob <- sob + sobli * lidf[i] + sof <- sof + sofli * lidf[i] + } + # Geometric factors to be used later in combination with rho and tau + sdb <- 0.5 * (ks + bf) + sdf <- 0.5 * (ks - bf) + dob <- 0.5 * (ko + bf) + dof <- 0.5 * (ko - bf) + ddb <- 0.5 * (1. + bf) + ddf <- 0.5 * (1. - bf) + + # LAIs in two layers + lai1 <- (1 - fb) * lai + lai2 <- fb * lai + + tss <- exp(-ks * lai) + ck <- exp(-ks * lai1) + alf <- 1e6 + if (hot > 0.0) { + alf <- (dso / hot) * 2.0 / (ks + ko) + } + if (alf > 200.0) { + alf <- 200.0 # inserted H. Bach 1 / 3 / 04 + } + if (alf == 0.0) { + # The pure hotspot + tsstoo <- tss + s1 <- (1 - ck) / (ks * lai) + s2 <- (ck - tss) / (ks * lai) + } else { + # Outside the hotspot + fhot <- lai * sqrt(ko * ks) + # Integrate 2 layers by exponential simpson method in 20 steps + # the steps are arranged according to equal partitioning + # of the derivative of the joint probability function + x1 <- y1 <- 0.0 + f1 <- 1.0 + ca <- exp(alf * (fb - 1.0)) + fint <- (1.0 - ca) * .05 + s1 <- 0.0 + for (istep in 1:20) { + if (istep < 20) { + x2 <- -log(1. - istep * fint) / alf + } else { + x2 <- 1. - fb + } + y2 <- -(ko + ks) * lai * x2 + fhot * (1.0 - exp(-alf * x2)) / alf + f2 <- exp(y2) + s1 <- s1 + (f2 - f1) * (x2 - x1) / (y2 - y1) + x1 <- x2 + y1 <- y2 + f1 <- f2 + } + fint <- (ca - exp(-alf)) * .05 + s2 <- 0.0 + for (istep in 1:20) { + if (istep < 20) { + x2 <- -log(ca - istep * fint) / alf + } else { + x2 <- 1.0 + } + y2 <- -(ko + ks) * lai * x2 + fhot * (1.0 - exp(-alf * x2)) / alf + f2 <- exp(y2) + s2 <- s2 + (f2 - f1) * (x2 - x1) / (y2 - y1) + x1 <- x2 + y1 <- y2 + f1 <- f2 + } + tsstoo <- f1 + } + + # Calculate reflectances and transmittances + # Bottom layer + tss <- exp(-ks * lai2) + too <- exp(-ko * lai2) + sb <- sdb * rho2 + sdf * tau2 + sf <- sdf * rho2 + sdb * tau2 + + vb <- dob * rho2 + dof * tau2 + vf <- dof * rho2 + dob * tau2 + + w2 <- sob * rho2 + sof * tau2 + + sigb <- ddb * rho2 + ddf * tau2 + sigf <- ddf * rho2 + ddb * tau2 + att <- 1.0 - sigf + m2 <- (att + sigb) * (att - sigb) + m2[m2 < 0] <- 0 + m <- sqrt(m2) + which_ncs <- which(m > 0.01) + which_cs <- which(m <= 0.01) + + tdd <- rdd <- tsd <- rsd <- tdo <- rdo <- 0 * m + rsod <- 0 * m + if (length(which_ncs) > 0) { + resncs <- nonconservativescattering(m[which_ncs], lai2, att[which_ncs], sigb[which_ncs], + ks, ko, sf[which_ncs], sb[which_ncs], vf[which_ncs], vb[which_ncs], tss, too) + tdd[which_ncs] <- resncs$tdd + rdd[which_ncs] <- resncs$rdd + tsd[which_ncs] <- resncs$tsd + rsd[which_ncs] <- resncs$rsd + tdo[which_ncs] <- resncs$tdo + rdo[which_ncs] <- resncs$rdo + rsod[which_ncs] <- resncs$rsod + } + if (length(which_cs) > 0) { + rescs <- conservativescattering(m[which_cs], lai2, att[which_cs], sigb[which_cs], + ks, ko, sf[which_cs], sb[which_cs], vf[which_cs], vb[which_cs], tss, too) + tdd[which_cs] <- rescs$tdd + rdd[which_cs] <- rescs$rdd + tsd[which_cs] <- rescs$tsd + rsd[which_cs] <- rescs$rsd + tdo[which_cs] <- rescs$tdo + rdo[which_cs] <- rescs$rdo + rsod[which_cs] <- rescs$rsod + } + + # Set background properties equal to those of the bottom layer on a black soil + rddb <- rdd + rsdb <- rsd + rdob <- rdo + rsodb <- rsod + tddb <- tdd + tsdb <- tsd + tdob <- tdo + toob <- too + tssb <- tss + # Top layer + tss <- exp(-ks * lai1) + too <- exp(-ko * lai1) + + sb <- sdb * rho1 + sdf * tau1 + sf <- sdf * rho1 + sdb * tau1 + + vb <- dob * rho1 + dof * tau1 + vf <- dof * rho1 + dob * tau1 + + w1 <- sob * rho1 + sof * tau1 + + sigb <- ddb * rho1 + ddf * tau1 + sigf <- ddf * rho1 + ddb * tau1 + att <- 1.0 - sigf + + m2 <- (att + sigb) * (att - sigb) + m2[m2 < 0] <- 0 + m <- sqrt(m2) + which_ncs <- which(m > 0.01) + which_cs <- which(m <= 0.01) + + tdd <- rdd <- tsd <- rsd <- tdo <- rdo <- 0 * m + rsod <- 0 * m + if (length(which_ncs) > 0) { + resncs <- nonconservativescattering(m[which_ncs], lai1, att[which_ncs], sigb[which_ncs], + ks, ko, sf[which_ncs], sb[which_ncs], vf[which_ncs], vb[which_ncs], tss, too) + tdd[which_ncs] <- resncs$tdd + rdd[which_ncs] <- resncs$rdd + tsd[which_ncs] <- resncs$tsd + rsd[which_ncs] <- resncs$rsd + tdo[which_ncs] <- resncs$tdo + rdo[which_ncs] <- resncs$rdo + rsod[which_ncs] <- resncs$rsod + } + if (length(which_cs) > 0) { + rescs <- conservativescattering(m[which_cs], lai1, att[which_cs], sigb[which_cs], + ks, ko, sf[which_cs], sb[which_cs], vf[which_cs], vb[which_cs], tss, too) + tdd[which_cs] <- rescs$tdd + rdd[which_cs] <- rescs$rdd + tsd[which_cs] <- rescs$tsd + rsd[which_cs] <- rescs$rsd + tdo[which_cs] <- rescs$tdo + rdo[which_cs] <- rescs$rdo + rsod[which_cs] <- rescs$rsod + } + + # combine with bottom layer reflectances and transmittances (adding method) + rn <- 1.0 - rdd * rddb + tup <- (tss * rsdb + tsd * rddb) / rn + tdn <- (tsd + tss * rsdb * rdd) / rn + rsdt <- rsd + tup * tdd + rdot <- rdo + tdd * (rddb * tdo + rdob * too) / rn + rsodt <- rsod + (tss * rsodb + tdn * rdob) * too + tup * tdo + + rsost <- (w1 * s1 + w2 * s2) * lai + + rsot <- rsost + rsodt + + # Diffuse reflectances at the top and the bottom are now different + rddt_t <- rdd + tdd * rddb * tdd / rn + rddt_b <- rddb + tddb * rdd * tddb / rn + + # Transmittances of the combined canopy layers + tsst <- tss * tssb + toot <- too * toob + tsdt <- tss * tsdb + tdn * tddb + tdot <- tdob * too + tddb * (tdo + rdd * rdob * too) / rn + tddt <- tdd * tddb / rn + + # Apply clumping effects to vegetation layer + rddcb <- cv * rddt_b + rddct <- cv * rddt_t + tddc <- 1 - cv + cv * tddt + rsdc <- cs * rsdt + tsdc <- cs * tsdt + rdoc <- co * rdot + tdoc <- co * tdot + tssc <- 1 - cs + cs * tsst + tooc <- 1 - co + co * toot + + # New weight function fcdc for crown contribution (W. Verhoef, 22-05-08) + rsoc <- fcdc * rsot + tssooc <- fcd * tsstoo + fcs * toot + fod * tsst + fos + # Canopy absorptance for black background (W. Verhoef, 02-03-04) + alfas <- 1. - tssc - tsdc - rsdc + alfad <- 1. - tddc - rddct + # Add the soil background + rn <- 1 - rddcb * rddsoil + tup <- (tssc * rsdsoil + tsdc * rddsoil) / rn + tdn <- (tsdc + tssc * rsdsoil * rddcb) / rn + + rddt <- rddct + tddc * rddsoil * tddc / rn + rsdt <- rsdc + tup * tddc + rdot <- rdoc + tddc * (rddsoil * tdoc + rdosoil * tooc) / rn + rsot <- rsoc + tssooc * rsosoil + tdn * rdosoil * tooc + tup * tdoc + + # Effect of soil background on canopy absorptances (W. Verhoef, 02-03-04) + alfast <- alfas + tup * alfad + alfadt <- alfad * (1. + tddc * rddsoil / rn) + } + my_list <- list("rdot" = rdot, "rsot" = rsot, "rddt" = rddt, "rsdt" = rsdt, + "alfast" = alfast, "alfadt" = alfadt) + return(my_list) +} + + + +#" computes non conservative scattering conditions +#" @param m numeric. +#" @param lai numeric. Leaf Area Index +#" @param att numeric. +#" @param sigb numeric. +#" @param ks numeric. +#" @param ko numeric. +#" @param sf numeric. +#" @param sb numeric. +#" @param vf numeric. +#" @param vb numeric. +#" @param tss numeric. +#" @param too numeric. +#" +#" @return list. tdd, rdd, tsd, rsd, tdo, rdo, rsod +#" +#" @export +nonconservativescattering <- function(m, lai, att, sigb, ks, ko, sf, sb, vf, vb, tss, too) { + + e1 <- exp(-m * lai) + e2 <- e1 * e1 + rinf <- (att - m) / sigb + rinf2 <- rinf * rinf + re <- rinf * e1 + denom <- 1. - rinf2 * e2 + + j1ks <- jfunc1(ks, m, lai) + j2ks <- jfunc2(ks, m, lai) + j1ko <- jfunc1(ko, m, lai) + j2ko <- jfunc2(ko, m, lai) + + ps <- (sf + sb * rinf) * j1ks + qs <- (sf * rinf + sb) * j2ks + pv <- (vf + vb * rinf) * j1ko + qv <- (vf * rinf + vb) * j2ko + + tdd <- (1. - rinf2) * e1 / denom + rdd <- rinf * (1. - e2) / denom + tsd <- (ps - re * qs) / denom + rsd <- (qs - re * ps) / denom + tdo <- (pv - re * qv) / denom + rdo <- (qv - re * pv) / denom + + z <- jfunc2(ks, ko, lai) + g1 <- (z - j1ks * too) / (ko + m) + g2 <- (z - j1ko * tss) / (ks + m) + + tv1 <- (vf * rinf + vb) * g1 + tv2 <- (vf + vb * rinf) * g2 + + t1 <- tv1 * (sf + sb * rinf) + t2 <- tv2 * (sf * rinf + sb) + t3 <- (rdo * qs + tdo * ps) * rinf + + # Multiple scattering contribution to bidirectional canopy reflectance + rsod <- (t1 + t2 - t3) / (1. - rinf2) + my_list <- list("tdd" = tdd, "rdd" = rdd, "tsd" = tsd, + "rsd" = rsd, "tdo" = tdo, "rdo" = rdo, + "rsod" = rsod) + return(my_list) +} + +#" computes conservative scattering conditions +#" @param m numeric. +#" @param lai numeric. Leaf Area Index +#" @param att numeric. +#" @param sigb numeric. +#" @param ks numeric. +#" @param ko numeric. +#" @param sf numeric. +#" @param sb numeric. +#" @param vf numeric. +#" @param vb numeric. +#" @param tss numeric. +#" @param too numeric. +#" +#" @return list. tdd, rdd, tsd, rsd, tdo, rdo, rsod +#" +#" @export +conservativescattering <- function(m, lai, att, sigb, ks, ko, sf, sb, vf, vb, tss, too) { + + # Near or complete conservative scattering + j4 <- jfunc4(m, lai) + amsig <- att - sigb + apsig <- att + sigb + rtp <- (1 - amsig * j4) / (1 + amsig * j4) + rtm <- (-1 + apsig * j4) / (1 + apsig * j4) + rdd <- 0.5 * (rtp + rtm) + tdd <- 0.5 * (rtp - rtm) + + dns <- ks * ks - m * m + dno <- ko * ko - m * m + cks <- (sb * (ks - att) - sf * sigb) / dns + cko <- (vb * (ko - att) - vf * sigb) / dno + dks <- (-sf * (ks + att) - sb * sigb) / dns + dko <- (-vf * (ko + att) - vb * sigb) / dno + ho <- (sf * cko + sb * dko) / (ko + ks) + + rsd <- cks * (1 - tss * tdd) - dks * rdd + rdo <- cko * (1 - too * tdd) - dko * rdd + tsd <- dks * (tss - tdd) - cks * tss * rdd + tdo <- dko * (too - tdd) - cko * too * rdd + # Multiple scattering contribution to bidirectional canopy reflectance + rsod <- ho * (1 - tss * too) - cko * tsd * too - dko * rsd + + my_list <- list("tdd" = tdd, "rdd" = rdd, "tsd" = tsd, + "rsd" = rsd, "tdo" = tdo, "rdo" = rdo, + "rsod" = rsod) + return(my_list) +} + + + + + + +#" computes the leaf angle distribution function value (freq) +#" +#" Ellipsoidal distribution function characterised by the average leaf +#" inclination angle in degree (ala) +#" Campbell 1986 +#" @param ala average leaf angle +#" @return foliar_distrib list. lidf and litab +#" @export +campbell <- function(ala) { + + tx1 <- c(10., 20., 30., 40., 50., 60., 70., 80., 82., 84., 86., 88., 90.) + tx2 <- c(0., 10., 20., 30., 40., 50., 60., 70., 80., 82., 84., 86., 88.) + litab <- (tx2 + tx1) / 2 + n <- length(litab) + tl1 <- tx1 * (pi / 180) + tl2 <- tx2 * (pi / 180) + excent <- exp(-1.6184e-5 * ala**3 + 2.1145e-3 * ala**2 - 1.2390e-1 * ala + 3.2491) + sum0 <- 0 + + freq <- c() + for (i in 1:n) { + x1 <- excent / (sqrt(1. + excent**2. * tan(tl1[i])**2)) + x2 <- excent / (sqrt(1. + excent**2. * tan(tl2[i])**2)) + if (excent == 1) { + freq[i] <- abs(cos(tl1[i]) - cos(tl2[i])) + } else { + alpha <- excent / sqrt(abs(1 - excent**2)) + alpha2 <- alpha**2 + x12 <- x1**2 + x22 <- x2**2 + alpx1 <- 0 * alpha2 + alpx2 <- 0 * alpha2 + almx1 <- 0 * alpha2 + almx2 <- 0 * alpha2 + if (excent > 1) { + alpx1 <- sqrt(alpha2[excent > 1] + x12[excent > 1]) + alpx2[excent > 1] <- sqrt(alpha2[excent > 1] + x22[excent > 1]) + dum <- x1 * alpx1 + alpha2 * log(x1 + alpx1) + freq[i] <- abs(dum - (x2 * alpx2 + alpha2 * log(x2 + alpx2))) + } else { + almx1 <- sqrt(alpha2 - x12) + almx2 <- sqrt(alpha2 - x22) + dum <- x1 * almx1 + alpha2 * asin(x1 / alpha) + freq[i] <- abs(dum - (x2 * almx2 + alpha2 * asin(x2 / alpha))) + } + } + } + sum0 <- sum(freq) + freq0 <- freq / sum0 + foliar_distrib <- list("lidf" = freq0, "litab" = litab) + return(foliar_distrib) +} + +#" computes the leaf angle distribution function value (freq) +#" +#" Using the original bimodal distribution function initially proposed in SAIL +#" references +#" ---------- +#" (Verhoef1998) Verhoef, Wout. Theory of radiative transfer models applied +#" in optical remote sensing of vegetation canopies. +#" Nationaal Lucht en Ruimtevaartlaboratorium, 1998. +#" http: / / library.wur.nl / WebQuery / clc / 945481. +#" @param a controls the average leaf slope +#" @param b controls the distribution"s bimodality +#" LIDF type a b +#" Planophile 1 0 +#" Erectophile -1 0 +#" Plagiophile 0 -1 +#" Extremophile 0 1 +#" Spherical -0.35 -0.15 +#" Uniform 0 0 +#" requirement: ||lidfa|| + ||lidfb|| < 1 +#" +#" @return foliar_distrib list. lidf and litab +#" @export +dladgen <- function(a, b) { + litab <- c(5., 15., 25., 35., 45., 55., 65., 75., 81., 83., 85., 87., 89.) + freq <- c() + for (i1 in 1:8) { + t <- i1 * 10 + freq[i1] <- dcum(a, b, t) + } + for (i2 in 9:12) { + t <- 80. + (i2 - 8) * 2. + freq[i2] <- dcum(a, b, t) + } + freq[13] <- 1 + for (i in 13:2) { + freq[i] <- freq[i] - freq[i - 1] + } + foliar_distrib <- list("lidf" = freq, "litab" = litab) + return(foliar_distrib) +} + +#" dcum function +#" @param a numeric. controls the average leaf slope +#" @param b numeric. controls the distribution"s bimodality +#" @param t numeric. angle +#" @return f +#" @export +dcum <- function(a, b, t) { + rd <- pi / 180 + if (a >= 1) { + f <- 1 - cos(rd * t) + } else { + eps <- 1e-8 + delx <- 1 + x <- 2 * rd * t + p <- x + while (delx >= eps) { + y <- a * sin(x) + .5 * b * sin(2. * x) + dx <- .5 * (y - x + p) + x <- x + dx + delx <- abs(dx) + } + f <- (2. * y + p) / pi + } + return(f) +} + +#" J1 function with avoidance of singularity problem +#" +#" @param k numeric. Extinction coefficient for direct (solar or observer) flux +#" @param l numeric. +#" @param t numeric. Leaf Area Index +#" @return jout numeric. +#" @export +jfunc1 <- function(k, l, t) { + # J1 function with avoidance of singularity problem + del <- (k - l) * t + jout <- 0 * l + jout[which(abs(del) > 1e-3)] <- (exp(-l[which(abs(del) > 1e-3)] * t) - exp(-k * t)) / (k - l[which(abs(del) > 1e-3)]) + jout[which(abs(del) <= 1e-3)] <- 0.5 * t * (exp(-k * t) + exp(-l[which(abs(del) <= 1e-3)] * t)) * (1 - del[which(abs(del) <= 1e-3)] * del[which(abs(del) <= 1e-3)] / 12) + return(jout) +} + +#" J2 function with avoidance of singularity problem +#" +#" @param k numeric. Extinction coefficient for direct (solar or observer) flux +#" @param l numeric. +#" @param t numeric. Leaf Area Index +#" @return jout numeric. +#" @export +jfunc2 <- function(k, l, t) { + # J2 function + jout <- (1. - exp(-(k + l) * t)) / (k + l) + return(jout) +} + +#" J3 function with avoidance of singularity problem +#" +#" @param k numeric. Extinction coefficient for direct (solar or observer) flux +#" @param l numeric. +#" @param t numeric. Leaf Area Index +#" @return jout numeric. +#" @export +jfunc3 <- function(k, l, t) { + out <- (1. - exp(-(k + l) * t)) / (k + l) + return(out) +} + + +#" j4 function for treating (near) conservative scattering +#" +#" @param m numeric. Extinction coefficient for direct (solar or observer) flux +#" @param t numeric. Leaf Area Index +#" @return jout numeric. +#" @export +jfunc4 <- function(m, t) { + + del <- m * t + out <- 0 * del + out[del > 1e-3] <- (1 - exp(-del)) / (m * (1 + exp(-del))) + out[del <= 1e-3] <- 0.5 * t * (1. - del * del / 12.) + return(out) +} + + +#" compute volume scattering functions and interception coefficients +#" for given solar zenith, viewing zenith, azimuth and leaf inclination angle. +#" +#" @param tts numeric. solar zenith +#" @param tto numeric. viewing zenith +#" @param psi numeric. azimuth +#" @param ttl numeric. leaf inclination angle +#" @return res list. includes chi_s, chi_o, frho, ftau +#" @export +volscatt <- function(tts, tto, psi, ttl) { + #******************************************************************************** + #* chi_s = interception functions + #* chi_o = interception functions + #* frho = function to be multiplied by leaf reflectance rho + #* ftau = functions to be multiplied by leaf transmittance tau + #******************************************************************************** + # Wout Verhoef, april 2001, for CROMA + + rd <- pi / 180 + costs <- cos(rd * tts) + costo <- cos(rd * tto) + sints <- sin(rd * tts) + sinto <- sin(rd * tto) + cospsi <- cos(rd * psi) + psir <- rd * psi + costl <- cos(rd * ttl) + sintl <- sin(rd * ttl) + cs <- costl * costs + co <- costl * costo + ss <- sintl * sints + so <- sintl * sinto + + #c .............................................................................. + #c betas -bts- and betao -bto- computation + #c Transition angles (beta) for solar (betas) and view (betao) directions + #c if thetav + thetal > pi / 2, bottom side of the leaves is observed for leaf azimut + #c interval betao + phi 1e-6) { + cosbts <- -cs / ss + } + cosbto <- 5 + if (abs(so) > 1e-6) { + cosbto <- -co / so + } + + if (abs(cosbts) < 1) { + bts <- acos(cosbts) + ds <- ss + } else { + bts <- pi + ds <- cs + } + chi_s <- 2. / pi * ((bts - pi * .5) * cs + sin(bts) * ss) + if (abs(cosbto) < 1) { + bto <- acos(cosbto) + doo <- so + } else if (tto < 90) { + bto <- pi + doo <- co + } else { + bto <- 0 + doo <- -co + } + chi_o <- 2. / pi * ((bto - pi * .5) * co + sin(bto) * so) + + #c .............................................................................. + #c computation of auxiliary azimut angles bt1, bt2, bt3 used + #c for the computation of the bidirectional scattering coefficient w + #c ............................................................................. + + btran1 <- abs(bts - bto) + btran2 <- pi - abs(bts + bto - pi) + + if (psir <= btran1) { + bt1 <- psir + bt2 <- btran1 + bt3 <- btran2 + } else { + bt1 <- btran1 + if (psir <= btran2) { + bt2 <- psir + bt3 <- btran2 + } else { + bt2 <- btran2 + bt3 <- psir + } + } + t1 <- 2. * cs * co + ss * so * cospsi + t2 <- 0 + if (bt2 > 0) { + t2 <- sin(bt2) * (2. * ds * doo + ss * so * cos(bt1) * cos(bt3)) + } + + denom <- 2. * pi * pi + frho <- ((pi - bt2) * t1 + t2) / denom + ftau <- (-bt2 * t1 + t2) / denom + + if (frho < 0) { + frho <- 0 + } + if (ftau < 0) { + ftau <- 0 + } + res <- list("chi_s" = chi_s, "chi_o" = chi_o, "frho" = frho, "ftau" = ftau) + return(res) +} diff --git a/tools/SRS_tools/prosail-master/R/Lib_PROSAIL_HybridInversion.R b/tools/SRS_tools/prosail-master/R/Lib_PROSAIL_HybridInversion.R new file mode 100644 index 000000000..e2f9a177d --- /dev/null +++ b/tools/SRS_tools/prosail-master/R/Lib_PROSAIL_HybridInversion.R @@ -0,0 +1,554 @@ +# ============================================================================= = +# prosail +# Lib_PROSAIL_HybridInversion.R +# ============================================================================= = +# PROGRAMMERS: +# Jean-Baptiste FERET +# Florian de BOISSIEU +# Copyright 2019 / 11 Jean-Baptiste FERET +# ============================================================================= = +# This Library includes functions dedicated to PROSAIL inversion using hybrid +# approach based on SVM regression +# ============================================================================= = + + +#" This function applies SVR model on raster data in order to estimate +#" vegetation biophysical properties +#" +#" @param raster_path character. path for a raster file +#" @param hybridmodel list. hybrid models produced from train_prosail_inversion +#" each element of the list corresponds to a set of hybrid models for a vegetation parameter +#" @param pathout character. path for directory where results are written +#" @param selectedbands list. list of spectral bands to be selected from raster (identified by name of vegetation parameter) +#" @param bandname character. spectral bands corresponding to the raster +#" @param maskraster character. path for binary mask defining ON (1) and OFF (0) pixels in the raster +#" @param multiplyingfactor numeric. multiplying factor used to write reflectance in the raster +#" --> PROSAIL simulates reflectance between 0 and 1, and raster data expected in the same range +#" +#" @return None +#" @importFrom progress progress_bar +#" @importFrom stars read_stars +#" @importFrom raster raster brick blockSize readStart readStop getValues writeStart writeStop writeValues +#" @import rgdal +#" @export +apply_prosail_inversion <- function(raster_path, hybridmodel, pathout, + selectedbands, bandname, + maskraster = FALSE, multiplyingfactor = 10000) { + + # explain which biophysical variables will be computed + bpvar <- names(hybridmodel) + print("The following biophysical variables will be computed") + print(bpvar) + + # get image dimensions + if (attr(rgdal::GDALinfo(raster_path, returnStats = FALSE), "driver") == "ENVI") { + hdr <- read_envi_header(get_hdr_name(raster_path)) + dimsraster <- list("rows" = hdr$lines, "cols" = hdr$samples, "bands" = hdr$bands) + } else { + dimsraster <- dim(read_stars(raster_path)) + dimsraster <- list("rows" = as.numeric(dimsraster[2]), "cols" = as.numeric(dimsraster[1]), "bands" = as.numeric(dimsraster[3])) + } + + # Produce a map for each biophysical property + for (parm in bpvar) { + print(paste("Computing", parm, sep = " ")) + # read by chunk to avoid memory problem + blk <- blockSize(brick(raster_path)) + # reflectance file + r_in <- readStart(brick(raster_path)) + # mask file + r_inmask <- FALSE + if (maskraster == FALSE) { + selectpixels <- "ALL" + } else if (!maskraster == FALSE) { + if (file.exists(maskraster)) { + r_inmask <- readStart(raster(maskraster)) + } else if (!file.exists(maskraster)) { + message("WARNING: Mask file does not exist:") + print(maskraster) + message("Processing all image") + selectpixels <- "ALL" + } + } + # initiate progress bar + pgbarlength <- length(hybridmodel[[parm]]) * blk$n + pb <- progress_bar$new( + format = "Hybrid inversion on raster [:bar] :percent in :elapsedfull, estimated time remaining :eta", + total = pgbarlength, clear = FALSE, width = 100) + + # output files + bpvarpath <- file.path(pathout, paste(basename(raster_path), parm, sep = "_")) + bpvarsdpath <- file.path(pathout, paste(basename(raster_path), parm, "STD", sep = "_")) + r_outmean <- writeStart(raster(raster_path), filename = bpvarpath, format = "ENVI", overwrite = TRUE) + r_outsd <- writeStart(raster(raster_path), filename = bpvarsdpath, format = "ENVI", overwrite = TRUE) + selbands <- match(selectedbands[[parm]], bandname) + + # loop over blocks + for (i in seq_along(blk$row)) { + # read values for block + # format is a matrix with rows the cells values and columns the layers + blockval <- getValues(r_in, row = blk$row[i], nrows = blk$nrows[i]) + fulllength <- dim(blockval)[1] + + if (typeof(r_inmask) == "logical") { + blockval <- blockval[, selbands] + # automatically filter pixels corresponding to negative values + selectpixels <- which(blockval[, 1] > 0) + blockval <- blockval[selectpixels, ] + } else if (typeof(r_inmask) == "S4") { + maskval <- getValues(r_inmask, row = blk$row[i], nrows = blk$nrows[i]) + selectpixels <- which(maskval == 1) + blockval <- blockval[selectpixels, selbands] + } + mean_estimatefull <- NA * vector(length = fulllength) + std_estimatefull <- NA * vector(length = fulllength) + if (length(selectpixels) > 0) { + blockval <- blockval / multiplyingfactor + modelsvr_estimate <- list() + for (modind in 1:seq_along(hybridmodel[[parm]])) { + pb$tick() + modelsvr_estimate[[modind]] <- predict(hybridmodel[[parm]][[modind]], blockval) + } + modelsvr_estimate <- do.call(cbind, modelsvr_estimate) + # final estimated value = mean parm value for all models + mean_estimate <- rowMeans(modelsvr_estimate) + # "uncertainty" = STD value for all models + std_estimate <- rowSds(modelsvr_estimate) + mean_estimatefull[selectpixels] <- mean_estimate + std_estimatefull[selectpixels] <- std_estimate + } else { + for (modind in 1:seq_along(hybridmodel[[parm]])) { + pb$tick() + } + } + r_outmean <- writeValues(r_outmean, mean_estimatefull, blk$row[i], format = "ENVI", overwrite = TRUE) + r_outsd <- writeValues(r_outsd, std_estimatefull, blk$row[i], format = "ENVI", overwrite = TRUE) + } + # close files + r_in <- readStop(r_in) + if (typeof(r_inmask) == "S4") { + r_inmask <- readStop(r_inmask) + } + r_outmean <- writeStop(r_outmean) + r_outsd <- writeStop(r_outsd) + # write biophysical variable name in headers + hdr <- read_envi_header(get_hdr_name(bpvarpath)) + hdr$`band names` <- paste("{", parm, "}", sep = "") + write_envi_header(hdr, get_hdr_name(bpvarpath)) + } + print("processing completed") + return(invisible()) +} + +#" get hdr name from image file name, assuming it is BIL format +#" +#" @param impath path of the image +#" +#" @return corresponding hdr +#" @importFrom tools file_ext file_path_sans_ext +#" @export +get_hdr_name <- function(impath) { + if (tools::file_ext(impath) == "") { + impathhdr <- paste(impath, ".hdr", sep = "") + } else if (tools::file_ext(impath) == "bil") { + impathhdr <- gsub(".bil", ".hdr", impath) + } else if (tools::file_ext(impath) == "zip") { + impathhdr <- gsub(".zip", ".hdr", impath) + } else { + impathhdr <- paste(tools::file_path_sans_ext(impath), ".hdr", sep = "") + } + + if (!file.exists(impathhdr)) { + message("WARNING : COULD NOT FIND hdr FILE") + print(impathhdr) + message("Process may stop") + } + return(impathhdr) +} + +#" This function applies the regression models trained with prosail_hybrid_train +#" +#" @param regressionmodels list. List of regression models produced by prosail_hybrid_train +#" @param refl numeric. LUT of bidirectional reflectances factors used for training +#" +#" @return hybridres list. Estimated values corresponding to refl. Includes +#" - meanestimate = mean value for the ensemble regression model +#" - stdestimate = std value for the ensemble regression model +#" @importFrom stats predict +#" @importFrom matrixStats rowSds +#" @importFrom progress progress_bar +#" @export + +prosail_hybrid_apply <- function(regressionmodels, refl) { + + # make sure refl is right dimensions + refl <- t(refl) + nbfeatures <- regressionmodels[[1]]$dim + if (!ncol(refl) == nbfeatures && nrow(refl) == nbfeatures) { + refl <- t(refl) + } + nbensemble <- length(regressionmodels) + estimatedval <- list() + pb <- progress_bar$new( + format = "Applying SVR models [:bar] :percent in :elapsed", + total = nbensemble, clear = FALSE, width = 100) + for (i in 1:nbensemble) { + pb$tick() + estimatedval[[i]] <- predict(regressionmodels[[i]], refl) + } + estimatedval <- do.call(cbind, estimatedval) + meanestimate <- rowMeans(estimatedval) + stdestimate <- rowSds(estimatedval) + hybridres <- list("meanestimate" = meanestimate, "stdestimate" = stdestimate) + return(hybridres) +} + +#" This function trains a suppot vector regression for a set of variables based on spectral data +#" +#" @param brf_lut numeric. LUT of bidirectional reflectances factors used for training +#" @param inputvar numeric. biophysical parameter corresponding to the reflectance +#" @param figplot Boolean. Set to TRUE if you want a scatterplot +#" @param nbensemble numeric. Number of individual subsets should be generated from brf_lut +#" @param withreplacement Boolean. should subsets be generated with or without replacement? +#" +#" @return modelssvr list. regression models trained for the retrieval of inputvar based on brf_lut +#" @importFrom liquidSVM svmRegression +#" @importFrom stats predict +#" @importFrom progress progress_bar +#" @importFrom graphics par +#" @importFrom expandFunctions reset.warnings +#" @importFrom stringr str_split +#" @importFrom simsalapar tryCatch.W.E +#" @import dplyr +#" @import ggplot2 +# @" @import caret +#" @export + +prosail_hybrid_train <- function(brf_lut, inputvar, figplot = FALSE, nbensemble = 20, withreplacement = FALSE) { + + x <- y <- ymean <- ystdmin <- ystdmax <- NULL + # split the LUT into nbensemble subsets + nbsamples <- length(inputvar) + if (dim(brf_lut)[2] == nbsamples) { + brf_lut <- t(brf_lut) + } + + # if subsets are generated from brf_lut with replacement + if (withreplacement == TRUE) { + subsets <- list() + samples_per_run <- round(nbsamples / nbensemble) + for (run in (1:nbensemble)) { + subsets[[run]] <- sample(seq(1, nbsamples), samples_per_run, replace = TRUE) + } + # if subsets are generated from brf_lut without replacement + } else if (withreplacement == FALSE) { + subsets <- split(sample(seq(1, nbsamples, by = 1)), seq(1, nbensemble, by = 1)) + } + + # run training for each subset + modelssvr <- list() + predictedyall <- list() + tunedmodelyall <- list() + pb <- progress_bar$new( + format = "Training SVR on subsets [:bar] :percent in :elapsed", + total = nbensemble, clear = FALSE, width = 100) + for (i in 1:nbensemble) { + pb$tick() + Sys.sleep(1 / 100) + trainingset <- list() + trainingset$X <- brf_lut[subsets[i][[1]], ] + trainingset$Y <- inputvar[subsets[i][[1]]] + # liquidSVM + r1 <- tryCatch.W.E(tunedmodel <- liquidSVM::svmRegression(trainingset$X, trainingset$Y)) + if (!is.null(r1$warning)) { + msg <- r1$warning$message + valgamma <- str_split(string = msg, pattern = "gamma=")[[1]][2] + vallambda <- str_split(string = msg, pattern = "lambda=")[[1]][2] + if (!is.na(as.numeric(valgamma))) { + message("Adjusting Gamma accordingly") + valgamma <- as.numeric(valgamma) + tunedmodel <- liquidSVM::svmRegression(trainingset$X, trainingset$Y, min_gamma = valgamma) + } + if (!is.na(as.numeric(vallambda))) { + message("Adjusting Lambda accordingly") + vallambda <- as.numeric(vallambda) + tunedmodel <- liquidSVM::svmRegression(trainingset$X, trainingset$Y, min_lambda = vallambda) + } + } + modelssvr[[i]] <- tunedmodel + } + + # if scatterplots needed + if (figplot == TRUE) { + # predict for full brf_lut + for (i in 1:nbensemble) { + tunedmodely <- stats::predict(modelssvr[[i]], brf_lut) + tunedmodelyall <- cbind(tunedmodelyall, matrix(tunedmodely, ncol = 1)) + } + # plot prediction + df <- data.frame(x = rep(1:nbsamples, nbensemble), y = as.numeric(matrix(tunedmodelyall, ncol = 1))) + df_summary <- df %>% + dplyr::group_by(x) %>% + summarize(ymin = min(y), ystdmin = mean(y) - sd(y), + ymax = max(y), ystdmax = mean(y) + sd(y), + ymean = mean(y)) + par(mar = rep(.1, 4)) + p <- ggplot(df_summary, aes(x = inputvar, y = ymean)) + + geom_point(size = 2) + + geom_errorbar(aes(ymin = ystdmin, ymax = ystdmax)) + meanpredict <- rowMeans(matrix(as.numeric(tunedmodelyall), ncol = nbensemble)) + print(p) + } + return(modelssvr) +} + +#" Reads ENVI hdr file +#" +#" @param hdrpath Path of the hdr file +#" +#" @return list of the content of the hdr file +#" @export +read_envi_header <- function(hdrpath) { + if (!grepl(".hdr$", hdrpath)) { + stop("File extension should be .hdr") + } + hdr <- readLines(hdrpath) + ## check ENVI at beginning of file + if (!grepl("ENVI", hdr[1])) { + stop("Not an ENVI header (ENVI keyword missing)") + } else { + hdr <- hdr [-1] + } + ## remove curly braces and put multi-line key-value-pairs into one line + hdr <- gsub("\\{([^}]*)\\}", "\\1", hdr) + l <- grep("\\{", hdr) + r <- grep("\\}", hdr) + + if (length(l) != length(r)) { + stop("Error matching curly braces in header (differing numbers).") + } + + if (any(r <= l)) { + stop("Mismatch of curly braces in header.") + } + + hdr[l] <- sub("\\{", "", hdr[l]) + hdr[r] <- sub("\\}", "", hdr[r]) + + for (i in rev(seq_along(l))) { + hdr <- c( + hdr [seq_len(l [i] - 1)], + paste(hdr [l [i]:r [i]], collapse = "\n"), + hdr [-seq_len(r [i])] + ) + } + + ## split key = value constructs into list with keys as names + hdr <- sapply(hdr, split_line, " = ", USE.NAMES = FALSE) + names(hdr) <- tolower(names(hdr)) + + ## process numeric values + tmp <- names(hdr) %in% c( + "samples", "lines", "bands", "header offset", "data type", + "byte order", "default bands", "data ignore value", + "wavelength", "fwhm", "data gain values" + ) + hdr [tmp] <- lapply(hdr [tmp], function(x) { + as.numeric(unlist(strsplit(x, ","))) + }) + + return(hdr) +} + +#" ENVI functions +#" +#" based on https: / / github.com / cran / hyperSpec / blob / master / R / read.ENVI.R +#" added wavelength, fwhm, ... to header reading +#" Title +#" +#" @param x character. +#" @param separator character +#" @param trim_blank boolean. +#" +#" @return list. +#" @export +split_line <- function(x, separator, trim_blank = TRUE) { + tmp <- regexpr(separator, x) + key <- substr(x, 1, tmp - 1) + value <- substr(x, tmp + 1, nchar(x)) + if (trim_blank) { + blank_pattern <- "^[[:blank:]]*([^[:blank:]] + .*[^[:blank:]] + )[[:blank:]]*$" + key <- sub(blank_pattern, "\\1", key) + value <- sub(blank_pattern, "\\1", value) + } + value <- as.list(value) + names(value) <- key + return(value) +} + +#" This function performs full training for hybrid invrsion using SVR with +#" values for default parameters +#" +#" @param minval list. minimum value for input parameters sampled to produce a training LUT +#" @param maxval list. maximum value for input parameters sampled to produce a training LUT +#" @param typedistrib list. Type of distribution. Either "Uniform" or "Gaussian" +#" @param gaussiandistrib list. Mean value and STD corresponding to the parameters sampled with gaussian distribution +#" @param parmset list. list of input parameters set to a specific value +#" @param nbsamples numeric. number of samples in training LUT +#" @param nbsamplesperrun numeric. number of training sample per individual regression model +#" @param nbmodels numeric. number of individual models to be run for ensemble +#" @param replacement bolean. is there replacement in subsampling? +#" @param sailversion character. Either 4SAIL or 4SAIL2 +#" @param parms2estimate list. list of input parameters to be estimated +#" @param bands2select list. list of bands used for regression for each input parameter +#" @param noiselevel list. list of noise value added to reflectance (defined per input parm) +#" @param specprospect list. Includes optical constants required for PROSPECT +#" @param specsoil list. Includes either dry soil and wet soil, or a unique soil sample if the psoil parameter is not inverted +#" @param specatm list. Includes direct and diffuse radiation for clear conditions +#" @param path_results character. path for results +#" @param figplot boolean. Set TRUE to get scatterplot of estimated biophysical variable during training step +#" @param force4lowlai boolean. Set TRUE to artificially reduce leaf chemical constituent content for low LAI +#" +#" +#" @return modelssvr list. regression models trained for the retrieval of inputvar based on brf_lut +#" @export + +train_prosail_inversion <- function(minval = NULL, maxval = NULL, + typedistrib = NULL, gaussiandistrib = NULL, parmset = NULL, + nbsamples = 2000, nbsamplesperrun = 100, nbmodels = 20, replacement = TRUE, + sailversion = "4SAIL", + parms2estimate = "lai", bands2select = NULL, noiselevel = NULL, + specprospect = NULL, specsoil = NULL, specatm = NULL, + path_results = "./", figplot = FALSE, force4lowlai = TRUE) { + + ###===================================================================### + ### 1- PRODUCE A LUT TO TRAIN THE HYBRID INVERSION ### + ###===================================================================### + # Define sensor characteristics + if (is.null(specprospect)) { + specprospect <- prosail::specprospect + } + if (is.null(specsoil)) { + specsoil <- prosail::specsoil + } + if (is.null(specprospect)) { + specatm <- prosail::specatm + } + # define distribution for parameters to be sampled + if (is.null(typedistrib)) { + typedistrib <- data.frame("CHL" = "Uniform", "CAR" = "Uniform", "EWT" = "Uniform", "ANT" = "Uniform", "LMA" = "Uniform", "N" = "Uniform", "BROWN" = "Uniform", + "psoil" = "Uniform", "LIDFa" = "Uniform", "lai" = "Uniform", "q" = "Uniform", "tto" = "Uniform", "tts" = "Uniform", "psi" = "Uniform") + } + if (is.null(gaussiandistrib)) { + gaussiandistrib <- list("Mean" = NULL, "Std" = NULL) + } + if (is.null(minval)) { + minval <- data.frame("CHL" = 10, "CAR" = 0, "EWT" = 0.01, "ANT" = 0, "LMA" = 0.005, "N" = 1.0, "psoil" = 0.0, "BROWN" = 0.0, + "LIDFa" = 20, "lai" = 0.5, "q" = 0.1, "tto" = 0, "tts" = 20, "psi" = 80) + } + if (is.null(maxval)) { + maxval <- data.frame("CHL" = 75, "CAR" = 15, "EWT" = 0.03, "ANT" = 2, "LMA" = 0.03, "N" = 2.0, "psoil" = 1.0, "BROWN" = 0.5, + "LIDFa" = 70, "lai" = 7, "q" = 0.2, "tto" = 5, "tts" = 30, "psi" = 110) + } + # define min and max values + # fixed parameters + if (is.null(parmset)) { + parmset <- data.frame("TypeLidf" = 2, "alpha" = 40) + } + # produce input parameters distribution + if (sailversion == "4SAIL") { + inputprosail <- get_distribution_input_prosail(minval, maxval, parmset, nbsamples, + typedistrib = typedistrib, + Mean = gaussiandistrib$Mean, Std = gaussiandistrib$Std, + force4lowlai = force4lowlai) + } else if (sailversion == "4SAIL2") { + inputprosail <- get_distribution_input_prosail2(minval, maxval, parmset, nbsamples, + typedistrib = typedistrib, + Mean = gaussiandistrib$Mean, Std = gaussiandistrib$Std, + force4lowlai = force4lowlai) + } + if (sailversion == "4SAIL2") { + # Definition of Cv && update LAI + maxlai <- min(c(maxval$lai), 4) + inputprosail$Cv <- NA * inputprosail$lai + inputprosail$Cv[which(inputprosail$lai > maxlai)] <- 1 + inputprosail$Cv[which(inputprosail$lai <= maxlai)] <- (1 / maxlai) + inputprosail$lai[which(inputprosail$lai <= maxlai)] / (maxlai + 1) + inputprosail$Cv <- inputprosail$Cv * matrix(rnorm(length(inputprosail$Cv), mean = 1, sd = 0.1)) + inputprosail$Cv[which(inputprosail$Cv < 0)] <- 0 + inputprosail$Cv[which(inputprosail$Cv > 1)] <- 1 + inputprosail$Cv[which(inputprosail$lai > maxlai)] <- 1 + inputprosail$fraction_brown <- 0 + 0 * inputprosail$lai + inputprosail$diss <- 0 + 0 * inputprosail$lai + inputprosail$Zeta <- 0.2 + 0 * inputprosail$lai + inputprosail$lai <- inputprosail$lai * inputprosail$Cv + } + + # generate LUT of BRF corresponding to inputprosail, for a sensor + brf_lut <- Generate_LUT_BRF(sailversion = sailversion, inputprosail = inputprosail, + specprospect = specprospect, specsoil = specsoil, specatm = specatm) + + # write parameters LUT + output <- matrix(unlist(inputprosail), ncol = length(inputprosail), byrow = FALSE) + filename <- file.path(path_results, "PROSAIL_LUT_InputParms.txt") + write.table(x = format(output, digits = 3), file = filename, append = FALSE, quote = FALSE, + col.names = names(inputprosail), row.names = FALSE, sep = "\t") + # Write BRF LUT corresponding to parameters LUT + filename <- file.path(path_results, "PROSAIL_LUT_reflectance.txt") + write.table(x = format(t(brf_lut), digits = 5), file = filename, append = FALSE, quote = FALSE, + col.names = specprospect$lambda, row.names = FALSE, sep = "\t") + + # Which bands will be used for inversion? + if (is.null(bands2select)) { + bands2select <- list() + for (parm in parms2estimate) { + bands2select[[parm]] <- seq(1, length(specprospect$lambda)) + } + } + # Add gaussian noise to reflectance LUT: one specific LUT per parameter + if (is.null(noiselevel)) { + noiselevel <- list() + for (parm in parms2estimate) { + noiselevel[[parm]] <- 0.01 + } + } + + # produce LIT with noise + brf_lut_noise <- list() + for (parm in parms2estimate) { + brf_lut_noise[[parm]] <- brf_lut[bands2select[[parm]], ] + brf_lut[bands2select[[parm]], ] * matrix(rnorm(nrow(brf_lut[bands2select[[parm]], ]) * ncol(brf_lut[bands2select[[parm]], ]), + 0, noiselevel[[parm]]), nrow = nrow(brf_lut[bands2select[[parm]], ])) + } + + ###===================================================================### + ### PERFORM HYBRID INVERSION ### + ###===================================================================### + # train SVR for each variable and each run + modelsvr <- list() + for (parm in parms2estimate) { + colparm <- which(parm == names(inputprosail)) + inputvar <- inputprosail[[colparm]] + modelsvr[[parm]] <- prosail_hybrid_train(brf_lut = brf_lut_noise[[parm]], inputvar = inputvar, + figplot = figplot, nbensemble = nbmodels, withreplacement = replacement) + } + return(modelsvr) +} + +#" writes ENVI hdr file +#" +#" @param hdr content to be written +#" @param hdrpath Path of the hdr file +#" +#" @return None +#" @importFrom stringr str_count +#" @export +write_envi_header <- function(hdr, hdrpath) { + h <- lapply(hdr, function(x) { + if (length(x) > 1 || (is.character(x) && stringr::str_count(x, "\\w + ") > 1)) { + x <- paste0("{", paste(x, collapse = ","), "}") + } + # convert last numerics + x <- as.character(x) + }) + writeLines(c("ENVI", paste(names(hdr), h, sep = "=")), con = hdrpath) + return(invisible()) +} diff --git a/tools/SRS_tools/prosail-master/R/Lib_SpectralIndices.R b/tools/SRS_tools/prosail-master/R/Lib_SpectralIndices.R new file mode 100644 index 000000000..d5afc44b9 --- /dev/null +++ b/tools/SRS_tools/prosail-master/R/Lib_SpectralIndices.R @@ -0,0 +1,679 @@ +# ============================================================================== = +# prosail +# Lib_spectralindices.R +# ============================================================================== = +# PROGRAMMERS: +# Jean-Baptiste FERET +# Florian de BOISSIEU +# Copyright 2019/11 Jean-Baptiste FERET +# ============================================================================== = +# This Library includes aims at computing spectral indices from reflectance data +# ============================================================================== = + +#" This function computes Area under curve for continuum removed reflectances +#" See Malenovský et al. (2013) for details +#" http://dx.doi.org/10.1016/j.rse.2012.12.015 +#" +#" @param refl RasterBrick, RasterStack or list. Raster bands in the order of sensorbands. +#" @param sensorbands numeric. vector containing central wavelength for each spectral band in the image +#" @param aucminmax list. wavelengths of lower and upper boundaries ("CRmin" and "CRmax") +#" @param reflfactor numeric. multiplying factor used to write reflectance in image (==10000 for S2) +#" +#" @return aucval raster +#" @export +auc <- function(refl, sensorbands, aucminmax, reflfactor = 1) { + + aucbands <- list() + aucbands[["CRmin"]] <- sensorbands[get_closest_bands(sensorbands, aucminmax[["CRmin"]])] + aucbands[["CRmax"]] <- sensorbands[get_closest_bands(sensorbands, aucminmax[["CRmax"]])] + bands <- get_closest_bands(sensorbands, aucbands) + for (i in bands[["CRmin"]]:bands[["CRmax"]]) { + if (is.na(match(i, bands))) { + aucbands[[paste("B", i, sep = "")]] <- sensorbands[i] + } + } + # compute continuum removal for all spectral bands + cr <- cr_wl(refl = refl, sensorbands = sensorbands, + crbands = aucbands, reflfactor = reflfactor) + + wl <- sort(unlist(aucbands), decreasing = FALSE) + aucval <- 0.5 * (1 - cr[[1]]) * (wl[2] - wl[1]) + for (i in 2:length(cr)) { + aucval <- aucval + 0.5 * (2 - cr[[i - 1]] - cr[[i]]) * (wl[i + 1] - wl[i]) + } + aucval <- aucval + 0.5 * (1 - cr[[length(cr)]]) * (wl[i + 2] - wl[i + 1]) + return(aucval) +} + +#" This function extracts boundaries to be used to compute continuum from reflectance data +#" +#" @param refl RasterBrick, RasterStack or list. Raster bands in the order of sensorbands. +#" @param sensorbands numeric. vector containing central wavelength for each spectral band in the image +#" @param crbands list. list of spectral bands (central wavelength) including CRmin and CRmax +#" @param reflfactor numeric. multiplying factor used to write reflectance in image ( == 10000 for S2) +#" +#" @return crminmax list. list of rasters corresponding to minimum and maximum wavelengths +#" @export +crbound <- function(refl, sensorbands, crbands, reflfactor = 1) { + + # get closest spectral bands from CR1 and CR2 + bands <- get_closest_bands(sensorbands, list(crbands[["CRmin"]], crbands[["CRmax"]])) + wl <- sensorbands[bands] + # get equation for line going from CR1 to CR2 + crminmax <- readrasterbands(refl = refl, bands = bands, reflfactor = reflfactor) + names(crminmax) <- paste("wl_", as.character(wl), sep = "") + return(crminmax) +} + +#" This function extracts boundaries to be used to compute continuum from reflectance data +#" +#" @param refl RasterBrick, RasterStack or list. Raster bands in the order of sensorbands. +#" @param sensorbands numeric. vector containing central wavelength for each spectral band in the image +#" @param crbands list. list of spectral bands (central wavelength) including CRmin and CRmax +#" @param reflfactor numeric. multiplying factor used to write reflectance in image ( == 10000 for S2) +#" +#" @return outlier_iqr numeric. band numbers of original sensor corresponding to S2 +#" @importFrom progress progress_bar +#" @export +cr_wl <- function(refl, sensorbands, crbands, reflfactor = 1) { + + # Make sure CRmin and CRmax are correctly defined + if (is.na(match("CRmin", names(crbands))) || is.na(match("CRmax", names(crbands)))) { + stop("Please define CRmin and CRmax (CRmin0 in order to avoid problems + selzero <- which(refl == 0) + refl[selzero] <- 0.005 + if (dim(refl)[1] == length(sensorbands)) { + refl <- t(refl) + } + + # inelegant but meeeeh + listindices <- list("ARI1", "ARI2", "ARVI", "BAI", "BAIS2", "CHL_RE", "CRI1", "CRI2", "EVI", "EVI2", + "GRVI1", "GNDVI", "IRECI", "LAI_SAVI", "MCARI", "mNDVI705", "MSAVI2", + "MSI", "mSR705", "MTCI", "nBR_RAW", "NDI_45", "NDII", "NDVI", "NDVI_G", + "NDVI705", "NDWI1", "NDWI2", "PSRI", "PSRI_NIR", "RE_NDVI", "RE_NDWI", "S2REP", + "SAVI", "SIPI", "SR", "CR_SWIR") + if (sel_indices == "ALL") { + sel_indices <- listindices + } + if ("ARI1" %in% sel_indices) { + ari1 <- (1 / refl[, sen2s2[["B3"]]]) - (1 / refl[, sen2s2[["B5"]]]) + spectralindices$ARI1 <- ari1 + } + if ("ARI2" %in% sel_indices) { + ari2 <- (refl[, sen2s2[["B8"]]] / refl[, sen2s2[["B2"]]]) - (refl[, sen2s2[["B8"]]] / refl[, sen2s2[["B3"]]]) + spectralindices$ARI2 <- ari2 + } + if ("ARVI" %in% sel_indices) { + arvi <- (refl[, sen2s2[["B8"]]] - (2 * refl[, sen2s2[["B4"]]] - refl[, sen2s2[["B2"]]])) / (refl[, sen2s2[["B8"]]] + (2 * refl[, sen2s2[["B4"]]] - refl[, sen2s2[["B2"]]])) + spectralindices$ARVI <- arvi + } + if ("BAI" %in% sel_indices) { + bai <- (1 / ((0.1 - refl[, sen2s2[["B4"]]])**2 + (0.06 - refl[, sen2s2[["B8"]]])**2)) + spectralindices$BAI <- bai + } + if ("BAIS2" %in% sel_indices) { + bais2 <- (1 - ((refl[, sen2s2[["B6"]]] * refl[, sen2s2[["B7"]]] * refl[, sen2s2[["B8A"]]]) / refl[, sen2s2[["B4"]]])**0.5) * ((refl[, sen2s2[["B12"]]] - refl[, sen2s2[["B8A"]]]) / ((refl[, sen2s2[["B12"]]] + refl[, sen2s2[["B8A"]]])**0.5) + 1) + spectralindices$BAIS2 <- bais2 + } + if ("CCCI" %in% sel_indices) { + ccci <- ((refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B5"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B5"]]])) / ((refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B4"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B4"]]])) + spectralindices$CCCI <- ccci + } + if ("CHL_RE" %in% sel_indices) { + chl_re <- refl[, sen2s2[["B5"]]] / refl[, sen2s2[["B8"]]] + spectralindices$CHL_RE <- chl_re + } + if ("CRI1" %in% sel_indices) { + cri1 <- (1 / refl[, sen2s2[["B2"]]]) - (1 / refl[, sen2s2[["B3"]]]) + spectralindices$CRI1 <- cri1 + } + if ("CRI2" %in% sel_indices) { + cri2 <- (1 / refl[, sen2s2[["B2"]]]) - (1 / refl[, sen2s2[["B5"]]]) + spectralindices$CRI2 <- cri2 + } + if ("EVI" %in% sel_indices) { + evi <- 2.5 * (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B4"]]]) / ((refl[, sen2s2[["B8"]]] + 6 * refl[, sen2s2[["B4"]]] - 7.5 * refl[, sen2s2[["B2"]]] + 1)) + spectralindices$EVI <- evi + } + if ("EVI2" %in% sel_indices) { + evi2 <- 2.5 * (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B4"]]]) / (refl[, sen2s2[["B8"]]] + 2.4 * refl[, sen2s2[["B4"]]] + 1) + spectralindices$EVI2 <- evi2 + } + if ("GRVI1" %in% sel_indices) { + grvi1 <- (refl[, sen2s2[["B4"]]] - refl[, sen2s2[["B3"]]]) / (refl[, sen2s2[["B4"]]] + refl[, sen2s2[["B3"]]]) + spectralindices$GRVI1 <- grvi1 + } + if ("GNDVI" %in% sel_indices) { + gndvi <- (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B3"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B3"]]]) + spectralindices$GNDVI <- gndvi + } + if ("IRECI" %in% sel_indices) { + ireci <- (refl[, sen2s2[["B7"]]] - refl[, sen2s2[["B4"]]]) * (refl[, sen2s2[["B6"]]] / (refl[, sen2s2[["B5"]]])) + spectralindices$IRECI <- ireci + } + if ("LAI_SAVI" %in% sel_indices) { + lai_savi <- - log(0.371 + 1.5 * (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B4"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B4"]]] + 0.5)) / 2.4 + spectralindices$LAI_SAVI <- lai_savi + } + if ("MCARI" %in% sel_indices) { + mcari <- (1 - 0.2 * (refl[, sen2s2[["B5"]]] - refl[, sen2s2[["B3"]]]) / (refl[, sen2s2[["B5"]]] - refl[, sen2s2[["B4"]]])) + spectralindices$MCARI <- mcari + } + if ("mNDVI705" %in% sel_indices) { + mndvi705 <- (refl[, sen2s2[["B6"]]] - refl[, sen2s2[["B5"]]]) / (refl[, sen2s2[["B6"]]] + refl[, sen2s2[["B5"]]] - 2 * refl[, sen2s2[["B2"]]]) + spectralindices$mNDVI705 <- mndvi705 + } + if ("MSAVI2" %in% sel_indices) { + msavi2 <- ((refl[, sen2s2[["B8"]]] + 1) - 0.5 * sqrt(((2 * refl[, sen2s2[["B8"]]]) - 1)**2 + 8 * refl[, sen2s2[["B4"]]])) + spectralindices$MSAVI2 <- msavi2 + } + if ("MSI" %in% sel_indices) { + msi <- refl[, sen2s2[["B11"]]] / refl[, sen2s2[["B8"]]] + spectralindices$MSI <- msi + } + if ("mSR705" %in% sel_indices) { + msr705 <- (refl[, sen2s2[["B6"]]] - refl[, sen2s2[["B2"]]]) / (refl[, sen2s2[["B5"]]] - refl[, sen2s2[["B2"]]]) + spectralindices$mSR705 <- msr705 + } + if ("MTCI" %in% sel_indices) { + mtci <- (refl[, sen2s2[["B6"]]] - refl[, sen2s2[["B5"]]]) / (refl[, sen2s2[["B5"]]] + refl[, sen2s2[["B4"]]]) + spectralindices$MTCI <- mtci + } + if ("nBR_RAW" %in% sel_indices) { + nbr_raw <- (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B12"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B12"]]]) + spectralindices$nBR_RAW <- nbr_raw + } + if ("NDI_45" %in% sel_indices) { + ndi_45 <- (refl[, sen2s2[["B5"]]] - refl[, sen2s2[["B4"]]]) / (refl[, sen2s2[["B5"]]] + refl[, sen2s2[["B4"]]]) + spectralindices$NDI_45 <- ndi_45 + } + if ("NDII" %in% sel_indices) { + ndii <- (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B11"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B11"]]]) + spectralindices$NDII <- ndii + } + if ("NDSI" %in% sel_indices) { + ndisi <- (refl[, sen2s2[["B3"]]] - refl[, sen2s2[["B11"]]]) / (refl[, sen2s2[["B3"]]] + refl[, sen2s2[["B11"]]]) + spectralindices$NDSI <- ndsi + } + if ("NDVI" %in% sel_indices) { + ndvi <- (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B4"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B4"]]]) + spectralindices$NDVI <- ndvi + } + if ("NDVI_G" %in% sel_indices) { + ndvi_g <- refl[, sen2s2[["B3"]]] * (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B4"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B4"]]]) + spectralindices$NDVI_G <- ndvi_g + } + if ("NDVI705" %in% sel_indices) { + ndvi705 <- (refl[, sen2s2[["B6"]]] - refl[, sen2s2[["B5"]]]) / (refl[, sen2s2[["B6"]]] + refl[, sen2s2[["B5"]]]) + spectralindices$NDVI705 <- ndvi705 + } + if ("NDWI1" %in% sel_indices) { + ndwi1 <- (refl[, sen2s2[["B8A"]]] - refl[, sen2s2[["B11"]]]) / (refl[, sen2s2[["B8A"]]] + refl[, sen2s2[["B11"]]]) + spectralindices$NDWI1 <- ndwi1 + } + if ("NDWI2" %in% sel_indices) { + ndwi2 <- (refl[, sen2s2[["B8A"]]] - refl[, sen2s2[["B12"]]]) / (refl[, sen2s2[["B8A"]]] + refl[, sen2s2[["B12"]]]) + spectralindices$NDWI2 <- ndwi2 + } + if ("PSRI" %in% sel_indices) { + psri <- (refl[, sen2s2[["B4"]]] - refl[, sen2s2[["B2"]]]) / (refl[, sen2s2[["B5"]]]) + spectralindices$PSRI <- psri + } + if ("PSRI_NIR" %in% sel_indices) { + psri_nir <- (refl[, sen2s2[["B4"]]] - refl[, sen2s2[["B2"]]]) / (refl[, sen2s2[["B8"]]]) + spectralindices$PSRI_NIR <- psri_nir + } + if ("RE_NDVI" %in% sel_indices) { + re_ndvi <- (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B6"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B6"]]]) + spectralindices$RE_NDVI <- re_ndvi + } + if ("RE_NDWI" %in% sel_indices) { + re_ndwi <- (refl[, sen2s2[["B4"]]] - refl[, sen2s2[["B6"]]]) / (refl[, sen2s2[["B4"]]] + refl[, sen2s2[["B6"]]]) + spectralindices$RE_NDWI <- re_ndwi + } + if ("S2REP" %in% sel_indices) { + s2rep <- 705 + 35 * (0.5 * (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B5"]]]) - refl[, sen2s2[["B6"]]]) / (refl[, sen2s2[["B7"]]] - refl[, sen2s2[["B6"]]]) + spectralindices$S2REP <- s2rep + } + if ("SAVI" %in% sel_indices) { + savi <- 1.5 * (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B5"]]]) / (refl[, sen2s2[["B8"]]] + refl[, sen2s2[["B5"]]] + 0.5) + spectralindices$SAVI <- savi + } + if ("SIPI" %in% sel_indices) { + sipi <- (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B2"]]]) / (refl[, sen2s2[["B8"]]] - refl[, sen2s2[["B4"]]]) + spectralindices$SIPI <- sipi + } + if ("SR" %in% sel_indices) { + sr <- refl[, sen2s2[["B8"]]] / refl[, sen2s2[["B4"]]] + spectralindices$SR <- sr + } + if ("CR_SWIR" %in% sel_indices) { + cr_swir <- refl[, sen2s2[["B11"]]] / (refl[, sen2s2[["B8A"]]] + (s2bands$B11 - s2bands$B8A) * (refl[, sen2s2[["B12"]]] - refl[, sen2s2[["B8A"]]]) / (s2bands$B12 - s2bands$B8A)) + spectralindices$CR_SWIR <- cr_swir + } + res <- list("spectralindices" = spectralindices, "listindices" = listindices) + return(res) +} + +#" this function identifies the bands of a given sensor with closest match to its spectral characteristics +#" +#" @param sensorbands numeric. wavelength in nanometer of the sensor of interest +#" @param listbands numeric or list. Named vector or list of spectral bands corresponding to sensor +#" +#" @return numeric. band numbers of original sensor +#" @export +get_closest_bands <- function(sensorbands, listbands) { + sapply(listbands, function(x) { + b <- which.min(abs(sensorbands - x)) + names(b) <- "" + b + }) +} + +#" This function computes interquartile range (IQR) criterion, which can be used +#" as a criterion for outlier detection +#" +#" @param distval numeric. vector of distribution of values +#" @param weightirq numeric. weighting factor appplied to IRQ to define lower and upper boudaries for outliers +#" +#" @return outlier_iqr numeric. band numbers of original sensor corresponding to S2 +#" @importFrom stats IQR quantile +#" @export +iqr_outliers <- function(distval, weightirq = 1.5) { + iqr <- IQR(distval, na.rm = TRUE) + range_iqr <- c(quantile(distval, 0.25, na.rm = TRUE), quantile(distval, 0.75, na.rm = TRUE)) + outlier_iqr <- c(range_iqr[1] - weightirq * iqr, range_iqr[2] + weightirq * iqr) + return(outlier_iqr) +} + +#" This function selects bands from a raster or stars object +#" +#" @param refl RasterBrick, RasterStack or list. Raster bands in the order of sensorbands. +#" @param bands numeric. rank of bands to be read in refl +#" @param reflfactor numeric. multiplying factor used to write reflectance in image ( == 10000 for S2) +#" +#" @return robj list. R object (default is raster, stars if refl is stars object) +#" @importFrom raster subset stack +#" @export +readrasterbands <- function(refl, bands, reflfactor = 1) { + + # get equation for line going from CR1 to CR2 + classraster <- class(refl) + if (classraster == "RasterBrick" || classraster == "RasterStack" || classraster == "stars") { + # if !reflfactor == 1 then apply a reflectance factor + if (classraster == "stars") { + robj <- refl[bands] + } else { + robj <- raster::subset(refl, bands) + } + if (!reflfactor == 1) { + robj <- robj / reflfactor + } + } else if (is.list(refl)) { + robj <- raster::stack(refl[bands]) # checks that all rasters have same crs/extent + if (!reflfactor == 1) { + robj <- robj / reflfactor + } + } else { + stop("refl is expected to be a RasterStack, RasterBrick, Stars object or a list of rasters") + } + return(robj) +} diff --git a/tools/SRS_tools/test-data/12a0b625-9ad5-4251-a57a-305e22edef2e.xml b/tools/SRS_tools/test-data/12a0b625-9ad5-4251-a57a-305e22edef2e.xml new file mode 100644 index 000000000..10f0d6021 --- /dev/null +++ b/tools/SRS_tools/test-data/12a0b625-9ad5-4251-a57a-305e22edef2e.xml @@ -0,0 +1,661 @@ + + + + 12a0b625-9ad5-4251-a57a-305e22edef2e + + + + + + + + + + + + + + Santiago Poggio + + + IFEVA/ Catédra de producción vegetal, facultad de agronomia, Universidad de Buenos Aires/ CONICET + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + spoggio@agro.uba.ar + + + + + + + + + + + + + + Jacques Baudry + + + BAGAP UMR 0980 INRAE Agrocampus Ouest ESA + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + jacques.baudry@inrae.fr + + + + + + + + + + + + + + Audrey Alignier + + + BAGAP UMR 0980 INRAE Agrocampus Ouest ESA + + + + + + + audrey.alignier@inrae.fr + + + + + + + + + + + + + + + + + Zone Atelier Armorique (ZAAr) + + + + + + + + + + + + + + + + + + + 2021-03-27T00:00:09 + + + ISO 19115:2003/19139 + + + 1.0 + + + + + + + RGF Lambert-93 + + + + + + + + + + + Relevés floristiques en maïs (Pleine-Fougères, 2010) - Zone Atelier Armorique ZAAr + + + + + 2010-09-14 + + + + + + + + + + + + + https://www.osuris.fr/geonetwork/srv/metadata/12a0b625-9ad5-4251-a57a-305e22edef2e + + + + + + + + + + "Relevés des adventices en cultures de maïs (2010) ZAAr" + +Relevés des adventices dans 28 parcelles de maïs pour séparer les effets de la séquence des cultures de l'hétérogénéité spatio-temporelle du paysage des 10 dernières années. + +Ce projet a été financé en partenariat par : +MINCyT: Ministerio de ciencia, tecnologia e innovation productiva (Argentine) +ECOS-SUD (France) + + + + + + + + + + + Santiago Poggio + + + IFEVA/ Catédra de producción vegetal, facultad de agronomia, Universidad de Buenos Aires/ CONICET + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + spoggio@agro.uba.ar + + + + + + + + + + + + + + Jacques Baudry + + + BAGAP UMR 0980 INRAE Agrocampus Ouest ESA + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + jacques.baudry@inrae.fr + + + + + + + + + + + + + + + + + + + + + https://www.osuris.fr/geonetwork/srv/api/records/12a0b625-9ad5-4251-a57a-305e22edef2e/attachments/sad_21_s.png + + + thumbnail + + + png + + + + + + + https://www.osuris.fr/geonetwork/srv/api/records/12a0b625-9ad5-4251-a57a-305e22edef2e/attachments/sad_21.png + + + large_thumbnail + + + png + + + + + + + Biodiversité + + + + + + + + + + Maïs + + + + + + + + + + Forêt / milieu végétal + + + Agriculture + + + Ecologie et environnement + + + Paysages et sites + + + + + + + + theme.EnvironnementFR.rdf + + + + + 2017-03-20 + + + + + + + + + + geonetwork.thesaurus.external.theme.EnvironnementFR + + + + + + + + + + + Installations de suivi environnemental + + + Habitats et biotopes + + + + + + + + Registre de thème INSPIRE + + + + + 2019-06-25 + + + + + + + + + + geonetwork.thesaurus.external.theme.httpinspireeceuropaeutheme-theme + + + + + + + + + + + + + + + + + licence CC-BY-NC 4.0 + + + + + + + Utilisation libre sous réserve de mentionner la source (a minima le nom du producteur) et la date de sa dernière mise à jour + + + + + + + + + + 20 + + + + + + + + + + + farming + + + + + + + + 2010-09-08 + 2008-09-14 + + + + + + + + + + + + -1.6259765625 + + + -0.9777832031250001 + + + 48.092651367188 + + + 48.696899414063 + + + + + + + France, Pleine-Fougères + + + + + + + + + ESRI Shapefile + + + 1.0 + + + + + + + + + + + + + + + + + + + + + + + + + L’article 7, paragraphe 1, de la directive 2007/2/CE correspond aux modalités techniques de l’interopérabilité : il s’agit du règlement relatif à l’interopérabilité : règlement n°1253/2013 du 21 octobre 2013 modifiant et complétant le règlement n°1089/2010 du 23 novembre 2010. + + + + + 2013-10-21 + + + + + + + + + + + + + true + + + + + + + + + Relevé sur le terrain des adventices du maïs + + + + + + \ No newline at end of file diff --git a/tools/SRS_tools/test-data/Metadata_validation.txt b/tools/SRS_tools/test-data/Metadata_validation.txt new file mode 100644 index 000000000..c3e83bde3 --- /dev/null +++ b/tools/SRS_tools/test-data/Metadata_validation.txt @@ -0,0 +1,5 @@ + +Validation of metadata according to ISO 19139 + +TRUE + according to ISO 19139 XML schemas! diff --git a/tools/SRS_tools/test-data/Mission.csv b/tools/SRS_tools/test-data/Mission.csv new file mode 100644 index 000000000..8575b1d17 --- /dev/null +++ b/tools/SRS_tools/test-data/Mission.csv @@ -0,0 +1,2 @@ +"","x" +"1","SAFE" diff --git a/tools/SRS_tools/test-data/NDVI.tabular b/tools/SRS_tools/test-data/NDVI.tabular new file mode 100644 index 000000000..bd581fec0 --- /dev/null +++ b/tools/SRS_tools/test-data/NDVI.tabular @@ -0,0 +1,10001 @@ +S2A_Subset longitude latitude NDVI +234.29 13.7115065213769 3.17883791054367 0.809475381273922 +233.06 13.7124063428304 3.17883903826377 0.814533326859802 +231.57 13.7133061646046 3.17884016519658 0.808063813546791 +228.73 13.7142059866993 3.17884129134209 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3.08939772975906 0.809455691014441 +246.08 13.799792558354 3.08939875143481 0.808684324455036 +251.94 13.8006923337187 3.08939977234536 0.827205483763792 diff --git a/tools/SRS_tools/test-data/S2A_MSIL2A_20200306T015621_N0214_R117_T51JXN_20200306T034744.zip b/tools/SRS_tools/test-data/S2A_MSIL2A_20200306T015621_N0214_R117_T51JXN_20200306T034744.zip new file mode 100644 index 000000000..17a19a10b Binary files /dev/null and b/tools/SRS_tools/test-data/S2A_MSIL2A_20200306T015621_N0214_R117_T51JXN_20200306T034744.zip differ diff --git a/tools/SRS_tools/test-data/S2A_Subset b/tools/SRS_tools/test-data/S2A_Subset new file mode 100644 index 000000000..61adf90ac Binary files /dev/null and b/tools/SRS_tools/test-data/S2A_Subset differ diff --git a/tools/SRS_tools/test-data/S2A_Subset.hdr b/tools/SRS_tools/test-data/S2A_Subset.hdr new file mode 100644 index 000000000..1318ef85a --- /dev/null +++ b/tools/SRS_tools/test-data/S2A_Subset.hdr @@ -0,0 +1,35 @@ +ENVI +description = { + File Resize Result, x resize factor: 1.000000, y resize factor: 1.000000. + [Thu Jul 11 09:00:28 2019]} +samples = 1000 +lines = 1000 +bands = 10 +header offset = 0 +file type = ENVI Standard +data type = 2 +interleave = bil +sensor type = Unknown +byte order = 0 +x start = 998 +y start = 766 +map info = {UTM, 1.000, 1.000, 356780.000, 351500.000, 1.0000000000e+001, 1.0000000000e+001, 33, North, WGS-84, units=Meters} +coordinate system string = {PROJCS["WGS_1984_UTM_Zone_33N",GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["false_easting",500000.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",15.0],PARAMETER["scale_factor",0.9996],PARAMETER["latitude_of_origin",0.0],UNIT["Meter",1.0]]} +wavelength units = Nanometers +z plot range = {-32768.00, 32767.00} +data ignore value = 0 +default stretch = 0.000000 1000.000000 linear +band names = { + Resize (band 02:S2A_T33NUD_20180104_Subset), + Resize (band 03:S2A_T33NUD_20180104_Subset), + Resize (band 04:S2A_T33NUD_20180104_Subset), + Resize (band 05:S2A_T33NUD_20180104_Subset), + Resize (band 06:S2A_T33NUD_20180104_Subset), + Resize (band 07:S2A_T33NUD_20180104_Subset), + Resize (band 08:S2A_T33NUD_20180104_Subset), + Resize (band 08A:S2A_T33NUD_20180104_Subset), + Resize (band 11:S2A_T33NUD_20180104_Subset), + Resize (band 12:S2A_T33NUD_20180104_Subset)} +wavelength = { + 496.600006, 560.000000, 664.500000, 703.900024, 740.200012, 782.500000, + 835.099976, 864.799988, 1613.699951, 2202.399902} diff --git a/tools/SRS_tools/test-data/S2A_T33NUD_Plots.zip b/tools/SRS_tools/test-data/S2A_T33NUD_Plots.zip new file mode 100644 index 000000000..8fc6acb70 Binary files /dev/null and b/tools/SRS_tools/test-data/S2A_T33NUD_Plots.zip differ diff --git a/tools/SRS_tools/val_metadata.r b/tools/SRS_tools/val_metadata.r new file mode 100644 index 000000000..6b17696c0 --- /dev/null +++ b/tools/SRS_tools/val_metadata.r @@ -0,0 +1,41 @@ +#Rscript + +############################################ +## Validate ISO 19139 metadata documen ## +############################################ + +#####Packages : ncdf4, +# geometa, +# httr +# xml +# xml2 +library(geometa) + +#####Load arguments + +args <- commandArgs(trailingOnly = TRUE) + +if (length(args) < 1) { + stop("This tool needs at least 1 argument") +}else { + input_data <- args[1] +} + +##------------------------------------------## +## Read ISO 19139 from a file or url ## +##------------------------------------------## + +# Test depuis catalogue Indores http://indores-tmp.in2p3.fr/geonetwork/srv/fre/catalog.search#/metadata/112ebeea-e79c-422c-8a43-a5a8323b446b +# +input_data <- xml2::read_xml(input_data) + +dir.create("results") +file.create("results/meta.xml") + +xml2::write_xml(input_data, file = "results/meta.xml") + +md <- geometa::readISO19139("results/meta.xml") + + +# validate iso +cat("\nValidation of metadata according to ISO 19139\n", md$validate(), file = "Metadata_validation.txt", fill = 1, append = FALSE) diff --git a/tools/SRS_tools/val_metadata.xml b/tools/SRS_tools/val_metadata.xml new file mode 100644 index 000000000..c0a48d5ae --- /dev/null +++ b/tools/SRS_tools/val_metadata.xml @@ -0,0 +1,59 @@ + + from files or Urls. + + macro.xml + + + r-ncdf4 + r-geometa + r-httr + r-xml + r-xml2 + + Metadata_validation.txt + ]]> + + + + + + + + + + + + + + + + diff --git a/tools/indic_ivr/.shed.yml b/tools/indic_ivr/.shed.yml new file mode 100644 index 000000000..d2f75d441 --- /dev/null +++ b/tools/indic_ivr/.shed.yml @@ -0,0 +1,14 @@ +categories: + - Ecology +owner: ecology +remote_repository_url: https://github.com/Marie59/indic_ivr +long_description: | + Calculate ivr and qecb indicators +type: unrestricted +auto_tool_repositories: + name_template: "{{ tool_id }}" + description_template: "Wrapper for ivr and qecb indicators: {{ tool_name }}." +suite: + name: "indic_ivr" + description: "A suite of biodiversity and ecology indicators" + type: unrestricted diff --git a/tools/indic_ivr/CB_ivr.tif b/tools/indic_ivr/CB_ivr.tif new file mode 100644 index 000000000..efbe25951 Binary files /dev/null and b/tools/indic_ivr/CB_ivr.tif differ diff --git a/tools/indic_ivr/cb_ecology.r b/tools/indic_ivr/cb_ecology.r new file mode 100644 index 000000000..a4be064be --- /dev/null +++ b/tools/indic_ivr/cb_ecology.r @@ -0,0 +1,1199 @@ +# author: "Jonathan Richir" +# date: "01 October 2022" + + +#Rscript + +############################### +## ## +############################### + +#####Packages : dplyr +# tidyr +# readr +# writexl +# stringr +# readxl +# tibble +# lubridate +# cowplot +# magrittr +# rmarkdown +library(magrittr) +#####Load arguments + +args <- commandArgs(trailingOnly = TRUE) + +#####Import data + +if (length(args) < 1) { + stop("This tool needs at least 1 argument") +}else { + ecology_input <- args[1] +} + +## load qecbNew data ; df saved from CB_qecb script + +qecb <- readRDS(ecology_input) + +`%notin%` <- Negate(`%in%`) + +## reorder and/or create new variables + +# variable site_year_month_day moved for clarity purpose, not needed necessarily +qecb <- tibble::add_column(qecb, qecb$site_year_month_day, .after = "Site_bis") +qecb <- qecb[, -which(names(qecb) %in% c("site_year_month_day"))] +qecb <- dplyr::rename(qecb, site_year_month_day = `qecb$site_year_month_day`) + +# new variable period (nothing to see with the existing périod variable) +period <- rep(NA, nrow(qecb)) +qecb <- tibble::add_column(qecb, period, .after = "Day") +qecb$period <- ifelse(as.numeric(qecb$Month) < 7, "p1", "p2") +qecb$period <- as.factor(qecb$period) +rm(period) + +qecb <- dplyr::arrange(qecb, region, site_year_month_day, Type.Bloc, Numéro.Bloc.échantillon, Face) + +# NB: les infos surface d'accolement sont dupliquées de la face inf vers la face sup de blocs mobiles (même si peu de sens d'avoir cette info pour les face sup ...) +# NB: les data "Abondance ressources ciblées par pêcheurs à pied" présentes uniquement pour les blocs mobiles sont dupliquées entre face inf et face sup. + +## SCRIPT I - NAs <- 0 + +# already performed for part in the CB_qecb script ; but here I also consider mobile organisms, logical observation (or not) according to boulders, faces etc ... so more complete. Could be some kind of script fusion to only keep Na to 0 correction in one script, i.e. moving this script to the CB_qecb script ... + +bretagne_bm <- dplyr::filter(qecb, region == "Bretagne" & Type.Bloc == "Bloc mobile") +bretagne_bf <- dplyr::filter(qecb, region == "Bretagne" & Type.Bloc %in% c("Bloc fixé", "Roche en place")) +egmp_basq_bm <- dplyr::filter(qecb, region == "EGMP.BASQ" & Type.Bloc == "Bloc mobile") +egmp_basq_bf <- dplyr::filter(qecb, region == "EGMP.BASQ" & Type.Bloc %in% c("Bloc fixé", "Roche en place")) + +# replace NAs by "0" for variables used in qecb determination + +{ + # bretagne_bm + bretagne_bm[, c( + "X..algues.brunes", + "Strate.algues.brunes", + "X..algues.rouges", + "Strate.algues.rouges", + "X..algues.vertes", + "Strate.algues.vertes", + "X..Cladophora", + "X..Lithophyllum", + "X..Recouvrement.Sediment", # is NA, then replace by 0 as well because no sense to have a NA value for "% recouvrement sédiment" as well. + #"Type.Sediment", + "X..Roche.Nue", # is NA, then replace by 0 as well because no sense to have a NA value for "% roche nue" as well. + "Nb.Littorina.obtusata", + "Nb.Gibbula.cineraria", + "Nb.Gibbula.pennanti", + "Nb.Gibbula.umbilicalis", + "Nb.Phallusia.mamillata", + "Nb.Tethya.aurantium", + #"Nb.Spirobranchus.lamarckii.1B", + #"Nb.Spirobranchus.lamarckii.2B", + #"Nb.Spirobranchus.lamarckii.3B", + #"Nb.Spirobranchus.lamarckii.4B", + #"Nb.Spirobranchus.lamarckii.5B", + "Nb.Spirobranchus.lamarckii.total", + #"Nb.spirorbis.1A", + #"Nb.spirorbis.2A", + #"Nb.spirorbis.3A", + #"Nb.spirorbis.4A", + #"Nb.spirorbis.5A", + "Nb.spirorbis.total", + #.."Nb.Crassostrea.gigas", + #.."Nb.Ostrea.edulis", + #.."X..Mytilus.sp.", + #.."X..Hermelles", + #.."X..Hydraires", + "X..Eponges", + "X..Ascidies.Coloniales", + "X..Ascidies.Solitaires", + "X..Bryozoaires.Dresses", + "X..Balanes.Vivantes", + #"Commentaires.Avant", + "X..Surface.Accolement", # is NA, then replace by 0 as well because no sense to have a NA value for "% surface accolement" as well. + #"Type.sustrat.observé", + #"Commentaires", + "Nb.Cancer.pagurus..Tourteau.", + "Nb.Necora.puber..Etrille.", + "Nb.Carcinus.maenas..Crabe.vert.", + "Nb.Nucella.lapilus..Pourpre.", + #.."Nb.Eriphia.verrucosa..Crabe.verruqueux.", + #.."Nb.Octopus.vulgaris..Poulpe.", + "Nb.Galathea..Galathées.", + #.."Nb.Paracentrotus.lividus..Oursin.", + "Nb.Lophozozymus.incisus..ancien.Xantho.incisus.", + "Nb.Palaemon.sp..Crevette.bouquet.ou.crevette.rose.", + "Nb.Haliotis.tuberculata..Ormeau.", + #"Nb.Stramonita.haemastoma..Pourpre.bouche.de.sang.", + "Nb.Littorina.littorea..Bigorneau.", + "Nb.Xantho.pilipes..Xanthe.poilu.", + "Nb.Mimachlamys.varia..Pétoncle.noir." + ) + ] <- lapply(bretagne_bm[, c( + "X..algues.brunes", + "Strate.algues.brunes", + "X..algues.rouges", + "Strate.algues.rouges", + "X..algues.vertes", + "Strate.algues.vertes", + "X..Cladophora", + "X..Lithophyllum", + "X..Recouvrement.Sediment", # is NA, then replace by 0 as well because no sense to have a NA value for "% recouvrement sédiment" as well. + #"Type.Sediment", + "X..Roche.Nue", # is NA, then replace by 0 as well because no sense to have a NA value for "% roche nue" as well. + "Nb.Littorina.obtusata", + "Nb.Gibbula.cineraria", + "Nb.Gibbula.pennanti", + "Nb.Gibbula.umbilicalis", + "Nb.Phallusia.mamillata", + "Nb.Tethya.aurantium", + #"Nb.Spirobranchus.lamarckii.1B", + #"Nb.Spirobranchus.lamarckii.2B", + #"Nb.Spirobranchus.lamarckii.3B", + #"Nb.Spirobranchus.lamarckii.4B", + #"Nb.Spirobranchus.lamarckii.5B", + "Nb.Spirobranchus.lamarckii.total", + #"Nb.spirorbis.1A", + #"Nb.spirorbis.2A", + #"Nb.spirorbis.3A", + #"Nb.spirorbis.4A", + #"Nb.spirorbis.5A", + "Nb.spirorbis.total", + #.."Nb.Crassostrea.gigas", + #.."Nb.Ostrea.edulis", + #.."X..Mytilus.sp.", + #.."X..Hermelles", + #.."X..Hydraires", + "X..Eponges", + "X..Ascidies.Coloniales", + "X..Ascidies.Solitaires", + "X..Bryozoaires.Dresses", + "X..Balanes.Vivantes", + #"Commentaires.Avant", + "X..Surface.Accolement", # is NA, then replace by 0 as well because no sense to have a NA value for "% surface accolement" as well. + #"Type.sustrat.observé", + #"Commentaires", + "Nb.Cancer.pagurus..Tourteau.", + "Nb.Necora.puber..Etrille.", + "Nb.Carcinus.maenas..Crabe.vert.", + "Nb.Nucella.lapilus..Pourpre.", + #.."Nb.Eriphia.verrucosa..Crabe.verruqueux.", + #.."Nb.Octopus.vulgaris..Poulpe.", + "Nb.Galathea..Galathées.", + #.."Nb.Paracentrotus.lividus..Oursin.", + "Nb.Lophozozymus.incisus..ancien.Xantho.incisus.", + "Nb.Palaemon.sp..Crevette.bouquet.ou.crevette.rose.", + "Nb.Haliotis.tuberculata..Ormeau.", + #"Nb.Stramonita.haemastoma..Pourpre.bouche.de.sang.", + "Nb.Littorina.littorea..Bigorneau.", + "Nb.Xantho.pilipes..Xanthe.poilu.", + "Nb.Mimachlamys.varia..Pétoncle.noir." + ) + ], function(x) replace(x, is.na(x), 0)) + + + # bretagne_bf + bretagne_bf[, c( + "X..algues.brunes", + "Strate.algues.brunes", + "X..algues.rouges", + "Strate.algues.rouges", + "X..algues.vertes", + "Strate.algues.vertes", + "X..Cladophora", + "X..Lithophyllum", + "X..Recouvrement.Sediment", # is NA, then replace by 0 as well because no sense to have a NA value for "% recouvrement sédiment" as well. + #"Type.Sediment", + "X..Roche.Nue", # is NA, then replace by 0 as well because no sense to have a NA value for "% roche nue" as well. + "Nb.Littorina.obtusata", + "Nb.Gibbula.cineraria", + "Nb.Gibbula.pennanti", + "Nb.Gibbula.umbilicalis", + "Nb.Phallusia.mamillata", + "Nb.Tethya.aurantium", + #"Nb.Spirobranchus.lamarckii.1B", + #"Nb.Spirobranchus.lamarckii.2B", + #"Nb.Spirobranchus.lamarckii.3B", + #"Nb.Spirobranchus.lamarckii.4B", + #"Nb.Spirobranchus.lamarckii.5B", + "Nb.Spirobranchus.lamarckii.total", + #"Nb.spirorbis.1A", + #"Nb.spirorbis.2A", + #"Nb.spirorbis.3A", + #"Nb.spirorbis.4A", + #"Nb.spirorbis.5A", + "Nb.spirorbis.total", + #.."Nb.Crassostrea.gigas", + #.."Nb.Ostrea.edulis", + #.."X..Mytilus.sp.", + #.."X..Hermelles", + #.."X..Hydraires", + "X..Eponges", + "X..Ascidies.Coloniales", + "X..Ascidies.Solitaires", + "X..Bryozoaires.Dresses", + "X..Balanes.Vivantes", + #"Commentaires.Avant", + "X..Surface.Accolement"#, # is NA, then replace by 0 as well because no sense to have a NA value for "% surface accolement" as well. + #"Type.sustrat.observé", + #"Commentaires", + #."Nb.Cancer.pagurus..Tourteau.", + #.."Nb.Necora.puber..Etrille.", + #."Nb.Carcinus.maenas..Crabe.vert.", + #."Nb.Nucella.lapilus..Pourpre.", + #.."Nb.Eriphia.verrucosa..Crabe.verruqueux.", + #.."Nb.Octopus.vulgaris..Poulpe.", + #."Nb.Galathea..Galathées.", + #.."Nb.Paracentrotus.lividus..Oursin.", + #."Nb.Lophozozymus.incisus..ancien.Xantho.incisus.", + #."Nb.Palaemon.sp..Crevette.bouquet.ou.crevette.rose.", + #."Nb.Haliotis.tuberculata..Ormeau.", + #."Nb.Stramonita.haemastoma..Pourpre.bouche.de.sang.", + #."Nb.Littorina.littorea..Bigorneau.", + #."Nb.Xantho.pilipes..Xanthe.poilu.", + #."Nb.Mimachlamys.varia..Pétoncle.noir." + ) + ] <- lapply(bretagne_bf[, c( + "X..algues.brunes", + "Strate.algues.brunes", + "X..algues.rouges", + "Strate.algues.rouges", + "X..algues.vertes", + "Strate.algues.vertes", + "X..Cladophora", + "X..Lithophyllum", + "X..Recouvrement.Sediment", # is NA, then replace by 0 as well because no sense to have a NA value for "% recouvrement sédiment" as well. + #"Type.Sediment", + "X..Roche.Nue", # is NA, then replace by 0 as well because no sense to have a NA value for "% roche nue" as well. + "Nb.Littorina.obtusata", + "Nb.Gibbula.cineraria", + "Nb.Gibbula.pennanti", + "Nb.Gibbula.umbilicalis", + "Nb.Phallusia.mamillata", + "Nb.Tethya.aurantium", + #"Nb.Spirobranchus.lamarckii.1B", + #"Nb.Spirobranchus.lamarckii.2B", + #"Nb.Spirobranchus.lamarckii.3B", + #"Nb.Spirobranchus.lamarckii.4B", + #"Nb.Spirobranchus.lamarckii.5B", + "Nb.Spirobranchus.lamarckii.total", + #"Nb.spirorbis.1A", + #"Nb.spirorbis.2A", + #"Nb.spirorbis.3A", + #"Nb.spirorbis.4A", + #"Nb.spirorbis.5A", + "Nb.spirorbis.total", + #.."Nb.Crassostrea.gigas", + #.."Nb.Ostrea.edulis", + #.."X..Mytilus.sp.", + #.."X..Hermelles", + #.."X..Hydraires", + "X..Eponges", + "X..Ascidies.Coloniales", + "X..Ascidies.Solitaires", + "X..Bryozoaires.Dresses", + "X..Balanes.Vivantes", + #"Commentaires.Avant", + "X..Surface.Accolement"#, # is NA, then replace by 0 as well because no sense to have a NA value for "% surface accolement" as well. + #"Type.sustrat.observé", + #"Commentaires", + #."Nb.Cancer.pagurus..Tourteau.", + #.."Nb.Necora.puber..Etrille.", + #."Nb.Carcinus.maenas..Crabe.vert.", + #."Nb.Nucella.lapilus..Pourpre.", + #.."Nb.Eriphia.verrucosa..Crabe.verruqueux.", + #.."Nb.Octopus.vulgaris..Poulpe.", + #."Nb.Galathea..Galathées.", + #.."Nb.Paracentrotus.lividus..Oursin.", + #."Nb.Lophozozymus.incisus..ancien.Xantho.incisus.", + #."Nb.Palaemon.sp..Crevette.bouquet.ou.crevette.rose.", + #."Nb.Haliotis.tuberculata..Ormeau.", + #."Nb.Stramonita.haemastoma..Pourpre.bouche.de.sang.", + #."Nb.Littorina.littorea..Bigorneau.", + #."Nb.Xantho.pilipes..Xanthe.poilu.", + #."Nb.Mimachlamys.varia..Pétoncle.noir." + ) + ], function(x) replace(x, is.na(x), 0)) + + + # egmp_basq_bm + egmp_basq_bm[, c( + "X..algues.brunes", + "Strate.algues.brunes", + "X..algues.rouges", + "Strate.algues.rouges", + "X..algues.vertes", + "Strate.algues.vertes", + "X..Cladophora", + "X..Lithophyllum", + "X..Recouvrement.Sediment", # is NA, then replace by 0 as well because no sense to have a NA value for "% recouvrement sédiment" as well. + #"Type.Sediment", + "X..Roche.Nue", # is NA, then replace by 0 as well because no sense to have a NA value for "% roche nue" as well. + "Nb.Littorina.obtusata", + "Nb.Gibbula.cineraria", + "Nb.Gibbula.pennanti", + "Nb.Gibbula.umbilicalis", + "Nb.Phallusia.mamillata", + "Nb.Tethya.aurantium", + #"Nb.Spirobranchus.lamarckii.1B", + #"Nb.Spirobranchus.lamarckii.2B", + #"Nb.Spirobranchus.lamarckii.3B", + #"Nb.Spirobranchus.lamarckii.4B", + #"Nb.Spirobranchus.lamarckii.5B", + "Nb.Spirobranchus.lamarckii.total", + #"Nb.spirorbis.1A", + #"Nb.spirorbis.2A", + #"Nb.spirorbis.3A", + #"Nb.spirorbis.4A", + #"Nb.spirorbis.5A", + "Nb.spirorbis.total", + "Nb.Crassostrea.gigas", + "Nb.Ostrea.edulis", + "X..Mytilus.sp.", + "X..Hermelles", + "X..Hydraires", + "X..Eponges", + "X..Ascidies.Coloniales", + "X..Ascidies.Solitaires", + "X..Bryozoaires.Dresses", + "X..Balanes.Vivantes", + #"Commentaires.Avant", + "X..Surface.Accolement", # is NA, then replace by 0 as well because no sense to have a NA value for "% surface accolement" as well. + #"Type.sustrat.observé", + #"Commentaires", + "Nb.Cancer.pagurus..Tourteau.", + "Nb.Necora.puber..Etrille.", + "Nb.Carcinus.maenas..Crabe.vert.", + "Nb.Nucella.lapilus..Pourpre.", + "Nb.Eriphia.verrucosa..Crabe.verruqueux.", + "Nb.Octopus.vulgaris..Poulpe.", + "Nb.Galathea..Galathées.", + "Nb.Paracentrotus.lividus..Oursin.", + "Nb.Lophozozymus.incisus..ancien.Xantho.incisus.", + "Nb.Palaemon.sp..Crevette.bouquet.ou.crevette.rose.", + "Nb.Haliotis.tuberculata..Ormeau.", + "Nb.Stramonita.haemastoma..Pourpre.bouche.de.sang.", + "Nb.Littorina.littorea..Bigorneau.", + "Nb.Xantho.pilipes..Xanthe.poilu.", + "Nb.Mimachlamys.varia..Pétoncle.noir." + ) + ] <- lapply(egmp_basq_bm[, c( + "X..algues.brunes", + "Strate.algues.brunes", + "X..algues.rouges", + "Strate.algues.rouges", + "X..algues.vertes", + "Strate.algues.vertes", + "X..Cladophora", + "X..Lithophyllum", + "X..Recouvrement.Sediment", # is NA, then replace by 0 as well because no sense to have a NA value for "% recouvrement sédiment" as well. + #"Type.Sediment", + "X..Roche.Nue", # is NA, then replace by 0 as well because no sense to have a NA value for "% roche nue" as well. + "Nb.Littorina.obtusata", + "Nb.Gibbula.cineraria", + "Nb.Gibbula.pennanti", + "Nb.Gibbula.umbilicalis", + "Nb.Phallusia.mamillata", + "Nb.Tethya.aurantium", + #"Nb.Spirobranchus.lamarckii.1B", + #"Nb.Spirobranchus.lamarckii.2B", + #"Nb.Spirobranchus.lamarckii.3B", + #"Nb.Spirobranchus.lamarckii.4B", + #"Nb.Spirobranchus.lamarckii.5B", + "Nb.Spirobranchus.lamarckii.total", + #"Nb.spirorbis.1A", + #"Nb.spirorbis.2A", + #"Nb.spirorbis.3A", + #"Nb.spirorbis.4A", + #"Nb.spirorbis.5A", + "Nb.spirorbis.total", + "Nb.Crassostrea.gigas", + "Nb.Ostrea.edulis", + "X..Mytilus.sp.", + "X..Hermelles", + "X..Hydraires", + "X..Eponges", + "X..Ascidies.Coloniales", + "X..Ascidies.Solitaires", + "X..Bryozoaires.Dresses", + "X..Balanes.Vivantes", + #"Commentaires.Avant", + "X..Surface.Accolement", # is NA, then replace by 0 as well because no sense to have a NA value for "% surface accolement" as well. + #"Type.sustrat.observé", + #"Commentaires", + "Nb.Cancer.pagurus..Tourteau.", + "Nb.Necora.puber..Etrille.", + "Nb.Carcinus.maenas..Crabe.vert.", + "Nb.Nucella.lapilus..Pourpre.", + "Nb.Eriphia.verrucosa..Crabe.verruqueux.", + "Nb.Octopus.vulgaris..Poulpe.", + "Nb.Galathea..Galathées.", + "Nb.Paracentrotus.lividus..Oursin.", + "Nb.Lophozozymus.incisus..ancien.Xantho.incisus.", + "Nb.Palaemon.sp..Crevette.bouquet.ou.crevette.rose.", + "Nb.Haliotis.tuberculata..Ormeau.", + "Nb.Stramonita.haemastoma..Pourpre.bouche.de.sang.", + "Nb.Littorina.littorea..Bigorneau.", + "Nb.Xantho.pilipes..Xanthe.poilu.", + "Nb.Mimachlamys.varia..Pétoncle.noir." + ) + ], function(x) replace(x, is.na(x), 0)) + + + # egmp_basq_bf + egmp_basq_bf[, c( + "X..algues.brunes", + "Strate.algues.brunes", + "X..algues.rouges", + "Strate.algues.rouges", + "X..algues.vertes", + "Strate.algues.vertes", + "X..Cladophora", + "X..Lithophyllum", + "X..Recouvrement.Sediment", # is NA, then replace by 0 as well because no sense to have a NA value for "% recouvrement sédiment" as well. + #"Type.Sediment", + "X..Roche.Nue", # is NA, then replace by 0 as well because no sense to have a NA value for "% roche nue" as well. + "Nb.Littorina.obtusata", + "Nb.Gibbula.cineraria", + "Nb.Gibbula.pennanti", + "Nb.Gibbula.umbilicalis", + "Nb.Phallusia.mamillata", + "Nb.Tethya.aurantium", + #"Nb.Spirobranchus.lamarckii.1B", + #"Nb.Spirobranchus.lamarckii.2B", + #"Nb.Spirobranchus.lamarckii.3B", + #"Nb.Spirobranchus.lamarckii.4B", + #"Nb.Spirobranchus.lamarckii.5B", + "Nb.Spirobranchus.lamarckii.total", + #"Nb.spirorbis.1A", + #"Nb.spirorbis.2A", + #"Nb.spirorbis.3A", + #"Nb.spirorbis.4A", + #"Nb.spirorbis.5A", + "Nb.spirorbis.total", + "Nb.Crassostrea.gigas", + "Nb.Ostrea.edulis", + "X..Mytilus.sp.", + "X..Hermelles", + "X..Hydraires", + "X..Eponges", + "X..Ascidies.Coloniales", + "X..Ascidies.Solitaires", + "X..Bryozoaires.Dresses", + "X..Balanes.Vivantes", + #"Commentaires.Avant", + "X..Surface.Accolement"#, # is NA, then replace by 0 as well because no sense to have a NA value for "% surface accolement" as well. + #"Type.sustrat.observé", + #"Commentaires", + #."Nb.Cancer.pagurus..Tourteau.", + #.."Nb.Necora.puber..Etrille.", + #."Nb.Carcinus.maenas..Crabe.vert.", + #."Nb.Nucella.lapilus..Pourpre.", + #.."Nb.Eriphia.verrucosa..Crabe.verruqueux.", + #.."Nb.Octopus.vulgaris..Poulpe.", + #."Nb.Galathea..Galathées.", + #.."Nb.Paracentrotus.lividus..Oursin.", + #."Nb.Lophozozymus.incisus..ancien.Xantho.incisus.", + #."Nb.Palaemon.sp..Crevette.bouquet.ou.crevette.rose.", + #."Nb.Haliotis.tuberculata..Ormeau.", + #."Nb.Stramonita.haemastoma..Pourpre.bouche.de.sang.", + #."Nb.Littorina.littorea..Bigorneau.", + #."Nb.Xantho.pilipes..Xanthe.poilu.", + #."Nb.Mimachlamys.varia..Pétoncle.noir." + ) + ] <- lapply(egmp_basq_bf[, c( + "X..algues.brunes", + "Strate.algues.brunes", + "X..algues.rouges", + "Strate.algues.rouges", + "X..algues.vertes", + "Strate.algues.vertes", + "X..Cladophora", + "X..Lithophyllum", + "X..Recouvrement.Sediment", # is NA, then replace by 0 as well because no sense to have a NA value for "% recouvrement sédiment" as well. + #"Type.Sediment", + "X..Roche.Nue", # is NA, then replace by 0 as well because no sense to have a NA value for "% roche nue" as well. + "Nb.Littorina.obtusata", + "Nb.Gibbula.cineraria", + "Nb.Gibbula.pennanti", + "Nb.Gibbula.umbilicalis", + "Nb.Phallusia.mamillata", + "Nb.Tethya.aurantium", + #"Nb.Spirobranchus.lamarckii.1B", + #"Nb.Spirobranchus.lamarckii.2B", + #"Nb.Spirobranchus.lamarckii.3B", + #"Nb.Spirobranchus.lamarckii.4B", + #"Nb.Spirobranchus.lamarckii.5B", + "Nb.Spirobranchus.lamarckii.total", + #"Nb.spirorbis.1A", + #"Nb.spirorbis.2A", + #"Nb.spirorbis.3A", + #"Nb.spirorbis.4A", + #"Nb.spirorbis.5A", + "Nb.spirorbis.total", + "Nb.Crassostrea.gigas", + "Nb.Ostrea.edulis", + "X..Mytilus.sp.", + "X..Hermelles", + "X..Hydraires", + "X..Eponges", + "X..Ascidies.Coloniales", + "X..Ascidies.Solitaires", + "X..Bryozoaires.Dresses", + "X..Balanes.Vivantes", + #"Commentaires.Avant", + "X..Surface.Accolement"#, # is NA, then replace by 0 as well because no sense to have a NA value for "% surface accolement" as well. + #"Type.sustrat.observé", + #"Commentaires", + #."Nb.Cancer.pagurus..Tourteau.", + #.."Nb.Necora.puber..Etrille.", + #."Nb.Carcinus.maenas..Crabe.vert.", + #."Nb.Nucella.lapilus..Pourpre.", + #.."Nb.Eriphia.verrucosa..Crabe.verruqueux.", + #.."Nb.Octopus.vulgaris..Poulpe.", + #."Nb.Galathea..Galathées.", + #.."Nb.Paracentrotus.lividus..Oursin.", + #."Nb.Lophozozymus.incisus..ancien.Xantho.incisus.", + #."Nb.Palaemon.sp..Crevette.bouquet.ou.crevette.rose.", + #."Nb.Haliotis.tuberculata..Ormeau.", + #."Nb.Stramonita.haemastoma..Pourpre.bouche.de.sang.", + #."Nb.Littorina.littorea..Bigorneau.", + #."Nb.Xantho.pilipes..Xanthe.poilu.", + #."Nb.Mimachlamys.varia..Pétoncle.noir." + ) + ], function(x) replace(x, is.na(x), 0)) + +} + +# merge dfs. +qecbnato0 <- dplyr::bind_rows(bretagne_bm, bretagne_bf) +qecbnato0 <- dplyr::bind_rows(qecbnato0, egmp_basq_bm) +qecbnato0 <- dplyr::bind_rows(qecbnato0, egmp_basq_bf) + +qecbnato0 <- dplyr::arrange(qecbnato0, region, site_year_month_day, Type.Bloc, Numéro.Bloc.échantillon, Face) + +rm(bretagne_bm, bretagne_bf, egmp_basq_bm, egmp_basq_bf) + + +## analyse matricielle + +# NB some variables were dplyr::renamed or created, cfr I originally merged qecb and ivr data in below script to do some correlation analysis. This is not the case anymore, so no more merging anymore. + +qecbnato0 <- tibble::add_column(qecbnato0, region.site_year_month_day = paste0(qecbnato0$region, qecbnato0$site_year_month_day), .before = "region") + + +numero_quadrat <- stringr::str_sub(qecbnato0$quadrat_bis, start = -1) +qecbnato0 <- tibble::add_column(qecbnato0, numero_quadrat, .after = "quadrat_bis") +rm(numero_quadrat) +qecbnato0$numero_quadrat <- as.integer(qecbnato0$numero_quadrat) + + + + +qecbnato0$Year <- as.integer(qecbnato0$Year) +qecbnato0$Month <- as.integer(qecbnato0$Month) +qecbnato0$Day <- as.integer(qecbnato0$Day) + +############################################################ +#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! +# Anna still hasn't corrected for boulder nb in FINS_Quemenes.2020.10.16 data encoding ! removed from the df_ +qecbnato0 <- qecbnato0 %>% dplyr::filter(site_year_month_day != "FINS_Quemenes.2020.10.16") +############################################################ + + +# first, create vector (4) for qecb and fishing by region (same as above) + +bret_egmp_basq_qecb <- c( + "X..algues.brunes", + "X..algues.rouges", + "X..algues.vertes", + "X..Cladophora", + "X..Lithophyllum", + "Nb.Littorina.obtusata", + "Nb.Gibbula.cineraria", + "Nb.Gibbula.pennanti", + "Nb.Gibbula.umbilicalis", + "Nb.Phallusia.mamillata", + "Nb.Tethya.aurantium", + "Nb.Spirobranchus.lamarckii.total", + "Nb.spirorbis.total", + "X..Eponges", + "X..Ascidies.Coloniales", + "X..Ascidies.Solitaires", + "X..Bryozoaires.Dresses", + "X..Balanes.Vivantes" + #, "X..Recouvrement.Sediment" + #, "X..Roche.Nue" + #, "X..Surface.Accolement" + ) + +egmp_basq_qecb <- c("Nb.Crassostrea.gigas", "Nb.Ostrea.edulis", "X..Mytilus.sp.", "X..Hermelles", "X..Hydraires") + +bret_egmp_basq_fishing <- c("Nb.Cancer.pagurus..Tourteau.", + "Nb.Necora.puber..Etrille.", + "Nb.Carcinus.maenas..Crabe.vert.", + "Nb.Nucella.lapilus..Pourpre.", + "Nb.Galathea..Galathées.", + "Nb.Lophozozymus.incisus..ancien.Xantho.incisus.", + "Nb.Palaemon.sp..Crevette.bouquet.ou.crevette.rose.", + "Nb.Haliotis.tuberculata..Ormeau.", + "Nb.Littorina.littorea..Bigorneau.", + "Nb.Xantho.pilipes..Xanthe.poilu.", + "Nb.Mimachlamys.varia..Pétoncle.noir.") + +egmp_basq_fishing <- c("Nb.Eriphia.verrucosa..Crabe.verruqueux.", "Nb.Octopus.vulgaris..Poulpe.", "Nb.Paracentrotus.lividus..Oursin.", "Nb.Stramonita.haemastoma..Pourpre.bouche.de.sang.") + +# what to do with spirorbes & Nb.Spirobranchus.lamarckii.total? log10 transformation + +qecbnato0 <- tibble::add_column(qecbnato0, log10.Nb.spirorbis.total = log10(qecbnato0$Nb.spirorbis.total + 1), .after = "Nb.spirorbis.total") +qecbnato0 <- tibble::add_column(qecbnato0, log10.Nb.Spirobranchus.lamarckii.total = log10(qecbnato0$Nb.Spirobranchus.lamarckii.total + 1), .after = "Nb.Spirobranchus.lamarckii.total") + +# here I can choose to either replace spirorbis and/or spirobranchus by their log10 transformation in bret_egmp_basq_qecb vector +bret_egmp_basq_qecb <- replace(bret_egmp_basq_qecb, bret_egmp_basq_qecb == "Nb.spirorbis.total", "log10.Nb.spirorbis.total") + + + +## determination of coefficient of dissimilarity face supérieure bloc mobile vs roche en place + +# loop in a fct + +matri_fct_bmf <- function(data, conca) { + + matri_df <- data + + for (x in c(1:length(unique(matri_df$site_year_month_day)))) { + + qecbnato0_x <- matri_df %>% dplyr::filter(site_year_month_day == unique(matri_df$site_year_month_day)[[x]]) + + rownames(qecbnato0_x) <- paste0(qecbnato0_x$Type.Bloc, "_", qecbnato0_x$Face, "_", qecbnato0_x$Numéro.Bloc.échantillon, "_", qecbnato0_x$quadrat_bis) + + + mtxdis <- vegan::vegdist( + sqrt(qecbnato0_x[, conca]), #Transform your species abundance data_ Typically, raw abundances are transformed prior to analysis. Usually you will use square root, fourth-root, log(X+1), or presence-absence (square root being least extreme, P/A being most). I would start with square root. (https://stats.stackexchange.com/questions/234495/double-zeroes-problem-with-euclidean-distance-and-abundance-data-is-the-proble) + + na.rm = TRUE, + method = "bray" #Construct species abundance dissimilarity matrices with Bray-Curtis. If your data contains samples that are all-zero you will run into the double zero problem. This can be overcome by using a zero-adjusted Bray-Curtis coefficient, which is sometimes referred to as a 'dummy variable' which damps down the similarity fluctuations between samples that are both zero (undefined). => see below for zero-adjusted Bray-Curtis coefficient ; #another possibility, sqrt + 1 ?? + ) + + + #library(ecole) #https://rdrr.io/github/phytomosaic/ecole/man/bray0.html + # mtxdis <- ecole::bray0( + # sqrt + # (qecbnato0_x[,conca]), na.rm = TRUE) + + mtxdis + expand.grid(mtxdis) + + mtxdisdf_ <- as.data.frame(as.matrix(mtxdis)) + + a_ <- NA + b_ <- NA + c_ <- NA + d_ <- NA + e_ <- NA + f_ <- NA + g_ <- NA + h_ <- NA + i_ <- NA + j_ <- NA + k_ <- NA + l_ <- NA + m_ <- NA + n_ <- NA + + for (z in c(1:nrow(mtxdisdf_))) { + + a_[[z]] <- (paste0(rownames(mtxdisdf_[z + 1,]), " & ", ifelse(nrow(mtxdisdf_) >= 1, colnames(mtxdisdf_[1]), NA))) + b_[[z]] <- (paste0(rownames(mtxdisdf_[z + 2,]), " & ", ifelse(nrow(mtxdisdf_) >= 2, colnames(mtxdisdf_[2]), NA))) + c_[[z]] <- (paste0(rownames(mtxdisdf_[z + 3,]), " & ", ifelse(nrow(mtxdisdf_) >= 3, colnames(mtxdisdf_[3]), NA))) + d_[[z]] <- (paste0(rownames(mtxdisdf_[z + 4,]), " & ", ifelse(nrow(mtxdisdf_) >= 4, colnames(mtxdisdf_[4]), NA))) + e_[[z]] <- (paste0(rownames(mtxdisdf_[z + 5,]), " & ", ifelse(nrow(mtxdisdf_) >= 5, colnames(mtxdisdf_[5]), NA))) + f_[[z]] <- (paste0(rownames(mtxdisdf_[z + 6,]), " & ", ifelse(nrow(mtxdisdf_) >= 6, colnames(mtxdisdf_[6]), NA))) + g_[[z]] <- (paste0(rownames(mtxdisdf_[z + 7,]), " & ", ifelse(nrow(mtxdisdf_) >= 7, colnames(mtxdisdf_[7]), NA))) + h_[[z]] <- (paste0(rownames(mtxdisdf_[z + 8,]), " & ", ifelse(nrow(mtxdisdf_) >= 8, colnames(mtxdisdf_[8]), NA))) + i_[[z]] <- (paste0(rownames(mtxdisdf_[z + 9,]), " & ", ifelse(nrow(mtxdisdf_) >= 9, colnames(mtxdisdf_[9]), NA))) + j_[[z]] <- (paste0(rownames(mtxdisdf_[z + 10,]), " & ", ifelse(nrow(mtxdisdf_) >= 10, colnames(mtxdisdf_[10]), NA))) + k_[[z]] <- (paste0(rownames(mtxdisdf_[z + 11,]), " & ", ifelse(nrow(mtxdisdf_) >= 11, colnames(mtxdisdf_[11]), NA))) + l_[[z]] <- (paste0(rownames(mtxdisdf_[z + 12,]), " & ", ifelse(nrow(mtxdisdf_) >= 12, colnames(mtxdisdf_[12]), NA))) + m_[[z]] <- (paste0(rownames(mtxdisdf_[z + 13,]), " & ", ifelse(nrow(mtxdisdf_) >= 13, colnames(mtxdisdf_[13]), NA))) + n_[[z]] <- (paste0(rownames(mtxdisdf_[z + 14,]), " & ", ifelse(nrow(mtxdisdf_) >= 14, colnames(mtxdisdf_[14]), NA))) + + } + + rm(z) + + y <- length(a_) - (ifelse(nrow(mtxdisdf_) >= 1, 1, nrow(mtxdisdf_))) + a_ <- a_[1:y] + y <- length(b_) - (ifelse(nrow(mtxdisdf_) >= 2, 2, nrow(mtxdisdf_))) + b_ <- b_[1:y] + y <- length(c_) - (ifelse(nrow(mtxdisdf_) >= 3, 3, nrow(mtxdisdf_))) + c_ <- c_[1:y] + y <- length(d_) - (ifelse(nrow(mtxdisdf_) >= 4, 4, nrow(mtxdisdf_))) + d_ <- d_[1:y] + y <- length(e_) - (ifelse(nrow(mtxdisdf_) >= 5, 5, nrow(mtxdisdf_))) + e_ <- e_[1:y] + y <- length(f_) - (ifelse(nrow(mtxdisdf_) >= 6, 6, nrow(mtxdisdf_))) + f_ <- f_[1:y] + y <- length(g_) - (ifelse(nrow(mtxdisdf_) >= 7, 7, nrow(mtxdisdf_))) + g_ <- g_[1:y] + y <- length(h_) - (ifelse(nrow(mtxdisdf_) >= 8, 8, nrow(mtxdisdf_))) + h_ <- h_[1:y] + y <- length(i_) - (ifelse(nrow(mtxdisdf_) >= 9, 9, nrow(mtxdisdf_))) + i_ <- i_[1:y] + y <- length(j_) - (ifelse(nrow(mtxdisdf_) >= 10, 10, nrow(mtxdisdf_))) + j_ <- j_[1:y] + y <- length(k_) - (ifelse(nrow(mtxdisdf_) >= 11, 11, nrow(mtxdisdf_))) + k_ <- k_[1:y] + y <- length(l_) - (ifelse(nrow(mtxdisdf_) >= 12, 12, nrow(mtxdisdf_))) + l_ <- l_[1:y] + y <- length(m_) - (ifelse(nrow(mtxdisdf_) >= 13, 13, nrow(mtxdisdf_))) + m_ <- m_[1:y] + y <- length(n_) - (ifelse(nrow(mtxdisdf_) >= 14, 14, nrow(mtxdisdf_))) + n_ <- n_[1:y] + + rm(y) + + name_ <- c(a_, b_, c_, d_, e_, f_, g_, h_, i_, j_, k_, l_, m_, n_) + df_ <- data.frame(expand.grid(mtxdis), name_[1:nrow(expand.grid(mtxdis))]) + names(df_) <- c("dist.", "name_") + + rm(a_, b_, c_, d_, e_, f_, g_, h_, i_, j_, k_, l_, m_, n_) + rm(name_) + + q_ <- strsplit(df_$name_, " & ") + mat_ <- matrix(unlist(q_), ncol = 2, byrow = TRUE) + q_df_ <- as.data.frame(matrix(unlist(q_), ncol = 2, byrow = TRUE)) + df_ <- dplyr::bind_cols(df_, q_df_) + + rm(q_, mat_, q_df_) + + split_ <- strsplit(df_$V1, "_") + v1_split_ <- as.data.frame(matrix(unlist(split_), ncol = 4, byrow = TRUE)) + split_ <- strsplit(df_$V2, "_") + v2_split_ <- as.data.frame(matrix(unlist(split_), ncol = 4, byrow = TRUE)) + + df_ <- dplyr::bind_cols(df_, v1_split_) + df_ <- dplyr::bind_cols(df_, v2_split_) + df_red_ <- subset(df_, V4...8 == V4...12 & V1...5 != V1...9) + site_year_month_day <- rep(unique(qecbnato0_x$site_year_month_day), nrow(df_red_)) + + df_red_ <- tibble::add_column(df_red_, site_year_month_day, .before = "dist.") + + rm(split_, v1_split_, v2_split_) + rm(mtxdis, mtxdisdf_, df_, site_year_month_day) + + matri_list[[x]] <- df_red_ + matri_list <<- matri_list + + rm(df_red_, qecbnato0_x, x) + + } + + matri_df <- do.call("rbind", matri_list) + + names(matri_df) <- c("site_year_month_day", "dist.", "name_", "name_left", "name_right", "Type.Bloc.left", "Face.left", "Numéro.Bloc.échantillon.left", "Quadrat.left", "Type.Bloc.right", "Face.right", "Numéro.Bloc.échantillon.right", "Quadrat.right") + + matri_df <<- matri_df + + hist(matri_df$dist.) + +} + +data_ <- dplyr::filter(qecbnato0, Face == "face supérieure") +data_$Type.Bloc <- ifelse(as.character(data_$Type.Bloc) == "Roche en place", "Bloc fixé", as.character(data_$Type.Bloc)) +matri_list <- vector("list", length(unique(data_$site_year_month_day))) + +matri_fct_bmf(data = data_, conca = c(bret_egmp_basq_qecb, egmp_basq_qecb)) +hist(matri_df$dist., main = c(paste("Histo. of Bray (0-adjusted) dist. dissimilarity measures"), paste(" (sqrt transfo) - BMfs vs BF -"))) + +matri_full_bm_bf_fs <- matri_df + +saveRDS(matri_full_bm_bf_fs, "matri_full_log.spi_BM.BF_FS.RDS") +rm(data_, matri_df, matri_list) + + +## determination of coefficient of dissimilarity between blocs mobiles face sup vs face inf. + +# loop in a fct + +matri_fct_bmm <- function(data, conca) { + + matri_df <- data + + for (x in c(1:length(unique(matri_df$site_year_month_day)))) { + + matri_df %>% dplyr::filter(site_year_month_day == unique(matri_df$site_year_month_day)[[x]]) -> qecbnato0_x + + rownames(qecbnato0_x) <- paste0(qecbnato0_x$Type.Bloc, "_", qecbnato0_x$Face, "_", qecbnato0_x$Numéro.Bloc.échantillon, "_", qecbnato0_x$quadrat_bis) + + + #mtxdis <- vegan::vegdist( + # sqrt + # (qecbnato0_x[,c(bret_egmp_basq_qecb)]), #Transform your species abundance data_ Typically, raw abundances are transformed prior to analysis. Usually you will use square root, fourth-root, log(X+1), or presence-absence (square root being least extreme, P/A being most). I would start with square root. (https://stats.stackexchange.com/questions/234495/double-zeroes-problem-with-euclidean-distance-and-abundance-data-is-the-proble) + # na.rm = T, + # method = "bray" #Construct species abundance dissimilarity matrices with Bray-Curtis. If your data contains samples that are all-zero you will run into the double zero problem. This can be overcome by using a zero-adjusted Bray-Curtis coefficient, which is sometimes referred to as a 'dummy variable' which damps down the similarity fluctuations between samples that are both zero (undefined). => see below for zero-adjusted Bray-Curtis coefficient ; #another possibility, sqrt + 1 ?? + #) + + + mtxdis <- ecole::bray0( + sqrt + (qecbnato0_x[,conca]), na.rm = TRUE) + + mtxdis + mtxdis + expand.grid(mtxdis) + + mtxdisdf_ <- as.data.frame(as.matrix(mtxdis)) + + a_ <- NA + b_ <- NA + c_ <- NA + d_ <- NA + e_ <- NA + f_ <- NA + g_ <- NA + h_ <- NA + i_ <- NA + j_ <- NA + k_ <- NA + l_ <- NA + m_ <- NA + n_ <- NA + o_ <- NA + p_ <- NA + q_ <- NA + r_ <- NA + s_ <- NA + + for (z in c(1:nrow(mtxdisdf_))) { + + a_[[z]] <- (paste0(rownames(mtxdisdf_[z + 1,]), " & ", ifelse(nrow(mtxdisdf_) >= 1, colnames(mtxdisdf_[1]), NA))) + b_[[z]] <- (paste0(rownames(mtxdisdf_[z + 2,]), " & ", ifelse(nrow(mtxdisdf_) >= 2, colnames(mtxdisdf_[2]), NA))) + c_[[z]] <- (paste0(rownames(mtxdisdf_[z + 3,]), " & ", ifelse(nrow(mtxdisdf_) >= 3, colnames(mtxdisdf_[3]), NA))) + d_[[z]] <- (paste0(rownames(mtxdisdf_[z + 4,]), " & ", ifelse(nrow(mtxdisdf_) >= 4, colnames(mtxdisdf_[4]), NA))) + e_[[z]] <- (paste0(rownames(mtxdisdf_[z + 5,]), " & ", ifelse(nrow(mtxdisdf_) >= 5, colnames(mtxdisdf_[5]), NA))) + f_[[z]] <- (paste0(rownames(mtxdisdf_[z + 6,]), " & ", ifelse(nrow(mtxdisdf_) >= 6, colnames(mtxdisdf_[6]), NA))) + g_[[z]] <- (paste0(rownames(mtxdisdf_[z + 7,]), " & ", ifelse(nrow(mtxdisdf_) >= 7, colnames(mtxdisdf_[7]), NA))) + h_[[z]] <- (paste0(rownames(mtxdisdf_[z + 8,]), " & ", ifelse(nrow(mtxdisdf_) >= 8, colnames(mtxdisdf_[8]), NA))) + i_[[z]] <- (paste0(rownames(mtxdisdf_[z + 9,]), " & ", ifelse(nrow(mtxdisdf_) >= 9, colnames(mtxdisdf_[9]), NA))) + j_[[z]] <- (paste0(rownames(mtxdisdf_[z + 10,]), " & ", ifelse(nrow(mtxdisdf_) >= 10, colnames(mtxdisdf_[10]), NA))) + k_[[z]] <- (paste0(rownames(mtxdisdf_[z + 11,]), " & ", ifelse(nrow(mtxdisdf_) >= 11, colnames(mtxdisdf_[11]), NA))) + l_[[z]] <- (paste0(rownames(mtxdisdf_[z + 12,]), " & ", ifelse(nrow(mtxdisdf_) >= 12, colnames(mtxdisdf_[12]), NA))) + m_[[z]] <- (paste0(rownames(mtxdisdf_[z + 13,]), " & ", ifelse(nrow(mtxdisdf_) >= 13, colnames(mtxdisdf_[13]), NA))) + n_[[z]] <- (paste0(rownames(mtxdisdf_[z + 14,]), " & ", ifelse(nrow(mtxdisdf_) >= 14, colnames(mtxdisdf_[14]), NA))) + o_[[z]] <- (paste0(rownames(mtxdisdf_[z + 15,]), " & ", ifelse(nrow(mtxdisdf_) >= 15, colnames(mtxdisdf_[15]), NA))) + p_[[z]] <- (paste0(rownames(mtxdisdf_[z + 16,]), " & ", ifelse(nrow(mtxdisdf_) >= 16, colnames(mtxdisdf_[16]), NA))) + q_[[z]] <- (paste0(rownames(mtxdisdf_[z + 17,]), " & ", ifelse(nrow(mtxdisdf_) >= 17, colnames(mtxdisdf_[17]), NA))) + r_[[z]] <- (paste0(rownames(mtxdisdf_[z + 18,]), " & ", ifelse(nrow(mtxdisdf_) >= 18, colnames(mtxdisdf_[18]), NA))) + s_[[z]] <- (paste0(rownames(mtxdisdf_[z + 19,]), " & ", ifelse(nrow(mtxdisdf_) >= 19, colnames(mtxdisdf_[19]), NA))) + + } + + rm(z) + + y <- length(a_) - (ifelse(nrow(mtxdisdf_) >= 1, 1, nrow(mtxdisdf_))) + a_ <- a_[1:y] + y <- length(b_) - (ifelse(nrow(mtxdisdf_) >= 2, 2, nrow(mtxdisdf_))) + b_ <- b_[1:y] + y <- length(c_) - (ifelse(nrow(mtxdisdf_) >= 3, 3, nrow(mtxdisdf_))) + c_ <- c_[1:y] + y <- length(d_) - (ifelse(nrow(mtxdisdf_) >= 4, 4, nrow(mtxdisdf_))) + d_ <- d_[1:y] + y <- length(e_) - (ifelse(nrow(mtxdisdf_) >= 5, 5, nrow(mtxdisdf_))) + e_ <- e_[1:y] + y <- length(f_) - (ifelse(nrow(mtxdisdf_) >= 6, 6, nrow(mtxdisdf_))) + f_ <- f_[1:y] + y <- length(g_) - (ifelse(nrow(mtxdisdf_) >= 7, 7, nrow(mtxdisdf_))) + g_ <- g_[1:y] + y <- length(h_) - (ifelse(nrow(mtxdisdf_) >= 8, 8, nrow(mtxdisdf_))) + h_ <- h_[1:y] + y <- length(i_) - (ifelse(nrow(mtxdisdf_) >= 9, 9, nrow(mtxdisdf_))) + i_ <- i_[1:y] + y <- length(j_) - (ifelse(nrow(mtxdisdf_) >= 10, 10, nrow(mtxdisdf_))) + j_ <- j_[1:y] + y <- length(k_) - (ifelse(nrow(mtxdisdf_) >= 11, 11, nrow(mtxdisdf_))) + k_ <- k_[1:y] + y <- length(l_) - (ifelse(nrow(mtxdisdf_) >= 12, 12, nrow(mtxdisdf_))) + l_ <- l_[1:y] + y <- length(m_) - (ifelse(nrow(mtxdisdf_) >= 13, 13, nrow(mtxdisdf_))) + m_ <- m_[1:y] + y <- length(n_) - (ifelse(nrow(mtxdisdf_) >= 14, 14, nrow(mtxdisdf_))) + n_ <- n_[1:y] + y <- length(o_) - (ifelse(nrow(mtxdisdf_) >= 15, 15, nrow(mtxdisdf_))) + o_ <- o_[1:y] + y <- length(p_) - (ifelse(nrow(mtxdisdf_) >= 16, 16, nrow(mtxdisdf_))) + p_ <- p_[1:y] + y <- length(q_) - (ifelse(nrow(mtxdisdf_) >= 17, 17, nrow(mtxdisdf_))) + q_ <- q_[1:y] + y <- length(r_) - (ifelse(nrow(mtxdisdf_) >= 18, 18, nrow(mtxdisdf_))) + r_ <- r_[1:y] + y <- length(s_) - (ifelse(nrow(mtxdisdf_) >= 19, 19, nrow(mtxdisdf_))) + s_ <- s_[1:y] + + rm(y) + + name_ <- c(a_, b_, c_, d_, e_, f_, g_, h_, i_, j_, k_, l_, m_, n_, o_, p_, q_, r_, s_) + df_ <- data.frame(expand.grid(mtxdis), name_[1:nrow(expand.grid(mtxdis))]) + names(df_) <- c("dist.", "name_") + + rm(a_, b_, c_, d_, e_, f_, g_, h_, i_, j_, k_, l_, m_, n_, o_, p_, q_, r_, s_) + rm(name_) + + q_ <- strsplit(df_$name_, " & ") + mat_ <- matrix(unlist(q_), ncol = 2, byrow = TRUE) + q_df_ <- as.data.frame(matrix(unlist(q_), ncol = 2, byrow = TRUE)) + df_ <- dplyr::bind_cols(df_, q_df_) + + rm(q_, mat_, q_df_) + + split_ <- strsplit(df_$V1, "_") + v1_split_ <- as.data.frame(matrix(unlist(split_), ncol = 4, byrow = TRUE)) + split_ <- strsplit(df_$V2, "_") + v2_split_ <- as.data.frame(matrix(unlist(split_), ncol = 4, byrow = TRUE)) + + df_ <- dplyr::bind_cols(df_, v1_split_) + df_ <- dplyr::bind_cols(df_, v2_split_) + df_red_ <- subset(df_, V4...8 == V4...12 & V3...7 == V3...11) + site_year_month_day <- rep(unique(qecbnato0_x$site_year_month_day), nrow(df_red_)) + + df_red_ <- tibble::add_column(df_red_, site_year_month_day, .before = "dist.") + + rm(split_, v1_split_, v2_split_) + rm(mtxdis, mtxdisdf_, df_, site_year_month_day) + + matri_list[[x]] <- df_red_ + matri_list <<- matri_list + + rm(df_red_, qecbnato0_x, x) + + } + + matri_df <- do.call("rbind", matri_list) + + names(matri_df) <- c("site_year_month_day", "dist.", "name_", "name_left", "name_right", "Type.Bloc.left", "Face.left", "Numéro.Bloc.échantillon.left", "Quadrat.left", "Type.Bloc.right", "Face.right", "Numéro.Bloc.échantillon.right", "Quadrat.right") + + matri_df <<- matri_df + + hist(matri_df$dist.) + +} + +data_ <- dplyr::filter(qecbnato0, Type.Bloc == "Bloc mobile") +matri_list <- vector("list", length(unique(data_$site_year_month_day))) + +matri_fct_bmm(data = data_, conca = c(bret_egmp_basq_qecb, egmp_basq_qecb)) +hist(matri_df$dist., main = c(paste("Histo. of Bray (0-adjusted) dist. dissimilarity measures"), paste(" (sqrt transfo) - BMfs vs BMfi -"))) + +matri_full_bm_bf_fi <- matri_df + +saveRDS(matri_full_bm_bf_fi, "matri_full_log.spi_BM_FS.FI.RDS") +rm(data_, matri_df, matri_list) + +## plot + +# activate line + +matri_full_bm_bf_fs <- tidyr::separate(matri_full_bm_bf_fs, "site_year_month_day", into = c("departement", "Site", "Year", "Month", "Day"), remove = FALSE) +matri_full_bm_bf_fs$Site <- paste0(matri_full_bm_bf_fs$departement, "_", matri_full_bm_bf_fs$Site) +matri_full_bm_bf_fs <- subset(matri_full_bm_bf_fs, select = - c(departement)) +matri_full_bm_bf_fs <- tibble::add_column(matri_full_bm_bf_fs, Date = as.Date(paste0(matri_full_bm_bf_fs$Year, "-", matri_full_bm_bf_fs$Month, "-", matri_full_bm_bf_fs$Day), origin = "1970-01-01"), .after = "Site") +matri_full_bm_bf_fs$Site <- as.factor(matri_full_bm_bf_fs$Site) + +matri_full_bm_bf_fi <- tidyr::separate(matri_full_bm_bf_fi, "site_year_month_day", into = c("departement", "Site", "Year", "Month", "Day"), remove = FALSE) +matri_full_bm_bf_fi$Site <- paste0(matri_full_bm_bf_fi$departement, "_", matri_full_bm_bf_fi$Site) +matri_full_bm_bf_fi <- subset(matri_full_bm_bf_fi, select = - c(departement)) +matri_full_bm_bf_fi <- tibble::add_column(matri_full_bm_bf_fi, Date = as.Date(paste0(matri_full_bm_bf_fi$Year, "-", matri_full_bm_bf_fi$Month, "-", matri_full_bm_bf_fi$Day), origin = "1970-01-01"), .after = "Site") +matri_full_bm_bf_fi$Site <- as.factor(matri_full_bm_bf_fi$Site) + +# if error message "Error in .Call.graphics(C_palette2, .Call(C_palette2, NULL)) : invalid graphics state" + + +bf_fs_plot <- ggplot2::ggplot(matri_full_bm_bf_fs, ggplot2::aes(x = Site, y = dist.)) + + ggplot2::geom_boxplot() + + #geom_jitter(shape = 16, position=position_jitter(0.2)) + + ggplot2::xlab("") + + ggplot2::ylab("distance diss. BM.BF_FS") + + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5, hjust = 1)) + +ggplot2::ggsave("distance_diss_BF_FS.png", bf_fs_plot) + +fs_fi_plot <- ggplot2::ggplot(matri_full_bm_bf_fi, ggplot2::aes(x = Site, y = dist.)) + + ggplot2::geom_boxplot() + + #geom_jitter(shape = 16, position=position_jitter(0.2)) + + ggplot2::xlab("") + + ggplot2::ylab("distance diss. BM_FS.FI") + + ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5, hjust = 1)) + +ggplot2::ggsave("distance_diss_FS_FI.png", fs_fi_plot) + +# issue with type de bloc, numéro de bloc and quadrat for df_ BM.BF_FS, cfr df_ left vs right variables doesn't give the right combination (variables with left vs right label in names come from the dissimilarity coefficient functions). +matri_full_bm_bf_fs$Quadrat <- NA +for (i in c(1:nrow(matri_full_bm_bf_fs))) { + ifelse(matri_full_bm_bf_fs$Type.Bloc.left[i] == "Bloc mobile", matri_full_bm_bf_fs$Quadrat[i] <- matri_full_bm_bf_fs$Quadrat.left[i], matri_full_bm_bf_fs$Quadrat[i] <- matri_full_bm_bf_fs$Quadrat.right[i]) +} +matri_full_bm_bf_fs$Numéro.Bloc <- NA +for (i in c(1:nrow(matri_full_bm_bf_fs))) { + ifelse(matri_full_bm_bf_fs$Type.Bloc.left[i] == "Bloc mobile", matri_full_bm_bf_fs$Numéro.Bloc[i] <- matri_full_bm_bf_fs$Numéro.Bloc.échantillon.left[i], matri_full_bm_bf_fs$Numéro.Bloc[i] <- matri_full_bm_bf_fs$Numéro.Bloc.échantillon.right[i]) +} + +matri_full_bm_bf_fs <- tibble::add_column(matri_full_bm_bf_fs, site_year_month_day.q_BMnb = paste0(matri_full_bm_bf_fs$site_year_month_day, "_", matri_full_bm_bf_fs$Quadrat, "_", matri_full_bm_bf_fs$Numéro.Bloc), .after = "site_year_month_day") +matri_full_bm_bf_fi <- tibble::add_column(matri_full_bm_bf_fi, site_year_month_day.q_BMnb = paste0(matri_full_bm_bf_fi$site_year_month_day, "_", matri_full_bm_bf_fi$Quadrat.left, "_", matri_full_bm_bf_fi$Numéro.Bloc.échantillon.left), .after = "site_year_month_day") + +colnames(matri_full_bm_bf_fs) <- paste("BM.BF_FS", colnames(matri_full_bm_bf_fs), sep = "_") +matri_full_bm_bf_fs <- dplyr::rename(matri_full_bm_bf_fs, site_year_month_day.q_BMnb = BM.BF_FS_site_year_month_day.q_BMnb) +colnames(matri_full_bm_bf_fi) <- paste("BM_FS.FI", colnames(matri_full_bm_bf_fi), sep = "_") +matri_full_bm_bf_fi <- dplyr::rename(matri_full_bm_bf_fi, site_year_month_day.q_BMnb = BM_FS.FI_site_year_month_day.q_BMnb) + +matri_full <- dplyr::full_join(matri_full_bm_bf_fs[, c("site_year_month_day.q_BMnb", "BM.BF_FS_dist.")], matri_full_bm_bf_fi[, c("site_year_month_day.q_BMnb", "BM_FS.FI_dist.")]) + +matri_full <- tidyr::separate(matri_full, "site_year_month_day.q_BMnb", into = c("departement", "Site", "Year", "Month", "Day", "Quadrat", "Bloc Mobile Number"), remove = FALSE) +matri_full$Site <- paste0(matri_full$departement, "_", matri_full$Site) +matri_full <- subset(matri_full, select = - c(departement)) +matri_full <- tibble::add_column(matri_full, Date = as.Date(paste0(matri_full$Year, "-", matri_full$Month, "-", matri_full$Day), origin = "1970-01-01"), .after = "Site") + +# Name for report/plot + +matri_full <- tibble::add_column(matri_full, Site_bis = NA, .after = "Site") + +matri_full$Site_bis <- ifelse(matri_full$Site == "GDMO_Locmariaquer", "Locmariaquer", matri_full$Site_bis) +matri_full$Site_bis <- ifelse(matri_full$Site == "GDMO_BegLann", "Beg Lann", matri_full$Site_bis) +matri_full$Site_bis <- ifelse(matri_full$Site == "FOUR_PlateauFour", "Plateau du Four", matri_full$Site_bis) +matri_full$Site_bis <- ifelse(matri_full$Site == "EGMP_GroinCou", "Groin du Cou", matri_full$Site_bis) +matri_full$Site_bis <- ifelse(matri_full$Site == "EGMP_PasEmsembert", "Le Pas d'Emsembert", matri_full$Site_bis) +matri_full$Site_bis <- ifelse(matri_full$Site == "EGMP_BreeBains", "La Brée-les-Bains", matri_full$Site_bis) +matri_full$Site_bis <- ifelse(matri_full$Site == "EGMP_PerreAntiochat", "Le Perré d'Antiochat", matri_full$Site_bis) +matri_full$Site_bis <- ifelse(matri_full$Site == "EGMP_Chassiron", "Chassiron", matri_full$Site_bis) +matri_full$Site_bis <- ifelse(matri_full$Site == "BASQ_FlotsBleusZP", "Les Flots Bleus / zone pêcheurs", matri_full$Site_bis) +matri_full$Site_bis <- ifelse(matri_full$Site == "BASQ_FlotsBleusZF", "Les Flots Bleus / zone familles", matri_full$Site_bis) +matri_full$Site_bis <- ifelse(matri_full$Site == "GONB_IlotStMichel", "Îlot Saint-Michel", matri_full$Site_bis) +matri_full$Site_bis <- ifelse(matri_full$Site == "FINS_Quemenes", "Quéménès", matri_full$Site_bis) +matri_full$Site_bis <- ifelse(matri_full$Site == "FINS_SeinGoulenez", "Île de Sein - Goulenez", matri_full$Site_bis) +matri_full$Site_bis <- ifelse(matri_full$Site == "FINS_SeinKilaourou", "Île de Sein - Kilaourou", matri_full$Site_bis) +matri_full$Site_bis <- ifelse(matri_full$Site == "ARMO_Verdelet", "Îlot du Verdelet", matri_full$Site_bis) +matri_full$Site_bis <- ifelse(matri_full$Site == "ARMO_Piegu", "Piégu", matri_full$Site_bis) +matri_full$Site_bis <- ifelse(matri_full$Site == "ARMO_Bilfot", "Pointe de Bilfot", matri_full$Site_bis) +matri_full$Site_bis <- ifelse(matri_full$Site == "ARMO_IlePlate", "Île Plate", matri_full$Site_bis) +matri_full$Site_bis <- ifelse(matri_full$Site == "PDMO_Perharidy", "Perharidy", matri_full$Site_bis) +matri_full$Site_bis <- ifelse(matri_full$Site == "BRES_Keraliou", "Keraliou", matri_full$Site_bis) +matri_full$Site_bis <- ifelse(matri_full$Site == "FINS_Mousterlin", "Pointe de Mousterlin", matri_full$Site_bis) +matri_full$Site_bis <- ifelse(matri_full$Site == "FINS_StNicolasGlenan", "Saint-Nicolas des Glénan", matri_full$Site_bis) + +unique(matri_full[, c("Site", "Site_bis")]) + +saveRDS(matri_full, "matri_full_log.spi.RDS") + + +## plot dissimilarity coefficient + +matri_full$Year <- as.integer(matri_full$Year) +matri_full$Month <- as.integer(matri_full$Month) +matri_full$Day <- as.integer(matri_full$Day) + + +## BM_FS.FI_dist => mobile boulder upper vs lower faces + +# Stats + +bm_fs_fi_dist_stat <- matri_full %>% dplyr::group_by(Site, Site_bis, Date, Year, Month, Day) %>% dplyr::summarize(BM_FS.FI_dist.moy = mean(BM_FS.FI_dist., na.rm = TRUE), BM_FS.FI_dist.et = sd(BM_FS.FI_dist., na.rm = TRUE), BM_FS.FI_dist.med = median(BM_FS.FI_dist., na.rm = TRUE), BM_FS.FI_dist.min = min(BM_FS.FI_dist., na.rm = TRUE), BM_FS.FI_dist.max = max(BM_FS.FI_dist., na.rm = TRUE), nb. = dplyr::n(), nb.notNa = sum(!is.na(BM_FS.FI_dist.))) + +bm_fs_fi_dist_stat <- dplyr::ungroup(bm_fs_fi_dist_stat) + +# Quality scale based on quartiles + +one <- round(mean(unlist(dplyr::filter(matri_full, BM_FS.FI_dist. <= quantile(matri_full$BM_FS.FI_dist., 0.25, na.rm = TRUE))["BM_FS.FI_dist."])), digits = 3) +two <- round(mean(unlist(dplyr::filter(matri_full, BM_FS.FI_dist. > quantile(matri_full$BM_FS.FI_dist., 0.25, na.rm = TRUE) & BM_FS.FI_dist. <= quantile(matri_full$BM_FS.FI_dist., 0.5, na.rm = TRUE))["BM_FS.FI_dist."])), digits = 3) +three <- round(mean(unlist(dplyr::filter(matri_full, BM_FS.FI_dist. > quantile(matri_full$BM_FS.FI_dist., 0.5, na.rm = TRUE) & BM_FS.FI_dist. <= quantile(matri_full$BM_FS.FI_dist., 0.75, na.rm = TRUE))["BM_FS.FI_dist."])), digits = 3) +four <- round(mean(unlist(dplyr::filter(matri_full, BM_FS.FI_dist. > quantile(matri_full$BM_FS.FI_dist., 0.75, na.rm = TRUE))["BM_FS.FI_dist."])), digits = 3) + +# Plot + +for (i in c(1:length(unique(bm_fs_fi_dist_stat$Site)))) { + + df1 <- dplyr::filter(bm_fs_fi_dist_stat, Site == unique(bm_fs_fi_dist_stat$Site)[i]) + + xmin_ <- as.Date(ifelse(min(df1$Year) >= 2014, "2014-01-01", paste0(min(matri_full$Year), "-01-01")), origin = "1970-01-01") + xmax_ <- as.Date(ifelse(max(df1$Year) <= 2017, "2018-01-01", #paste0(max(matri_full$Year)+1, + "2022-01-01") + #) + , origin = "1970-01-01") + + ymin_ <- 0 + ymax_ <- 1 + + png(paste0("diss_", unique(bm_fs_fi_dist_stat$Site), ".png")) + plot(bm_fs_fi_dist_stat$Date, bm_fs_fi_dist_stat$BM_FS.FI_dist.med, xlim = c(xmin_, xmax_), ylim = c(ymin_, ymax_), pch = 19, main = "", xlab = "", ylab = "", type = "n", axes = FALSE) + + rect(as.Date("2013-01-01", origin = "1970-01-01"), -0.1, as.Date("2023-01-01", origin = "1970-01-01"), one, col = "red", border = NA) + rect(as.Date("2013-01-01", origin = "1970-01-01"), one, as.Date("2023-01-01", origin = "1970-01-01"), two, col = "orange", border = NA) + rect(as.Date("2013-01-01", origin = "1970-01-01"), two, as.Date("2023-01-01", origin = "1970-01-01"), three, col = "yellow", border = NA) + rect(as.Date("2013-01-01", origin = "1970-01-01"), three, as.Date("2023-01-01", origin = "1970-01-01"), four, col = "olivedrab", border = NA) + rect(as.Date("2013-01-01", origin = "1970-01-01"), four, as.Date("2023-01-01", origin = "1970-01-01"), 1.1, col = "blue", border = NA) + + par(new = TRUE) + plot(bm_fs_fi_dist_stat$Date, bm_fs_fi_dist_stat$BM_FS.FI_dist.med, xlim = c(xmin_, xmax_), ylim = c(ymin_, ymax_), pch = 19, cex = 1, main = unique(df1$Site_bis), xlab = "Année", + ylab = "Coef. dissi. BM_FS.FI", col = "grey") + points(df1$Date, df1$BM_FS.FI_dist.med, pch = 19, cex = 1.5) + arrows(df1$Date, df1$BM_FS.FI_dist.med, df1$Date, df1$BM_FS.FI_dist.max, code = 3, angle = 90, length = 0.00) + arrows(df1$Date, df1$BM_FS.FI_dist.med, df1$Date, df1$BM_FS.FI_dist.min, code = 3, angle = 90, length = 0.00) + +} + +rm(df1, four, i, one, three, two, xmax_, xmin_, ymax_, ymin_) + + +## BM.BF_FS_dist => mobile boulder vs fixed boulder upper faces + +# Stats + +bm_bf_fs_dist_stat <- matri_full %>% dplyr::group_by(Site, Site_bis, Date, Year, Month, Day) %>% dplyr::summarize(BM.BF_FS_dist.moy = mean(BM.BF_FS_dist., na.rm = TRUE), BM.BF_FS_dist.et = sd(BM.BF_FS_dist., na.rm = TRUE), BM.BF_FS_dist.med = median(BM.BF_FS_dist., na.rm = TRUE), BM.BF_FS_dist.min = min(BM.BF_FS_dist., na.rm = TRUE), BM.BF_FS_dist.max = max(BM.BF_FS_dist., na.rm = TRUE), nb. = dplyr::n(), nb.notNa = sum(!is.na(BM.BF_FS_dist.))) + +bm_bf_fs_dist_stat <- dplyr::ungroup(bm_bf_fs_dist_stat) + +# Quality scale based on quartiles + +one <- round(mean(unlist(dplyr::filter(matri_full, BM.BF_FS_dist. <= quantile(matri_full$BM.BF_FS_dist., 0.25, na.rm = TRUE))["BM.BF_FS_dist."])), digits = 3) +two <- round(mean(unlist(dplyr::filter(matri_full, BM.BF_FS_dist. > quantile(matri_full$BM.BF_FS_dist., 0.25, na.rm = TRUE) & BM.BF_FS_dist. <= quantile(matri_full$BM.BF_FS_dist., 0.5, na.rm = TRUE))["BM.BF_FS_dist."])), digits = 3) +three <- round(mean(unlist(dplyr::filter(matri_full, BM.BF_FS_dist. > quantile(matri_full$BM.BF_FS_dist., 0.5, na.rm = TRUE) & BM.BF_FS_dist. <= quantile(matri_full$BM.BF_FS_dist., 0.75, na.rm = TRUE))["BM.BF_FS_dist."])), digits = 3) +four <- round(mean(unlist(dplyr::filter(matri_full, BM.BF_FS_dist. > quantile(matri_full$BM.BF_FS_dist., 0.75, na.rm = TRUE))["BM.BF_FS_dist."])), digits = 3) + +# Plot + +for (i in c(1:length(unique(bm_bf_fs_dist_stat$Site)))) { + + df1 <- dplyr::filter(bm_bf_fs_dist_stat, Site == unique(bm_bf_fs_dist_stat$Site)[i]) + + xmin_ <- as.Date(ifelse(min(df1$Year) >= 2014, "2014-01-01", paste0(min(matri_full$Year), "-01-01")), origin = "1970-01-01") + xmax_ <- as.Date(ifelse(max(df1$Year) <= 2017, "2018-01-01", #paste0(max(matri_full$Year)+1, + "2022-01-01") + #) + , origin = "1970-01-01") + + ymin_ <- 0 + ymax_ <- 1 + + png(paste0("diss_", unique(bm_bf_fs_dist_stat$Site), ".png")) + plot(bm_bf_fs_dist_stat$Date, bm_bf_fs_dist_stat$BM.BF_FS_dist.med, xlim = c(xmin_, xmax_), ylim = c(ymin_, ymax_), pch = 19, main = "", xlab = "", ylab = "", type = "n", axes = FALSE) + + rect(as.Date("2013-01-01", origin = "1970-01-01"), -0.1, as.Date("2023-01-01", origin = "1970-01-01"), one, col = "blue", border = NA) + rect(as.Date("2013-01-01", origin = "1970-01-01"), one, as.Date("2023-01-01", origin = "1970-01-01"), two, col = "olivedrab", border = NA) + rect(as.Date("2013-01-01", origin = "1970-01-01"), two, as.Date("2023-01-01", origin = "1970-01-01"), three, col = "yellow", border = NA) + rect(as.Date("2013-01-01", origin = "1970-01-01"), three, as.Date("2023-01-01", origin = "1970-01-01"), four, col = "orange", border = NA) + rect(as.Date("2013-01-01", origin = "1970-01-01"), four, as.Date("2023-01-01", origin = "1970-01-01"), 1.1, col = "red", border = NA) + + par(new = TRUE) + plot(bm_bf_fs_dist_stat$Date, bm_bf_fs_dist_stat$BM.BF_FS_dist.med, xlim = c(xmin_, xmax_), ylim = c(ymin_, ymax_), pch = 19, cex = 1, main = unique(df1$Site_bis), xlab = "Année", + ylab = "Coef. dissi. BM.BF_FS", col = "grey") + points(df1$Date, df1$BM.BF_FS_dist.med, pch = 19, cex = 1.5) + arrows(df1$Date, df1$BM.BF_FS_dist.med, df1$Date, df1$BM.BF_FS_dist.max, code = 3, angle = 90, length = 0.00) + arrows(df1$Date, df1$BM.BF_FS_dist.med, df1$Date, df1$BM.BF_FS_dist.min, code = 3, angle = 90, length = 0.00) + +} + +rm(df1, four, i, one, three, two, xmax_, xmin_, ymax_, ymin_) + +write.table(bm_bf_fs_dist_stat, "Valeurs_stat.tabular", row.names = FALSE, quote = FALSE, sep = "\t", dec = ".", fileEncoding = "UTF-8") + +saveRDS(bm_fs_fi_dist_stat, "matri_full_log.spi_bm_fs_fi_dist_statRDS") + +saveRDS(bm_bf_fs_dist_stat, "matri_full_log.spi_bm_bf_fs_dist_statRDS") diff --git a/tools/indic_ivr/cb_ecology.xml b/tools/indic_ivr/cb_ecology.xml new file mode 100644 index 000000000..de7c1ca03 --- /dev/null +++ b/tools/indic_ivr/cb_ecology.xml @@ -0,0 +1,101 @@ + + indicator + + r-dplyr + r-tidyr + r-readr + r-writexl + r-stringr + r-readxl + r-tibble + r-lubridate + r-cowplot + + r-rmarkdown + r-officedown + r-knitr + r-vegan + r-reshape2 + r-ggplot2 + r-gridExtra + + r-Rcpp + r-arsenal + r-magrittr + r-remotes + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @Manual{, + title = {Les habitats rocheux intertidaux sous l’influence d’activités anthropiques : structure, dynamique et enjeux de conservation. Sciences agricoles}, + author = {Bernard M.}, + year = {2012}, + note = {https://tel.archives-ouvertes.fr/tel-00720611/} + + + diff --git a/tools/indic_ivr/cb_ivr.r b/tools/indic_ivr/cb_ivr.r new file mode 100644 index 000000000..8d5e8f7c3 --- /dev/null +++ b/tools/indic_ivr/cb_ivr.r @@ -0,0 +1,340 @@ +# author: "Jonathan Richir" +# date: "19 April 2021" ) + +#Rscript + +############################### +## ## +############################### + +#####Packages : dplyr +# tidyr +# readr +# writexl +# stringr +# readxl +# tibble +# lubridate +# cowplot +# magrittr +# rmarkdown +library(magrittr) +#####Load arguments + +args <- commandArgs(trailingOnly = TRUE) + +#####Import data + +if (length(args) < 1) { + stop("This tool needs at least 1 argument") +}else { + input_data <- args[1] + input_data2 <- args[2] + fiche_val <- args[3] + fiche_term <- args[4] + +} + +## load ivr data + +ivr <- read.csv2(input_data, header = FALSE, fileEncoding = "Latin1") +names_ <- as.vector(unlist(ivr[1, ])) +names_ <- gsub(" ", ".", names_) +colnames(ivr) <- names_ +ivr <- ivr[-1, ] +ivr <- ivr[, -17] + +ivr_next <- read.csv2(input_data2, header = FALSE, fileEncoding = "Latin1") +names_ <- as.vector(unlist(ivr_next[1, ])) +names_ <- gsub(" ", ".", names_) +colnames(ivr_next) <- names_ +rm(names_) +ivr_next <- ivr_next[-1, ] +ivr_next <- ivr_next[, -17] + +# bind ivr dfs. + +ivr <- dplyr::bind_rows(ivr, ivr_next) + +# NB inversion between id and ID.Fiche variable names +ivr <- dplyr::rename(ivr, XX = ID.Fiche) +ivr <- dplyr::rename(ivr, ID.Fiche = id) +ivr <- dplyr::rename(ivr, id = XX) + +rm(ivr_next) + + +## import excel files "Fiche terrain" + +fiche <- read.csv2(fiche_val, fileEncoding = "Latin1") # fileEncoding = "Latin1" cfr é in variable names + +fiche_next <- read.csv2(fiche_term, fileEncoding = "Latin1") # fileEncoding = "Latin1" cfr é in variable names + +fiche <- dplyr::bind_rows(fiche, fiche_next) +date_fiche <- as.Date(stringr::str_sub(fiche$date.sortie, end = 10), origin = "1970-01-01") +fiche <- tibble::add_column(fiche, date_fiche, .after = "date.sortie") +rm(date_fiche) + +rm(fiche_next) + + +## ivr vs fiche terrain + +ivr$id <- as.numeric(ivr[, c("id")]) + +fiche_red <- dplyr::filter(fiche, fiche$ID.Fiche %in% unique(ivr[, c("id")])) + +id_count <- ivr %>% dplyr::group_by(id) %>% dplyr::count() +id_count <- dplyr::rename(id_count, "ID.Fiche" = "id") +id_count <- dplyr::ungroup(id_count) +id_count <- as.data.frame(id_count) + +fiche_red <- dplyr::left_join(fiche_red, id_count) + +# rep fiche terrain information +fiche_expanded <- fiche_red[rep(row.names(fiche_red), fiche_red$n), 1:ncol(fiche_red)] +fiche_expanded <- dplyr::rename(fiche_expanded, "id" = "ID.Fiche") + +## merge ivr data and ficheterrain information + +ivr <- dplyr::bind_cols(ivr, fiche_expanded) +ivr <- dplyr::rename(ivr, "id.ivr" = "id...1") +ivr <- dplyr::rename(ivr, "id.fiche" = "id...17") + +rm(fiche_expanded, fiche_red, id_count) + +ivr <- ivr %>% tidyr::separate(date_fiche, c("Year", "Month", "Day"), sep = "-", remove = FALSE) + + +## I create two new variables for Site names, one for data analysis and one for data reporting. Only works for actual ivr df with 22 sites ! + +# Name for data analysis + +ivr <- tibble::add_column(ivr, Site = NA, .after = "ID.Fiche") +unique(ivr$Site) + +ivr$Site <- ifelse(ivr$zone.habitat == unique(ivr$zone.habitat)[1], "GDMO_Locmariaquer", ivr$Site) +ivr$Site <- ifelse(ivr$zone.habitat == unique(ivr$zone.habitat)[2], "GDMO_BegLann", ivr$Site) +ivr$Site <- ifelse(ivr$zone.habitat == unique(ivr$zone.habitat)[3], "FOUR_PlateauFour", ivr$Site) +ivr$Site <- ifelse(ivr$zone.habitat == unique(ivr$zone.habitat)[4], "EGMP_GroinCou", ivr$Site) +ivr$Site <- ifelse(ivr$zone.habitat == unique(ivr$zone.habitat)[5], "EGMP_PasEmsembert", ivr$Site) +ivr$Site <- ifelse(ivr$zone.habitat == unique(ivr$zone.habitat)[6], "EGMP_BreeBains", ivr$Site) +ivr$Site <- ifelse(ivr$zone.habitat == unique(ivr$zone.habitat)[7], "EGMP_PerreAntiochat", ivr$Site) +ivr$Site <- ifelse(ivr$zone.habitat == unique(ivr$zone.habitat)[8], "EGMP_Chassiron", ivr$Site) +ivr$Site <- ifelse(ivr$zone.habitat == unique(ivr$zone.habitat)[9], "BASQ_FlotsBleusZP", ivr$Site) +ivr$Site <- ifelse(ivr$zone.habitat == unique(ivr$zone.habitat)[10], "BASQ_FlotsBleusZF", ivr$Site) +ivr$Site <- ifelse(ivr$zone.habitat == unique(ivr$zone.habitat)[11], "GONB_IlotStMichel", ivr$Site) +ivr$Site <- ifelse(ivr$zone.habitat == unique(ivr$zone.habitat)[12], "FINS_Quemenes", ivr$Site) +ivr$Site <- ifelse(ivr$zone.habitat == unique(ivr$zone.habitat)[13], "FINS_SeinGoulenez", ivr$Site) +ivr$Site <- ifelse(ivr$zone.habitat == unique(ivr$zone.habitat)[14], "FINS_SeinKilaourou", ivr$Site) +ivr$Site <- ifelse(ivr$zone.habitat == unique(ivr$zone.habitat)[15], "ARMO_Verdelet", ivr$Site) +ivr$Site <- ifelse(ivr$zone.habitat == unique(ivr$zone.habitat)[16], "ARMO_Piegu", ivr$Site) +ivr$Site <- ifelse(ivr$zone.habitat == unique(ivr$zone.habitat)[17], "ARMO_Bilfot", ivr$Site) +ivr$Site <- ifelse(ivr$zone.habitat == unique(ivr$zone.habitat)[18], "ARMO_IlePlate", ivr$Site) +ivr$Site <- ifelse(ivr$zone.habitat == unique(ivr$zone.habitat)[19], "PDMO_Perharidy", ivr$Site) +ivr$Site <- ifelse(ivr$zone.habitat == unique(ivr$zone.habitat)[20], "BRES_Keraliou", ivr$Site) +ivr$Site <- ifelse(ivr$zone.habitat == unique(ivr$zone.habitat)[21], "FINS_Mousterlin", ivr$Site) +ivr$Site <- ifelse(ivr$zone.habitat == unique(ivr$zone.habitat)[22], "FINS_StNicolasGlenan", ivr$Site) + +unique(ivr$Site) + +# Anne Boulet forgot to specify zone.habitat in 2020. I asked her to correct it in ESTAMP +ivr$Site <- ifelse(ivr$zone.habitat == unique(ivr$zone.habitat)[23], "GDMO_Locmariaquer", ivr$Site) +unique(ivr$Site) +unique(ivr[, c("Site", "zone.habitat")]) + +# Name for report/plot + +ivr <- tibble::add_column(ivr, Site_bis = NA, .after = "Site") + +ivr$Site_bis <- ifelse(ivr$Site == "GDMO_Locmariaquer", "Locmariaquer", ivr$Site_bis) +ivr$Site_bis <- ifelse(ivr$Site == "GDMO_BegLann", "Beg Lann", ivr$Site_bis) +ivr$Site_bis <- ifelse(ivr$Site == "FOUR_PlateauFour", "Plateau du Four", ivr$Site_bis) +ivr$Site_bis <- ifelse(ivr$Site == "EGMP_GroinCou", "Groin du Cou", ivr$Site_bis) +ivr$Site_bis <- ifelse(ivr$Site == "EGMP_PasEmsembert", "Le Pas d'Emsembert", ivr$Site_bis) +ivr$Site_bis <- ifelse(ivr$Site == "EGMP_BreeBains", "La Brée-les-Bains", ivr$Site_bis) +ivr$Site_bis <- ifelse(ivr$Site == "EGMP_PerreAntiochat", "Le Perré d'Antiochat", ivr$Site_bis) +ivr$Site_bis <- ifelse(ivr$Site == "EGMP_Chassiron", "Chassiron", ivr$Site_bis) +ivr$Site_bis <- ifelse(ivr$Site == "BASQ_FlotsBleusZP", "Les Flots Bleus / zone pêcheurs", ivr$Site_bis) +ivr$Site_bis <- ifelse(ivr$Site == "BASQ_FlotsBleusZF", "Les Flots Bleus / zone familles", ivr$Site_bis) +ivr$Site_bis <- ifelse(ivr$Site == "GONB_IlotStMichel", "Îlot Saint-Michel", ivr$Site_bis) +ivr$Site_bis <- ifelse(ivr$Site == "FINS_Quemenes", "Quéménès", ivr$Site_bis) +ivr$Site_bis <- ifelse(ivr$Site == "FINS_SeinGoulenez", "Île de Sein - Goulenez", ivr$Site_bis) +ivr$Site_bis <- ifelse(ivr$Site == "FINS_SeinKilaourou", "Île de Sein - Kilaourou", ivr$Site_bis) +ivr$Site_bis <- ifelse(ivr$Site == "ARMO_Verdelet", "Îlot du Verdelet", ivr$Site_bis) +ivr$Site_bis <- ifelse(ivr$Site == "ARMO_Piegu", "Piégu", ivr$Site_bis) +ivr$Site_bis <- ifelse(ivr$Site == "ARMO_Bilfot", "Pointe de Bilfot", ivr$Site_bis) +ivr$Site_bis <- ifelse(ivr$Site == "ARMO_IlePlate", "Île Plate", ivr$Site_bis) +ivr$Site_bis <- ifelse(ivr$Site == "PDMO_Perharidy", "Perharidy", ivr$Site_bis) +ivr$Site_bis <- ifelse(ivr$Site == "BRES_Keraliou", "Keraliou", ivr$Site_bis) +ivr$Site_bis <- ifelse(ivr$Site == "FINS_Mousterlin", "Pointe de Mousterlin", ivr$Site_bis) +ivr$Site_bis <- ifelse(ivr$Site == "FINS_StNicolasGlenan", "Saint-Nicolas des Glénan", ivr$Site_bis) + +unique(ivr[, c("Site", "Site_bis")]) + + +## change some variable format to integer + +ivr$Nb.Blocs.Non.Retournes <- as.integer(ivr$Nb.Blocs.Non.Retournes) +ivr$Nb.Blocs.Retournes <- as.integer(ivr$Nb.Blocs.Retournes) + +ivr$Year <- as.integer(ivr$Year) +ivr$Month <- as.integer(ivr$Month) +ivr$Day <- as.integer(ivr$Day) +ivr$Numero.Quadrat <- as.integer(ivr$Numero.Quadrat) + + +## save the final, commplete ivr df. + +ivr <- ivr[, c(19:54, 1:18)] + +saveRDS(ivr, "ivr.RDS") + + +## percentage of unturned vs overturned boulders and IVR previous 0-5 discrete scale values calculation + +# create two new variables first + +site_year_month_day <- paste0(ivr$Site, ".", gsub("-", ".", as.character(ivr$date_fiche))) +ivr <- tibble::add_column(ivr, site_year_month_day, .after = "Site_bis") +rm(site_year_month_day) + +site_year_month_day_qdnb <- paste0(ivr$Site, ".", gsub("-", ".", as.character(ivr$Date)), ".", ivr$Numero.Quadrat) +ivr <- tibble::add_column(ivr, site_year_month_day_qdnb, .after = "site_year_month_day") +rm(site_year_month_day_qdnb) + +ivr <- dplyr::arrange(ivr, Site, Year, Month, Numero.Quadrat) + +# remove data with NA value for Nb.Blocs.Retournes & Nb.Blocs.Non.Retournes +ivr_naomit <- ivr %>% dplyr::filter(!is.na(ivr$Nb.Blocs.Retournes)) +ivr_naomit <- as.data.frame(ivr_naomit) +colnames(ivr_naomit) <- colnames(ivr) +ivr_naomit <- ivr_naomit %>% dplyr::filter(!is.na(ivr_naomit$Nb.Blocs.Non.Retournes)) +ivr_naomit <- as.data.frame(ivr_naomit) + +# also remove data with Nb.Blocs.Retournes = 0 & Nb.Blocs.Non.Retournes = 0, cfr equivalent to quadrat with no boulders ... makes no sense to consider quadrat without boulder for ivr determination. +ivr_rm <- dplyr::filter(ivr_naomit, ivr_naomit$Nb.Blocs.Retournes == 0 && ivr_naomit$Nb.Blocs.Non.Retournes == 0) +ivr_naomit <- ivr_naomit %>% dplyr::anti_join(ivr_rm) +rm(ivr_rm) + +ivr_val_qu_ <- ivr_naomit + +for (i in 1:nrow(ivr_naomit)) { + (bm <- sum(ivr_naomit$Nb.Blocs.Non.Retournes[i], ivr_naomit$Nb.Blocs.Retournes[i])) + (ivr_val_qu_$blocs.retournes.fr.[i] <- (ivr_naomit$Nb.Blocs.Retournes[i] / bm) * 100) + (ivr_val_qu_$blocs.non.retournes.fr.[i] <- (ivr_naomit$Nb.Blocs.Non.Retournes[i] / bm) * 100) +} + +rm(bm, i) + +ivr_val_qu_$blocs.non.retournes.fr. <- ifelse(is.nan(ivr_val_qu_$blocs.non.retournes.fr.), NA, ivr_val_qu_$blocs.non.retournes.fr.) +ivr_val_qu_$blocs.retournes.fr. <- ifelse(is.nan(ivr_val_qu_$blocs.retournes.fr.), NA, ivr_val_qu_$blocs.retournes.fr.) + +# ivr for loop by quadrat. + +for (i in 1:nrow(ivr_val_qu_)) { + if (ivr_val_qu_$Nb.Blocs.Non.Retournes[i] == 0 && ivr_val_qu_$Nb.Blocs.Retournes[i] == 0) { + ivr_ <- NA + }else { + if (ivr_val_qu_$blocs.retournes.fr.[i] < 5) { + ivr_ <- 0 + } else if (ivr_val_qu_$blocs.retournes.fr.[i] >= 5 && ivr_val_qu_$blocs.retournes.fr.[i] < 25) { + ivr_ <- 1 + } else if (ivr_val_qu_$blocs.retournes.fr.[i] >= 25 && ivr_val_qu_$blocs.retournes.fr.[i] < 45) { + ivr_ <- 2 + } else if (ivr_val_qu_$blocs.retournes.fr.[i] >= 45 && ivr_val_qu_$blocs.retournes.fr.[i] < 65) { + ivr_ <- 3 + } else if (ivr_val_qu_$blocs.retournes.fr.[i] >= 65 && ivr_val_qu_$blocs.retournes.fr.[i] < 85) { + ivr_ <- 4 + } else { + ivr_ <- 5 + } + + ivr_val_qu_$valeur.ivr_quadrat[i] <- ivr_ + } +} + +rm(i, ivr_) + +# reorder variables for logical purpose +ivr_val_qu_ <- ivr_val_qu_[, c(1:56, 58, 57, 59)] +indic_full <- ivr_val_qu_ +saveRDS(ivr_val_qu_, "ivr_val_qu.RDS") + +rm(ivr_naomit) + + +## Calculate ivr statistics now + +ivr_val_qu_stat_ <- ivr_val_qu_ %>% dplyr::group_by(id.ivr, Site, Site_bis, Year, Month, Day) %>% dplyr::summarize(ivr_moy = mean(valeur.ivr_quadrat), ivr_et = sd(valeur.ivr_quadrat), ivr_med = median(valeur.ivr_quadrat), ivr_min = min(valeur.ivr_quadrat), ivr_max = max(valeur.ivr_quadrat), fr.r.moy = mean(blocs.retournes.fr.), fr.r.et = sd(blocs.retournes.fr.), fr.r.med = median(blocs.retournes.fr.), fr.r.min = min(blocs.retournes.fr.), fr.r.max = max(blocs.retournes.fr.), fr.nr.moy = mean(blocs.non.retournes.fr.), fr.nr.et = sd(blocs.non.retournes.fr.), fr.nr.med = median(blocs.non.retournes.fr.), fr.nr.min = min(blocs.non.retournes.fr.), fr.nr.max = max(blocs.non.retournes.fr.), nb. = dplyr::n()) + +Date <- as.Date(paste0(ivr_val_qu_stat_$Year, "-", ivr_val_qu_stat_$Month, "-", ivr_val_qu_stat_$Day), origin = "1970-01-01") +ivr_val_qu_stat_ <- tibble::add_column(ivr_val_qu_stat_, Date, .after = "Site_bis") +rm(Date) + +ivr_val_qu_stat_ <- as.data.frame(ivr_val_qu_stat_) +indic <- ivr_val_qu_stat_ +saveRDS(ivr_val_qu_stat_, "ivr_val_qu_stat.RDS") + + +## plot ivr (NB: Year, Month, Day variable names are replace by Annee, Mois, Jour, cfr previous label use in the script) + +ivr_val_qu_stat_ <- dplyr::rename(ivr_val_qu_stat_, Annee = Year) +ivr_val_qu_stat_ <- dplyr::rename(ivr_val_qu_stat_, Mois = Month) +ivr_val_qu_stat_ <- dplyr::rename(ivr_val_qu_stat_, Jour = Day) +write.table(ivr_val_qu_stat_, "Valeurs_stat.tabular", row.names = FALSE, quote = FALSE, sep = "\t", dec = ".", fileEncoding = "UTF-8") +# old IVR scale with discrete 0 to 5 environmental status levels, plus other site data + +for (i in c(1:length(unique(ivr_val_qu_stat_$Site)))) { + ivr_val_eg <- dplyr::filter(ivr_val_qu_stat_, ivr_val_qu_stat_$Site == unique(ivr_val_qu_stat_$Site)[i]) + + xmin_ <- as.Date(ifelse(min(ivr_val_eg$Annee) >= 2014, "2014-01-01", paste0(min(ivr_val_eg$Annee), "-01-01")), origin = "1970-01-01") + xmax_ <- as.Date(ifelse(max(ivr_val_eg$Annee) <= 2017, "2018-01-01", #paste0(max(ivr_val_eg$Annee) + "2022-01-01") + #) + , origin = "1970-01-01") + + png(paste0("old_ivr_", unique(ivr_val_eg$Site), ".png")) + plot(ivr_val_qu_stat_$Date, ivr_val_qu_stat_$ivr_med, xlim = c(xmin_, xmax_), ylim = c(-0.5, 5.5), pch = 19, main = unique(ivr_val_eg$Site), xlab = "Date", ylab = "IVR (médiane, min. et max de 5 quadrats)", col = "grey") + + points(ivr_val_eg$Date, ivr_val_eg$ivr_med, pch = 19, cex = 1.5) + arrows(ivr_val_eg$Date, ivr_val_eg$ivr_med, ivr_val_eg$Date, ivr_val_eg$ivr_max, code = 3, angle = 90, length = 0.00) + arrows(ivr_val_eg$Date, ivr_val_eg$ivr_med, ivr_val_eg$Date, ivr_val_eg$ivr_min, code = 3, angle = 90, length = 0.00) +} + +rm(ivr_val_eg) + +# new IVR scale with continuous 0 to 5 environmental status levels based on % of overturned boulders /20, plus other site data + +par(mar = c(5, 4, 2, 2) + 0.1) + +par(mfrow = c(1, 1)) + +for (i in c(1:length(unique(ivr_val_qu_stat_$Site)))) { + ivr_val_eg <- dplyr::filter(ivr_val_qu_stat_, ivr_val_qu_stat_$Site == unique(ivr_val_qu_stat_$Site)[i]) + + xmin_ <- as.Date(ifelse(min(ivr_val_eg$Annee) >= 2014, "2014-01-01", paste0(min(ivr_val_eg$Annee), "-01-01")), origin = "1970-01-01") + xmax_ <- as.Date(ifelse(max(ivr_val_eg$Annee) <= 2017, "2018-01-01", "2022-01-01"), origin = "1970-01-01") + + png(paste0("new_ivr_", unique(ivr_val_eg$Site), ".png")) + plot(ivr_val_eg$Date, ivr_val_eg$fr.r.moy / 20, xlim = c(xmin_, xmax_), ylim = c(0, 5), pch = 19, main = unique(ivr_val_eg$Site), xlab = "", ylab = "", type = "n", axes = FALSE) + + rect(as.Date("2013-01-01", origin = "1970-01-01"), 85 / 20, as.Date("2023-01-01", origin = "1970-01-01"), 5.5, col = "red", border = NA) + rect(as.Date("2013-01-01", origin = "1970-01-01"), 65 / 20, as.Date("2023-01-01", origin = "1970-01-01"), 85 / 20, col = "orange1", border = NA) + rect(as.Date("2013-01-01", origin = "1970-01-01"), 45 / 20, as.Date("2023-01-01", origin = "1970-01-01"), 65 / 20, col = "gold1", border = NA) + rect(as.Date("2013-01-01", origin = "1970-01-01"), 25 / 20, as.Date("2023-01-01", origin = "1970-01-01"), 45 / 20, col = "yellow", border = NA) + rect(as.Date("2013-01-01", origin = "1970-01-01"), 5 / 20, as.Date("2023-01-01", origin = "1970-01-01"), 25 / 20, col = "olivedrab", border = NA) + rect(as.Date("2013-01-01", origin = "1970-01-01"), -0.5, as.Date("2023-01-01", origin = "1970-01-01"), 5 / 20, col = "blue", border = NA) + + par(new = TRUE) + plot(ivr_val_qu_stat_$Date, ivr_val_qu_stat_$fr.r.moy / 20, xlim = c(xmin_, xmax_), ylim = c(0, 5), pch = 19, main = unique(ivr_val_eg$Site), xlab = "Date", ylab = "IVR", col = "grey") + + points(ivr_val_eg$Date, ivr_val_eg$fr.r.moy / 20, pch = 19, cex = 1.5) + + arrows(ivr_val_eg$Date, ivr_val_eg$fr.r.moy / 20 + ivr_val_eg$fr.r.et / 20, ivr_val_eg$Date, ivr_val_eg$fr.r.moy / 20 - ivr_val_eg$fr.r.et / 20, code = 3, angle = 90, length = 0.00) + + rm(ivr_val_eg, i, xmax_, xmin_) +} + +report <- args[5] +loop_file <- source(args[6]) diff --git a/tools/indic_ivr/cb_ivr.xml b/tools/indic_ivr/cb_ivr.xml new file mode 100644 index 000000000..43104f457 --- /dev/null +++ b/tools/indic_ivr/cb_ivr.xml @@ -0,0 +1,112 @@ + + indicator + + r-dplyr + r-tidyr + r-readr + r-writexl + r-stringr + r-readxl + r-tibble + r-lubridate + r-cowplot + r-magrittr + r-rmarkdown + r-officedown + r-knitr + r-tidyverse + r-officer + r-flextable + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @Manual{, + title = {Les habitats rocheux intertidaux sous l’influence d’activités anthropiques : structure, dynamique et enjeux de conservation. Sciences agricoles}, + author = {Bernard M.}, + year = {2012}, + note = {https://tel.archives-ouvertes.fr/tel-00720611/} + + + diff --git a/tools/indic_ivr/cb_ivr_site_report_col_scale_loop.Rmd b/tools/indic_ivr/cb_ivr_site_report_col_scale_loop.Rmd new file mode 100644 index 000000000..118a8dc1f --- /dev/null +++ b/tools/indic_ivr/cb_ivr_site_report_col_scale_loop.Rmd @@ -0,0 +1,182 @@ +--- +params: + Site: v +title: "Indice Visuel de Retournement des blocs (IVR) - suivi 2014-2017" +#author: "Jonathan Richir, Christian Kerbiriou, Pauline Poisson, Maud Bernard, Juliette Delavenne, Isabelle Le Viol" +#date: "30 Avril 2021" +output: + #word_document: + #reference_docx: word_template.docx + officedown::rdocx_document +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set( + echo = FALSE + , message = FALSE + , warning = FALSE + ) + +df <- indic +df_full <- indic_full + +df1 <- df %>% dplyr::filter(df$Site == v) +df1 <- droplevels(df1) +``` + +# Contexte + +Mieux comprendre les effets des changements globaux et locaux sur les habitats marins, et l’efficacité des mesures de gestion adoptées sous-entend de disposer de suivis standardisés et d’indicateurs robustes et sensibles reflétant l’état des habitats. + +L’habitat « Champs de blocs médiolittoraux » abrite une forte diversité de micro-habitats et d’espèces de par sa forte hétérogénéité structurelle et sa position intermédiaire sur l’estran, qui en font un des habitats les plus diversifiés du médiolittoral et à fort intérêt écologique, le long de la façade Manche-Atlantique française. Il est aussi un habitat très attractif pour la pratique de pêche à pied de loisir, qui via le remaniement des blocs, peut impacter les communautés. + +Ainsi, l’habitat « Champs de blocs médiolittoraux » a-t-il fait l’objet de plusieurs initiatives nationales et locales (dont LIFE+ « Expérimentation pour une gestion durable et concertée de la pêche à pied récréative en France » 2013-2017) pour mieux évaluer son état et le mettre en relation avec la pression de pêche à pied en vue d’adapter la gestion locale, notamment à travers le réseau d’Aires Marines Protégées (Natura 2000, PNM, PNR etc.). + +Ces projets ont en particulier permis de développer un réseau d’acteurs-trices de terrain-gestionnaires impliqué-e-s et des outils d’évaluation de l’état écologique et de la pression de pêche à pied: (1) l’Indicateur Visuel de Retournement des blocs (IVR) - objet du présent rapport - qui s’apparente à un indicateur « paysager » pour évaluer la pression de pêche sur la base de critères architecturaux; (2) L’indice de Qualité Écologique des Champs de Blocs (QECB) basé sur des variables biotiques et abiotiques qui répondent à la perturbation « retournement des blocs ». + + +# Application et calcul de l'Indicateur Visuel de Retournement des blocs (IVR) + +Basé sur les proportions de blocs « retournés « et « non retournés » et variant entre 0 et 5, cet indicateur peut être appliqué très rapidement et plusieurs fois dans l'année par le-la gestionnaire. + +Pour son application, cinq quadrats de 25m^2^ sont répartis à l’échelle du champ de blocs. Dans chaque quadrat, les blocs retournables à dominance d'algues brunes et/ou rouges (blocs « bruns/rouges »), et les blocs retournables à dominance d'algues vertes opportunistes ou de roche nue (blocs « blancs/verts ») sont dénombrés. + +Le rapport, par quadrat, entre le nombre de blocs « bruns/rouges » ou le nombre de blocs « blancs/verts » sur le nombre total de blocs comptés donnent deux valeurs de pourcentage (qui additionnées égalent 100%). A partir de ces pourcentages, la valeur de l'IVR peut être déterminée pour chaque quadrat. + +Dans sa version originale, ces plages de pourcentages étaient transformées en des équivalents entiers numériques allant de 0 à 5 comme suit: + +- IVR 0: présence quasi-exclusive de blocs « bruns/rouges » (entre 95% et 100%), pas de bloc ou de rares blocs retournés par les pêcheurs à pied (entre 0% et <5%); +- IVR 1: forte dominance des blocs « bruns/rouges » (entre 75% et <95%) et faible représentation des blocs « blancs/verts » (entre 5% et <25%); +- IVR 2: dominance des blocs « bruns/rouges » (entre 55% et <75%) mais bonne proportion de blocs « blancs/verts » (entre 25% et <45%); +- IVR 3: représentation équivalente de blocs « bruns/rouges » (entre 35% et <55%) et des blocs « blancs/verts » (entre 45% et <65%); +- IVR 4: faible représentation des blocs « bruns/rouges » (entre 15% et <35%) et dominance des blocs « blancs/verts » (entre 65% et <85%); +- IVR 5: très faible représentation des blocs « bruns/rouges » (entre 0% et <15%) et très forte dominance des blocs « blancs/verts » (entre 85% et 100%). + +
+ +![](CB_ivr.tif){width=1000} + +Légende de la figure. Photos de champs de blocs caractéristiques aux valeurs d'IVR allant de 0 (en haut à gauche, champ de blocs non impacté) à 5 (en bas à droite, très forte pression de pêche à pied) (© Maud Bernard). + +Cependant, passer d'une variable continue allant de 0% à 100% à une variable discrète ne contenant qu'un nombre réduit de valeurs réelles (de 0 à 5) peut-être considéré comme une simplification, justifiable, des observations de terrain. Afin de conserver cette dimension de continuité de la fréquence d'occurrence des blocs « bruns/rouges » vs « blancs/verts », une alternative consiste à considérer ces valeurs calculées de pourcentages, et à les diviser par 20 pour ainsi en revenir à une échelle de valeurs continues cette fois, toujours comprises entre 0 et 5. A chacune des 6 classes de valeurs continues d'indice IVR ainsi obtenues peut lui être associé un code couleur, allant du bleu pour une occurrence inférieure à 5% des blocs « blancs/verts » à rouge pour une occurrence supérieure à 85% des blocs « blancs/verts ». + +```{r} + +ivr_scale <- data.frame(matrix(NA, nrow = 6, ncol = 3)) +colnames(ivr_scale) <- c("Occurrence des blocs blancs/verts", "Valeur d'IVR", "Signification") + +ivr_scale$`Occurrence des blocs blancs/verts` <- c( + "entre 0% et <5%", + "entre 5% et <25%", + "entre 25% et <45%", + "entre 45% et <65%", + "entre 65% et <85%", + "entre 85% et 100%") +ivr_scale$`Valeur d'IVR` <- c( + "0 = IVR < 0.25", + "0.25 = IVR < 1.25", + "1.25 = IVR < 2.25", + "2.25 = IVR < 3.25", + "3.25 = IVR < 4.25", + "4.25 = IVR = 5") +ivr_scale$Signification <- c("Pas de bloc ou de rares blocs retournés", "Faible représentation des blocs blancs/verts", "Bonne proportion de blocs blancs/verts", "Représentation équivalente de blocs bruns/rouges et blancs/verts", "Dominance des blocs blancs/verts", "Très forte dominance des blocs blancs/verts") + +#ivr_scale + +``` + +```{r} +library(magrittr) + +qu_tbl <- flextable::flextable(ivr_scale) +qu_tbl <- flextable::bg(qu_tbl, i = 1, bg = "blue") +qu_tbl <- flextable::bg(qu_tbl, i = 2, bg = "olivedrab") +qu_tbl <- flextable::bg(qu_tbl, i = 3, bg = "yellow") +qu_tbl <- flextable::bg(qu_tbl, i = 4, bg = "gold1") +qu_tbl <- flextable::bg(qu_tbl, i = 5, bg = "orange1") +qu_tbl <- flextable::bg(qu_tbl, i = 6, bg = "red") + +qu_tbl %>% flextable::autofit() %>% flextable::fit_to_width(7) + +``` + +Légende de la table. Pourcentages de blocs « blancs/verts », exprimés en une échelle continue de valeurs équivalentes d'IVR allant de 0 à 5. + + +# Observations pour le suivi du site `r unique(df1$Site_bis)` + +Les observations des taux de retournement/non retournement de blocs de vingt-quatre champs distribués le long de la façade Manche-Atlantique française sont stockées dans la base de données ESTAMP (http://www.pecheapied-loisir.fr/base-de-donnees-estamp/), hébergée par l'Office Français de la Biodiversité, créée dans le cadre du projet LIFE+ Pêche à Pied de Loisir, et que vous avez contribué à enrichir de vos observations. L'évolution des valeurs médianes de l'indice IVR: au site `r unique(df1$Site.bis)` et tous champs de blocs confondus, pour la période de suivi 2014-2017, est représentée graphiquement sur les deux figures ci-dessous. A des fins de comparaisons entre la précédente échelle discrète et la nouvelle échelle continue de valeurs d'IVR, et des distributions associées des valeurs d'indice en escalier ou continues, les deux représentations graphiques sont données. + +Selon l'échelle discrète de valeurs de l'IVR, les valeurs médianes au site `r unique(df1$Site.bis)` (points noirs) sont comprises entre un minimum égal à `r min(df1$ivr.med, na.rm = T)` et un maximum égal à `r max(df1$ivr.med, na.rm = T)`. Les valeurs individuelles, par quadrat (barres), sont quant à elles comprises entre un minimum égal à `r min(df1$ivr.min, na.rm = T)` et un maximum égal à `r max(df1$ivr.max, na.rm = T)`. Selon l'échelle continue de valeurs de l'IVR, les valeurs médianes sont comprises entre un minimum égal à `r round(min(df1$fr.r.med/20, na.rm = T), digits = 2)` et un maximum égal à `r round(max(df1$fr.r.med/20, na.rm = T), digits = 2)`. Les valeurs individuelles, par quadrat (barres), sont quant à elles comprises entre un minimum égal à `r round(min(df1$fr.r.min/20, na.rm = T), digits = 2)` et un maximum égal à `r round(max(df1$fr.r.max/20, na.rm = T), digits = 2)`. + +```{r} + +par(mar = c(5, 5, 4, 2) + 0.1) + + xmin_ <- as.Date(ifelse(min(df1$Annee) >= 2014, "2014-01-01", paste0(min(df$Annee), "-01-01")), origin = "1970-01-01") + xmax_ <- as.Date(ifelse(max(df1$Annee) <= 2017, "2018-01-01", #paste0(max(qecb.val.eg$Annee)+1, + "2022-01-01") + #) + , origin = "1970-01-01") + + plot(df$Date, df$ivr.med, xlim = c(xmin_, xmax_), ylim = c(-0.2, 5.2), pch = 19, cex = 1, main = unique(df1$Site_bis), xlab = "Année", ylab = "IVR", col = "grey") + + points(df1$Date, df1$ivr.med, pch = 19, cex = 1.5) + arrows(df1$Date, df1$ivr_med, df1$Date, df1$ivr_max, code = 3, angle = 90, length = 0.00) + arrows(df1$Date, df1$ivr_med, df1$Date, df1$ivr_min, code = 3, angle = 90, length = 0.00) + +``` + +```{r} + +par(mar = c(5, 5, 4, 2) + 0.1) + + xmin_ <- as.Date(ifelse(min(df1$Annee) >= 2014, "2014-01-01", paste0(min(df$Annee), "-01-01")), origin = "1970-01-01") + xmax_ <- as.Date(ifelse(max(df1$Annee) <= 2017, "2018-01-01", #paste0(max(qecb.val.eg$Annee)+1, + "2022-01-01") + #) + , origin = "1970-01-01") + + plot(df$Date, df$fr.r.med / 20, xlim = c(xmin_, xmax_), ylim = c(-0.2, 5.2), pch = 19, main = unique(df1$Site_bis), xlab = "", ylab = "", type = "n", axes = FALSE) + + rect(as.Date("2013-01-01", origin = "1970-01-01"), 85 / 20, as.Date("2023-01-01", origin = "1970-01-01"), 6, col = "red", border = NA) + rect(as.Date("2013-01-01", origin = "1970-01-01"), 65 / 20, as.Date("2023-01-01", origin = "1970-01-01"), 85 / 20, col = "orange1", border = NA) + rect(as.Date("2013-01-01", origin = "1970-01-01"), 45 / 20, as.Date("2023-01-01", origin = "1970-01-01"), 65 / 20, col = "gold1", border = NA) + rect(as.Date("2013-01-01", origin = "1970-01-01"), 25 / 20, as.Date("2023-01-01", origin = "1970-01-01"), 45 / 20, col = "yellow", border = NA) + rect(as.Date("2013-01-01", origin = "1970-01-01"), 5 / 20, as.Date("2023-01-01", origin = "1970-01-01"), 25 / 20, col = "olivedrab", border = NA) + rect(as.Date("2013-01-01", origin = "1970-01-01"), -1, as.Date("2023-01-01", origin = "1970-01-01"), 5 / 20, col = "blue", border = NA) + + par(new = TRUE) + plot(df$Date, df$fr.r.med / 20, xlim = c(xmin_, xmax_), ylim = c(-0.2, 5.2), pch = 19, main = unique(df1$Site_bis), xlab = "Année", ylab = "IVR", col = "grey") + points(df1$Date, df1$fr.r.med / 20, pch = 19, cex = 1.5) + arrows(df1$Date, df1$fr.r.med / 20, df1$Date, df1$fr.r.max / 20, code = 3, angle = 90, length = 0.00) + arrows(df1$Date, df1$fr.r.med / 20, df1$Date, df1$fr.r.min / 20, code = 3, angle = 90, length = 0.00) +``` + +Légende des figures. Evolution temporelle de la valeur mediane de l'IVR au site `r unique(df1$Site.bis)` (points noirs), en comparaison à celles des vingt-trois autres champs de blocs suivis le long de la façade Manche-Atlantique française (points gris). Les barres représentent la distribution des valeurs individuelles d'IVR par quadrat au site `r unique(df1$Site.bis)` (n = `r sort(unique(df1$nb.))`) entre valeurs minimums et maximums. Graphique du haut - La distribution en escalier des valeurs médianes de l'IVR reflète le caractère discret de l'échelle utilisée. Graphique du bas - Le découpage horizontal de la fenêtre graphique représente les 5 classes d'occurrence de retournement des blocs mobiles, de quasi-inexistant en bleu à dominant en rouge, ramenée sur une échelle continue de 0 à 5. + +Ces valeurs propres au site `r unique(df1$Site.bis)` peuvent être resituées par rapport aux autres valeurs médianes de l'IVR obtenues pour les vingt-trois autres sites suivis sur la période 2014-2017 à l’échelle de la façade Manche-Atlantique française (points gris). Tous sites confondus, la pression de pêche à pied est très variable dans le temps et dans l'espace, ce qui se traduit par une distribution des valeurs médianes de l'IVR occupant toute la fenêtre graphique. + + +# Continuité du suivi du site `r unique(df1$Site_bis)` + +Ce bref rapport présente l'évolution de la valeur de l'indice IVR au site `r unique(df1$Site.bis)`, suivi de `r min(unique(df1$Annee), na.rm = T)` à `r max(unique(df1$Annee), na.rm = T)`, et la resitue par rapport aux valeurs IVR des vingt-trois autres champs de blocs suivis de 2014 à 2017 sur la façade Manche-Atlantique française. Il illustre l'évolution dans le temps de la pression de la pêche à pied, et rappelle la nécessité de continuer d'en évaluer l'impact, que les observations récoltées traduisent du maintien d'une faible pression de pêche, d'une diminution de cette dernière au fil du temps, au plus préoccupant d'un retournement dommageable d'un grand nombre de blocs. + +Au nom du Groupe de Travail Champs de Blocs, nous espérons pouvoir continuer à compter sur vous pour participer à ces suivis; et bien évidemment, si vous avez la possibilité dès le printemps prochain de retourner sur le terrain effectuer de nouveaux relevés, ceux-ci, très utiles, permettront d'évaluer les tendances observées graphiquement ci-dessus sur une plus longue période. + + +# Remerciements et bibliographie + +Le projet est financé, à daté du 15 avril 2021, par une convention OFB Muséum national d'Histoire naturelle (MNHN) dans le cadre du Life Marha (LIFE 16 IPE FR001). Le Groupe de Travail Champs de Blocs remercie tous les participants au projet. + +Contacts : jonathan.richir@mnhn.fr (post-doctorant MNHN, en charge du projet), isabelle.le-viol@mnhn.fr (responsable MNHN du projet), christian.kerbiriou@mnhn.fr (responsable MNHN du projet), pour le Groupe de Travail. + +Littérature conseillée: + +- Bernard M., (2012). Les habitats rocheux intertidaux sous l’influence d’activités anthropiques : structure, dynamique et enjeux de conservation. Sciences agricoles. Université de Bretagne occidentale - Brest. NNT : 2012BRES0010. 423 pp. Thèse disponible sur https://tel.archives-ouvertes.fr/tel-00720611/. + +- Verbeke G., Maison E. (2013). Fiche S8. Définition d’indicateurs de perturbations des champs de blocs, dans : La gestion de la pêche de loisir dans les aires marines protégées, Recueil d’expériences des gestionnaires. Coll. « Cahiers techniques », Montpellier, Aten, n°87: 63-66. Fiche disponible sur http://ct87.espaces-naturels.fr/. + +
+ +![](logo_merged.png){width=1000} diff --git a/tools/indic_ivr/cb_qecb.r b/tools/indic_ivr/cb_qecb.r new file mode 100644 index 000000000..e9897e155 --- /dev/null +++ b/tools/indic_ivr/cb_qecb.r @@ -0,0 +1,1311 @@ +# author: "Jonathan Richir" +# date: "19 April 2021" + + +#Rscript + +############################### +## ## +############################### + +#####Packages : dplyr +# tidyr +# readr +# writexl +# stringr +# readxl +# tibble +# lubridate +# cowplot +# magrittr +# rmarkdown +library(magrittr) +#####Load arguments + +args <- commandArgs(trailingOnly = TRUE) + +#####Import data + +if (length(args) < 1) { + stop("This tool needs at least 1 argument") +}else { + input_data <- args[1] + input_data2 <- args[2] + fiche_val <- args[3] + fiche_term <- args[4] + +} + + +# load qecb data + +qecb <- read.csv2(input_data, header = TRUE, fileEncoding = "Latin1") # fileEncoding = "Latin1", cfr é in variable names + +qecb_next <- read.csv2(input_data2, header = TRUE, fileEncoding = "Latin1") # fileEncoding = "Latin1", cfr é in variable names + +# bind qecb dfs. + +qecb <- dplyr::bind_rows(qecb, qecb_next) +rm(qecb_next) + +# import csv files ficheterrain + +fiche <- read.csv2(fiche_val, header = TRUE, fileEncoding = "Latin1") # fileEncoding = "Latin1", cfr é in variable names + +fiche_next <- read.csv2(fiche_term, header = TRUE, fileEncoding = "Latin1") # fileEncoding = "Latin1", cfr é in variable names + +# bind ficheterrain +fiche <- dplyr::bind_rows(fiche, fiche_next) +rm(fiche_next) + +## work on "Fiche terrain" + +date_fiche <- as.Date(stringr::str_sub(fiche$date.sortie, end = 10), origin = "1970-01-01") +fiche <- tibble::add_column(fiche, date_fiche, .after = "date.sortie") +rm(date_fiche) + +## qecb vs fiche terrain + +fiche_red <- dplyr::filter(fiche, fiche$ID.Fiche %in% unique(qecb[, c("id")])) + +id_count <- qecb %>% dplyr::group_by(id) %>% dplyr::count() +id_count <- dplyr::rename(id_count, "ID.Fiche" = "id") +id_count <- data.frame(id_count) +fiche_red <- dplyr::left_join(fiche_red, id_count) + +# rep fiche terrain information +fiche_expanded <- fiche_red[rep(row.names(fiche_red), fiche_red$n), 1:ncol(fiche_red)] +fiche_expanded <- dplyr::rename(fiche_expanded, "id" = "ID.Fiche") + +## merge qecb data and ficheterrain information + +qecb <- dplyr::bind_cols(qecb, fiche_expanded) +qecb <- dplyr::rename(qecb, "id_qecb" = "id...1") +qecb <- dplyr::rename(qecb, "id_fiche" = "id...68") + +rm(fiche_expanded, fiche_red, id_count) + +qecb <- qecb %>% tidyr::separate(date_fiche, c("Year", "Month", "Day"), sep = "-", remove = FALSE) + + +## quadrat nb : in contrast to ivr df, quadrat number is missing for many observations in qecb df ; but from the Numero.Photo variable (boulder photo associated to field data collection), I could get back most missing quadrat numbers + +quadrat <- stringr::str_extract(qecb$Numero.Photo, "Q[12345]") +qecb <- tibble::add_column(qecb, quadrat, .after = "Numero.Photo") +rm(quadrat) + +# check +quadrat_bis <- rep(NA, length = nrow(qecb)) +qecb <- tibble::add_column(qecb, quadrat_bis, .after = "quadrat") +rm(quadrat_bis) + +qecb$quadrat_bis <- ifelse(qecb$Numéro.Bloc.échantillon %in% c(1, 2) & qecb$Type.Bloc == "Bloc mobile", "Q1", qecb$quadrat_bis) +qecb$quadrat_bis <- ifelse(qecb$Numéro.Bloc.échantillon %in% c(3, 4) & qecb$Type.Bloc == "Bloc mobile", "Q2", qecb$quadrat_bis) +qecb$quadrat_bis <- ifelse(qecb$Numéro.Bloc.échantillon %in% c(5, 6) & qecb$Type.Bloc == "Bloc mobile", "Q3", qecb$quadrat_bis) +qecb$quadrat_bis <- ifelse(qecb$Numéro.Bloc.échantillon %in% c(7, 8) & qecb$Type.Bloc == "Bloc mobile", "Q4", qecb$quadrat_bis) +qecb$quadrat_bis <- ifelse(qecb$Numéro.Bloc.échantillon %in% c(9, 10) & qecb$Type.Bloc == "Bloc mobile", "Q5", qecb$quadrat_bis) + + +qecb$quadrat_bis <- ifelse(qecb$Numéro.Bloc.échantillon == 1 & qecb$Type.Bloc %in% c("Bloc fixé", "Roche en place"), "Q1", qecb$quadrat_bis) +qecb$quadrat_bis <- ifelse(qecb$Numéro.Bloc.échantillon == 2 & qecb$Type.Bloc %in% c("Bloc fixé", "Roche en place"), "Q2", qecb$quadrat_bis) +qecb$quadrat_bis <- ifelse(qecb$Numéro.Bloc.échantillon == 3 & qecb$Type.Bloc %in% c("Bloc fixé", "Roche en place"), "Q3", qecb$quadrat_bis) +qecb$quadrat_bis <- ifelse(qecb$Numéro.Bloc.échantillon == 4 & qecb$Type.Bloc %in% c("Bloc fixé", "Roche en place"), "Q4", qecb$quadrat_bis) +qecb$quadrat_bis <- ifelse(qecb$Numéro.Bloc.échantillon == 5 & qecb$Type.Bloc %in% c("Bloc fixé", "Roche en place"), "Q5", qecb$quadrat_bis) + +## I create two new variables for Site names, one for data analysis and one for data reporting. Only works for actual ivr df with 22 sites ! + +# Name for data analysis + +qecb <- tibble::add_column(qecb, Site = NA, .after = "ID.Fiche") +unique(qecb$Site) + +qecb$Site <- ifelse(qecb$zone.habitat == unique(qecb$zone.habitat)[1], "GDMO_Locmariaquer", qecb$Site) +qecb$Site <- ifelse(qecb$zone.habitat == unique(qecb$zone.habitat)[2], "GDMO_BegLann", qecb$Site) +qecb$Site <- ifelse(qecb$zone.habitat == unique(qecb$zone.habitat)[3], "FOUR_PlateauFour", qecb$Site) +qecb$Site <- ifelse(qecb$zone.habitat == unique(qecb$zone.habitat)[4], "EGMP_GroinCou", qecb$Site) +qecb$Site <- ifelse(qecb$zone.habitat == unique(qecb$zone.habitat)[5], "EGMP_PasEmsembert", qecb$Site) +qecb$Site <- ifelse(qecb$zone.habitat == unique(qecb$zone.habitat)[6], "EGMP_BreeBains", qecb$Site) +qecb$Site <- ifelse(qecb$zone.habitat == unique(qecb$zone.habitat)[7], "EGMP_PerreAntiochat", qecb$Site) +qecb$Site <- ifelse(qecb$zone.habitat == unique(qecb$zone.habitat)[8], "EGMP_Chassiron", qecb$Site) +qecb$Site <- ifelse(qecb$zone.habitat == unique(qecb$zone.habitat)[9], "BASQ_FlotsBleusZP", qecb$Site) +qecb$Site <- ifelse(qecb$zone.habitat == unique(qecb$zone.habitat)[10], "BASQ_FlotsBleusZF", qecb$Site) +qecb$Site <- ifelse(qecb$zone.habitat == unique(qecb$zone.habitat)[11], "GONB_IlotStMichel", qecb$Site) +qecb$Site <- ifelse(qecb$zone.habitat == unique(qecb$zone.habitat)[12], "FINS_Quemenes", qecb$Site) +qecb$Site <- ifelse(qecb$zone.habitat == unique(qecb$zone.habitat)[13], "FINS_SeinGoulenez", qecb$Site) +qecb$Site <- ifelse(qecb$zone.habitat == unique(qecb$zone.habitat)[14], "FINS_SeinKilaourou", qecb$Site) +qecb$Site <- ifelse(qecb$zone.habitat == unique(qecb$zone.habitat)[15], "ARMO_Verdelet", qecb$Site) +qecb$Site <- ifelse(qecb$zone.habitat == unique(qecb$zone.habitat)[16], "ARMO_Piegu", qecb$Site) +qecb$Site <- ifelse(qecb$zone.habitat == unique(qecb$zone.habitat)[17], "ARMO_Bilfot", qecb$Site) +qecb$Site <- ifelse(qecb$zone.habitat == unique(qecb$zone.habitat)[18], "ARMO_IlePlate", qecb$Site) +qecb$Site <- ifelse(qecb$zone.habitat == unique(qecb$zone.habitat)[19], "PDMO_Perharidy", qecb$Site) +qecb$Site <- ifelse(qecb$zone.habitat == unique(qecb$zone.habitat)[20], "BRES_Keraliou", qecb$Site) +qecb$Site <- ifelse(qecb$zone.habitat == unique(qecb$zone.habitat)[21], "FINS_Mousterlin", qecb$Site) +qecb$Site <- ifelse(qecb$zone.habitat == unique(qecb$zone.habitat)[22], "FINS_StNicolasGlenan", qecb$Site) + +unique(qecb$Site) + +# Anne Boulet forgot to specify zone.habitat in 2020. I asked her to correct it in ESTAMP +qecb$Site <- ifelse(qecb$zone.habitat == unique(qecb$zone.habitat)[23], "GDMO_Locmariaquer", qecb$Site) +unique(qecb$Site) +unique(qecb[, c("Site", "zone.habitat")]) + +# Name for report/plot + +qecb <- tibble::add_column(qecb, Site_bis = NA, .after = "Site") + +qecb$Site_bis <- ifelse(qecb$Site == "GDMO_Locmariaquer", "Locmariaquer", qecb$Site_bis) +qecb$Site_bis <- ifelse(qecb$Site == "GDMO_BegLann", "Beg Lann", qecb$Site_bis) +qecb$Site_bis <- ifelse(qecb$Site == "FOUR_PlateauFour", "Plateau du Four", qecb$Site_bis) +qecb$Site_bis <- ifelse(qecb$Site == "EGMP_GroinCou", "Groin du Cou", qecb$Site_bis) +qecb$Site_bis <- ifelse(qecb$Site == "EGMP_PasEmsembert", "Le Pas d'Emsembert", qecb$Site_bis) +qecb$Site_bis <- ifelse(qecb$Site == "EGMP_BreeBains", "La Brée-les-Bains", qecb$Site_bis) +qecb$Site_bis <- ifelse(qecb$Site == "EGMP_PerreAntiochat", "Le Perré d'Antiochat", qecb$Site_bis) +qecb$Site_bis <- ifelse(qecb$Site == "EGMP_Chassiron", "Chassiron", qecb$Site_bis) +qecb$Site_bis <- ifelse(qecb$Site == "BASQ_FlotsBleusZP", "Les Flots Bleus / zone pêcheurs", qecb$Site_bis) +qecb$Site_bis <- ifelse(qecb$Site == "BASQ_FlotsBleusZF", "Les Flots Bleus / zone familles", qecb$Site_bis) +qecb$Site_bis <- ifelse(qecb$Site == "GONB_IlotStMichel", "Îlot Saint-Michel", qecb$Site_bis) +qecb$Site_bis <- ifelse(qecb$Site == "FINS_Quemenes", "Quéménès", qecb$Site_bis) +qecb$Site_bis <- ifelse(qecb$Site == "FINS_SeinGoulenez", "Île de Sein - Goulenez", qecb$Site_bis) +qecb$Site_bis <- ifelse(qecb$Site == "FINS_SeinKilaourou", "Île de Sein - Kilaourou", qecb$Site_bis) +qecb$Site_bis <- ifelse(qecb$Site == "ARMO_Verdelet", "Îlot du Verdelet", qecb$Site_bis) +qecb$Site_bis <- ifelse(qecb$Site == "ARMO_Piegu", "Piégu", qecb$Site_bis) +qecb$Site_bis <- ifelse(qecb$Site == "ARMO_Bilfot", "Pointe de Bilfot", qecb$Site_bis) +qecb$Site_bis <- ifelse(qecb$Site == "ARMO_IlePlate", "Île Plate", qecb$Site_bis) +qecb$Site_bis <- ifelse(qecb$Site == "PDMO_Perharidy", "Perharidy", qecb$Site_bis) +qecb$Site_bis <- ifelse(qecb$Site == "BRES_Keraliou", "Keraliou", qecb$Site_bis) +qecb$Site_bis <- ifelse(qecb$Site == "FINS_Mousterlin", "Pointe de Mousterlin", qecb$Site_bis) +qecb$Site_bis <- ifelse(qecb$Site == "FINS_StNicolasGlenan", "Saint-Nicolas des Glénan", qecb$Site_bis) + +unique(qecb[, c("Site", "Site_bis")]) + + +## change some variables to factor + +# change 'X..' variables that are indeed % to numeric; https://stackoverflow.com/questions/59410939/apply-function-to-all-variables-with-string-in-name +ix <- grep("^X..", names(qecb)) +qecb[ix] <- lapply(qecb[ix], as.numeric) +rm(ix) + + +## save the final, complete qecb df_ + +qecb <- qecb[, c(72:107, 1:71)] + +saveRDS(qecb, "qecb.RDS") + + +## qecb df preparation prior qecb calculation + +# Several issues to solve in the df first + + +qecb$Type.Bloc <- factor(qecb$Type.Bloc, levels = c("Bloc mobile", "Bloc fixé", "Roche en place")) + +qecb$Face <- factor(qecb$Face, levels = c("face supérieure", "face inférieure")) + +qecb <- dplyr::arrange(qecb, Type.Bloc, Face, Numéro.Bloc.échantillon) + +qecb <- tibble::add_column(qecb, site_year_month_day = paste0(qecb$Site, ".", qecb$Year, ".", qecb$Month, ".", qecb$Day), .after = "Site_bis") + +# save qecb as a new df_ for analysis purpose => several changes to operate to run the code and functions + +qecbnew <- qecb + +# df with list object nb and corresponding site_year_month_day value to solve for loop issues + +df_list_loop <- data.frame("site_year_month_day" = unique(qecbnew$site_year_month_day), + "loop nb" = c(1:length(unique(qecbnew$site_year_month_day)))) + +# dplyr::filter for df that makes problem, then eventually correct in the dataframe for wrong coding; brackets (xx) for nb because will change when qecb df_ enlarged. +# these listed boulder field survey error when highlighted when running the loop, that ran into an error ; it was a step by step procedure with solving one listed observation after another when issues appeared. Surely not the best way to proceed, maybe better just to skip these surveys (site + date), but in the present case I wanted to keep most of the observations, therefore I corrected them manually whenever needed. + +# list nb (28) - EGMP_BreeBains.2016.04.06 +qecbnew$Face <- as.character(qecbnew$Face) +qecbnew$Face <- ifelse(qecbnew$ID.Fiche == "BDD_IVR&QECB_La Bree_20160406_VImport.xlsx" & qecbnew$Référence.bloc == "avr16-LaBreeB9sup", "face supérieure", qecbnew$Face) +qecbnew$Face <- ifelse(qecbnew$ID.Fiche == "BDD_IVR&QECB_La Bree_20160406_VImport.xlsx" & qecbnew$Référence.bloc == "avr16-LaBreeB10sup", "face supérieure", qecbnew$Face) +qecbnew$Face <- as.factor(qecbnew$Face) +unique(qecbnew$Face) + +# list nb 33 - EGMP_PerreAntiochat.2016.04.07 +qecbnew$Face <- as.character(qecbnew$Face) +qecbnew$Face <- ifelse(qecbnew$ID.Fiche == "BDD_IVR&QECB_PerAnt_20160407_VImport.xlsx" & qecbnew$Référence.bloc == "avr16-PerAntB9sup", "face supérieure", qecbnew$Face) +qecbnew$Face <- ifelse(qecbnew$ID.Fiche == "BDD_IVR&QECB_PerAnt_20160407_VImport.xlsx" & qecbnew$Référence.bloc == "avr16-PerAntB10sup", "face supérieure", qecbnew$Face) +qecbnew$Face <- as.factor(qecbnew$Face) +unique(qecbnew$Face) + +# list nb 37 - EGMP_Chassiron.2016.03.09 +qecbnew$Face <- as.character(qecbnew$Face) +qecbnew$Face <- ifelse(qecbnew$ID.Fiche == "BDD_IVR&QECB_Chassiron_20160309&10_VImport.xlsx" & qecbnew$Référence.bloc == "mars16-ChassB9sup", "face supérieure", qecbnew$Face) +qecbnew$Face <- ifelse(qecbnew$ID.Fiche == "BDD_IVR&QECB_Chassiron_20160309&10_VImport.xlsx" & qecbnew$Référence.bloc == "mars16-ChasB10sup", "face supérieure", qecbnew$Face) +qecbnew$Face <- as.factor(qecbnew$Face) +unique(qecbnew$Face) + +# list nb 76 - ARMO_Verdelet.2015.03.23 +qecbnew$Face <- as.character(qecbnew$Face) +qecbnew$Face <- ifelse(qecbnew$ID.Fiche == "BDD_IVR&QECB_Verdelet_20150323_VImport.xlsx" & qecbnew$Référence.bloc == "mar15-VerB10inf", "face inférieure", qecbnew$Face) +qecbnew$Face <- as.factor(qecbnew$Face) +unique(qecbnew$Face) + +# list nb 116 - "GDMO_Locmariaquer.2018.09.10" +qecbnew$Type.Bloc <- as.character(qecbnew$Type.Bloc) +qecbnew$Type.Bloc <- ifelse(qecbnew$ID.Fiche == "2018-09-10-GDMO-CDB-001" & qecbnew$Numero.Photo == "2018-09-10_GDMO_01_CDB-5_sup_392578.jpg", "Roche en place", qecbnew$Type.Bloc) +qecbnew$Type.Bloc <- as.factor(qecbnew$Type.Bloc) +qecbnew$quadrat_bis <- ifelse(qecbnew$ID.Fiche == "2018-09-10-GDMO-CDB-001" & qecbnew$Numero.Photo == "2018-09-10_GDMO_01_CDB-5_sup_392578.jpg", "Q5", qecbnew$quadrat_bis) +qecbnew <- qecbnew %>% dplyr::filter(!(ID.Fiche == "2018-09-10-GDMO-CDB-001" & Numero.Photo == "")) + +# Few sites to remove prior running the for loop because it was not just a encoding mistake for one data, but a globally wroing coding for the site + date survey. + +qecb_i <- qecbnew %>% dplyr::filter(site_year_month_day == "FINS_StNicolasGlenan.2016.04.08") # no bloc fixe ! +qecbnew <- qecbnew %>% dplyr::filter(site_year_month_day != "FINS_StNicolasGlenan.2016.04.08") +qecb_i <- qecbnew %>% dplyr::filter(site_year_month_day == "GDMO_Locmariaquer.2019.09.30") # most faces of blocs mobiles do not correspond to each other; only 3 over 10 boulder have data for both face supérieure and face inférieure +qecbnew <- qecbnew %>% dplyr::filter(site_year_month_day != "GDMO_Locmariaquer.2019.09.30") +rm(df_list_loop, qecb_i) + + +# check for species with count within sub-0.1m^2-quadrat (i.e. reduced size quadrat compare to most organisms on boulder to count them, because abundant ; then some extrapolation) + +# first for Spirobranchus + +qecbnew$Nb.Spirobranchus.lamarckii.total.ini <- qecbnew$Nb.Spirobranchus.lamarckii.total +qecbnew$Nb.Spirobranchus.lamarckii.total <- as.character(qecbnew$Nb.Spirobranchus.lamarckii.total) + +table(qecbnew$Nb.Spirobranchus.lamarckii.total) +subset(qecbnew, is.na(qecbnew$Nb.Spirobranchus.lamarckii.total)) +nrow(subset(qecbnew, is.na(qecbnew$Nb.Spirobranchus.lamarckii.total))) +subset(qecbnew, is.nan(qecbnew$Nb.Spirobranchus.lamarckii.total)) +subset(qecbnew, is.finite(qecbnew$Nb.Spirobranchus.lamarckii.total)) +nrow(dplyr::filter(qecbnew, qecbnew$Nb.Spirobranchus.lamarckii.total %in% c(NA, "NaN", "Inf", "-Inf"))) + +qecbnew_spirobranchus <- (dplyr::filter(qecbnew, Nb.Spirobranchus.lamarckii.total %in% c(NA, "NaN", "Inf", "-Inf"))) +qecbnew_spirobranchus[, c("Nb.Spirobranchus.lamarckii.1B", "Nb.Spirobranchus.lamarckii.2B", "Nb.Spirobranchus.lamarckii.3B", "Nb.Spirobranchus.lamarckii.4B", "Nb.Spirobranchus.lamarckii.5B")] <- sapply(qecbnew_spirobranchus[, c("Nb.Spirobranchus.lamarckii.1B", "Nb.Spirobranchus.lamarckii.2B", "Nb.Spirobranchus.lamarckii.3B", "Nb.Spirobranchus.lamarckii.4B", "Nb.Spirobranchus.lamarckii.5B")], as.character) +(spirobranchus_data <- subset(qecbnew_spirobranchus, !is.na(qecbnew_spirobranchus$Nb.Spirobranchus.lamarckii.1B) || !is.na(qecbnew_spirobranchus$Nb.Spirobranchus.lamarckii.2B) || !is.na(qecbnew_spirobranchus$Nb.Spirobranchus.lamarckii.3B) || !is.na(qecbnew_spirobranchus$Nb.Spirobranchus.lamarckii.4B) || !is.na(qecbnew_spirobranchus$Nb.Spirobranchus.lamarckii.5B))[, c("site_year_month_day", "Nb.Spirobranchus.lamarckii.1B", "Nb.Spirobranchus.lamarckii.2B", "Nb.Spirobranchus.lamarckii.3B", "Nb.Spirobranchus.lamarckii.4B", "Nb.Spirobranchus.lamarckii.5B", "Nb.Spirobranchus.lamarckii.total")]) +unique(spirobranchus_data$site_year_month_day) + +quemenes <- dplyr::filter(qecbnew, Site == "FINS_Quemenes") +quemenes <- dplyr::arrange(quemenes, date_fiche) +# for Quemenes, issue because for sampling date "FINS_Quemenes.2015.09.30" the 5 counts of Spirobranchus were encoded in 1B instead of total !!! I noticed this issue when mining data (see below), therefore I corrected before running below script for Spirobranchus. +qecbnew$Nb.Spirobranchus.lamarckii.total <- ifelse(qecbnew$site_year_month_day == "FINS_Quemenes.2015.09.30" & is.na(qecbnew$Nb.Spirobranchus.lamarckii.total), qecbnew$Nb.Spirobranchus.lamarckii.1B, qecbnew$Nb.Spirobranchus.lamarckii.total) +(quemenes <- dplyr::filter(qecbnew, site_year_month_day == "FINS_Quemenes.2015.09.30")[, c("site_year_month_day", "Nb.Spirobranchus.lamarckii.1B", "Nb.Spirobranchus.lamarckii.2B", "Nb.Spirobranchus.lamarckii.3B", "Nb.Spirobranchus.lamarckii.4B", "Nb.Spirobranchus.lamarckii.5B", "Nb.Spirobranchus.lamarckii.total")]) +rm(quemenes) + +seinkilaourou <- dplyr::filter(qecbnew, Site == "FINS_SeinKilaourou") +seinkilaourou <- dplyr::arrange(seinkilaourou, date_fiche) +# same issue with SeinKilaourou +qecbnew$Nb.Spirobranchus.lamarckii.total <- ifelse(qecbnew$site_year_month_day == "FINS_SeinKilaourou.2015.04.21" & is.na(qecbnew$Nb.Spirobranchus.lamarckii.total), qecbnew$Nb.Spirobranchus.lamarckii.1B, qecbnew$Nb.Spirobranchus.lamarckii.total) +(seinkilaourou <- dplyr::filter(qecbnew, site_year_month_day == "FINS_SeinKilaourou.2015.04.21")[, c("site_year_month_day", "Nb.Spirobranchus.lamarckii.1B", "Nb.Spirobranchus.lamarckii.2B", "Nb.Spirobranchus.lamarckii.3B", "Nb.Spirobranchus.lamarckii.4B", "Nb.Spirobranchus.lamarckii.5B", "Nb.Spirobranchus.lamarckii.total")]) +rm(seinkilaourou) + +# some more issues however with "x100"count data + +Spirobranchus <- subset(qecbnew, !is.na(qecbnew$Nb.Spirobranchus.lamarckii.1B) & !is.na(qecbnew$Nb.Spirobranchus.lamarckii.2B) & !is.na(qecbnew$Nb.Spirobranchus.lamarckii.3B) & !is.na(qecbnew$Nb.Spirobranchus.lamarckii.4B) & !is.na(qecbnew$Nb.Spirobranchus.lamarckii.5B) & !is.na(qecbnew$Nb.Spirobranchus.lamarckii.total))[, c("site_year_month_day", "Nb.Spirobranchus.lamarckii.1B", "Nb.Spirobranchus.lamarckii.2B", "Nb.Spirobranchus.lamarckii.3B", "Nb.Spirobranchus.lamarckii.4B", "Nb.Spirobranchus.lamarckii.5B", "Nb.Spirobranchus.lamarckii.total")] +for (i in c(1:nrow(Spirobranchus))) { + Spirobranchus$mean.x.100[[i]] <- sum(Spirobranchus[i, c(2:6)], na.rm = TRUE) / sum(!is.na(Spirobranchus[i, c(2:6)])) * 100 +} +Spirobranchus$mean.x.100 <- unlist(Spirobranchus$mean.x.100) +Spirobranchus$Nb.Spirobranchus.lamarckii.total <- as.numeric(Spirobranchus$Nb.Spirobranchus.lamarckii.total) +for (i in c(1:nrow(Spirobranchus))) { + Spirobranchus$diff[[i]] <- Spirobranchus[i, "Nb.Spirobranchus.lamarckii.total"] - Spirobranchus[i, "mean.x.100"] +} +Spirobranchus$diff <- abs(as.integer(Spirobranchus$diff)) +Spirobranchus <- dplyr::arrange(Spirobranchus, desc(diff), mean.x.100) +Spirobranchus <- dplyr::arrange(dplyr::filter(Spirobranchus, diff != 0 & mean.x.100 != 0), desc(diff)) + +# check it all in the qecbnew df + +for (i in c(1:nrow(qecbnew))) { + qecbnew$mean.x.100[[i]] <- + #ifelse(qecbnew$Nb.Spirobranchus.lamarckii.total[[i]] %in% c(NA, "NaN", "Inf", "-Inf"), + sum(qecbnew[i, c("Nb.Spirobranchus.lamarckii.1B", "Nb.Spirobranchus.lamarckii.2B", "Nb.Spirobranchus.lamarckii.3B", "Nb.Spirobranchus.lamarckii.4B", "Nb.Spirobranchus.lamarckii.5B")], na.rm = TRUE) / sum(!is.na(qecbnew[i, c("Nb.Spirobranchus.lamarckii.1B", "Nb.Spirobranchus.lamarckii.2B", "Nb.Spirobranchus.lamarckii.3B", "Nb.Spirobranchus.lamarckii.4B", "Nb.Spirobranchus.lamarckii.5B")])) * 100 + #, qecbnew$Nb.Spirobranchus.lamarckii.total[[i]]) +} # sum of only NAs/0 = NaN; so replace NaN by Na +qecbnew$mean.x.100 <- as.character(qecbnew$mean.x.100) + +sort(dplyr::filter(qecbnew, paste0(qecbnew$id_qecb, "_", qecbnew$site_year_month_day, "_", qecbnew$Type.Bloc, "_", qecbnew$Numéro.Bloc.échantillon, "_", qecbnew$Face) %in% paste0(qecbnew_spirobranchus$id_qecb, "_", qecbnew_spirobranchus$site_year_month_day, "_", qecbnew_spirobranchus$Type.Bloc, "_", qecbnew_spirobranchus$Numéro.Bloc.échantillon, "_", qecbnew_spirobranchus$Face))[, "mean.x.100"]) + +for (i in c(1:nrow(qecbnew))) { + qecbnew$mean.x.100[[i]] <- ifelse(qecbnew$mean.x.100[[i]] == "NaN", NA, qecbnew$mean.x.100[[i]]) +} +nrow(subset(qecbnew, is.na(qecbnew$mean.x.100))) +qecbnew$mean.x.100 <- as.integer(qecbnew$mean.x.100) + +qecbnew$Nb.Spirobranchus.lamarckii.total <- as.integer(qecbnew$Nb.Spirobranchus.lamarckii.total) +unique(qecbnew$Nb.Spirobranchus.lamarckii.total - qecbnew$mean.x.100) +table(qecbnew$Nb.Spirobranchus.lamarckii.total - qecbnew$mean.x.100) +qecbnew$Nb.Spirobranchus.lamarckii.total.diff <- abs((qecbnew$Nb.Spirobranchus.lamarckii.total - qecbnew$mean.x.100)) +spirobranchus_diff <- qecbnew[, c("Nb.Spirobranchus.lamarckii.1B", "Nb.Spirobranchus.lamarckii.2B", "Nb.Spirobranchus.lamarckii.3B", "Nb.Spirobranchus.lamarckii.4B", "Nb.Spirobranchus.lamarckii.5B", "Nb.Spirobranchus.lamarckii.total", "Nb.Spirobranchus.lamarckii.total.ini", "mean.x.100", "Nb.Spirobranchus.lamarckii.total.diff")] +spirobranchus_diff <- dplyr::arrange(spirobranchus_diff, desc(Nb.Spirobranchus.lamarckii.total.diff), mean.x.100) +spirobranchus_diff <- dplyr::arrange(dplyr::filter(spirobranchus_diff, Nb.Spirobranchus.lamarckii.total.diff != 0 & mean.x.100 != 0), desc(Nb.Spirobranchus.lamarckii.total.diff)) + +qecbnew$Nb.Spirobranchus.lamarckii.total <- ifelse(qecbnew$Nb.Spirobranchus.lamarckii.total.diff != 0 & qecbnew$mean.x.100 != 0, qecbnew$mean.x.100, qecbnew$Nb.Spirobranchus.lamarckii.total) +spirobranchus_diff <- qecbnew[, c("Nb.Spirobranchus.lamarckii.1B", "Nb.Spirobranchus.lamarckii.2B", "Nb.Spirobranchus.lamarckii.3B", "Nb.Spirobranchus.lamarckii.4B", "Nb.Spirobranchus.lamarckii.5B", "Nb.Spirobranchus.lamarckii.total", "Nb.Spirobranchus.lamarckii.total.ini", "mean.x.100", "Nb.Spirobranchus.lamarckii.total.diff")] +spirobranchus_diff$Nb.Spirobranchus.lamarckii.total.diff <- abs(as.integer(spirobranchus_diff$Nb.Spirobranchus.lamarckii.total.diff)) +spirobranchus_diff <- dplyr::arrange(spirobranchus_diff, desc(Nb.Spirobranchus.lamarckii.total.diff), mean.x.100) +spirobranchus_diff <- dplyr::arrange(dplyr::filter(spirobranchus_diff, Nb.Spirobranchus.lamarckii.total.diff != 0 & mean.x.100 != 0), desc(Nb.Spirobranchus.lamarckii.total.diff)) +# ok, change made when data x 100 was not correct. + +# finally, change NA by mean.x100 for Spirobranchus total +qecbnew$Nb.Spirobranchus.lamarckii.total <- as.character(qecbnew$Nb.Spirobranchus.lamarckii.total) +for (i in c(1:nrow(qecbnew))) { + qecbnew$Nb.Spirobranchus.lamarckii.total[[i]] <- ifelse(qecbnew$Nb.Spirobranchus.lamarckii.total[[i]] %in% c(NA, "NaN", "Inf", "-Inf"), sum(qecbnew[i, c("Nb.Spirobranchus.lamarckii.1B", "Nb.Spirobranchus.lamarckii.2B", "Nb.Spirobranchus.lamarckii.3B", "Nb.Spirobranchus.lamarckii.4B", "Nb.Spirobranchus.lamarckii.5B")], na.rm = TRUE) / sum(!is.na(qecbnew[i, c("Nb.Spirobranchus.lamarckii.1B", "Nb.Spirobranchus.lamarckii.2B", "Nb.Spirobranchus.lamarckii.3B", "Nb.Spirobranchus.lamarckii.4B", "Nb.Spirobranchus.lamarckii.5B")])) * 100, qecbnew$Nb.Spirobranchus.lamarckii.total[[i]]) +} # sum of only NAs/0 = NaN; so replace NaN by Na + +sort(dplyr::filter(qecbnew, paste0(qecbnew$id_qecb, "_", qecbnew$site_year_month_day, "_", qecbnew$Type.Bloc, "_", qecbnew$Numéro.Bloc.échantillon, "_", qecbnew$Face) %in% paste0(qecbnew_spirobranchus$id_qecb, "_", qecbnew_spirobranchus$site_year_month_day, "_", qecbnew_spirobranchus$Type.Bloc, "_", qecbnew_spirobranchus$Numéro.Bloc.échantillon, "_", qecbnew_spirobranchus$Face))[, "Nb.Spirobranchus.lamarckii.total"]) + +for (i in c(1:nrow(qecbnew))) { + qecbnew$Nb.Spirobranchus.lamarckii.total[[i]] <- ifelse(qecbnew$Nb.Spirobranchus.lamarckii.total[[i]] == "NaN", NA, qecbnew$Nb.Spirobranchus.lamarckii.total[[i]]) +} +nrow(subset(qecbnew, is.na(qecbnew$Nb.Spirobranchus.lamarckii.total))) +qecbnew$Nb.Spirobranchus.lamarckii.total <- as.integer(qecbnew$Nb.Spirobranchus.lamarckii.total) + +unique(qecbnew$Nb.Spirobranchus.lamarckii.total - qecbnew$Nb.Spirobranchus.lamarckii.total.ini) +table(qecbnew$Nb.Spirobranchus.lamarckii.total - qecbnew$Nb.Spirobranchus.lamarckii.total.ini) +qecbnew$Nb.Spirobranchus.lamarckii.total.diff <- abs(qecbnew$Nb.Spirobranchus.lamarckii.total - qecbnew$Nb.Spirobranchus.lamarckii.total.ini) +spirobranchus_diff <- qecbnew[, c("Nb.Spirobranchus.lamarckii.1B", "Nb.Spirobranchus.lamarckii.2B", "Nb.Spirobranchus.lamarckii.3B", "Nb.Spirobranchus.lamarckii.4B", "Nb.Spirobranchus.lamarckii.5B", "Nb.Spirobranchus.lamarckii.total", "Nb.Spirobranchus.lamarckii.total.ini", "mean.x.100", "Nb.Spirobranchus.lamarckii.total.diff")] +spirobranchus_diff <- dplyr::arrange(spirobranchus_diff, desc(Nb.Spirobranchus.lamarckii.total.diff), mean.x.100) +spirobranchus_diff <- dplyr::arrange(dplyr::filter(spirobranchus_diff, Nb.Spirobranchus.lamarckii.total.diff != 0 & mean.x.100 != 0), desc(Nb.Spirobranchus.lamarckii.total.diff)) +table(qecbnew$Nb.Spirobranchus.lamarckii.total.diff) +length(na.omit(qecbnew$Nb.Spirobranchus.lamarckii.total)) +sum(is.na(qecbnew$Nb.Spirobranchus.lamarckii.total)) +length(na.omit(qecbnew$Nb.Spirobranchus.lamarckii.total)) + sum(is.na(qecbnew$Nb.Spirobranchus.lamarckii.total)) + +qecbnew <- subset(qecbnew, select = -c(Nb.Spirobranchus.lamarckii.total.ini, mean.x.100, Nb.Spirobranchus.lamarckii.total.diff)) + +rm(qecbnew_spirobranchus, Spirobranchus, spirobranchus_data, spirobranchus_diff) + +# do the same for spirorbis + +qecbnew$Nb.spirorbis.total.ini <- qecbnew$Nb.spirorbis.total +qecbnew$Nb.spirorbis.total <- as.character(qecbnew$Nb.spirorbis.total) + +table(qecbnew$Nb.spirorbis.total) +subset(qecbnew, is.na(qecbnew$Nb.spirorbis.total)) +nrow(subset(qecbnew, is.na(qecbnew$Nb.spirorbis.total))) +subset(qecbnew, is.nan(qecbnew$Nb.spirorbis.total)) +subset(qecbnew, is.finite(qecbnew$Nb.spirorbis.total)) + +nrow(dplyr::filter(qecbnew, qecbnew$Nb.spirorbis.total %in% c(NA, "NaN", "Inf", "-Inf"))) + +qecbnew_spirorbis <- (dplyr::filter(qecbnew, Nb.spirorbis.total %in% c(NA, "NaN", "Inf", "-Inf"))) +qecbnew_spirorbis[, c("Nb.spirorbis.1A", "Nb.spirorbis.2A", "Nb.spirorbis.3A", "Nb.spirorbis.4A", "Nb.spirorbis.5A")] <- sapply(qecbnew_spirorbis[, c("Nb.spirorbis.1A", "Nb.spirorbis.2A", "Nb.spirorbis.3A", "Nb.spirorbis.4A", "Nb.spirorbis.5A")], as.character) +(spirobranchus_data <- subset(qecbnew_spirorbis, !is.na(qecbnew_spirorbis$Nb.spirorbis.1A) || !is.na(qecbnew_spirorbis$Nb.spirorbis.2A) || !is.na(qecbnew_spirorbis$Nb.spirorbis.3A) || !is.na(qecbnew_spirorbis$Nb.spirorbis.4A) || !is.na(qecbnew_spirorbis$Nb.spirorbis.5A))[, c("site_year_month_day", "Nb.spirorbis.1A", "Nb.spirorbis.2A", "Nb.spirorbis.3A", "Nb.spirorbis.4A", "Nb.spirorbis.5A", "Nb.spirorbis.total")]) +unique(spirobranchus_data$site_year_month_day) + +# In contrast to Spirobranchus data, no encoding issues for spirorbis data, cfr when sub-quadrat 1A-5A are ALL encoded, NA for total. + +# some more issues however with "x200"count data + +spirorbis <- subset(qecbnew, !is.na(qecbnew$Nb.spirorbis.1A) & !is.na(qecbnew$Nb.spirorbis.2A) & !is.na(qecbnew$Nb.spirorbis.3A) & !is.na(qecbnew$Nb.spirorbis.4A) & !is.na(qecbnew$Nb.spirorbis.5A) & !is.na(qecbnew$Nb.spirorbis.total))[, c("site_year_month_day", "Nb.spirorbis.1A", "Nb.spirorbis.2A", "Nb.spirorbis.3A", "Nb.spirorbis.4A", "Nb.spirorbis.5A", "Nb.spirorbis.total")] +for (i in c(1:nrow(spirorbis))) { + spirorbis$mean.x.200[[i]] <- sum(spirorbis[i, c(2:6)], na.rm = TRUE) / sum(!is.na(spirorbis[i, c(2:6)])) * 200 +} +spirorbis$mean.x.200 <- unlist(spirorbis$mean.x.200) +spirorbis$Nb.spirorbis.total <- as.numeric(spirorbis$Nb.spirorbis.total) +for (i in c(1:nrow(spirorbis))) { + spirorbis$diff[[i]] <- spirorbis[i, "Nb.spirorbis.total"] - spirorbis[i, "mean.x.200"] +} +spirorbis$diff <- abs(as.integer(spirorbis$diff)) +spirorbis <- dplyr::arrange(spirorbis, desc(diff), mean.x.200) +(gonb_ilotstmichel_2015_04_18 <- dplyr::filter(spirorbis, site_year_month_day == "GONB_IlotStMichel.2015.04.18")) +rm(gonb_ilotstmichel_2015_04_18) +spirorbis <- dplyr::arrange(dplyr::filter(spirorbis, diff != 0 & mean.x.200 != 0), desc(diff)) + +# check it all in the qecbnew df + +for (i in c(1:nrow(qecbnew))) { + qecbnew$mean.x.200[[i]] <- + #ifelse(qecbnew$Nb.spirorbis.total[[i]] %in% c(NA, "NaN", "Inf", "-Inf"), + sum(qecbnew[i, c("Nb.spirorbis.1A", "Nb.spirorbis.2A", "Nb.spirorbis.3A", "Nb.spirorbis.4A", "Nb.spirorbis.5A")], na.rm = TRUE) / sum(!is.na(qecbnew[i, c("Nb.spirorbis.1A", "Nb.spirorbis.2A", "Nb.spirorbis.3A", "Nb.spirorbis.4A", "Nb.spirorbis.5A")])) * 200 + #, qecbnew$Nb.spirorbis.total[[i]]) +} # sum of only NAs/0 = NaN; so replace NaN by Na +qecbnew$mean.x.200 <- as.character(qecbnew$mean.x.200) + +sort(dplyr::filter(qecbnew, paste0(qecbnew$id_qecb, "_", qecbnew$site_year_month_day, "_", qecbnew$Type.Bloc, "_", qecbnew$Numéro.Bloc.échantillon, "_", qecbnew$Face) %in% paste0(qecbnew_spirorbis$id_qecb, "_", qecbnew_spirorbis$site_year_month_day, "_", qecbnew_spirorbis$Type.Bloc, "_", qecbnew_spirorbis$Numéro.Bloc.échantillon, "_", qecbnew_spirorbis$Face))[, "mean.x.200"]) + +for (i in c(1:nrow(qecbnew))) { + qecbnew$mean.x.200[[i]] <- ifelse(qecbnew$mean.x.200[[i]] == "NaN", NA, qecbnew$mean.x.200[[i]]) +} +nrow(subset(qecbnew, is.na(qecbnew$mean.x.200))) +qecbnew$mean.x.200 <- as.integer(qecbnew$mean.x.200) + +qecbnew$Nb.spirorbis.total <- as.integer(qecbnew$Nb.spirorbis.total) +unique(qecbnew$Nb.spirorbis.total - qecbnew$mean.x.200) +table(qecbnew$Nb.spirorbis.total - qecbnew$mean.x.200) +qecbnew$Nb.spirorbis.total.diff <- abs((qecbnew$Nb.spirorbis.total - qecbnew$mean.x.200)) +spirorbis_diff <- qecbnew[, c("Nb.spirorbis.1A", "Nb.spirorbis.2A", "Nb.spirorbis.3A", "Nb.spirorbis.4A", "Nb.spirorbis.5A", "Nb.spirorbis.total", "Nb.spirorbis.total.ini", "mean.x.200", "Nb.spirorbis.total.diff")] +spirorbis_diff <- dplyr::arrange(spirorbis_diff, desc(Nb.spirorbis.total.diff), mean.x.200) +spirorbis_diff <- dplyr::arrange(dplyr::filter(spirorbis_diff, Nb.spirorbis.total.diff != 0 & mean.x.200 != 0), desc(Nb.spirorbis.total.diff)) + +qecbnew$Nb.spirorbis.total <- ifelse(qecbnew$Nb.spirorbis.total.diff != 0 & qecbnew$mean.x.200 != 0, qecbnew$mean.x.200, qecbnew$Nb.spirorbis.total) +spirorbis_diff <- qecbnew[, c("Nb.spirorbis.1A", "Nb.spirorbis.2A", "Nb.spirorbis.3A", "Nb.spirorbis.4A", "Nb.spirorbis.5A", "Nb.spirorbis.total", "Nb.spirorbis.total.ini", "mean.x.200", "Nb.spirorbis.total.diff")] +spirorbis_diff$Nb.spirorbis.total.diff <- abs(as.integer(spirorbis_diff$Nb.spirorbis.total.diff)) +spirorbis_diff <- dplyr::arrange(spirorbis_diff, desc(Nb.spirorbis.total.diff), mean.x.200) +spirorbis_diff <- dplyr::arrange(dplyr::filter(spirorbis_diff, Nb.spirorbis.total.diff != 0 & mean.x.200 != 0), desc(Nb.spirorbis.total.diff)) +# ok, change made when data x 200 was not correct. + +# finally, change NA by mean.x200 for spirorbis total +qecbnew$Nb.spirorbis.total <- as.character(qecbnew$Nb.spirorbis.total) +for (i in c(1:nrow(qecbnew))) { + qecbnew$Nb.spirorbis.total[[i]] <- ifelse(qecbnew$Nb.spirorbis.total[[i]] %in% c(NA, "NaN", "Inf", "-Inf"), sum(qecbnew[i, c("Nb.spirorbis.1A", "Nb.spirorbis.2A", "Nb.spirorbis.3A", "Nb.spirorbis.4A", "Nb.spirorbis.5A")], na.rm = TRUE) / sum(!is.na(qecbnew[i, c("Nb.spirorbis.1A", "Nb.spirorbis.2A", "Nb.spirorbis.3A", "Nb.spirorbis.4A", "Nb.spirorbis.5A")])) * 200, qecbnew$Nb.spirorbis.total[[i]]) +} # sum of only NAs/0 = NaN; so replace NaN by Na + +sort(dplyr::filter(qecbnew, paste0(qecbnew$id_qecb, "_", qecbnew$site_year_month_day, "_", qecbnew$Type.Bloc, "_", qecbnew$Numéro.Bloc.échantillon, "_", qecbnew$Face) %in% paste0(qecbnew_spirorbis$id_qecb, "_", qecbnew_spirorbis$site_year_month_day, "_", qecbnew_spirorbis$Type.Bloc, "_", qecbnew_spirorbis$Numéro.Bloc.échantillon, "_", qecbnew_spirorbis$Face))[, "Nb.spirorbis.total"]) + +for (i in c(1:nrow(qecbnew))) { + qecbnew$Nb.spirorbis.total[[i]] <- ifelse(qecbnew$Nb.spirorbis.total[[i]] == "NaN", NA, qecbnew$Nb.spirorbis.total[[i]]) +} +nrow(subset(qecbnew, is.na(qecbnew$Nb.spirorbis.total))) +qecbnew$Nb.spirorbis.total <- as.integer(qecbnew$Nb.spirorbis.total) + +unique(qecbnew$Nb.spirorbis.total - qecbnew$Nb.spirorbis.total.ini) +table(qecbnew$Nb.spirorbis.total - qecbnew$Nb.spirorbis.total.ini) +qecbnew$Nb.spirorbis.total.diff <- abs(qecbnew$Nb.spirorbis.total - qecbnew$Nb.spirorbis.total.ini) +spirorbis_diff <- qecbnew[, c("Nb.spirorbis.1A", "Nb.spirorbis.2A", "Nb.spirorbis.3A", "Nb.spirorbis.4A", "Nb.spirorbis.5A", "Nb.spirorbis.total", "Nb.spirorbis.total.ini", "mean.x.200", "Nb.spirorbis.total.diff")] +spirorbis_diff <- dplyr::arrange(spirorbis_diff, desc(Nb.spirorbis.total.diff), mean.x.200) +spirorbis_diff <- dplyr::arrange(dplyr::filter(spirorbis_diff, Nb.spirorbis.total.diff != 0 & mean.x.200 != 0), desc(Nb.spirorbis.total.diff)) +table(qecbnew$Nb.spirorbis.total.diff) +length(na.omit(qecbnew$Nb.spirorbis.total)) +sum(is.na(qecbnew$Nb.spirorbis.total)) +length(na.omit(qecbnew$Nb.spirorbis.total)) + sum(is.na(qecbnew$Nb.spirorbis.total)) + +qecbnew <- subset(qecbnew, select = -c(Nb.spirorbis.total.ini, mean.x.200, Nb.spirorbis.total.diff)) + +rm(qecbnew_spirorbis, spirorbis, spirobranchus_data, spirorbis_diff, i) + + +# dplyr::filter for abnormal data, based on histogram distribution of data + +qecbnewhist_ <- qecbnew +ylab_ <- "fréquence" + +hist_ <- qecbnewhist_[, c( + "Type.Bloc", + "Face", + "X..algues.brunes", + "Strate.algues.brunes", + "X..algues.rouges", + "Strate.algues.rouges", + "X..algues.vertes", + "Strate.algues.vertes", + "X..Cladophora", + "X..Lithophyllum", + "X..Recouvrement.Sediment", + #"Type.Sediment" , + "X..Roche.Nue", + "Nb.Littorina.obtusata", + "Nb.Gibbula.cineraria", + "Nb.Gibbula.pennanti", + "Nb.Gibbula.umbilicalis", + "Nb.Phallusia.mamillata", + "Nb.Tethya.aurantium", + #"Nb.Spirobranchus.lamarckii.1B" , + #"Nb.Spirobranchus.lamarckii.2B" , + #"Nb.Spirobranchus.lamarckii.3B" , + #"Nb.Spirobranchus.lamarckii.4B" , + #"Nb.Spirobranchus.lamarckii.5B" , + "Nb.Spirobranchus.lamarckii.total", + #"Nb.spirorbis.1A" , + #"Nb.spirorbis.2A" , + #"Nb.spirorbis.3A" , + #"Nb.spirorbis.4A" , + #"Nb.spirorbis.5A" , + "Nb.spirorbis.total", + "Nb.Crassostrea.gigas", + "Nb.Ostrea.edulis", + "X..Mytilus.sp.", + "X..Hermelles", + "X..Hydraires", + "X..Eponges", + "X..Ascidies.Coloniales", + "X..Ascidies.Solitaires", + "X..Bryozoaires.Dresses", + "X..Balanes.Vivantes", + #"Commentaires.Avant" , + "X..Surface.Accolement", + #"Type.sustrat.observé" , + #"Commentaires" , + "Nb.Cancer.pagurus..Tourteau.", + "Nb.Necora.puber..Etrille.", + "Nb.Carcinus.maenas..Crabe.vert.", + "Nb.Nucella.lapilus..Pourpre.", + "Nb.Eriphia.verrucosa..Crabe.verruqueux.", + "Nb.Octopus.vulgaris..Poulpe.", + "Nb.Galathea..Galathées.", + "Nb.Paracentrotus.lividus..Oursin.", + "Nb.Lophozozymus.incisus..ancien.Xantho.incisus.", + "Nb.Palaemon.sp..Crevette.bouquet.ou.crevette.rose.", + "Nb.Haliotis.tuberculata..Ormeau.", + "Nb.Stramonita.haemastoma..Pourpre.bouche.de.sang.", + "Nb.Littorina.littorea..Bigorneau.", + "Nb.Xantho.pilipes..Xanthe.poilu.", + "Nb.Mimachlamys.varia..Pétoncle.noir." +)] + +par(mfrow = c(2, 3)) + +sapply(names(hist_[, c(3:ncol(hist_))]), + function(cname) { + print(hist(hist_[, c(3:ncol(hist_))][[cname]], main = "", xlab = cname, ylab = ylab_, breaks = length(unique(hist_[, c(3:ncol(hist_))][[cname]])))) + }) + +par(mfrow = c(1, 1)) + +dplyr::filter(qecbnew, X..algues.brunes > 100)[, c("Site", "date_fiche", "Type.Bloc", "Numéro.Bloc.échantillon", "Face", "X..algues.brunes")] +qecbnew$X..algues.brunes <- ifelse(qecbnew$X..algues.brunes > 100, 100, qecbnew$X..algues.brunes) +dplyr::filter(qecbnew, X..algues.rouges > 100)[, c("Site", "date_fiche", "Type.Bloc", "Numéro.Bloc.échantillon", "Face", "X..algues.rouges")] +qecbnew$X..algues.rouges <- ifelse(qecbnew$X..algues.rouges > 100, 100, qecbnew$X..algues.rouges) +dplyr::filter(qecbnew, Nb.Phallusia.mamillata > 10)[, c("Site", "date_fiche", "Type.Bloc", "Numéro.Bloc.échantillon", "Face", "Nb.Phallusia.mamillata")] +dplyr::filter(qecbnew, Nb.Tethya.aurantium > 2)[, c("Site", "date_fiche", "Type.Bloc", "Numéro.Bloc.échantillon", "Face", "Nb.Tethya.aurantium")] +dplyr::filter(qecbnew, Nb.spirorbis.total > 15000)[, c("Site", "date_fiche", "Type.Bloc", "Numéro.Bloc.échantillon", "Face", "Nb.spirorbis.total")] +armo_ileplate <- dplyr::filter(qecbnew, Site == "ARMO_IlePlate" & date_fiche == "2015-10-29") +rm(armo_ileplate) +dplyr::filter(qecbnew, Nb.Nucella.lapilus..Pourpre. > 20)[, c("Site", "date_fiche", "Type.Bloc", "Numéro.Bloc.échantillon", "Face", "Nb.Nucella.lapilus..Pourpre.")] + +rm(qecbnewhist_, ylab_, hist_) + + +## SCRIPT I - NAs <- 0 ; cfr previous comment makes no sense to have NA encoded when the presence of an organism is in reality = 0 + +# We are facing an issues with NA observations, because either they were not measured/counted, then they are effectively NAs; or these NAs = indeed "0"; but I cannot have NAs for variables that are included in the index determination, cfr if 5+0 = 5, 5+NA = NA; see for example site_year_month_day == "ARMO_Bilfot.2014.04.28", Nb.Spirobranchus.lamarckii.total is NA ... +# I theregore change these NAs by 0 + +# replace NAs by "0" for variables used in qecb determination +qecbnew[, c("X..algues.brunes", + "X..algues.rouges", + "X..Lithophyllum", + "X..Cladophora", + "Nb.Littorina.obtusata", + "Nb.Gibbula.cineraria", + "Nb.Gibbula.pennanti", + "Nb.Gibbula.umbilicalis", + "X..Eponges", + "X..Ascidies.Coloniales", + "X..Ascidies.Solitaires", + "X..Bryozoaires.Dresses", + "X..algues.vertes", + "X..Roche.Nue", + "Nb.spirorbis.total", + "X..Balanes.Vivantes", + "Nb.Spirobranchus.lamarckii.total", + "X..Surface.Accolement")] <- lapply(qecbnew[, + c("X..algues.brunes", + "X..algues.rouges", + "X..Lithophyllum", + "X..Cladophora", + "Nb.Littorina.obtusata", + "Nb.Gibbula.cineraria", + "Nb.Gibbula.pennanti", + "Nb.Gibbula.umbilicalis", + "X..Eponges", + "X..Ascidies.Coloniales", + "X..Ascidies.Solitaires", + "X..Bryozoaires.Dresses", + "X..algues.vertes", + "X..Roche.Nue", + "Nb.spirorbis.total", + "X..Balanes.Vivantes", + "Nb.Spirobranchus.lamarckii.total", + "X..Surface.Accolement")], + function(x) replace(x, is.na(x), 0)) + +# and also replace NA for bivalve by 0 for EGMP and BASQ surveys cfr for accollement correction later on. + +qecbnew$X..Mytilus.sp. <- ifelse((substr(qecbnew$Site, 1, 4) %in% c("EGMP", "BASQ")) & is.na(qecbnew$X..Mytilus.sp.), 0, qecbnew$X..Mytilus.sp.) +qecbnew$Nb.Crassostrea.gigas <- ifelse((substr(qecbnew$Site, 1, 4) %in% c("EGMP", "BASQ")) & is.na(qecbnew$Nb.Crassostrea.gigas), 0, qecbnew$Nb.Crassostrea.gigas) +qecbnew$Nb.Ostrea.edulis <- ifelse((substr(qecbnew$Site, 1, 4) %in% c("EGMP", "BASQ")) & is.na(qecbnew$Nb.Ostrea.edulis), 0, qecbnew$Nb.Ostrea.edulis) + + +# add a region variable +region <- rep(NA, nrow(qecbnew)) +qecbnew <- tibble::add_column(qecbnew, region, .after = "Site_bis") +qecbnew$region <- ifelse(qecbnew$Site %in% c("EGMP_GroinCou", "EGMP_PasEmsembert", "EGMP_BreeBains", "EGMP_PerreAntiochat", "EGMP_Chassiron", "BASQ_FlotsBleusZP", "BASQ_FlotsBleusZF"), "EGMP.BASQ", "Bretagne") +rm(region) +qecbnew <- dplyr::arrange(qecbnew, region, site_year_month_day, Type.Bloc, Numéro.Bloc.échantillon, Face) + +# accolement function according to recent 'retournement' + +## before I go further ahead, I have to correct for surface d'accollement for several variable for BM.FI !! + +# not the same file name between script qecb script (qecbNew) and this script (qecbNew); doesn't matter, only appears here in the first dplyr::filter lines. + +qecbnew <- tibble::add_column(qecbnew, terri_ = substr(qecbnew$Site, 1, 4), .after = "Site_bis") + +qecbnew$X..Eponges_ini <- qecbnew$X..Eponges +qecbnew$X..Ascidies.Coloniales_ini <- qecbnew$X..Ascidies.Coloniales +qecbnew$X..Ascidies.Solitaires_ini <- qecbnew$X..Ascidies.Solitaires +qecbnew$X..Bryozoaires.Dresses_ini <- qecbnew$X..Bryozoaires.Dresses +qecbnew$X..Lithophyllum_ini <- qecbnew$X..Lithophyllum +qecbnew$X..Balanes.Vivantes_ini <- qecbnew$X..Balanes.Vivantes + +df_bm_fs <- qecbnew %>% dplyr::filter(Type.Bloc == "Bloc mobile" & Face == "face supérieure") +df_bm_fi <- qecbnew %>% dplyr::filter(Type.Bloc == "Bloc mobile" & Face == "face inférieure") +df_bf <- qecbnew %>% dplyr::filter(Type.Bloc != "Bloc mobile") + +`%notin%` <- Negate(`%in%`) + +acco_fct <- function(var_) { + + df_bm_fi$var_cor.acco. <<- NA + + for (i in c(1:nrow(df_bm_fi))) { + + df_bm_fi$var_cor.acco.[[i]] <<- if (df_bm_fi$terri_[[i]] %notin% c("EGMP", "BASQ")) { + ifelse(#df_$Couleur.dominante %in% c("Rouge", "Brune", "Brune-Rouge") || + df_bm_fs$Couleur.dominante[[i]] %in% c("Blanche", "Verte", "Blanche-Verte", "Colorée"), df_bm_fi[i, var_] / (100 - df_bm_fi$X..Surface.Accolement[[i]]) * 100, df_bm_fi[i, var_]) + } else { + ifelse(df_bm_fs$Couleur.dominante[[i]] %in% c("Blanche", "Verte", "Blanche-Verte", "Colorée") + & df_bm_fi$X..Surface.Accolement[[i]] != 0 # I have to use it in dplyr::filter this time as well for EGMP- BASQ (but not for Bretagne, although could be added, same result); identical/repeated measure for BM.FI and BM.FS + & df_bm_fs$X..Mytilus.sp.[[i]] == 0, df_bm_fi[i, var_] / (100 - df_bm_fi$X..Surface.Accolement[[i]]) * 100, df_bm_fi[i, var_]) + } + + } + +} + +# I would only consider colors in c("Rouge", "Brune", "Brune-Rouge") for BM.FI correction [ and not the series c("Blanche-Brune", "Rouge", "Brune", "Blanche-Rouge", "Brune-Rouge", "Rouge-Verte", "Brune-Verte") ] ; and for BM.FS, the list c("Blanche", "Verte", "Colorée") => we do the correction for BM.FI accollement based on BM.FS color !!! + + +# apply acco_fct to BM.FI variables + +apply_acco_fct <- function(var_) { + + + + show(sort(df_bm_fi[, var_], decreasing = TRUE, index.return = FALSE)[1:50]) + pre_ <- as.vector(df_bm_fi[, var_]) + acco_fct(var_) + df_bm_fi <<- tibble::add_column(df_bm_fi, var_cor. = df_bm_fi$var_cor.acco., .after = var_) + show(sort(df_bm_fi$var_cor., decreasing = TRUE, index.return = FALSE)[1:50]) + df_bm_fi$var_cor. <<- as.numeric(ifelse(as.character(df_bm_fi$var_cor.) %in% c(NA, "NaN", "-Inf", "Inf"), "0", as.character(df_bm_fi$var_cor.))) + df_bm_fi$var_cor. <<- ifelse(df_bm_fi$var_cor. > 100, 100, df_bm_fi$var_cor.) + show(sort(df_bm_fi$var_cor., decreasing = TRUE, index.return = FALSE)[1:50]) + show(length(na.omit(which(abs(as.vector(df_bm_fi$var_cor.) - pre_) != 0))) / na.omit(length(df_bm_fi$var_cor.)) * 100) + par(mfrow = c(1, 3)) + hist(pre_, main = var_, xlab = "pre-corection") + hist(df_bm_fi$var_cor., main = var_, xlab = "post-corection") + hist(df_bm_fi[as.vector(which(abs(as.vector(df_bm_fi$var_cor.) - pre_) != 0)), var_], main = var_, xlab = "diff. post-pre != 0") + par(mfrow = c(1, 1)) + df_bm_fi <<- df_bm_fi[, -which(names(df_bm_fi) %in% c(var_, "var_cor.acco."))] + colnames(df_bm_fi)[colnames(df_bm_fi) == "var_cor."] <<- var_ + + rm(pre_) + +} + +apply_acco_fct("X..Eponges") +apply_acco_fct("X..Ascidies.Coloniales") +apply_acco_fct("X..Ascidies.Solitaires") +apply_acco_fct("X..Bryozoaires.Dresses") +apply_acco_fct("X..Lithophyllum") +apply_acco_fct("X..Balanes.Vivantes") + +qecbnew <- dplyr::bind_rows(df_bm_fs, df_bm_fi) +qecbnew <- dplyr::bind_rows(qecbnew, df_bf) + +qecbnew <- dplyr::arrange(qecbnew, region, site_year_month_day, Type.Bloc, Numéro.Bloc.échantillon, Face) + +# do remove some more data ... +# "FINS_Quemenes.2020.10.16", bad encoding, I let know Anna Capietto to make changes => was corrected normally, so unactivate next time I download ESTAMP data +qecbnew <- dplyr::filter(qecbnew, site_year_month_day != "FINS_Quemenes.2020.10.16") + +# save the final qecbnew df_ + +saveRDS(qecbnew, "qecbnew.RDS") + +ecology_input <- qecbnew + +write.table(ecology_input, "Valeurs_stat.tabular", row.names = FALSE, quote = FALSE, sep = "\t", dec = ".", fileEncoding = "UTF-8") + +## do calculate QECB values now + +# create lists + +qecb_val_qu_list <- vector("list", length(unique(qecbnew$site_year_month_day))) +qecb_val_list <- vector("list", length(unique(qecbnew$site_year_month_day))) + +for (i in c(1:length(unique(qecbnew$site_year_month_day)))) { + + qecb_i <- qecbnew %>% dplyr::filter(site_year_month_day == unique(qecbnew$site_year_month_day)[[i]]) + + (terri_ <- unique(substr(qecb_i$Site, 1, 4))) + + nb. <- as.vector(unlist(intersect(dplyr::filter(qecb_i, Type.Bloc == "Bloc mobile" & Face == "face supérieure")["Numéro.Bloc.échantillon"], dplyr::filter(qecb_i, Type.Bloc == "Bloc mobile" & Face == "face inférieure")["Numéro.Bloc.échantillon"]))) + + bloc_nb <- c( + paste0("Bloc mobile", " - ", "face supérieure", " - ", nb.), + paste0("Bloc mobile", " - ", "face inférieure", " - ", nb.), + paste0(as.vector(unlist(dplyr::filter(qecb_i, Type.Bloc != "Bloc mobile")["Type.Bloc"])), " - ", as.vector(unlist(dplyr::filter(qecb_i, Type.Bloc != "Bloc mobile")["Face"])), " - ", as.vector(unlist(dplyr::filter(qecb_i, Type.Bloc != "Bloc mobile")["Numéro.Bloc.échantillon"]))) + ) + + qecb_i$Bloc <- paste0(qecb_i$Type.Bloc, " - ", qecb_i$Face, " - ", qecb_i$Numéro.Bloc.échantillon) + + qecb_i <- qecb_i %>% subset(Bloc %in% bloc_nb) + ## qebm_1 + + + # vfs_bm Bloc mobile + + { + df_bm_fs <- qecb_i %>% dplyr::filter(qecb_i$Type.Bloc == "Bloc mobile" & qecb_i$Face == "face supérieure") # to keep a version of it for later on correction for accollement for BM FI + df_ <- df_bm_fs + df_ <- dplyr::arrange(df_, Numéro.Bloc.échantillon) + + a_bms <- df_$X..algues.brunes + df_$X..algues.rouges + df_$X..Cladophora + b_bms <- df_$X..Lithophyllum + c_bms <- df_$Nb.Littorina.obtusata + df_$Nb.Gibbula.cineraria + df_$Nb.Gibbula.pennanti + df_$Nb.Gibbula.umbilicalis + d_bms <- df_$X..Eponges + df_$X..Ascidies.Coloniales + df_$X..Ascidies.Solitaires + df_$X..Bryozoaires.Dresses + e_bms <- df_$X..algues.vertes + f_bms <- df_$X..Roche.Nue + + vfs_bm <- ((df_$X..algues.brunes + df_$X..algues.rouges + df_$X..Cladophora) + + df_$X..Lithophyllum + + (df_$Nb.Littorina.obtusata + df_$Nb.Gibbula.cineraria + df_$Nb.Gibbula.pennanti + df_$Nb.Gibbula.umbilicalis) + + (df_$X..Eponges + df_$X..Ascidies.Coloniales + df_$X..Ascidies.Solitaires + df_$X..Bryozoaires.Dresses) + ) - + (df_$X..algues.vertes + + df_$X..Roche.Nue + ) + vfs_bm + + + # vfi_bm Bloc mobile + + df_ <- qecb_i %>% dplyr::filter(Type.Bloc == "Bloc mobile" & Face == "face inférieure") + df_ <- dplyr::arrange(df_, Numéro.Bloc.échantillon) + + df_bm_fi <- df_ + + # accolement function according to recent 'retournement' + + `%notin%` <- Negate(`%in%`) + + acco_fct <- function(var_) { + + if (terri_ %notin% c("EGMP", "BASQ")) { + ifelse(#df_$Couleur.dominante %in% c("Rouge", "Brune", "Brune-Rouge") || + df_bm_fs$Couleur.dominante %in% c("Blanche", "Verte", "Blanche-Verte", "Colorée"), df_bm_fi[, var_] / (100 - df_bm_fi$X..Surface.Accolement) * 100, df_bm_fi[, var_]) + } else { + ifelse(df_bm_fs$Couleur.dominante %in% c("Blanche", "Verte", "Blanche-Verte", "Colorée") + & df_bm_fi$X..Surface.Accolement != 0 # I have to use it in dplyr::filter this time as well for EGMP- BASQ (but not for Bretagne, altough could be added, same result); identical/repeated measure for BM.FI and BM.FS + & df_bm_fs$X..Mytilus.sp. == 0, df_bm_fi[, var_] / (100 - df_bm_fi$X..Surface.Accolement) * 100, df_bm_fi[, var_]) + } + + } + + # I would only consider colors in c("Rouge", "Brune", "Brune-Rouge") for BM.FI correction [ and not the series c("Blanche-Brune", "Rouge", "Brune", "Blanche-Rouge", "Brune-Rouge", "Rouge-Verte", "Brune-Verte") ] ; and for BM.FS, the list c("Blanche", "Verte", "Colorée") => we do the correction for BM.FI accollement based on BM.FS color !!! + + df_bm_fs$Couleur.dominante + df_bm_fs$X..Mytilus.sp. + + df_[, "X..Eponges"] + df_[, "X..Surface.Accolement"] + df_[, "X..Eponges"] <- acco_fct("X..Eponges") + df_[, "X..Eponges"] <- as.numeric(ifelse(as.character(df_[, "X..Eponges"]) %in% c(NA, "NaN", "-Inf", "Inf"), "0", as.character(df_[, "X..Eponges"]))) + df_[, "X..Eponges"] <- ifelse(df_[, "X..Eponges"] > 100, 100, df_[, "X..Eponges"]) + df_[, "X..Eponges"] + + df_[, "X..Ascidies.Coloniales"] + df_[, "X..Surface.Accolement"] + df_[, "X..Ascidies.Coloniales"] <- acco_fct("X..Ascidies.Coloniales") + df_[, "X..Ascidies.Coloniales"] <- as.numeric(ifelse(as.character(df_[, "X..Ascidies.Coloniales"]) %in% c(NA, "NaN", "-Inf", "Inf"), "0", as.character(df_[, "X..Ascidies.Coloniales"]))) + df_[, "X..Ascidies.Coloniales"] <- ifelse(df_[, "X..Ascidies.Coloniales"] > 100, 100, df_[, "X..Ascidies.Coloniales"]) + df_[, "X..Ascidies.Coloniales"] + + df_[, "X..Ascidies.Solitaires"] + df_[, "X..Surface.Accolement"] + df_[, "X..Ascidies.Solitaires"] <- acco_fct("X..Ascidies.Solitaires") + df_[, "X..Ascidies.Solitaires"] <- as.numeric(ifelse(as.character(df_[, "X..Ascidies.Solitaires"]) %in% c(NA, "NaN", "-Inf", "Inf"), "0", as.character(df_[, "X..Ascidies.Solitaires"]))) + df_[, "X..Ascidies.Solitaires"] <- ifelse(df_[, "X..Ascidies.Solitaires"] > 100, 100, df_[, "X..Ascidies.Solitaires"]) + df_[, "X..Ascidies.Solitaires"] + + df_[, "X..Bryozoaires.Dresses"] + df_[, "X..Surface.Accolement"] + df_[, "X..Bryozoaires.Dresses"] <- acco_fct("X..Bryozoaires.Dresses") + df_[, "X..Bryozoaires.Dresses"] <- as.numeric(ifelse(as.character(df_[, "X..Bryozoaires.Dresses"]) %in% c(NA, "NaN", "-Inf", "Inf"), "0", as.character(df_[, "X..Bryozoaires.Dresses"]))) + df_[, "X..Bryozoaires.Dresses"] <- ifelse(df_[, "X..Bryozoaires.Dresses"] > 100, 100, df_[, "X..Bryozoaires.Dresses"]) + df_[, "X..Bryozoaires.Dresses"] + + df_[, "X..Lithophyllum"] + df_[, "X..Surface.Accolement"] + df_[, "X..Lithophyllum"] <- acco_fct("X..Lithophyllum") + df_[, "X..Lithophyllum"] <- as.numeric(ifelse(as.character(df_[, "X..Lithophyllum"]) %in% c(NA, "NaN", "-Inf", "Inf"), "0", as.character(df_[, "X..Lithophyllum"]))) + df_[, "X..Lithophyllum"] <- ifelse(df_[, "X..Lithophyllum"] > 100, 100, df_[, "X..Lithophyllum"]) + df_[, "X..Lithophyllum"] + + d_bmi <- df_$X..Eponges + df_$X..Ascidies.Coloniales + df_$X..Ascidies.Solitaires + df_$X..Bryozoaires.Dresses + b_bmi <- df_$X..Lithophyllum + a_bmi <- df_$X..algues.brunes + df_$X..algues.rouges + df_$X..Cladophora + c_bmi <- df_$Nb.Littorina.obtusata + df_$Nb.Gibbula.cineraria + df_$Nb.Gibbula.pennanti + df_$Nb.Gibbula.umbilicalis + e_bmi <- df_$X..algues.vertes + f_bmi <- df_$X..Roche.Nue + + vfi_bm <- ((df_$X..Eponges + df_$X..Ascidies.Coloniales + df_$X..Ascidies.Solitaires + df_$X..Bryozoaires.Dresses) + + df_$X..Lithophyllum + ) - + ((df_$X..algues.brunes + df_$X..algues.rouges + df_$X..Cladophora) + + (df_$Nb.Littorina.obtusata + df_$Nb.Gibbula.cineraria + df_$Nb.Gibbula.pennanti + df_$Nb.Gibbula.umbilicalis) + + df_$X..algues.vertes + + df_$X..Roche.Nue + ) + vfi_bm + + # vfsi_bm Bloc mobile + + df_ <- qecb_i %>% dplyr::filter(Type.Bloc == "Bloc mobile") + df_ <- dplyr::arrange(df_, desc(Face), Numéro.Bloc.échantillon) + num_bloc <- as.vector(sort(unique(df_$Numéro.Bloc.échantillon))) + + g_bmsi <- NA + h_bmsi <- NA + l_bmsi <- NA + + df_bm_fs <- dplyr::filter(df_, Face == "face inférieure") + df_bm_fi <- dplyr::filter(df_, Face == "face inférieure") + + df_bm_fs$Couleur.dominante + df_bm_fs$X..Mytilus.sp. + + df_[, "X..Balanes.Vivantes"] + df_[, "X..Surface.Accolement"] + df_ <- dplyr::mutate(df_, row.nb = dplyr::row_number()) + dplyr::filter(df_, Face == "face inférieure")["row.nb"] + df_[c(dplyr::filter(df_, Face == "face inférieure")[1, "row.nb"]:unlist(tail(dplyr::filter(df_, Face == "face inférieure"), n = 1)["row.nb"])), "X..Balanes.Vivantes"] <- acco_fct("X..Balanes.Vivantes") + df_[, "X..Balanes.Vivantes"] <- as.numeric(ifelse(as.character(df_[, "X..Balanes.Vivantes"]) %in% c(NA, "NaN", "-Inf", "Inf"), "0", as.character(df_[, "X..Balanes.Vivantes"]))) + df_[, "X..Balanes.Vivantes"] <- ifelse(df_[, "X..Balanes.Vivantes"] > 100, 100, df_[, "X..Balanes.Vivantes"]) + df_[, "X..Balanes.Vivantes"] + + for (k in c(1:length(na.omit(num_bloc)))) { + + j_ <- num_bloc[k] + + gin_ <- unname(unlist( + (dplyr::filter(df_, Numéro.Bloc.échantillon == j_ & Face == "face supérieure")["Nb.spirorbis.total"] + + dplyr::filter(df_, Numéro.Bloc.échantillon == j_ & Face == "face inférieure")["Nb.spirorbis.total"] + ) / 1000)) + + g_bmsi <<- c(g_bmsi, gin_) + + hin_ <- unname(unlist( + (dplyr::filter(df_, Numéro.Bloc.échantillon == j_ & Face == "face supérieure")["X..Balanes.Vivantes"] + + dplyr::filter(df_, Numéro.Bloc.échantillon == j_ & Face == "face inférieure")["X..Balanes.Vivantes"] + ) / 100)) + + h_bmsi <<- c(h_bmsi, hin_) + + lin_ <- unname(unlist( + (dplyr::filter(df_, Numéro.Bloc.échantillon == j_ & Face == "face supérieure")["Nb.Spirobranchus.lamarckii.total"] + + dplyr::filter(df_, Numéro.Bloc.échantillon == j_ & Face == "face inférieure")["Nb.Spirobranchus.lamarckii.total"] + ) / 100)) + + l_bmsi <<- c(l_bmsi, lin_) # To avoid error message "Error in I <<- c(I, IIn.) : cannot change value of locked binding for 'I'" + + } + + g_bmsi <- g_bmsi[2:length(g_bmsi)] + g_bmsi + h_bmsi <- h_bmsi[2:length(h_bmsi)] + h_bmsi + i_bmsi <- l_bmsi[2:length(l_bmsi)] + i_bmsi + + vfsi_bm <- NA + + for (k in c(1:length(na.omit(num_bloc)))) { + + j_ <- num_bloc[k] + + vfsin_ <- unname(unlist( + ((dplyr::filter(df_, Numéro.Bloc.échantillon == j_ & Face == "face supérieure")["Nb.spirorbis.total"] + + dplyr::filter(df_, Numéro.Bloc.échantillon == j_ & Face == "face inférieure")["Nb.spirorbis.total"] + ) / 1000) + - + (((dplyr::filter(df_, Numéro.Bloc.échantillon == j_ & Face == "face supérieure")["X..Balanes.Vivantes"] + + dplyr::filter(df_, Numéro.Bloc.échantillon == j_ & Face == "face inférieure")["X..Balanes.Vivantes"] + ) / 100) + + ((dplyr::filter(df_, Numéro.Bloc.échantillon == j_ & Face == "face supérieure")["Nb.Spirobranchus.lamarckii.total"] + + dplyr::filter(df_, Numéro.Bloc.échantillon == j_ & Face == "face inférieure")["Nb.Spirobranchus.lamarckii.total"] + ) / 100) + ) + )) + + vfsi_bm <<- c(vfsi_bm, vfsin_) + + } + + vfsi_bm <- vfsi_bm[2:length(vfsi_bm)] + vfsi_bm + + + # qebm_1 + + (qebm_1 <- vfs_bm + vfi_bm + vfsi_bm) + + ## qebm_2 + + + # vrfs_bf moyenne Bloc fixé ; = VDRmoyenne in excel file + + df_ <- qecb_i %>% dplyr::filter(Type.Bloc %in% c("Bloc fixé", "Roche en place")) + df_ <- dplyr::arrange(df_, Numéro.Bloc.échantillon) + + a_bf <- df_$X..algues.brunes + df_$X..algues.rouges + df_$X..Cladophora + b_bf <- df_$X..Lithophyllum + c_bf <- df_$Nb.Littorina.obtusata + df_$Nb.Gibbula.cineraria + df_$Nb.Gibbula.pennanti + df_$Nb.Gibbula.umbilicalis + d_bf <- df_$X..Eponges + df_$X..Ascidies.Coloniales + df_$X..Ascidies.Solitaires + df_$X..Bryozoaires.Dresses + e_bf <- df_$X..algues.vertes + f_bf <- df_$X..Roche.Nue + + a_bf <- c(a_bf, rep(NA, length(unique(dplyr::filter(qecb_i, Type.Bloc == "Bloc mobile")["Numéro.Bloc.échantillon"])[, 1]) - length(a_bf))) + b_bf <- c(b_bf, rep(NA, length(unique(dplyr::filter(qecb_i, Type.Bloc == "Bloc mobile")["Numéro.Bloc.échantillon"])[, 1]) - length(b_bf))) + c_bf <- c(c_bf, rep(NA, length(unique(dplyr::filter(qecb_i, Type.Bloc == "Bloc mobile")["Numéro.Bloc.échantillon"])[, 1]) - length(c_bf))) + d_bf <- c(d_bf, rep(NA, length(unique(dplyr::filter(qecb_i, Type.Bloc == "Bloc mobile")["Numéro.Bloc.échantillon"])[, 1]) - length(d_bf))) + e_bf <- c(e_bf, rep(NA, length(unique(dplyr::filter(qecb_i, Type.Bloc == "Bloc mobile")["Numéro.Bloc.échantillon"])[, 1]) - length(e_bf))) + f_bf <- c(f_bf, rep(NA, length(unique(dplyr::filter(qecb_i, Type.Bloc == "Bloc mobile")["Numéro.Bloc.échantillon"])[, 1]) - length(f_bf))) + + vrfs_bf <- ((df_$X..algues.brunes + df_$X..algues.rouges + df_$X..Cladophora) + + df_$X..Lithophyllum + + (df_$Nb.Littorina.obtusata + df_$Nb.Gibbula.cineraria + df_$Nb.Gibbula.pennanti + df_$Nb.Gibbula.umbilicalis) + + (df_$X..Eponges + df_$X..Ascidies.Coloniales + df_$X..Ascidies.Solitaires + df_$X..Bryozoaires.Dresses) + ) - + (df_$X..algues.vertes + + df_$X..Roche.Nue + ) # different from Pauline, check with her + vrfs_bf + vrfs_bf <- c(vrfs_bf, rep(NA, length(unique(dplyr::filter(qecb_i, Type.Bloc == "Bloc mobile")["Numéro.Bloc.échantillon"])[, 1]) - length(vrfs_bf))) + + # (G - (H + I)) Bloc fixé & Roche en place + + g_bf <- df_$Nb.spirorbis.total / 1000 + h_bf <- df_$X..Balanes.Vivantes / 100 + i_bf <- df_$Nb.Spirobranchus.lamarckii.total / 100 + + g_bf <- c(g_bf, rep(NA, length(unique(dplyr::filter(qecb_i, Type.Bloc == "Bloc mobile")["Numéro.Bloc.échantillon"])[, 1]) - length(g_bf))) + h_bf <- c(h_bf, rep(NA, length(unique(dplyr::filter(qecb_i, Type.Bloc == "Bloc mobile")["Numéro.Bloc.échantillon"])[, 1]) - length(h_bf))) + i_bf <- c(i_bf, rep(NA, length(unique(dplyr::filter(qecb_i, Type.Bloc == "Bloc mobile")["Numéro.Bloc.échantillon"])[, 1]) - length(i_bf))) + + `(G - (H + I))BF` <- (df_$Nb.spirorbis.total / 1000 + - (df_$X..Balanes.Vivantes / 100 + df_$Nb.Spirobranchus.lamarckii.total / 100) + ) + `(G - (H + I))BF` + `(G - (H + I))BF` <- c(`(G - (H + I))BF`, rep(NA, length(unique(dplyr::filter(qecb_i, Type.Bloc == "Bloc mobile")["Numéro.Bloc.échantillon"])[, 1]) - length(`(G - (H + I))BF`))) + + + # vrfs_bf.moy + + (mean(vrfs_bf + `(G - (H + I))BF`, na.rm = TRUE) -> vrfs_bf.moy) + + + # (G - (H + I)S.BM) Bloc mobile face supérieure + + df_ <- qecb_i %>% dplyr::filter(Type.Bloc == "Bloc mobile" & Face == "face supérieure") + df_ <- dplyr::arrange(df_, Numéro.Bloc.échantillon) + + g_bms <- df_$Nb.spirorbis.total / 1000 + h_bms <- df_$X..Balanes.Vivantes / 100 + i_bms <- df_$Nb.Spirobranchus.lamarckii.total / 100 + + `(G - (H + I))S.BM` <- (df_$Nb.spirorbis.total / 1000 + - (df_$X..Balanes.Vivantes / 100 + df_$Nb.Spirobranchus.lamarckii.total / 100) + ) + `(G - (H + I))S.BM` + + + # vrfs_bm + + (vrfs_bm <- vfs_bm + `(G - (H + I))S.BM`) + + + # vrfs_bm_moy + + (vrfs_bm_moy <- mean(vrfs_bm#[val.vrfs_bm_moy.i] + , na.rm = TRUE)) + + + # ||vrfs_bm_moy/vrfs_bf.moy|| + + (`||vrfs_bm_moy/vrfs_bf.moy||` <- abs(mean(vrfs_bm#[val.vrfs_bm_moy.i] + , na.rm = TRUE)/vrfs_bf.moy)) + + + # qebm_2 + + (qebm_2 <- qebm_1 * `||vrfs_bm_moy/vrfs_bf.moy||`) + + + ## QECB + + (QECB <- mean(qebm_2#[val.qecb_i] + , na.rm = TRUE)) + + } + + qecb_val_qu_list[[i]] <- data.frame(id_qecb = rep(unique(qecb_i$id_qecb), length(qebm_2)), + Site = rep(unique(qecb_i$Site), length(qebm_2)), + Site_bis = rep(unique(qecb_i$Site_bis), length(qebm_2)), + site_year_month_day = rep(unique(qecb_i$site_year_month_day), length(qebm_2)), + Boulder.nb_bms = sort(unique(dplyr::filter(qecb_i, Type.Bloc == "Bloc mobile" & Face == "face supérieure")["Numéro.Bloc.échantillon"])[, 1]), + Boulder.nb_bmi = sort(unique(dplyr::filter(qecb_i, Type.Bloc == "Bloc mobile" & Face == "face inférieure")["Numéro.Bloc.échantillon"])[, 1]), + Boulder.nb_bf = c(sort(unique(dplyr::filter(qecb_i, Type.Bloc %in% c("Bloc fixé", "Roche en place"))["Numéro.Bloc.échantillon"])[, 1]), rep(NA, length(unique(dplyr::filter(qecb_i, Type.Bloc == "Bloc mobile" & Face == "face supérieure")["Numéro.Bloc.échantillon"])[, 1]) - length(unique(dplyr::filter(qecb_i, Type.Bloc %in% c("Bloc fixé", "Roche en place"))["Numéro.Bloc.échantillon"])[, 1]))), + quadrat.bmS = sort(dplyr::filter(qecb_i, Type.Bloc == "Bloc mobile" & Face == "face supérieure")["quadrat_bis"][, 1]), + quadrat.bmI = sort(dplyr::filter(qecb_i, Type.Bloc == "Bloc mobile" & Face == "face inférieure")["quadrat_bis"][, 1]), + quadrat.bf = c(sort(dplyr::filter(qecb_i, Type.Bloc %in% c("Bloc fixé", "Roche en place"))["quadrat_bis"][, 1]), rep(NA, length(unique(dplyr::filter(qecb_i, Type.Bloc == "Bloc mobile")["Numéro.Bloc.échantillon"])[, 1]) - length(dplyr::filter(qecb_i, Type.Bloc %in% c("Bloc fixé", "Roche en place"))["quadrat_bis"][, 1]))), + a_bms, + b_bms, + c_bms, + d_bms, + e_bms, + f_bms, + a_bmi, + b_bmi, + c_bmi, + d_bmi, + e_bmi, + f_bmi, + g_bmsi, + h_bmsi, + i_bmsi, + a_bf, + b_bf, + c_bf, + d_bf, + e_bf, + f_bf, + g_bf, + h_bf, + i_bf, + g_bms, + h_bms, + i_bms, + vfs_bm, + vfi_bm, + vfsi_bm, + qebm_1, + vrfs_bf, + `(G - (H + I))S.BM`, + `(G - (H + I))BF`, + vrfs_bm, + qebm_2) + + qecb_val_list[[i]] <- data.frame(id_qecb = unique(qecb_i$id_qecb), + Site = unique(qecb_i$Site), + Site_bis = unique(qecb_i$Site_bis), + site_year_month_day = unique(qecb_i$site_year_month_day), + vrfs_bm_moy, + vrfs_bf.moy, + `||vrfs_bm_moy/vrfs_bf.moy||`, + QECB) + + rm(qecb_i) + rm(df_bm_fs, df_bm_fi) + rm("(G - (H + I))BF", "(G - (H + I))S.BM", "||vrfs_bm_moy/vrfs_bf.moy||", "a_bf", "a_bmi", "a_bms", "b_bf", "b_bmi", "b_bms", "bloc_nb", "c_bf", "c_bmi", "c_bms", "d_bf", "d_bmi", "d_bms", "e_bf", "e_bmi", "e_bms", "f_bf", "f_bmi", "f_bms", "g_bf", "g_bms", "g_bmsi", "gin_", "h_bf", "h_bms", "h_bmsi", "hin_", "i", "i_bf", "i_bms", "i_bmsi", "j_", "k", "l_bmsi", "lin_", "nb.", "num_bloc", "qebm_1", "qebm_2", "QECB", "vfi_bm", "vfs_bm", "vfsi_bm", "vfsin_", "vrfs_bf", "vrfs_bf.moy", "vrfs_bm", "vrfs_bm_moy", "terri_") + +} + +qecb_val_qu_ <- do.call("rbind", qecb_val_qu_list) +qecb_val_qu_ <- dplyr::arrange(qecb_val_qu_, site_year_month_day, Boulder.nb_bms) +Date <- as.Date(stringr::str_sub(qecb_val_qu_$site_year_month_day,-10,-1), format = "%Y.%m.%d", origin = "1970-01-01") +qecb_val_qu_ <- tibble::add_column(qecb_val_qu_, Date, .after = "Site_bis") +rm(Date) + +qebm_2_list <- vector("list", length(unique(qecb_val_qu_$site_year_month_day))) + +for (i in c(1:length(unique(qecb_val_qu_$site_year_month_day)))) { + +qebm_2_i <- qecb_val_qu_ %>% dplyr::filter(site_year_month_day == unique(qecb_val_qu_$site_year_month_day)[[i]]) + + qebm_2_bis <- qebm_2_i$qebm_1 * + abs((mean(((qebm_2_i$a_bms + + qebm_2_i$b_bms + + qebm_2_i$c_bms + + qebm_2_i$d_bms) + - + (qebm_2_i$e_bms + + qebm_2_i$f_bms) + ) + + + (qebm_2_i$g_bms + - (qebm_2_i$h_bms + + qebm_2_i$i_bms) + ) + , na.rm = TRUE) + ) + / + (mean( + ( + (qebm_2_i$a_bf + + qebm_2_i$b_bf + + qebm_2_i$c_bf + + qebm_2_i$d_bf) + - + (qebm_2_i$e_bf + + qebm_2_i$f_bf) + ) + + + (qebm_2_i$g_bf + - + (qebm_2_i$h_bf + + qebm_2_i$i_bf) + ) + , na.rm = TRUE) + ) + ) + +qebm_2_list[[i]] <- data.frame(site_year_month_day = unique(qebm_2_i$site_year_month_day), qebm_2_bis) + +rm(i, qebm_2_i, qebm_2_bis) + +} + +qecb_val_qu_[, ncol(qecb_val_qu_) + 1] <- do.call("rbind", qebm_2_list)[2] + +qecb_val_ <- do.call("rbind", qecb_val_list) +qecb_val_ <- dplyr::arrange(qecb_val_, site_year_month_day) +Date <- as.Date(stringr::str_sub(qecb_val_$site_year_month_day, -10, -1), format = "%Y.%m.%d", origin = "1970-01-01") +qecb_val_ <- tibble::add_column(qecb_val_, Date, .after = "Site_bis") +rm(Date) + +rm(list = ls()[!ls() %in% c("fiche", "qecb", "qecbnew", "qecb_val_qu_")]) + +qecb_val_qu_ <- tidyr::separate(qecb_val_qu_, Date, c("Annee", "Mois", "Jour"), remove = FALSE) +qecb_val_qu_$Annee <- as.integer(qecb_val_qu_$Annee) +qecb_val_qu_$Mois <- as.integer(qecb_val_qu_$Mois) +qecb_val_qu_$Jour <- as.integer(qecb_val_qu_$Jour) + +dplyr::filter(qecb_val_qu_, qebm_2 %in% c("Inf", "NaN")) + +qecb_val_qu_nan <- qecb_val_qu_ +qecb_val_qu_nan$qebm_2 <- ifelse(qecb_val_qu_nan$qebm_2 %in% c("-Inf", "NaN"), NA, qecb_val_qu_nan$qebm_2) + +qecb_val_qu_stat_ <- qecb_val_qu_nan %>% dplyr::group_by(id_qecb, Site, Site_bis, Annee, Mois, Jour) %>% dplyr::summarize(qecb.moy = mean(qebm_2, na.rm = TRUE), qecb.et = sd(qebm_2, na.rm = TRUE), qecb.med = median(qebm_2, na.rm = TRUE), qecb.min = min(qebm_2, na.rm = TRUE), qecb.max = max(qebm_2, na.rm = TRUE), nb. = dplyr::n(), nb.notNa = sum(!is.na(qebm_2))) + +Date <- as.Date(paste0(qecb_val_qu_stat_$Annee, "-", qecb_val_qu_stat_$Mois, "-", qecb_val_qu_stat_$Jour), origin = "1970-01-01") +qecb_val_qu_stat_ <- tibble::add_column(qecb_val_qu_stat_, Date, .after = "Site_bis") +rm(Date) + +qecb_val_qu_stat_ <- as.data.frame(qecb_val_qu_stat_) +indic <- qecb_val_qu_stat_ +saveRDS(qecb_val_qu_stat_, "qecb_val_qu_stat.RDS") + + +survey_list <- vector("list", length(unique(qecb_val_qu_$site_year_month_day))) + +for (i in c(1:length(unique(qecb_val_qu_$site_year_month_day)))) { + + qecb_i <- qecb_val_qu_ %>% dplyr::filter(site_year_month_day == unique(qecb_val_qu_$site_year_month_day)[[i]]) + + survey_list[[i]] <- data.frame( + site_year_month_day = rep(unique(qecb_i$site_year_month_day), nrow(qecb_i)), + survey_nb = rep(i, nrow(qecb_i)) + ) + +} + +survey <- do.call("rbind", survey_list) + +qecb_val_qu_ <- tibble::add_column(qecb_val_qu_, survey_nb = survey$survey_nb, .after = "site_year_month_day") +indic_full <- qecb_val_qu_ +rm(i, survey_list, survey, qecb_i) + +survey_nb <- c(1:nrow(qecb_val_qu_)) +qecb_val_ <- tibble::add_column(qecb_val_qu_, survey_nb, .after = "site_year_month_day") + +rm(survey_nb, qecb_val_qu_nan) + +saveRDS(qecb_val_, "qecb_val.RDS") +saveRDS(qecb_val_qu_, "qecb_val_qu.RDS") + + +## Plots qecb + +qecb_val_qu_nan <- qecb_val_qu_ +qecb_val_qu_stat_nan <- qecb_val_qu_stat_ + +`%notin%` <- Negate(`%in%`) + +qecb_val_qu_nan$qebm_2 <- ifelse(qecb_val_qu_nan$qebm_2 %in% c("-Inf", "NaN"), NA, qecb_val_qu_nan$qebm_2) + +qecb_val_qu_stat_nan[, c("qecb.moy", "qecb.et", "qecb.med", "qecb.min", "qecb.max")] <- ifelse(qecb_val_qu_stat_nan[, c("qecb.moy", "qecb.et", "qecb.med", "qecb.min", "qecb.max")] %in% c("-Inf", "NaN"), NA, qecb_val_qu_stat_nan[, c("qecb.moy", "qecb.et", "qecb.med", "qecb.min", "qecb.max")]) + + + +for (i in c(1:length(unique(qecb_val_qu_stat_nan$Site)))) { + + df1 <- dplyr::filter(qecb_val_qu_stat_nan, Site == unique(qecb_val_qu_stat_nan$Site)[i]) + + + xmin_ <- as.Date(ifelse(min(df1$Annee) >= 2014, "2014-01-01", paste0(min(df$Annee), "-01-01")), origin = "1970-01-01") + xmax_ <- as.Date(ifelse(max(df1$Annee) <= 2017, "2018-01-01", #paste0(max(qecb_val_eg$Annee)+1, + "2023-01-01") + #) + , origin = "1970-01-01") + + png(paste0("old_qecb_", unique(qecb_val_qu_stat_nan$Site), ".png")) + plot(qecb_val_qu_stat_nan$Date, qecb_val_qu_stat_nan$qecb.med, xlim = c(xmin_, xmax_), ylim = c(-360, 360), pch = 19, cex = 1, main = unique(df1$Site_bis), xlab = "Année", + ylab = "QECB", col = "grey") + points(df1$Date, df1$qecb.med, pch = 19, cex = 1.5) + arrows(df1$Date, df1$qecb.med, df1$Date, df1$qecb.max, code = 3, angle = 90, length = 0.00) + arrows(df1$Date, df1$qecb.med, df1$Date, df1$qecb.min, code = 3, angle = 90, length = 0.00) + + abline(h = c(-216, -72, 72, 216), lty = "dashed") + text(xmax_, -288, "1") + text(xmax_, -146, "2") + text(xmax_, 0, "3") + text(xmax_, 146, "4") + text(xmax_, 288, "5") + +} + +# New quality scale based on quartiles + +dt_ <- dplyr::filter(qecb_val_qu_nan, qebm_2 >= -360) +dt_ <- dplyr::filter(dt_, qebm_2 <= 360) +dt_bis <- dplyr::filter(dt_, qebm_2 >= quantile(dt_$qebm_2, c(0.05), na.rm = TRUE)) +dt_bis <- dplyr::filter(dt_bis, qebm_2 <= quantile(dt_bis$qebm_2, c(0.95), na.rm = TRUE)) + +one <- round(mean(unlist(dplyr::filter(dt_bis, qebm_2 <= quantile(dt_bis$qebm_2, 0.25, na.rm = TRUE))["qebm_2"])), digits = 0) +two <- round(mean(unlist(dplyr::filter(dt_bis, qebm_2 > quantile(dt_bis$qebm_2, 0.25, na.rm = TRUE) & qebm_2 <= quantile(dt_bis$qebm_2, 0.5, na.rm = TRUE))["qebm_2"])), digits = 0) +three <- round(mean(unlist(dplyr::filter(dt_bis, qebm_2 > quantile(dt_bis$qebm_2, 0.5, na.rm = TRUE) & qebm_2 <= quantile(dt_bis$qebm_2, 0.75, na.rm = TRUE))["qebm_2"])), digits = 0) +four <- round(mean(unlist(dplyr::filter(dt_bis, qebm_2 > quantile(dt_bis$qebm_2, 0.75, na.rm = TRUE))["qebm_2"])), digits = 0) + + +# I have unactivated the model line drawing because aberant for some sites with bad qecb values +for (i in c(1:length(unique(qecb_val_qu_stat_nan$Site)))) { + + df1 <- dplyr::filter(qecb_val_qu_stat_nan, Site == unique(qecb_val_qu_stat_nan$Site)[i]) + + xmin_ <- as.Date(ifelse(min(df1$Annee) >= 2014, "2014-01-01", paste0(min(df$Annee), "-01-01")), origin = "1970-01-01") + xmax_ <- as.Date(ifelse(max(df1$Annee) <= 2017, "2018-01-01", #paste0(max(qecb_val_eg$Annee)+1, + "2022-01-01") + #) + , origin = "1970-01-01") + + ymin_ <- ifelse(min(df1$qecb.med, na.rm = TRUE) < -70, -360, -70) + ymax_ <- ifelse(max(df1$qecb.med, na.rm = TRUE) > 200, 360, 200) + + png(paste0("new_qecb_", unique(qecb_val_qu_stat_nan$Site), ".png")) + plot(qecb_val_qu_stat_nan$Date, qecb_val_qu_stat_nan$qecb.med, xlim = c(xmin_, xmax_), ylim = c(ymin_, ymax_), pch = 19, main = "", xlab = "", ylab = "", type = "n", axes = FALSE) + + rect(as.Date("2013-01-01", origin = "1970-01-01"), -400, as.Date("2023-01-01", origin = "1970-01-01"), one, col = "red", border = NA) + rect(as.Date("2013-01-01", origin = "1970-01-01"), one, as.Date("2023-01-01", origin = "1970-01-01"), two, col = "orange", border = NA) + rect(as.Date("2013-01-01", origin = "1970-01-01"), two, as.Date("2023-01-01", origin = "1970-01-01"), three, col = "yellow", border = NA) + rect(as.Date("2013-01-01", origin = "1970-01-01"), three, as.Date("2023-01-01", origin = "1970-01-01"), four, col = "olivedrab", border = NA) + rect(as.Date("2013-01-01", origin = "1970-01-01"), four, as.Date("2023-01-01", origin = "1970-01-01"), 400, col = "blue", border = NA) + + par(new = TRUE) + plot(qecb_val_qu_stat_nan$Date, qecb_val_qu_stat_nan$qecb.med, xlim = c(xmin_, xmax_), ylim = c(ymin_, ymax_), pch = 19, cex = 1, main = unique(df1$Site_bis), xlab = "Année", + ylab = "QECB", col = "grey") + points(df1$Date, df1$qecb.med, pch = 19, cex = 1.5) + arrows(df1$Date, df1$qecb.med, df1$Date, df1$qecb.max, code = 3, angle = 90, length = 0.00) + arrows(df1$Date, df1$qecb.med, df1$Date, df1$qecb.min, code = 3, angle = 90, length = 0.00) + +} +rm(df1, dt_, dt_bis, four, i, one, three, two, xmax_, xmin_, ymax_, ymin_) +args <- commandArgs(trailingOnly = TRUE) + +#####Import data + +if (length(args) < 1) { + stop("This tool needs at least 1 argument") +}else { + report <- args[5] + loop_file <- source(args[6]) +} diff --git a/tools/indic_ivr/cb_qecb.xml b/tools/indic_ivr/cb_qecb.xml new file mode 100644 index 000000000..627ed6655 --- /dev/null +++ b/tools/indic_ivr/cb_qecb.xml @@ -0,0 +1,115 @@ + + indicator + + r-dplyr + r-tidyr + r-readr + r-writexl + r-stringr + r-readxl + r-tibble + r-lubridate + r-cowplot + + r-rmarkdown + r-officedown + r-knitr + r-vegan + r-reshape2 + r-ggplot2 + r-gridExtra + + r-Rcpp + r-arsenal + r-magrittr + r-tidyverse + r-officer + r-flextable + r-kableextra + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @Manual{, + title = {Les habitats rocheux intertidaux sous l’influence d’activités anthropiques : structure, dynamique et enjeux de conservation. Sciences agricoles}, + author = {Bernard M.}, + year = {2012}, + note = {https://tel.archives-ouvertes.fr/tel-00720611/} + + + diff --git a/tools/indic_ivr/cb_qecb_site_report_col_scale_loop.Rmd b/tools/indic_ivr/cb_qecb_site_report_col_scale_loop.Rmd new file mode 100644 index 000000000..071675087 --- /dev/null +++ b/tools/indic_ivr/cb_qecb_site_report_col_scale_loop.Rmd @@ -0,0 +1,242 @@ +--- +title: "Indice de Qualité Écologique des Champs de Blocs (QECB) - suivi 2014-2017" +#author: "Jonathan Richir, Christian Kerbiriou, Pauline Poisson, Maud Bernard, Juliette Delavenne, Isabelle Le Viol" +#date: "21 Juin 2021" +output: + #word_document: + #reference_docx: word_template.docx + #fig_width: 7 + #fig_height: 5 + officedown::rdocx_document + #word_document: default + #html_document: default +#always_allow_html: true # to be able to knit the kableExtra table +--- + +```{r setup, include = FALSE} +knitr::opts_chunk$set( + echo = FALSE + , message = FALSE + , warning = FALSE + ) + +df <- indic +df_full <- indic_full + +df[, c("qecb.moy", "qecb.et", "qecb.med", "qecb.min", "qecb.max")] <- apply(df[, c("qecb.moy", "qecb.et", "qecb.med", "qecb.min", "qecb.max")], 2, function(x) {ifelse(x %in% c("Inf", "NaN"), NA, x)}) +df_full$qebm_2 <- ifelse(df_full$qebm_2 %in% c("-Inf", "NaN"), NA, df_full$qebm_2) + +df1 <- df %>% dplyr::filter(Site == v) +df1 <- droplevels(df1) + +``` + +--- +title: "Indice de Qualité Écologique des Champs de Blocs (QECB) - suivi 2014-`r ifelse(max(df1$Annee) <= 2017, 2017, max(df1$Annee))`" +--- + +# Contexte + +Mieux comprendre les effets des changements globaux et locaux sur les habitats marins, et l’efficacité des mesures de gestion adoptées sous-entend de disposer de suivis standardisés et d’indicateurs robustes et sensibles reflétant l’état des habitats. + +L’habitat « Champs de blocs médiolittoraux » abrite une forte diversité de micro-habitats et d’espèces de par sa forte hétérogénéité structurelle et sa position intermédiaire sur l’estran, qui en font un des habitats les plus diversifiés du médiolittoral et à fort intérêt écologique, le long de la façade Manche-Atlantique française. Il est aussi un habitat très attractif pour la pratique de pêche à pied de loisir, qui via le remaniement des blocs, peut impacter les communautés. + +Ainsi, l’habitat « Champs de blocs médiolittoraux » a-t-il fait l’objet de plusieurs initiatives nationales et locales (dont LIFE+ « Expérimentation pour une gestion durable et concertée de la pêche à pied récréative en France » 2013-2017) pour mieux évaluer son état et le mettre en relation avec la pression de pêche à pied en vue d’adapter la gestion locale, notamment à travers le réseau d’Aires Marines Protégées (Natura 2000, PNM, PNR etc.). + +Ces projets ont en particulier permis de développer un réseau d’acteurs-trices de terrain-gestionnaires impliqué-e-s et des outils d’évaluation de l’état écologique et de la pression de pêche à pied: (1) l’Indicateur Visuel de Retournement des blocs (IVR) qui s’apparente à un indicateur « paysager » pour évaluer la pression de pêche sur la base de critères architecturaux; (2) L’indice de Qualité Écologique des Champs de Blocs (QECB) - objet du présent rapport - basé sur des variables biotiques et abiotiques qui répondent à la perturbation « retournement des blocs ». + + +# Application et calcul de l'indice de Qualité Écologique des Champs de Blocs (QECB) + +L’indice QECB est basé sur 16 variables biotiques ou abiotiques qui répondent à la perturbation « retournement des blocs mobiles ». Parmi ces 16 variables et selon les surfaces - supérieures ou inférieures - de blocs considérées, certaines sont indicatrices d’un « bon état écologique » (recouvrement des faces supérieures en algues brunes et rouges, recouvrement élevés des faces inférieures en éponges …) et d’autres d’une « dégradation » des blocs (recouvrement des faces supérieures en algues vertes opportunistes, taux élevés de roche nue sur les deux faces …). + +L’indice QECB intègre également dans sa formule les variables prélevées au niveau des faces supérieures de blocs fixés, ou de roche en place. Les communautés des faces supérieures de ces blocs de très grande taille ne subissent pas de variations anthropiques liées au retournement des blocs. Elles peuvent donc être considérées comme représentatives des communautés des faces supérieures des blocs retournables, avant retournement. L'échantillonnage de blocs fixés, ou de roche en place permet de faire des comparaisons intra-site « blocs mobiles / blocs fixés - roche en place », nécessaire en raison de la complexité de l’habitat. + +
+ +![](CB_qecb.tif){width=1000} + +Légende de la figure. Photos d'organismes végétaux et animaux des faces supérieures et inférieures de blocs mobiles (© Maud Bernard). + +Pour son application, cinq quadrats de 25m^2^ sont répartis à l’échelle du champ de blocs. + +- Dans chaque quadrat, deux blocs retournables sont sélectionnés au hasard. Les blocs doivent impérativement présenter une surface égale ou supérieure à 0,1 m^2^ (surface du quadrat). Dix blocs seront ainsi sélectionnés au total. +- Chaque bloc observé est numéroté puis échantillonné au +niveau de sa face supérieure puis de sa face inférieure. Le +bloc est ensuite remis à l’endroit. +- En parallèle, 5 blocs non retournables sont sélectionnés +à l’échelle du champ de blocs. L’échantillonnage de leurs +faces supérieures permet d'obtenir un état de référence +pour chaque champ de blocs. La méthodologie (ainsi que la fréquence de suivi) est la même que pour les blocs retournables, à l’exception que seules les faces supérieures sont échantillonnées. +- L'indice QECB, calculé à partir des mesures de terrain des 10 blocs mobiles et 5 blocs fixés - roche en place - échantillonnés, peut prendre une valeur théoriquement comprise entre -360 et 360. +- Sur la base de ces bornes théoriques, l’indice multivarié QECB peut être décomposé en 5 classes numérotées de 1 à 5. La classe 1 témoigne d’un très mauvais état écologique du champ de blocs sous la pression « retournement des blocs », et la classe 5 d’un très bon état écologique. + +Dans leur version originale, les limites de classes étaient obtenues en découpant l'intervalle entre les bornes théoriques minimales et maximales en 5 blocs de taille identique de 144 unités [(360-(-360))/5 = 144]. Cela étant, bon nombre de valeurs d'indice se trouvaient - tous sites confondus - dans la classe état écologique moyen. Ce manque de finesse dans l'évaluation de l'état écologique des champs de blocs, nous avons cherché à y remédier en découpant différemment les classes de l'indice. + +```{r} + +qecb_scale <- data.frame(matrix(NA, nrow = 5, ncol = 3)) +colnames(qecb_scale) <- c("Classe de l'indice", "Limites équidistantes", "Signification") + +qecb_scale$`Classe de l'indice` <- c("1", "2", "3", "4", "5") +qecb_scale$`Limites équidistantes` <- c("-360 ≤ QECB < -216", "-216 ≤ QECB < -72", "-72 ≤ QECB < +72", "+72 ≤ QECB < +216", "+216 ≤ QECB = +360") +qecb_scale$Signification <- c("Très mauvais état écologique", "Mauvais état écologique", "État écologique moyen", "Bon état écologique", "Très bon état écologique") + +#qecb_scale + +``` + +```{r} + +qu_tbl <- qecb_scale %>% + flextable::flextable() + +flextable::autofit(qu_tbl) + +``` +Légende de la table. Classes de l'indice de Qualité Ecologique des Champs de Blocs, dont les limites de classes couvrent 5 plages de valeurs identiques de 144 unités. + +```{r} + +dt_ <- dplyr::filter(df_full, qebm_2 >= -360) +dt_ <- dplyr::filter(dt_, qebm_2 <= 360) +dt_bis <- dplyr::filter(dt_, qebm_2 >= quantile(dt_$qebm_2, c(0.05), na.rm = TRUE)) +dt_bis <- dplyr::filter(dt_bis, qebm_2 <= quantile(dt_bis$qebm_2, c(0.95), na.rm = TRUE)) + +one <- round(mean(unlist(dplyr::filter(dt_bis, qebm_2 <= quantile(dt_bis$qebm_2, 0.25, na.rm = TRUE))["qebm_2"])), digits = 0) +two <- round(mean(unlist(dplyr::filter(dt_bis, qebm_2 > quantile(dt_bis$qebm_2, 0.25, na.rm = TRUE) & qebm_2 <= quantile(dt_bis$qebm_2, 0.5, na.rm = TRUE))["qebm_2"])), digits = 0) +three <- round(mean(unlist(dplyr::filter(dt_bis, qebm_2 > quantile(dt_bis$qebm_2, 0.5, na.rm = TRUE) & qebm_2 <= quantile(dt_bis$qebm_2, 0.75, na.rm = TRUE))["qebm_2"])), digits = 0) +four <- round(mean(unlist(dplyr::filter(dt_bis, qebm_2 > quantile(dt_bis$qebm_2, 0.75, na.rm = TRUE))["qebm_2"])), digits = 0) + +``` + +Les limites entre classes peuvent être définies à partir de la méthode des quantiles. Les quantiles sont les valeurs qui divisent un jeu de données ordonnées (classées du plus petit au plus grand) en intervalles contenant le même nombre de données. Nous avons donc classé et divisé l'ensemble des valeurs QECB des suivis terrain - tous sites confondus - en 4 suites de valeurs de taille égale. Nous avons ensuite calculé la moyenne de chacune de ces 4 suites de valeurs d'indice (respectivement `r one`, `r two`, `r three`, `r four`), pour ainsi obtenir comme précédemment une échelle de qualité en 5 classes. Ces nouvelles limites de classes, calculées à partir des données de terrain, sont ainsi représentatives de cette réalité de terrain. Enfin, à chacune de ces 5 classes leur a été associé un code couleur, allant du rouge pour un très mauvais état écologique au bleu pour un très bon état écologique. + +```{r} + +qecb_scale <- data.frame(matrix(NA, nrow = 5, ncol = 3)) +colnames(qecb_scale) <- c("Classe de l'indice", "Limites quantiles", "Signification") + +qecb_scale$`Classe de l'indice` <- c("1", "2", "3", "4", "5") +qecb_scale$`Limites quantiles` <- c( + paste0("-360 = QECB < ", as.character(one)), + paste0(as.character(one), " = QECB < +", as.character(two)), + paste0("+", as.character(two), " = QECB < +", as.character(three)), + paste0("+", as.character(three), " = QECB < +", as.character(four)), + paste0("+", as.character(four), " = QECB = +360")) + +qecb_scale$Signification <- c("Très mauvais état écologique", "Mauvais état écologique", "État écologique moyen", "Bon état écologique", "Très bon état écologique") + +``` + +```{r} + +qu_tbl <- qecb_scale %>% + flextable::flextable() +qu_tbl <- flextable::bg(qu_tbl, i = 1, bg = "red") +qu_tbl <- flextable::bg(qu_tbl, i = 2, bg = "orange") +qu_tbl <- flextable::bg(qu_tbl, i = 3, bg = "yellow") +qu_tbl <- flextable::bg(qu_tbl, i = 4, bg = "olivedrab") +qu_tbl <- flextable::bg(qu_tbl, i = 5, bg = "blue") + +flextable::autofit(qu_tbl) + +``` +Légende de la table. Classes de l'indice de Qualité Ecologique des Champs de Blocs, dont les limites de classes ont été calculées à partir de la méthode des quantiles. + + +# Observations pour le suivi du site `r unique(df1$Site.bis)` + +Les mesures des variables biotiques et abiotiques qui répondent à la perturbation « retournement des blocs mobiles » de vingt-quatre champs distribués le long de la façade Manche-Atlantique française sont stockées dans la base de données ESTAMP (http://www.pecheapied-loisir.fr/base-de-donnees-estamp/), hébergée par l'Office Français de la Biodiversité, créée dans le cadre du projet LIFE+ Pêche à Pied de Loisir, et que vous avez contribué à enrichir de vos observations. L'évolution des valeurs médianes de l'indice QECB: au site `r unique(df1$Site.bis)` et tous champs de blocs confondus, pour la période de suivi 2014-`r ifelse(max(df1$Annee) > 2017, max(df1$Annee), 2017)`, est représentée graphiquement sur les deux figures ci-dessous. A des fins de comparaisons entre la précédente et la nouvelle délimitation des 5 classes de qualité écologique des champs de blocs, et de la distribution des valeurs d'indice dans ces classes, les deux représentations graphiques en lien avec les tables ci-dessus sont données. + +```{r} + +txt_ <- data.frame(matrix(NA, nrow = 2, ncol = 1)) +colnames(txt_) <- "res." +txt_[1, 1] <- ifelse(min(df1$qecb.min, na.rm = TRUE) < -360, " (le minimum est en dehors des bornes théoriques)", "") +txt_[2, 1] <- ifelse(max(df1$qecb.max, na.rm = TRUE) > 360, " (le maximum est en dehors des bornes théoriques)", "") + +``` + +Les valeurs médianes du QECB au site `r unique(df1$Site.bis)` (points noirs) sont comprises entre un minimum égal à `r round(min(df1$qecb.med, na.rm = TRUE), digits = 0)` et un maximum égal à `r round(max(df1$qecb.med, na.rm = TRUE), digits = 0)`. Les valeurs individuelles, par bloc mobile (barres), sont quant à elles comprises entre un minimum égal à `r round(min(df1$qecb.min, na.rm = TRUE), digits = 0)``r txt_[1,1]` et un maximum égal à `r round(max(df1$qecb.max, na.rm = TRUE), digits = 0)``r txt_[2,1]`. Ces valeurs propres au site `r unique(df1$Site.bis)` peuvent être resituées par rapport aux autres valeurs médianes du QECB obtenues pour les vingt-trois autres sites suivis sur la période 2014-`r ifelse(max(df1$Annee) <= 2017, 2017, max(df1$Annee))` à l’échelle de la façade Manche-Atlantique française (points gris). Tous sites confondus, les valeurs médianes de cet indice occupent une fenêtre plus restreinte que celle entre leurs extrema théoriques, centrées sur la valeur de 6. + +```{r} + +par(mar = c(5, 5, 4, 2) + 0.1) + + xmin_ <- as.Date(ifelse(min(df1$Annee) >= 2014, "2014-01-01", paste0(min(df$Annee), "-01-01")), origin = "1970-01-01") + xmax_ <- as.Date(ifelse(max(df1$Annee) <= 2017, "2018-01-01", #paste0(max(qecb.val.eg$Annee)+1, + "2023-01-01") + #) + , origin = "1970-01-01") + + plot(df$Date, df$qecb.med, xlim = c(xmin_, xmax_), ylim = c(-360, 360), pch = 19, cex = 1, main = unique(df1$Site.bis), xlab = "Année", + ylab = "QECB", col = "grey") + points(df1$Date, df1$qecb.med, pch = 19, cex = 1.5) + arrows(df1$Date, df1$qecb.med, df1$Date, df1$qecb.max, code = 3, angle = 90, length = 0.00) + arrows(df1$Date, df1$qecb.med, df1$Date, df1$qecb.min, code = 3, angle = 90, length = 0.00) + + abline(h = c(-216, -72, 72, 216), lty = "dashed") + text(xmax_, -288, "1") + text(xmax_, -146, "2") + text(xmax_, 0, "3") + text(xmax_, 146, "4") + text(xmax_, 288, "5") + +``` + +```{r} + +par(mar = c(5, 5, 4, 2) + 0.1) + + xmin_ <- as.Date(ifelse(min(df1$Annee) >= 2014, "2014-01-01", paste0(min(df$Annee), "-01-01")), origin = "1970-01-01") + xmax_ <- as.Date(ifelse(max(df1$Annee) <= 2017, "2018-01-01", #paste0(max(qecb.val.eg$Annee)+1, + "2022-01-01") + #) + , origin = "1970-01-01") + + + ymin_ <- ifelse(min(df1$qecb.med, na.rm = TRUE) < -70, -360, -70) + ymax_ <- ifelse(max(df1$qecb.med, na.rm = TRUE) > 200, 360, 200) + + plot(df$Date, df$qecb.med, xlim = c(xmin_, xmax_), ylim = c(ymin_, ymax_), pch = 19, main = "", xlab = "", ylab = "", type = "n", axes = FALSE) + + rect(as.Date("2013-01-01", origin = "1970-01-01"), -400, as.Date("2023-01-01", origin = "1970-01-01"), one, col = "red", border = NA) + rect(as.Date("2013-01-01", origin = "1970-01-01"), one, as.Date("2023-01-01", origin = "1970-01-01"), two, col = "orange", border = NA) + rect(as.Date("2013-01-01", origin = "1970-01-01"), two, as.Date("2023-01-01", origin = "1970-01-01"), three, col = "yellow", border = NA) + rect(as.Date("2013-01-01", origin = "1970-01-01"), three, as.Date("2023-01-01", origin = "1970-01-01"), four, col = "olivedrab", border = NA) + rect(as.Date("2013-01-01", origin = "1970-01-01"), four, as.Date("2023-01-01", origin = "1970-01-01"), 400, col = "blue", border = NA) + + par(new = TRUE) + plot(df$Date, df$qecb.med, xlim = c(xmin_, xmax_), ylim = c(ymin_, ymax_), pch = 19, cex = 1, main = unique(df1$Site.bis), xlab = "Année", + ylab = "QECB", col = "grey") + points(df1$Date, df1$qecb.med, pch = 19, cex = 1.5) + arrows(df1$Date, df1$qecb.med, df1$Date, df1$qecb.max, code = 3, angle = 90, length = 0.00) + arrows(df1$Date, df1$qecb.med, df1$Date, df1$qecb.min, code = 3, angle = 90, length = 0.00) +``` + +Légende des figures. Evolution temporelle de la valeur médiane du QECB au site `r unique(df1$Site.bis)` (points noirs), en comparaison à celles des vingt-trois autres champs de blocs suivis le long de la façade Manche-Atlantique française (points gris). Les barres représentent la distribution des valeurs individuelles de QECB par bloc mobile au site `r unique(df1$Site.bis)` (n = `r sort(unique(df1$nb.notNa))[sort(unique(df1$nb.notNa)) != 0] `) entre valeurs minimums et maximums. Graphique du haut - Le découpage horizontal de la fenêtre graphique et les chiffres associés 1 à 5 représentent les classes de taille équivalente (144 unités) d'états écologiques. Graphique du bas - Le découpage horizontal de la fenêtre graphique représente les 5 classes d'états écologiques, aux limites calculées d'après la méthode des quantiles, dont le code couleur définit les états de très mauvais en rouge à très bon en bleu. + + +# Continuité du suivi du site `r unique(df1$Site.bis)` + +Ce bref rapport présente l'évolution de la valeur de l'indice QECB au site `r unique(df1$Site.bis)`, suivi de `r min(unique(df1$Annee), na.rm = TRUE)` à `r max(unique(df1$Annee), na.rm = TRUE)`, et la resitue par rapport aux valeurs QECB des vingt-trois autres champs de blocs suivis pour l'essentiel de 2014 à 2017 sur la façade Manche-Atlantique française. Il illustre l'évolution dans le temps de l'impact de la pression de la pêche à pied et du retournement des blocs sur la qualité écologique de l'habitat. + +Au nom du Groupe de Travail Champs de Blocs, nous espérons pouvoir continuer à compter sur vous pour participer à ces suivis; et bien évidemment, si vous avez la possibilité le printemps prochain de retourner sur le terrain effectuer de nouveaux relevés, ceux-ci, très utiles, permettront d'évaluer les tendances observées graphiquement ci-dessus sur une plus longue période. + + +# Remerciements et bibliographie + +Le projet est financé, à daté du 15 avril 2021, par une convention OFB Muséum national d'Histoire naturelle (MNHN) dans le cadre du Life Marha (LIFE 16 IPE FR001). Le Groupe de Travail Champs de Blocs remercie tous les participants au projet. + +Contacts : jonathan.richir@mnhn.fr (post-doctorant MNHN, en charge du projet), isabelle.le-viol@mnhn.fr (responsable MNHN du projet), christian.kerbiriou@mnhn.fr (responsable MNHN du projet), pour le Groupe de Travail. + +Littérature conseillée: + +- Bernard M., (2012). Les habitats rocheux intertidaux sous l’influence d’activités anthropiques : structure, dynamique et enjeux de conservation. Sciences agricoles. Université de Bretagne occidentale - Brest. NNT : 2012BRES0010. 423 pp. Thèse disponible sur https://tel.archives-ouvertes.fr/tel-00720611/. + +- Verbeke G., Maison E. (2013). Fiche S8. Définition d’indicateurs de perturbations des champs de blocs, dans : La gestion de la pêche de loisir dans les aires marines protégées, Recueil d’expériences des gestionnaires. Coll. « Cahiers techniques », Montpellier, Aten, n°87: 63-66. Fiche disponible sur http://ct87.espaces-naturels.fr/. + +
+ +![](logo_merged.png){width=1000} diff --git a/tools/indic_ivr/logo_merged.png b/tools/indic_ivr/logo_merged.png new file mode 100644 index 000000000..be264dc5f Binary files /dev/null and b/tools/indic_ivr/logo_merged.png differ diff --git a/tools/indic_ivr/loop_col_scale.r b/tools/indic_ivr/loop_col_scale.r new file mode 100644 index 000000000..ec32c53a7 --- /dev/null +++ b/tools/indic_ivr/loop_col_scale.r @@ -0,0 +1,26 @@ +library("rmarkdown") + +df <- indic +df_full <- indic_full + +slices <- unique(df$Site)[!is.na(unique(df$Site))] + +#if (choice == "pdf") { +# for (v in slices) { +# pdf(paste0(v, ".pdf")) +# rmarkdown::render(report, +# output_file = paste0(v, ".docx"), +# output_dir = getwd(), +# params = list(Site = v) +# ) +# dev.off() +# } +#}else { + for (v in slices) { + rmarkdown::render(report, + output_file = paste0(v, ".docx"), + params = list(Site = v) + ) + dev.off() + } +#} diff --git a/tools/indic_ivr/test-data/Valeurs_stat.tabular b/tools/indic_ivr/test-data/Valeurs_stat.tabular new file mode 100644 index 000000000..2087b8c8b --- /dev/null +++ b/tools/indic_ivr/test-data/Valeurs_stat.tabular @@ -0,0 +1,146 @@ +id.ivr Site Site_bis Date Annee Mois Jour ivr_moy ivr_et ivr_med ivr_min ivr_max fr.r.moy fr.r.et fr.r.med fr.r.min fr.r.max fr.nr.moy fr.nr.et fr.nr.med fr.nr.min fr.nr.max nb. +373462 GDMO_Locmariaquer Locmariaquer 2014-10-10 2014 10 10 1.6 0.894427190999916 1 1 3 28.2417615777664 11.2421196405189 22.5806451612903 17.3913043478261 45.4545454545455 71.7582384222336 11.2421196405189 77.4193548387097 54.5454545454545 82.6086956521739 5 +373529 GDMO_Locmariaquer Locmariaquer 2015-03-22 2015 3 22 2 1.22474487139159 2 1 4 32.0357039706226 21.6986144640573 32.3529411764706 12.8205128205128 65.9574468085106 67.9642960293774 21.6986144640573 67.6470588235294 34.0425531914894 87.1794871794872 5 +373596 GDMO_Locmariaquer Locmariaquer 2015-10-30 2015 10 30 1.8 0.447213595499958 2 1 2 32.7278416028416 11.7928800412532 38.4615384615385 15.625 44.4444444444444 67.2721583971584 11.7928800412532 61.5384615384615 55.5555555555556 84.375 5 +373663 GDMO_Locmariaquer Locmariaquer 2016-03-09 2016 3 9 1 0 1 1 1 17.5238095238095 2.46057346338358 16.6666666666667 14.2857142857143 20 82.4761904761905 2.46057346338358 83.3333333333333 80 85.7142857142857 5 +373730 GDMO_Locmariaquer Locmariaquer 2016-09-17 2016 9 17 1.2 1.30384048104053 1 0 3 18.8888888888889 22.082896571502 11.1111111111111 0 50 81.1111111111111 22.082896571502 88.8888888888889 50 100 5 +373797 GDMO_Locmariaquer Locmariaquer 2016-10-20 2016 10 20 1.6 1.14017542509914 2 0 3 25.7575757575758 18.4947812359602 27.2727272727273 0 50 74.2424242424242 18.4947812359602 72.7272727272727 50 100 5 +373804 GDMO_BegLann Beg Lann 2014-11-06 2014 11 6 1.8 0.836660026534076 2 1 3 32.0161616161616 18.6935581802127 27.7777777777778 16.6666666666667 63.6363636363636 67.9838383838384 18.6935581802127 72.2222222222222 36.3636363636364 83.3333333333333 5 +373871 GDMO_BegLann Beg Lann 2015-03-23 2015 3 23 1.8 1.30384048104053 1 1 4 27.8475654562423 24.945742155208 12.2448979591837 10.3448275862069 67.9012345679012 72.1524345437577 24.945742155208 87.7551020408163 32.0987654320988 89.6551724137931 5 +373938 GDMO_BegLann Beg Lann 2015-10-31 2015 10 31 3.8 0.447213595499958 4 3 4 73.5478829212845 10.681210664357 77.7777777777778 58.3333333333333 82.6086956521739 26.4521170787155 10.681210664357 22.2222222222222 17.3913043478261 41.6666666666667 5 +374005 GDMO_BegLann Beg Lann 2016-03-10 2016 3 10 3 0.707106781186548 3 2 4 56.5849673202614 12.7880487530146 61.1111111111111 37.5 70.8333333333333 43.4150326797386 12.7880487530146 38.8888888888889 29.1666666666667 62.5 5 +374072 GDMO_BegLann Beg Lann 2016-09-18 2016 9 18 4 0.707106781186548 4 3 5 80.0023809523809 11.3510528245955 81.25 64 95.2380952380952 19.997619047619 11.3510528245955 18.75 4.76190476190476 36 5 +374139 FOUR_PlateauFour Plateau du Four 2015-03-19 2015 3 19 3.66666666666667 0.577350269189626 4 3 4 68.1481481481482 8.98100418739418 66.6666666666667 60 77.7777777777778 31.8518518518519 8.98100418739418 33.3333333333333 22.2222222222222 40 3 +374194 FOUR_PlateauFour Plateau du Four 2015-04-20 2015 4 20 1.6 2.07364413533277 1 0 5 29.6923076923077 42.3412454362027 10 0 100 70.3076923076923 42.3412454362027 90 0 100 5 +374261 FOUR_PlateauFour Plateau du Four 2015-08-03 2015 8 3 0.6 0.894427190999916 0 0 2 13.2773109243697 19.4224933064649 0 0 42.8571428571429 86.7226890756303 19.4224933064649 100 57.1428571428571 100 5 +374268 FOUR_PlateauFour Plateau du Four 2015-09-28 2015 9 28 2.75 1.70782512765993 2.5 1 5 50.9456185220083 35.2415503409813 41.0216718266254 21.7391304347826 100 49.0543814779917 35.2415503409813 58.9783281733746 0 78.2608695652174 4 +374335 FOUR_PlateauFour Plateau du Four 2016-04-07 2016 4 7 2 1.22474487139159 2 1 4 36.2985896112831 18.9601066515641 33.3333333333333 22.2222222222222 68.4210526315789 63.7014103887169 18.9601066515641 66.6666666666667 31.5789473684211 77.7777777777778 5 +374402 FOUR_PlateauFour Plateau du Four 2016-06-06 2016 6 6 2.6 1.51657508881031 2 1 5 44.8625687156422 26.3977236537726 41.6666666666667 20.6896551724138 86.9565217391304 55.1374312843578 26.3977236537726 58.3333333333333 13.0434782608696 79.3103448275862 5 +374469 FOUR_PlateauFour Plateau du Four 2016-09-19 2016 9 19 3.2 1.78885438199983 3 1 5 59.9771241830065 37.0556815015956 52.9411764705882 12.5 100 40.0228758169935 37.0556815015956 47.0588235294118 0 87.5 5 +374536 EGMP_GroinCou Groin du Cou 2014-10-08 2014 10 8 2.6 1.81659021245849 2 1 5 48.6721463406907 37.1303305797349 28.3582089552239 17.3469387755102 100 51.3278536593093 37.1303305797349 71.6417910447761 0 82.6530612244898 5 +374599 EGMP_GroinCou Groin du Cou 2015-03-20 2015 3 20 2.6 0.894427190999916 3 1 3 49.0455273392816 18.9397429770386 54 16 63.8297872340426 50.9544726607184 18.9397429770386 46 36.1702127659575 84 5 +374666 EGMP_GroinCou Groin du Cou 2015-09-29 2015 9 29 3.6 0.547722557505166 4 3 4 66.2977605303581 10.6617136695677 68.5714285714286 50 79.0322580645161 33.7022394696418 10.6617136695677 31.4285714285714 20.9677419354839 50 5 +374733 EGMP_GroinCou Groin du Cou 2016-04-06 2016 4 6 3.6 1.14017542509914 4 2 5 66.675181040158 20.1458247656749 70.9677419354839 42.8571428571429 92 33.324818959842 20.1458247656749 29.0322580645161 8 57.1428571428571 5 +374800 EGMP_GroinCou Groin du Cou 2016-10-16 2016 10 16 4.4 1.34164078649987 5 2 5 80.957560957561 25.6652353344208 90.9090909090909 35.7142857142857 96.2962962962963 19.042439042439 25.6652353344208 9.09090909090909 3.7037037037037 64.2857142857143 5 +374867 EGMP_PasEmsembert Le Pas d'Emsembert 2015-10-30 2015 10 30 1 0 1 1 1 15.8170884632923 2.54930787132588 16.2790697674419 11.7647058823529 18.75 84.1829115367077 2.54930787132588 83.7209302325581 81.25 88.2352941176471 5 +374934 EGMP_PasEmsembert Le Pas d'Emsembert 2016-03-10 2016 3 10 2.4 0.894427190999916 2 2 4 44.35448469659 12.2858657776686 39.2857142857143 35.0877192982456 65.4545454545455 55.64551530341 12.2858657776686 60.7142857142857 34.5454545454545 64.9122807017544 5 +375001 EGMP_PasEmsembert Le Pas d'Emsembert 2016-10-18 2016 10 18 0.8 1.09544511501033 0 0 2 11.5833333333333 15.943694330013 0 0 31.25 88.4166666666667 15.943694330013 100 68.75 100 5 +375068 EGMP_BreeBains La Brée-les-Bains 2014-09-08 2014 9 8 1.8 0.447213595499958 2 1 2 29.6785421785422 5.73880710041291 31.8181818181818 20.8333333333333 35.1351351351351 70.3214578214578 5.73880710041291 68.1818181818182 64.8648648648649 79.1666666666667 5 +375075 EGMP_BreeBains La Brée-les-Bains 2014-09-11 2014 9 11 1.2 0.447213595499958 1 1 2 20.2614584630714 7.62329343535137 18.75 13.5135135135135 32.258064516129 79.7385415369286 7.62329343535137 81.25 67.741935483871 86.4864864864865 5 +375082 EGMP_BreeBains La Brée-les-Bains 2014-10-10 2014 10 10 1.4 1.14017542509914 1 0 3 25.6432339590234 19.039432372354 21.2121212121212 3.84615384615385 50 74.3567660409766 19.039432372354 78.7878787878788 50 96.1538461538462 5 +375149 EGMP_BreeBains La Brée-les-Bains 2015-03-23 2015 3 23 1.6 0.547722557505166 2 1 2 25.9055330634278 8.923820438574 30 11.5384615384615 33.3333333333333 74.0944669365722 8.923820438574 70 66.6666666666667 88.4615384615385 5 +375216 EGMP_BreeBains La Brée-les-Bains 2015-10-27 2015 10 27 1 0.707106781186548 1 0 2 14.5284454107984 15.1505050883432 9.09090909090909 3.57142857142857 41.1764705882353 85.4715545892016 15.1505050883432 90.9090909090909 58.8235294117647 96.4285714285714 5 +375283 EGMP_BreeBains La Brée-les-Bains 2016-04-06 2016 4 6 1 1.22474487139159 1 0 3 18.1038961038961 25.0153604354666 9.09090909090909 0 60 81.8961038961039 25.0153604354666 90.9090909090909 40 100 5 +375350 EGMP_BreeBains La Brée-les-Bains 2016-10-16 2016 10 16 1 0.707106781186548 1 0 2 12.65639589169 9.34955397672037 11.1111111111111 0 25 87.34360410831 9.34955397672037 88.8888888888889 75 100 5 +375417 EGMP_PerreAntiochat Le Perré d'Antiochat 2014-10-09 2014 10 9 0.6 0.547722557505166 1 0 1 9.99325210419435 7.82620004336438 6.38297872340426 4.16666666666667 22.7272727272727 90.0067478958056 7.82620004336439 93.6170212765958 77.2727272727273 95.8333333333333 5 +375484 EGMP_PerreAntiochat Le Perré d'Antiochat 2015-04-18 2015 4 18 1 0.707106781186548 1 0 2 12.4289044289044 9.21713442269648 8.33333333333333 3.84615384615385 27.2727272727273 87.5710955710956 9.21713442269648 91.6666666666667 72.7272727272727 96.1538461538462 5 +375551 EGMP_PerreAntiochat Le Perré d'Antiochat 2015-10-27 2015 10 27 0.6 0.547722557505166 1 0 1 7.94266820011501 6.64371273180172 8.51063829787234 0 16 92.057331799885 6.64371273180172 91.4893617021277 84 100 5 +375618 EGMP_PerreAntiochat Le Perré d'Antiochat 2016-04-07 2016 4 7 1 0 1 1 1 13.2083333333333 6.03163018111384 9.52380952380952 9.375 23.3333333333333 86.7916666666667 6.03163018111384 90.4761904761905 76.6666666666667 90.625 5 +375685 EGMP_PerreAntiochat Le Perré d'Antiochat 2016-10-17 2016 10 17 0.6 0.547722557505166 1 0 1 10.8182729148246 8.72559143832662 5.55555555555556 3.84615384615385 20.6896551724138 89.1817270851754 8.72559143832662 94.4444444444444 79.3103448275862 96.1538461538462 5 +375752 EGMP_Chassiron Chassiron 2015-04-20 2015 4 20 1.25 1.5 1 0 3 24.0804468436047 23.5206972909376 22.9757085020243 3.7037037037037 46.6666666666667 75.9195531563953 23.5206972909376 77.0242914979757 53.3333333333333 96.2962962962963 4 +375819 EGMP_Chassiron Chassiron 2015-10-28 2015 10 28 0.4 0.547722557505166 0 0 1 3.45450225706819 5.1386643911238 0 0 11.4754098360656 96.5454977429318 5.1386643911238 100 88.5245901639344 100 5 +375886 EGMP_Chassiron Chassiron 2016-03-10 2016 3 10 2 1 2 1 3 35.4653763738163 21.5561398088561 33.3333333333333 7.84313725490196 64 64.5346236261837 21.5561398088561 66.6666666666667 36 92.156862745098 5 +375953 EGMP_Chassiron Chassiron 2016-09-19 2016 9 19 0.6 0.894427190999916 0 0 2 9.46685028526461 14.795638362216 4.34782608695652 0 35.2941176470588 90.5331497147354 14.795638362216 95.6521739130435 64.7058823529412 100 5 +376020 BASQ_FlotsBleusZP Les Flots Bleus / zone pêcheurs 2014-11-24 2014 11 24 1.4 0.894427190999916 2 0 2 22.399577167019 13.5456361967697 25 0 36.3636363636364 77.600422832981 13.5456361967697 75 63.6363636363636 100 5 +376087 BASQ_FlotsBleusZP Les Flots Bleus / zone pêcheurs 2015-03-19 2015 3 19 2 1.22474487139159 2 1 4 34.9678501467975 28.4742978408597 29.6296296296296 13.6363636363636 83.7837837837838 65.0321498532025 28.4742978408597 70.3703703703704 16.2162162162162 86.3636363636364 5 +376154 BASQ_FlotsBleusZP Les Flots Bleus / zone pêcheurs 2015-05-18 2015 5 18 3.6 1.14017542509914 4 2 5 62.7678571428571 21.9664819047702 68.4210526315789 31.5789473684211 85.7142857142857 37.2321428571429 21.9664819047702 31.5789473684211 14.2857142857143 68.4210526315789 5 +376221 BASQ_FlotsBleusZP Les Flots Bleus / zone pêcheurs 2015-08-03 2015 8 3 2.4 1.51657508881031 3 0 4 42.7959549411162 29.1141826942559 45.8333333333333 3.2258064516129 80 57.2040450588838 29.1141826942559 54.1666666666667 20 96.7741935483871 5 +376288 BASQ_FlotsBleusZP Les Flots Bleus / zone pêcheurs 2015-09-30 2015 9 30 2.8 0.836660026534076 3 2 4 50.7780815675553 24.2639073060226 57.8947368421053 25 81.8181818181818 49.2219184324448 24.2639073060226 42.1052631578947 18.1818181818182 75 5 +376355 BASQ_FlotsBleusZP Les Flots Bleus / zone pêcheurs 2016-03-10 2016 3 10 1 0.707106781186548 1 0 2 17.7820512820513 12.4388378760018 20 0 33.3333333333333 82.2179487179487 12.4388378760018 80 66.6666666666667 100 5 +376422 BASQ_FlotsBleusZP Les Flots Bleus / zone pêcheurs 2016-05-09 2016 5 9 1.4 1.14017542509914 1 0 3 19.7753932342168 18.4125474257914 10 3.84615384615385 47.6190476190476 80.2246067657832 18.4125474257914 90 52.3809523809524 96.1538461538462 5 +376489 BASQ_FlotsBleusZP Les Flots Bleus / zone pêcheurs 2016-08-22 2016 8 22 1.6 0.547722557505166 2 1 2 24.262432012432 8.0613468909796 27.7777777777778 13.3333333333333 31.25 75.737567987568 8.06134689097961 72.2222222222222 68.75 86.6666666666667 5 +376556 BASQ_FlotsBleusZP Les Flots Bleus / zone pêcheurs 2016-09-20 2016 9 20 2.4 1.14017542509914 2 1 4 41.737380266792 26.2953514839257 38.4615384615385 9.09090909090909 71.4285714285714 58.262619733208 26.2953514839257 61.5384615384615 28.5714285714286 90.9090909090909 5 +376623 BASQ_FlotsBleusZF Les Flots Bleus / zone familles 2015-03-24 2015 3 24 4 1 4 3 5 72.0049792038885 18.9739093665491 81.4814814814815 47.8260869565217 89.7959183673469 27.9950207961115 18.9739093665491 18.5185185185185 10.2040816326531 52.1739130434783 5 +376690 BASQ_FlotsBleusZF Les Flots Bleus / zone familles 2015-08-17 2015 8 17 3.4 1.67332005306815 3 1 5 64.2620905664384 29.3773324016899 57.1428571428571 23.0769230769231 94.2307692307692 35.7379094335616 29.37733240169 42.8571428571429 5.76923076923077 76.9230769230769 5 +376757 BASQ_FlotsBleusZF Les Flots Bleus / zone familles 2015-10-26 2015 10 26 2 2.34520787991171 1 0 5 36.6769730970292 43.7477810490541 12.5 0 96.7741935483871 63.3230269029708 43.7477810490541 87.5 3.2258064516129 100 5 +376824 BASQ_FlotsBleusZF Les Flots Bleus / zone familles 2016-03-08 2016 3 8 2 1 2 1 3 32.2821566044072 14.5017470801281 26.0869565217391 17.6470588235294 50 67.7178433955928 14.5017470801281 73.9130434782609 50 82.3529411764706 5 +376891 BASQ_FlotsBleusZF Les Flots Bleus / zone familles 2016-08-18 2016 8 18 1.2 1.09544511501033 1 0 3 21.4285714285714 22.0157642963178 21.4285714285714 0 57.1428571428571 78.5714285714286 22.0157642963178 78.5714285714286 42.8571428571429 100 5 +376958 BASQ_FlotsBleusZF Les Flots Bleus / zone familles 2016-09-15 2016 9 15 2.4 1.14017542509914 2 1 4 46.1699346405229 21.3130831096963 40 22.2222222222222 77.7777777777778 53.8300653594771 21.3130831096963 60 22.2222222222222 77.7777777777778 5 +377025 GONB_IlotStMichel ÃŽlot Saint-Michel 2014-10-08 2014 10 8 2.33333333333333 0.577350269189626 2 2 3 45.6837196843593 4.87522269089479 43.3962264150943 42.3728813559322 51.2820512820513 54.3162803156407 4.87522269089479 56.6037735849057 48.7179487179487 57.6271186440678 3 +377052 GONB_IlotStMichel ÃŽlot Saint-Michel 2015-04-18 2015 4 18 1.75 0.5 2 1 2 29.4126696436978 5.40385218175188 31.6636851520572 21.4285714285714 32.8947368421053 70.5873303563022 5.40385218175188 68.3363148479428 67.1052631578947 78.5714285714286 4 +377119 GONB_IlotStMichel ÃŽlot Saint-Michel 2015-10-29 2015 10 29 2.2 0.836660026534076 2 1 3 35.2773704789834 13.2894744877552 32.258064516129 21.2121212121212 52.0833333333333 64.7226295210166 13.2894744877551 67.741935483871 47.9166666666667 78.7878787878788 5 +377186 GONB_IlotStMichel ÃŽlot Saint-Michel 2016-05-07 2016 5 7 2 0.707106781186548 2 1 3 31.3711230854088 9.93081763707341 28.3783783783784 20 46.9387755102041 68.6288769145912 9.93081763707341 71.6216216216216 53.0612244897959 80 5 +377253 GONB_IlotStMichel ÃŽlot Saint-Michel 2016-10-18 2016 10 18 1.6 0.547722557505166 2 1 2 26.6649499925362 14.6711336218575 34.2857142857143 6.66666666666667 41.8181818181818 73.3350500074638 14.6711336218575 65.7142857142857 58.1818181818182 93.3333333333333 5 +377320 FINS_Quemenes Quéménès 2015-04-20 2015 4 20 0.2 0.447213595499958 0 0 1 2.52404546779365 3.79030535400707 0 0 8.51063829787234 97.4759545322063 3.79030535400707 100 91.4893617021277 100 5 +377387 FINS_Quemenes Quéménès 2015-09-30 2015 9 30 0 0 0 0 0 0 0 0 0 0 100 0 100 100 100 5 +377454 FINS_Quemenes Quéménès 2016-04-07 2016 4 7 0 0 0 0 0 0 0 0 0 0 100 0 100 100 100 5 +377521 FINS_Quemenes Quéménès 2016-09-17 2016 9 17 0 0 0 0 0 0 0 0 0 0 100 0 100 100 100 4 +377576 FINS_Quemenes Quéménès 2017-04-27 2017 4 27 0.2 0.447213595499958 0 0 1 3.52941176470588 7.89200462646985 0 0 17.6470588235294 96.4705882352941 7.89200462646985 100 82.3529411764706 100 5 +377643 FINS_Quemenes Quéménès 2017-09-20 2017 9 20 0 0 0 0 0 0.925925925925926 1.60375074774896 0 0 2.77777777777778 99.0740740740741 1.60375074774896 100 97.2222222222222 100 3 +377686 FINS_Quemenes Quéménès 2017-10-07 2017 10 7 1 0 1 1 1 14.5145145145145 10.0509672601091 14.5145145145145 7.40740740740741 21.6216216216216 85.4854854854855 10.0509672601091 85.4854854854855 78.3783783783784 92.5925925925926 2 +377717 FINS_SeinGoulenez ÃŽle de Sein - Goulenez 2016-04-08 2016 4 8 0.6 0.547722557505166 1 0 1 7.89037108901285 5.15056173421976 7.14285714285714 3.33333333333333 15.7894736842105 92.1096289109871 5.15056173421976 92.8571428571429 84.2105263157895 96.6666666666667 5 +377784 FINS_SeinGoulenez ÃŽle de Sein - Goulenez 2016-10-18 2016 10 18 0.666666666666667 0.577350269189626 1 0 1 9.94306418219462 4.90693876499534 12.5 4.28571428571429 13.0434782608696 90.0569358178054 4.90693876499534 87.5 86.9565217391304 95.7142857142857 3 +377827 FINS_SeinGoulenez ÃŽle de Sein - Goulenez 2017-04-27 2017 4 27 0 0 0 0 0 2.53602084014257 1.47822747481387 2.34733700642792 1.21212121212121 4.23728813559322 97.4639791598574 1.47822747481387 97.6526629935721 95.7627118644068 98.7878787878788 4 +377894 FINS_SeinGoulenez ÃŽle de Sein - Goulenez 2017-11-06 2017 11 6 0 0 0 0 0 1.27513409299417 1.44430313208988 0.934579439252336 0 3.73134328358209 98.7248659070058 1.44430313208988 99.0654205607477 96.2686567164179 100 5 +377961 FINS_SeinKilaourou ÃŽle de Sein - Kilaourou 2015-04-21 2015 4 21 0.75 0.957427107756338 0.5 0 2 9.3362480127186 14.5216136896962 3.37837837837838 0 30.5882352941176 90.6637519872814 14.5216136896962 96.6216216216216 69.4117647058823 100 4 +378028 FINS_SeinKilaourou ÃŽle de Sein - Kilaourou 2015-09-29 2015 9 29 0.6 0.894427190999916 0 0 2 10.61468745835 13.9489114337475 3.33333333333333 0 31.7073170731707 89.38531254165 13.9489114337475 96.6666666666667 68.2926829268293 100 5 +378095 FINS_SeinKilaourou ÃŽle de Sein - Kilaourou 2016-04-08 2016 4 8 0.2 0.447213595499958 0 0 1 2.58792815371763 2.01753256282825 2.14285714285714 0 5.55555555555556 97.4120718462824 2.01753256282825 97.8571428571428 94.4444444444444 100 5 +378162 FINS_SeinKilaourou ÃŽle de Sein - Kilaourou 2016-09-19 2016 9 19 0.5 0.577350269189626 0.5 0 1 6.40756926471212 5.10133681736421 7.40973312401884 0 10.8108108108108 93.5924307352879 5.1013368173642 92.5902668759812 89.1891891891892 100 4 +378229 FINS_SeinKilaourou ÃŽle de Sein - Kilaourou 2017-04-27 2017 4 27 3.25 1.5 4 1 4 60.099194771852 27.8749100694006 68.0931263858093 20 84.2105263157895 39.900805228148 27.8749100694006 31.9068736141907 15.7894736842105 80 4 +378280 FINS_SeinKilaourou ÃŽle de Sein - Kilaourou 2017-11-06 2017 11 6 0.25 0.5 0 0 1 1.78571428571429 3.57142857142857 0 0 7.14285714285714 98.2142857142857 3.57142857142857 100 92.8571428571429 100 4 +378335 ARMO_Verdelet ÃŽlot du Verdelet 2014-05-15 2014 5 15 2.2 1.6431676725155 2 1 5 43.0920060331825 34.2161940780082 38.4615384615385 12.8205128205128 100 56.9079939668175 34.2161940780082 61.5384615384615 0 87.1794871794872 5 +378402 ARMO_Verdelet ÃŽlot du Verdelet 2014-10-08 2014 10 8 1.4 0.547722557505166 1 1 2 24.8675525283736 10.0447297867476 23.8095238095238 14.6341463414634 40.9836065573771 75.1324474716264 10.0447297867476 76.1904761904762 59.0163934426229 85.3658536585366 5 +378469 ARMO_Verdelet ÃŽlot du Verdelet 2015-03-23 2015 3 23 1.8 0.836660026534076 2 1 3 30.2428985556361 15.1865867489171 31.5789473684211 12.1951219512195 52.9411764705882 69.7571014443639 15.1865867489171 68.4210526315789 47.0588235294118 87.8048780487805 5 +378536 ARMO_Verdelet ÃŽlot du Verdelet 2015-10-26 2015 10 26 1.8 0.836660026534076 2 1 3 29.8039629805411 21.4613245308648 28 6.25 60 70.1960370194589 21.4613245308648 72 40 93.75 5 +378603 ARMO_Verdelet ÃŽlot du Verdelet 2016-04-09 2016 4 9 1.2 0.836660026534076 1 0 2 17.0521498015104 10.3408954367461 18.5185185185185 4.34782608695652 27.9411764705882 82.9478501984896 10.340895436746 81.4814814814815 72.0588235294118 95.6521739130435 5 +378670 ARMO_Verdelet ÃŽlot du Verdelet 2016-10-18 2016 10 18 1.6 0.547722557505166 2 1 2 28.5884634615016 13.5137659095912 36.9565217391304 11.1111111111111 40.4255319148936 71.4115365384984 13.5137659095912 63.0434782608696 59.5744680851064 88.8888888888889 5 +378737 ARMO_Piegu Piégu 2014-05-14 2014 5 14 4.4 0.894427190999916 5 3 5 81.2869875222816 23.143407799003 93.3333333333333 45.4545454545455 100 18.7130124777184 23.143407799003 6.66666666666667 0 54.5454545454545 5 +378804 ARMO_Piegu Piégu 2014-11-05 2014 11 5 3.2 0.836660026534076 3 2 4 60.3256217466744 21.4257109648899 60 28.5714285714286 84.2105263157895 39.6743782533256 21.42571096489 40 15.7894736842105 71.4285714285714 5 +378871 ARMO_Piegu Piégu 2015-03-19 2015 3 19 2.2 1.6431676725155 2 1 5 34.302597058013 32.1560511875708 27.906976744186 7.14285714285714 89.3617021276596 65.697402941987 32.1560511875708 72.0930232558139 10.6382978723404 92.8571428571429 5 +378934 ARMO_Piegu Piégu 2015-10-01 2015 10 1 3.2 1.09544511501033 3 2 5 54.0982199331345 18.2185108388066 47.6190476190476 39.2857142857143 85.4838709677419 45.9017800668655 18.2185108388066 52.3809523809524 14.5161290322581 60.7142857142857 5 +379001 ARMO_Piegu Piégu 2016-04-06 2016 4 6 2.8 1.48323969741913 3 1 5 51.7209099040494 27.1410184498267 50 21.875 93.0232558139535 48.2790900959506 27.1410184498267 50 6.97674418604651 78.125 5 +379068 ARMO_Piegu Piégu 2016-10-16 2016 10 16 3.2 1.30384048104053 3 2 5 59.9437411625862 27.9356771004796 55 27.6595744680851 100 40.0562588374139 27.9356771004796 45 0 72.3404255319149 5 +379135 ARMO_Bilfot Pointe de Bilfot 2014-04-28 2014 4 28 0.6 0.547722557505166 1 0 1 10.0212298653418 9.91246789686066 6.06060606060606 0 24.5283018867925 89.9787701346582 9.91246789686066 93.9393939393939 75.4716981132076 100 5 +379202 ARMO_Bilfot Pointe de Bilfot 2014-10-10 2014 10 10 2.2 1.30384048104053 2 1 4 38.6882061271053 23.2631845466373 36.8421052631579 13.953488372093 69.4444444444444 61.3117938728947 23.2631845466373 63.1578947368421 30.5555555555556 86.046511627907 5 +379269 ARMO_Bilfot Pointe de Bilfot 2015-04-18 2015 4 18 2.2 1.09544511501033 2 1 4 32.9857397504456 20.9522164004544 32.3529411764706 8.33333333333333 65.9090909090909 67.0142602495544 20.9522164004544 67.6470588235294 34.0909090909091 91.6666666666667 5 +379336 ARMO_Bilfot Pointe de Bilfot 2015-10-27 2015 10 27 2.8 0.447213595499958 3 2 3 52.3291812665315 10.1311001235753 53.6585365853659 36.2068965517241 64.2857142857143 47.6708187334685 10.1311001235753 46.3414634146341 35.7142857142857 63.7931034482759 5 +379403 ARMO_Bilfot Pointe de Bilfot 2016-04-07 2016 4 7 3.4 0.894427190999916 4 2 4 65.7948508957984 18.5261191353454 75.8620689655172 37.7777777777778 80 34.2051491042016 18.5261191353454 24.1379310344828 20 62.2222222222222 5 +379470 ARMO_Bilfot Pointe de Bilfot 2016-10-15 2016 10 15 2.4 0.547722557505166 2 2 3 42.4854231974922 14.3351920703859 37.5 27.5862068965517 59.0909090909091 57.5145768025078 14.3351920703859 62.5 40.9090909090909 72.4137931034483 5 +379537 ARMO_IlePlate ÃŽle Plate 2014-04-29 2014 4 29 1 0 1 1 1 14.5513983940077 6.59060288570236 14.2857142857143 5.88235294117647 21.0526315789474 85.4486016059923 6.59060288570236 85.7142857142857 78.9473684210526 94.1176470588235 5 +379604 ARMO_IlePlate ÃŽle Plate 2015-04-17 2015 4 17 0.4 0.547722557505166 0 0 1 4.46492575524834 4.69249470267624 4.76190476190476 0 11.1111111111111 95.5350742447517 4.69249470267624 95.2380952380952 88.8888888888889 100 5 +379671 ARMO_IlePlate ÃŽle Plate 2015-10-29 2015 10 29 0.8 0.447213595499958 1 0 1 10.0148643626904 4.55566042273653 10.2564102564103 4.34782608695652 16.6666666666667 89.9851356373096 4.55566042273653 89.7435897435898 83.3333333333333 95.6521739130435 5 +379738 ARMO_IlePlate ÃŽle Plate 2016-03-24 2016 3 24 0.8 0.447213595499958 1 0 1 8.84978991596639 5.40003068620694 9.375 0 14.2857142857143 91.1502100840336 5.40003068620694 90.625 85.7142857142857 100 5 +379805 ARMO_IlePlate ÃŽle Plate 2016-11-16 2016 11 16 0.4 0.547722557505166 0 0 1 6.7041507898706 4.55344281331021 4.6875 1.96078431372549 12.5 93.2958492101294 4.55344281331021 95.3125 87.5 98.0392156862745 5 +379872 PDMO_Perharidy Perharidy 2014-05-17 2014 5 17 1.8 0.836660026534076 2 1 3 29.07684968994 19.5164204692144 26.9230769230769 5.63380281690141 54.9019607843137 70.92315031006 19.5164204692144 73.0769230769231 45.0980392156863 94.3661971830986 5 +379879 PDMO_Perharidy Perharidy 2014-09-09 2014 9 9 1 0 1 1 1 16.22073531607 5.88382759331326 18.1818181818182 10 23.5294117647059 83.77926468393 5.88382759331326 81.8181818181818 76.4705882352941 90 5 +379934 PDMO_Perharidy Perharidy 2015-03-19 2015 3 19 1.8 0.836660026534076 2 1 3 32.1335403726708 14.7237180638778 30 20.8333333333333 57.1428571428571 67.8664596273292 14.7237180638778 70 42.8571428571429 79.1666666666667 5 +380001 PDMO_Perharidy Perharidy 2015-09-28 2015 9 28 1.2 0.447213595499958 1 1 2 15.9569026500517 11.7260579816644 14.4927536231884 6.38297872340426 35.8695652173913 84.0430973499483 11.7260579816644 85.5072463768116 64.1304347826087 93.6170212765958 5 +380068 PDMO_Perharidy Perharidy 2016-03-10 2016 3 10 1.4 0.547722557505166 1 1 2 22.5490895577852 7.77007495599773 19.5652173913043 13.0434782608696 32.6923076923077 77.4509104422148 7.77007495599773 80.4347826086957 67.3076923076923 86.9565217391304 5 +380135 PDMO_Perharidy Perharidy 2016-09-19 2016 9 19 1 0.707106781186548 1 0 2 13.8382815518121 9.88005387680422 11.6279069767442 1.96078431372549 28.9473684210526 86.1617184481879 9.88005387680422 88.3720930232558 71.0526315789474 98.0392156862745 5 +380202 BRES_Keraliou Keraliou 2014-10-10 2014 10 10 1.8 1.30384048104053 1 1 4 31.6033282878369 25.0303831183052 21.7391304347826 12.5 74.1176470588235 68.3966717121631 25.0303831183052 78.2608695652174 25.8823529411765 87.5 5 +380269 BRES_Keraliou Keraliou 2015-01-22 2015 1 22 0.25 0.5 0 0 1 3.08695652173913 3.86378007543514 2.17391304347826 0 8 96.9130434782609 3.86378007543514 97.8260869565217 92 100 4 +380276 BRES_Keraliou Keraliou 2015-03-21 2015 3 21 0.8 1.30384048104053 0 0 3 13.7804327238289 20.9656319749622 2.7027027027027 0 49.0566037735849 86.219567276171 20.9656319749622 97.2972972972973 50.9433962264151 100 5 +380343 BRES_Keraliou Keraliou 2016-03-10 2016 3 10 0.8 0.836660026534076 1 0 2 9.05555555555556 11.6494582081404 5 0 27.7777777777778 90.9444444444444 11.6494582081404 95 72.2222222222222 100 5 +380410 BRES_Keraliou Keraliou 2016-10-17 2016 10 17 2 1.58113883008419 2 0 4 34.9044499044499 32.720380943022 38.6363636363636 0 81.9444444444444 65.0955500955501 32.720380943022 61.3636363636364 18.0555555555556 100 5 +380477 FINS_Mousterlin Pointe de Mousterlin 2014-10-09 2014 10 9 1.8 0.836660026534076 2 1 3 35.624302134647 19.8846618871322 37.9310344827586 16.6666666666667 64 64.375697865353 19.8846618871322 62.0689655172414 36 83.3333333333333 5 +380540 FINS_Mousterlin Pointe de Mousterlin 2015-03-21 2015 3 21 1.4 0.547722557505166 1 1 2 21.2208624708625 8.04184983120332 18.75 13.6363636363636 33.3333333333333 78.7791375291375 8.04184983120333 81.25 66.6666666666667 86.3636363636364 5 +380607 FINS_Mousterlin Pointe de Mousterlin 2015-10-28 2015 10 28 1.4 0.547722557505166 1 1 2 25.0826775016736 13.3018945922566 19.3548387096774 11.6279069767442 43.4782608695652 74.9173224983264 13.3018945922566 80.6451612903226 56.5217391304348 88.3720930232558 5 +380670 FINS_Mousterlin Pointe de Mousterlin 2016-03-10 2016 3 10 1 0 1 1 1 13.6277056277056 5.58758928576079 12 8 20 86.3722943722944 5.58758928576079 88 80 92 5 +380733 FINS_Mousterlin Pointe de Mousterlin 2016-11-14 2016 11 14 1.2 0.447213595499958 1 1 2 19.4725274725275 12.288643029003 15.3846153846154 7.69230769230769 40 80.5274725274725 12.288643029003 84.6153846153846 60 92.3076923076923 5 +380800 FINS_StNicolasGlenan Saint-Nicolas des Glénan 2014-10-10 2014 10 10 2 1 2 1 3 36.3494008714597 20.111405704751 27.4509803921569 22.2222222222222 59.375 63.6505991285403 20.111405704751 72.5490196078431 40.625 77.7777777777778 3 +380859 FINS_StNicolasGlenan Saint-Nicolas des Glénan 2015-03-22 2015 3 22 1.6 0.547722557505166 2 1 2 25.2873015873016 14.0024432774236 26.7857142857143 6.66666666666667 38.8888888888889 74.7126984126984 14.0024432774236 73.2142857142857 61.1111111111111 93.3333333333333 5 +380926 FINS_StNicolasGlenan Saint-Nicolas des Glénan 2016-04-08 2016 4 8 1.25 0.5 1 1 2 20.2298145117294 8.7975747021922 16.3461538461538 14.8936170212766 33.3333333333333 79.7701854882706 8.7975747021922 83.6538461538462 66.6666666666667 85.1063829787234 4 +380973 FINS_StNicolasGlenan Saint-Nicolas des Glénan 2016-10-18 2016 10 18 1.2 1.09544511501033 1 0 3 18.0606060606061 18.778834577768 13.3333333333333 0 50 81.9393939393939 18.778834577768 86.6666666666667 50 100 5 +392417 GDMO_Locmariaquer Locmariaquer 2018-04-17 2018 4 17 1.8 0.836660026534076 2 1 3 29.7115384615385 20.6462258539384 30.7692307692308 11.5384615384615 62.5 70.2884615384615 20.6462258539384 69.2307692307692 37.5 88.4615384615385 5 +392521 GDMO_Locmariaquer Locmariaquer 2018-09-10 2018 9 10 4.2 1.30384048104053 5 2 5 80.8445251058681 28.8066208826745 93.3333333333333 31.5789473684211 100 19.1554748941319 28.8066208826745 6.66666666666667 0 68.4210526315789 5 +406229 GDMO_Locmariaquer Locmariaquer 2019-04-19 2019 4 19 2.4 0.547722557505166 2 2 3 47.0310245310245 11.104781536097 42.8571428571429 36.3636363636364 62.5 52.9689754689755 11.104781536097 57.1428571428571 37.5 63.6363636363636 5 +424249 GDMO_Locmariaquer Locmariaquer 2019-09-30 2019 9 30 3.8 2.16794833886788 5 0 5 74.6666666666667 43.3076847170979 100 0 100 25.3333333333333 43.3076847170979 0 0 100 5 +444815 GDMO_Locmariaquer Locmariaquer 2020-05-07 2020 5 7 1.4 0.547722557505166 1 1 2 20.1791470940407 6.30056924704725 17.0212765957447 13.6363636363636 28.5714285714286 79.8208529059593 6.30056924704725 82.9787234042553 71.4285714285714 86.3636363636364 5 +455567 GDMO_Locmariaquer Locmariaquer 2020-09-19 2020 9 19 4.4 1.34164078649987 5 2 5 85 33.5410196624968 100 25 100 15 33.5410196624968 0 0 75 5 +477617 FINS_SeinGoulenez ÃŽle de Sein - Goulenez 2020-10-16 2020 10 16 1 0 1 1 1 NA NA NA NA NA NA NA NA NA NA 5 +477640 FINS_SeinKilaourou ÃŽle de Sein - Kilaourou 2020-10-16 2020 10 16 0 0 0 0 0 0 0 0 0 0 100 0 100 100 100 4 +477714 FINS_SeinGoulenez ÃŽle de Sein - Goulenez 2020-03-12 2020 3 12 0.8 0.447213595499958 1 0 1 10.4725158562368 6.23861101344281 12.5 0 15.9090909090909 89.5274841437632 6.23861101344281 87.5 84.0909090909091 100 5 +477733 FINS_SeinKilaourou ÃŽle de Sein - Kilaourou 2019-03-22 2019 3 22 0 0 0 0 0 0.588235294117647 1.31533410441164 0 0 2.94117647058824 99.4117647058823 1.31533410441164 100 97.0588235294118 100 5 +477743 FINS_SeinKilaourou ÃŽle de Sein - Kilaourou 2019-10-29 2019 10 29 0 0 0 0 0 0 0 0 0 0 100 0 100 100 100 4 +477750 FINS_SeinGoulenez ÃŽle de Sein - Goulenez 2019-10-29 2019 10 29 0.8 0.447213595499958 1 0 1 9.74604029254722 7.40151854470847 7.89473684210526 0 17.6470588235294 90.2539597074528 7.40151854470847 92.1052631578947 82.3529411764706 100 5 +477757 FINS_SeinGoulenez ÃŽle de Sein - Goulenez 2019-03-22 2019 3 22 0.6 0.894427190999916 0 0 2 9.95129679220749 11.4676754859719 4.41176470588235 0 28.5714285714286 90.0487032077925 11.4676754859719 95.5882352941177 71.4285714285714 100 5 +478379 FINS_Quemenes Quéménès 2021-03-31 2021 3 31 0.6 0.547722557505166 1 0 1 7.35635370585309 5.89560482882574 6.17283950617284 2.1505376344086 16.3265306122449 92.6436462941469 5.89560482882573 93.8271604938272 83.6734693877551 97.8494623655914 5 +483642 GDMO_Locmariaquer Locmariaquer 2021-05-28 2021 5 28 1.4 1.34164078649987 2 0 3 26.5714285714286 24.5282011786683 40 0 50 73.4285714285714 24.5282011786683 60 50 100 5 +487404 FINS_SeinGoulenez ÃŽle de Sein - Goulenez 2021-03-31 2021 3 31 0.6 0.547722557505166 1 0 1 10.1755030475077 6.77437335154763 10.3448275862069 3.33333333333333 17.1428571428571 89.8244969524923 6.77437335154762 89.6551724137931 82.8571428571429 96.6666666666667 5 +487405 FINS_SeinKilaourou ÃŽle de Sein - Kilaourou 2021-03-31 2021 3 31 0.4 0.547722557505166 0 0 1 6.90381360853802 3.74597137512406 4.28571428571429 4.05405405405405 11.0236220472441 93.096186391462 3.74597137512407 95.7142857142857 88.9763779527559 95.9459459459459 5 +494799 FINS_Quemenes Quéménès 2020-10-16 2020 10 16 0.2 0.447213595499958 0 0 1 3.32932556321441 2.07748955547336 2.4390243902439 1.53846153846154 6.09756097560976 96.6706744367856 2.07748955547337 97.5609756097561 93.9024390243902 98.4615384615385 5 +504857 GDMO_Locmariaquer Locmariaquer 2021-09-09 2021 9 9 2 1.73205080756888 3 0 3 37.8787878787879 33.5050672537818 50 0 63.6363636363636 62.1212121212121 33.5050672537818 50 36.3636363636364 100 3 +504964 GDMO_Locmariaquer Locmariaquer 2017-12-06 2017 12 6 2.8 1.09544511501033 3 1 4 52.9386973180077 18.5844767149475 55.5555555555556 24.1379310344828 75 47.0613026819923 18.5844767149475 44.4444444444444 25 75.8620689655172 5 +505032 GDMO_Locmariaquer Locmariaquer 2017-03-14 2017 3 14 2.2 1.09544511501033 2 1 4 34.4047619047619 21.029686931248 25 12.5 66.6666666666667 65.5952380952381 21.029686931248 75 33.3333333333333 87.5 5 +512569 EGMP_Chassiron Chassiron 2021-10-07 2021 10 7 4.25 0.5 4 4 5 77.7010512173852 7.42781596124523 78.0622009569378 68.9655172413793 85.7142857142857 22.2989487826148 7.42781596124523 21.9377990430622 14.2857142857143 31.0344827586207 4 +513924 EGMP_BreeBains La Brée-les-Bains 2021-10-07 2021 10 7 0.25 0.5 0 0 1 3.2258064516129 6.45161290322581 0 0 12.9032258064516 96.7741935483871 6.45161290322581 100 87.0967741935484 100 4 diff --git a/tools/indic_ivr/test-data/champbloc_ivr.csv b/tools/indic_ivr/test-data/champbloc_ivr.csv new file mode 100644 index 000000000..6aa52a7dd --- /dev/null +++ b/tools/indic_ivr/test-data/champbloc_ivr.csv @@ -0,0 +1,143 @@ +"ID Fiche";"id";"Numero Quadrat";"GPS1 Latitude";"GPS1 Longitude";"GPS2 Latitude";"GPS2 Longitude";"GPS3 Latitude";"GPS3 Longitude";"GPS4 Latitude";"GPS4 Longitude";"Numero Photo";"Numéro strate";"Description Strate";"Nb Blocs Non Retournes";"Nb Blocs Retournes" +392417;"2018-04-17-GDMO-CDB-001";5;;;;;;;;;"2018-04-17_GDMO_01_CDB-Q5_392479.jpg";"2";"10% d'algues vertes, 90% algues brunes, cuvettes et blocs moyens et sable";23;3; +392417;"2018-04-17-GDMO-CDB-001";3;;;;;;;;;"2018-04-17_GDMO_01_CDB-Q3_392477.jpg";"2";"40% d'algues vertes, 60% algues brunes, cuvettes et blocs moyens";14;2; +392417;"2018-04-17-GDMO-CDB-001";4;;;;;;;;;"2018-04-17_GDMO_01_CDB-Q4_392478.jpg";"2";"75% d'algues vertes, 25% algues brunes, 1 cuvette et blocs moyens";3;5; +392417;"2018-04-17-GDMO-CDB-001";2;;;;;;;;;"2018-04-17_GDMO_01_CDB-Q2_392476.jpg";"2";"40% d'algues vertes, 60% algues brunes, cuvettes et petits blocs ";11;5; +392417;"2018-04-17-GDMO-CDB-001";1;;;;;;;;;"2018-04-17_GDMO_01_CDB-Q1_392475.jpg";"2";"60% d'algues vertes, 40% algues brunes, cuvettes et blocs moyens";9;4; +392521;"2018-09-10-GDMO-CDB-001";5;;;;;;;;;"2018-09-10_GDMO_01_CDB-Q5_392587.jpg";"2";"10% algues vertes, 60% algues brunes, blocs moyens, cuvettes et sable";6;23; +392521;"2018-09-10-GDMO-CDB-001";4;;;;;;;;;"2018-09-10_GDMO_01_CDB-Q4_392586.jpg";"2";"principalement vert, pas mal de cuvettes";0;12; +392521;"2018-09-10-GDMO-CDB-001";3;;;;;;;;;"2018-09-10_GDMO_01_CDB-Q3_392585.jpg";"2";"principalement vert, quelques cuvettes";0;11; +392521;"2018-09-10-GDMO-CDB-001";2;;;;;;;;;"2018-09-10_GDMO_01_CDB-Q2_392584.jpg";"2";"principalement vert, quelques cuvettes";1;14; +392521;"2018-09-10-GDMO-CDB-001";1;;;;;;;;;"2018-09-10_GDMO_01_CDB-Q1_392583.jpg";"2";"40% algues vertes, 40% algues brunes, blocs moyens et souvent blocs mixtes (algues vertes et brunes en même temps)";13;6; +406229;"2019-04-19-GDMO-CDB-001";5;;;;;;;;;"2019-04-19_GDMO_01_CDB-Q5_406370.JPG";"2";"petits blocs sur sable grossier, beaucoup de petits blocs verts";11;7; +406229;"2019-04-19-GDMO-CDB-001";4;;;;;;;;;"2019-04-19_GDMO_01_CDB-Q4_406369.JPG";"2";"petits blocs sur sable grossier, beaucoup d'algues";4;3; +406229;"2019-04-19-GDMO-CDB-001";3;;;;;;;;;"2019-04-19_GDMO_01_CDB-Q3_406368.JPG";"2";"petits blocs sur sable grossier";5;6; +406229;"2019-04-19-GDMO-CDB-001";2;;;;;;;;;"2019-04-19_GDMO_01_CDB-Q2_406367.JPG";"2";"petits blocs sur sable grossier";3;5; +406229;"2019-04-19-GDMO-CDB-001";1;;;;;;;;;"2019-04-19_GDMO_01_CDB-Q1_406366.JPG";"2";"blocs sur sable grossier";7;4; +424249;"2019-09-30-GDMO-CDB-001";3;;;;;;;;;"2019-09-30_GDMO_01_CDB-Q3_424316.jpg";"2";"Très petits blocs, importante partie ensablée, plutôt des algues rouges et vertes";0;5; +424249;"2019-09-30-GDMO-CDB-001";2;;;;;;;;;"2019-09-30_GDMO_01_CDB-Q2_424315.jpg";"2";"Très petits blocs, importante partie ensablée, plutôt des algues rouges et vertes";0;6; +424249;"2019-09-30-GDMO-CDB-001";1;;;;;;;;;"2019-09-30_GDMO_01_CDB-Q1_424314.jpg";"2";"Dominance d'algues brunes, peu de sable";7;0; +424249;"2019-09-30-GDMO-CDB-001";5;;;;;;;;;"2019-09-30_GDMO_01_CDB-Q5_424318.jpg";"2";"petits blocs, cuvettes, algues vertes, huitres mortes";4;11; +424249;"2019-09-30-GDMO-CDB-001";4;;;;;;;;;"2019-09-30_GDMO_01_CDB-Q4_424317.jpg";"2";"Très petits blocs, importante partie ensablée, plutôt des algues rouges et vertes, sable très grossier";0;9; +444815;"2020-05-07-GDMO-CDB-001";5;;;;;;;;;"2020-05-07_GDMO_01_CDB-Q5_444881.JPG";"2";"petits blocs, cuvettes, sable grossier";38;6; +444815;"2020-05-07-GDMO-CDB-001";4;;;;;;;;;"2020-05-07_GDMO_01_CDB-Q4_444880.JPG";"2";"petits blocs, cuvettes, sable grossier, présence de quelques sargasses";35;7; +444815;"2020-05-07-GDMO-CDB-001";2;;;;;;;;;"2020-05-07_GDMO_01_CDB-Q2_444878.JPG";"2";"petits blocs, cuvettes, sable grossier";39;8; +444815;"2020-05-07-GDMO-CDB-001";1;;;;;;;;;"2020-05-07_GDMO_01_CDB-Q1_444877.JPG";"2";"blocs moyens et petits cailloutis, beaucoup de fucus serratus";20;8; +444815;"2020-05-07-GDMO-CDB-001";3;;;;;;;;;"2020-05-07_GDMO_01_CDB-Q3_444879.JPG";"2";"petits blocs, cuvettes, sable grossier, présence de quelques sargasses";12;4; +455567;"2020-09-19-GDMO-CDB-001";5;;;;;;;;;"2020-09-19_GDMO_01_CDB-Q5_455633.JPG";"Pas de stratification";"peu de blocs, assez petits";0;9; +455567;"2020-09-19-GDMO-CDB-001";4;;;;;;;;;"2020-09-19_GDMO_01_CDB-Q4_455632.JPG";"Pas de stratification";"cuvettes, petits blocs verts";0;14; +455567;"2020-09-19-GDMO-CDB-001";3;;;;;;;;;"2020-09-19_GDMO_01_CDB-Q3_455631.JPG";"Pas de stratification";"cuvette, petits blocs verts";0;11; +455567;"2020-09-19-GDMO-CDB-001";2;;;;;;;;;"2020-09-19_GDMO_01_CDB-Q2_455630.JPG";"Pas de stratification";"peu de blocs, verts";0;15; +455567;"2020-09-19-GDMO-CDB-001";1;;;;;;;;;"2020-09-19_GDMO_01_CDB-Q1_455629.JPG";"Pas de stratification";"algues brunes, petits blocs";9;3; +477617;"2020-10-16-PNMI-CDB-001";4;48.0435;4.872067;;;;;;;"2020-10-16_PNMI_07_CDB-Q4_477637.jpg";"Pas de stratification";"CHAMPS DE BLOCS ABSENT +Présence de petites pierres +fucale et AR présente";0;0; +477617;"2020-10-16-PNMI-CDB-001";3;48.043467;4.872333;;;;;;;"2020-10-16_PNMI_07_CDB-Q3_477636.jpg";"2";"Himenthalia +chondrus +Ulves +Asparogopsis +Algues rouges sp +";45;12; +477617;"2020-10-16-PNMI-CDB-001";2;48.042617;4.873833;;;;;;;"2020-10-16_PNMI_07_CDB-Q2_477635.jpg";"4";"Laminaria digitata +Ulva sp. +Chondrus crispus +";44;12; +477617;"2020-10-16-PNMI-CDB-001";1;48.043417;4.87325;;;;;;;"2020-10-16_PNMI_07_CDB-Q1_477634.jpg";"2";"Chondrus crispus +Himentalia +Ulva +Bifurcaria bifurcata +";76;10; +477617;"2020-10-16-PNMI-CDB-001";5;48.043583;4.87015;;;;;;;"2020-10-16_PNMI_07_CDB-Q5_477638.jpg";"4";"L. sacchoriza (un peu) +Chondrus crispus +Palmaria palmata +ulva sp +Laurencia pinatifida";76;15; +477640;"2020-10-16-PNMI-CDB-002";5;49.033817;4.834617;;;;;;;"2020-10-16_PNMI_07_CDB-Q5_477653.jpg";"4";"80% L. digitata +10% himanthalia +Chondus +fucus";20;0; +477640;"2020-10-16-PNMI-CDB-002";4;48.033717;4.83445;;;;;;;"2020-10-16_PNMI_07_CDB-Q4_477652.jpg";"4";"50% fucus +10% L. digitata +20% Chondrus crispus +20% autres";81;; +477640;"2020-10-16-PNMI-CDB-002";3;48.039667;4.834233;;;;;;;"2020-10-16_PNMI_07_CDB-Q3_477651.jpg";"4";"60% L. digitata +30% himenthale +10% fucus +Chondus crispus";81;0; +477640;"2020-10-16-PNMI-CDB-002";2;48.040167;4.8341;;;;;;;"2020-10-16_PNMI_07_CDB-Q2_477650.jpg";"4";"80% L. digitata +10% Himenthalia el +fucus sp. +Chondrus ";27;0; +477640;"2020-10-16-PNMI-CDB-002";1;48.033683;4.834617;;;;;;;"2020-10-16_PNMI_07_CDB-Q1_477649.jpg";"3";"50% chondrus +20% Himenthalia +20% fucus +10% divers AR";79;0; +477714;"2020-03-12-PNMI-CDB-001";5;48.043583;4.87015;;;;;;;;"2";"Chondrus/ Algues rouges";37;7; +477714;"2020-03-12-PNMI-CDB-001";4;48.0435;4.872067;;;;;;;;"4";"Laminaria digitata/Chondrus crispus";22;0; +477714;"2020-03-12-PNMI-CDB-001";3;48.043467;4.872333;;;;;;;;"2";"Laurencia pinatifida/ Algues rouges";42;6; +477714;"2020-03-12-PNMI-CDB-001";1;48.043417;4.87325;;;;;;;;"4";"Chondrus crispus/ Laminaria Digitata/ ulves/ Hymenthales";37;6; +477714;"2020-03-12-PNMI-CDB-001";2;48.042617;4.873833;;;;;;;;"3";"Chondrus crispus/ Fucus seratus/ Himenthales";18;2; +477733;"2019-03-22-PNMI-CDB-001";4;48.03388;4.83445;;;;;;;;"4";"Pas de photos +laminaire";66;2; +477733;"2019-03-22-PNMI-CDB-001";5;48.03368;4.83403;;;;;;;"2019-03-22_PNMI_07_CDB-Q5_477739.JPG";"4";"Laminaires ; pas plus d'informations";51;0; +477733;"2019-03-22-PNMI-CDB-001";3;48.033733;4.8335;;;;;;;"2019-03-22_PNMI_07_CDB-Q3_477737.JPG";"4";"Laminaires- pas d'autres remarques";47;0; +477733;"2019-03-22-PNMI-CDB-001";1;48.0382;4.83462;;;;;;;;"4";"Laminaires- pas de photo disponible";61;0; +477733;"2019-03-22-PNMI-CDB-001";2;48.03403;4.8341;;;;;;;"2019-03-22_PNMI_07_CDB-Q2_477736.JPG";"4";"Laminaires ; pas plus de description donnée";78;0; +477743;"2019-10-29-PNMI-CDB-001";5;48.03368;4.83403;;;;;;;"2019-10-29_PNMI_07_CDB-Q5_477749.jpg";"4";"Hymenthalles, laminaires, Chondrus, Dulse";25;0; +477743;"2019-10-29-PNMI-CDB-001";1;48.0382;4.83462;;;;;;;"2019-10-29_PNMI_07_CDB-Q1_477745.jpg";"4";"Hymenthalles, laminaires, Chondrus, Dulse";51;0; +477743;"2019-10-29-PNMI-CDB-001";4;48.03388;4.83445;;;;;;;;"4";"Pas de photo +Hymenthalles, laminaires, Chondrus, Dulse";57;0; +477743;"2019-10-29-PNMI-CDB-001";3;48.033733;4.8335;;;;;;;"2019-10-29_PNMI_07_CDB-Q3_477747.jpg";"4";"Hymenthalles, laminaires, Chondrus, Dulse";21;0; +477743;"2019-10-29-PNMI-CDB-001";2;;;;;;;;;;"Pas de stratification";"NB : Le point Q2 n'a pas été réalisé ; probablement marnage trop important pour y avoir accès? +";;; +477750;"2019-10-29-PNMI-CDB-002";1;48.043417;4.87325;;;;;;;;"2";"Chondrus crispus, Hymenthalles, laminaires";28;6; +477750;"2019-10-29-PNMI-CDB-002";5;48.043583;4.87015;;;;;;;;"3";"Lauwrancia, himenthalles, bifurcaria, dychota +Pas de photo";35;3; +477750;"2019-10-29-PNMI-CDB-002";4;48.0435;4.872067;;;;;;;;"3";"Chondrus, fucales, himenthales";20;4; +477750;"2019-10-29-PNMI-CDB-002";3;48.043467;4.872333;;;;;;;;"Pas de stratification";"Chondrus hymenthalle";43;3; +477750;"2019-10-29-PNMI-CDB-002";2;48.042617;4.873833;;;;;;;;"4";"LAminaire chondrus +Pas de photo exploitable";19;0; +477757;"2019-03-22-PNMI-CDB-002";5;48.043833;4.870533;;;;;;;"2019-03-22_PNMI_07_CDB-Q5_477763.JPG";"3";"Pas d'information voir photo";35;14; +477757;"2019-03-22-PNMI-CDB-002";4;48.0435;4.872067;;;;;;;"2019-03-22_PNMI_07_CDB-Q4_477762.JPG";"Pas de stratification";"Chondrus/ himenthallia/ Fucus/ palmaria";53;8; +477757;"2019-03-22-PNMI-CDB-002";3;48.043467;4.872333;;;;;;;"2019-03-22_PNMI_07_CDB-Q3_477761.JPG";"3";"Chondrus +Himenthalles";65;3; +477757;"2019-03-22-PNMI-CDB-002";2;48.042967;4.8734;;;;;;;"2019-03-22_PNMI_07_CDB-Q2_477760.JPG";"Pas de stratification";"Chondrus Himenthalle";22;0; +477757;"2019-03-22-PNMI-CDB-002";1;48.043417;4.87325;;;;;;;"2019-03-22_PNMI_07_CDB-Q1_477759.JPG";"Pas de stratification";"Algues rouges Chondrus et autres +himenthalle";79;3; +478379;"2021-03-31-PNMI-CDB-001";5;;;;;;;;;;"Pas de stratification";"blocs moyens avec bloc sur bloc +substrat caillouteux +algues rouges (pioka)";91;2; +478379;"2021-03-31-PNMI-CDB-001";2;;;;;;;;;;"Pas de stratification";"blocs éparses, moyen à petits blocs +Laminaires, algues rouges (pioka)";65;7; +478379;"2021-03-31-PNMI-CDB-001";3;;;;;;;;;"P3310001.JPG";"Pas de stratification";"blocs moyens sur cailloux et débris coquilliers +Algues rouges et himanthales";76;5; +478379;"2021-03-31-PNMI-CDB-001";1;;;;;;;;;"2021-03-31_PNMI_02_CDB-Q1_478381.JPG";"Pas de stratification";"Algues rouges et laminaires. Quelques algues vertes. Sable grossier et cailloux. Blocs moyens à gros.";81;2; +478379;"2021-03-31-PNMI-CDB-001";4;;;;;;;;;"2021-03-31_PNMI_02_CDB-Q4_478384.JPG";"Pas de stratification";"gros blocs +laminaires et algues rouges +dans l'eau (ruisseau) +sur sable grossier coquillier";41;8; +483642;"2021-05-28-GDMO-CDB-001";3;;;;;;;;;"2021-05-28_GDMO_01_CDB-Q3_483646.JPG";"2";"peu de blocs, sur substrat sablo-vaseux, 50% verts, 50% bruns";4;3; +483642;"2021-05-28-GDMO-CDB-001";2;;;;;;;;;"2021-05-28_GDMO_01_CDB-Q2_483645.JPG";"2";"petits blocs, 35% verts, 65% bruns, quelques cuvettes";6;4; +483642;"2021-05-28-GDMO-CDB-001";1;;;;;;;;;"2021-05-28_GDMO_01_CDB-Q1_483644.JPG";"2";"petits blocs, quelques cuvettes, sable";7;0; +483642;"2021-05-28-GDMO-CDB-001";5;;;;;;;;;"2021-05-28_GDMO_01_CDB-Q5_483648.JPG";"2";"petits blocs, 60% bruns et 40% verts, une dizaine de lièvres de mer ";9;0; +483642;"2021-05-28-GDMO-CDB-001";4;;;;;;;;;"2021-05-28_GDMO_01_CDB-Q4_483647.JPG";"2";"petits blocs, 70% verts";6;6; +494799;"2020-10-16-PNMI-CDB-003";5;;;;;;;;;;"Pas de stratification";"Le champ de blocs de Quéménes présente une stratification assez homogène. Une première strate est constituée de « blocs sur petits blocs, cailloutis et sédiments grossiers » recouverts de Bifurcaria bifurcata et d’Himanthalia elongata en couverture assez ";122;2; +494799;"2020-10-16-PNMI-CDB-003";4;;;;;;;;;;"Pas de stratification";"Le champ de blocs de Quéménes présente une stratification assez homogène. Une première strate est constituée de « blocs sur petits blocs, cailloutis et sédiments grossiers » recouverts de Bifurcaria bifurcata et d’Himanthalia elongata en couverture assez ";64;1; +494799;"2020-10-16-PNMI-CDB-003";3;;;;;;;;;;"Pas de stratification";"Le champ de blocs de Quéménes présente une stratification assez homogène. Une première strate est constituée de « blocs sur petits blocs, cailloutis et sédiments grossiers » recouverts de Bifurcaria bifurcata et d’Himanthalia elongata en couverture assez ";77;5; +494799;"2020-10-16-PNMI-CDB-003";2;;;;;;;;;;"Pas de stratification";"Le champ de blocs de Quéménes présente une stratification assez homogène. Une première strate est constituée de « blocs sur petits blocs, cailloutis et sédiments grossiers » recouverts de Bifurcaria bifurcata et d’Himanthalia elongata en couverture assez ";115;6; +494799;"2020-10-16-PNMI-CDB-003";1;;;;;;;;;;"Pas de stratification";"Le champ de blocs de Quéménes présente une stratification assez homogène. Une première strate est constituée de « blocs sur petits blocs, cailloutis et sédiments grossiers » recouverts de Bifurcaria bifurcata et d’Himanthalia elongata en couverture assez ";80;2; +504857;"2021-09-09-GDMO-CDB-001";2;;;;;;;;;"2021-09-09_GDMO_01_CDB-Q2_504860.JPG";"2";"présence d'eau, blocs moyens, débris coquillers";2;2; +504857;"2021-09-09-GDMO-CDB-001";5;;;;;;;;;"2021-09-09_GDMO_01_CDB-Q5_504861.JPG";"2";"blocs moyens, débris coquillers + +(données changées à postériori du à la confusion entre de l'algue rouge décolorée et des algues vertes)";12;0; +504857;"2021-09-09-GDMO-CDB-001";1;;;;;;;;;"2021-09-09_GDMO_01_CDB-Q1_504859.JPG";"2";"petits blocs, débris coquillers, fucus";4;7; +504964;"2017-12-06-GDMO-CDB-001";5;;;;;;;;;"2017-12-06_GDMO_01_CDB-Q5_505030.JPG";"Pas de stratification";"blocs moyens, dominance algues brunes, quelques vertes";22;7; +504964;"2017-12-06-GDMO-CDB-001";1;;;;;;;;;"DSCN8824.JPG";"Pas de stratification";"Petits blocs sur sable coquiller grossier. ";6;6; +504964;"2017-12-06-GDMO-CDB-001";4;;;;;;;;;"DSCN8831.JPG";"Pas de stratification";"Dominance algues vertes";4;6; +504964;"2017-12-06-GDMO-CDB-001";3;;;;;;;;;"DSCN8830.JPG";"Pas de stratification";"Petits blocs sur sable grossier. Algues vertes, roche nue";4;5; +504964;"2017-12-06-GDMO-CDB-001";2;;;;;;;;;"2017-12-06_GDMO_01_CDB-Q2_505027.JPG";"Pas de stratification";"Petits blocs sur sable coquiller, quelques gros blocs non déplaçables. Dominance algues brunes/rouges. Quelques algues vertes.";4;12; +505032;"2017-03-14-GDMO-CDB-001";3;;;;;;;;;"2017-03-14_GDMO_01_CDB-Q3_505146.JPG";"1";"sable grossier, très peu de blocs plus grand qu'une feuille A4, dominance brun/rouge, quelques algues vertes";4;3; +505032;"2017-03-14-GDMO-CDB-001";4;;;;;;;;;"2017-03-14_GDMO_01_CDB-Q4_505147.JPG";"1";"blocs de taille moyenne, sable grossier, algues rouges, ""algues voleuse d'huitre""";3;6; +505032;"2017-03-14-GDMO-CDB-001";1;;;;;;;;;"2017-03-14_GDMO_01_CDB-Q1_505144.JPG";"2";"Débris coquillers de moules, sable grossier, petits blocs bruns";3;1; +505032;"2017-03-14-GDMO-CDB-001";5;;;;;;;;;"2017-03-14_GDMO_01_CDB-Q5_505148.JPG";"2";"algues brunes/rouges, sable grossier, blocs de taille moyenne";6;2; +505032;"2017-03-14-GDMO-CDB-001";2;;;;;;;;;"2017-03-14_GDMO_01_CDB-Q2_505145.JPG";"1";"sable grossier, dominance brun/rouge, quelques algues vertes, petits blocs";7;1; diff --git a/tools/indic_ivr/test-data/champbloc_ivr_val.csv b/tools/indic_ivr/test-data/champbloc_ivr_val.csv new file mode 100644 index 000000000..0098a68d7 --- /dev/null +++ b/tools/indic_ivr/test-data/champbloc_ivr_val.csv @@ -0,0 +1,718 @@ +"ID Fiche";"id";"Numero Quadrat";"GPS1 Latitude";"GPS1 Longitude";"GPS2 Latitude";"GPS2 Longitude";"GPS3 Latitude";"GPS3 Longitude";"GPS4 Latitude";"GPS4 Longitude";"Numero Photo";"Numéro strate";"Description Strate";"Nb Blocs Non Retournes";"Nb Blocs Retournes" +373462;"BDD_IVR&QECB_Locmariaquer_20141010_VImport.xlsx";5;47.548053;-2.951211;;;;;;;"Strate2_Q5.JPG";"2";"Zone de blocs mobiles sur platier rocher qui s'ensable, dominés par le Fucus serratus, blocs de taille moyenne.";38;11; +373462;"BDD_IVR&QECB_Locmariaquer_20141010_VImport.xlsx";1;47.54815;-2.951336;;;;;;;"Strate2_Q1.JPG";"2";"Zone de blocs mobiles sur platier rocher qui s'ensable, dominés par le Fucus serratus, blocs de taille moyenne.";24;20; +373462;"BDD_IVR&QECB_Locmariaquer_20141010_VImport.xlsx";4;47.547729;-2.950839;;;;;;;"Strate1_Q4.JPG";"1";"Zone de blocs mobiles de taille moyenne sur sable grossier, cailloux et cailloutis. Blocs dont les faces supérieures sont dominées par des algues vertes opportunistes, des algues rouges en ménage + nombreux patchs de roche nue";19;4; +373462;"BDD_IVR&QECB_Locmariaquer_20141010_VImport.xlsx";2;47.547829;-2.950839;;;;;;;"Strate1_Q2.JPG";"1";"Zone de blocs mobiles de taille moyenne sur sable grossier, cailloux et cailloutis. Blocs dont les faces supérieures sont dominées par des algues vertes opportunistes, des algues rouges en ménage + nombreux patchs de roche nue";24;7; +373462;"BDD_IVR&QECB_Locmariaquer_20141010_VImport.xlsx";3;47.547866;-2.950759;;;;;;;"Strate1_Q3.JPG";"1";"Zone de blocs mobiles de taille moyenne sur sable grossier, cailloux et cailloutis. Blocs dont les faces supérieures sont dominées par des algues vertes opportunistes, des algues rouges en ménage + nombreux patchs de roche nue";20;10; +373529;"BDD_IVR&QECB_Locmariaquer_22032015_VImport.xlsx";5;47.54803;-2.95118;;;;;;;"DSCN1175.JPG";"2";"Zone de blocs mobiles sur platier rocher qui s'ensable, dominés par le Fucus serratus, blocs de taille moyenne.";39;6; +373529;"BDD_IVR&QECB_Locmariaquer_22032015_VImport.xlsx";4;47.5477;-2.95079;;;;;;;"DSCN1167.JPG";"1";"Zone de blocs mobiles de taille moyenne sur sable grossier, cailloux et cailloutis. Blocs dont les faces supérieures sont dominées par des algues vertes opportunistes, des algues rouges en ménage + nombreux patchs de roche nue";23;11; +373529;"BDD_IVR&QECB_Locmariaquer_22032015_VImport.xlsx";3;47.54779;-2.95073;;;;;;;"DSCN1160.JPG";"1";"Zone de blocs mobiles de taille moyenne sur sable grossier, cailloux et cailloutis. Blocs dont les faces supérieures sont dominées par des algues vertes opportunistes, des algues rouges en ménage + nombreux patchs de roche nue";18;10; +373529;"BDD_IVR&QECB_Locmariaquer_22032015_VImport.xlsx";2;47.54783;-2.95083;;;;;;;"DSCN1149.JPG";"1";"Zone de blocs mobiles de taille moyenne sur sable grossier, cailloux et cailloutis. Blocs dont les faces supérieures sont dominées par des algues vertes opportunistes, des algues rouges en ménage + nombreux patchs de roche nue";16;31; +373529;"BDD_IVR&QECB_Locmariaquer_22032015_VImport.xlsx";1;47.54812;-2.95133;;;;;;;"DSCN1141.JPG";"2";"Zone de blocs mobiles sur platier rocher qui s'ensable, dominés par le Fucus serratus, blocs de taille moyenne.";34;5; +373596;"BDD_IVR&QECB_Locmariaquer_20151030_VImport.xlsx";5;47.54803;-2.95118;;;;;;;"DSCN2997.JPG";"2";"Zone de blocs mobiles sur platier rocher qui s'ensable, dominés par le Fucus serratus, blocs de taille moyenne.";27;5; +373596;"BDD_IVR&QECB_Locmariaquer_20151030_VImport.xlsx";4;47.5477;-2.95079;;;;;;;"DSCN2991.JPG";"1";"Zone de blocs mobiles de taille moyenne sur sable grossier, cailloux et cailloutis. Blocs dont les faces supérieures sont dominées par des algues vertes opportunistes, des algues rouges en ménage + nombreux patchs de roche nue";20;13; +373596;"BDD_IVR&QECB_Locmariaquer_20151030_VImport.xlsx";3;47.54779;-2.95073;;;;;;;"DSCN2985.JPG";"1";"Zone de blocs mobiles de taille moyenne sur sable grossier, cailloux et cailloutis. Blocs dont les faces supérieures sont dominées par des algues vertes opportunistes, des algues rouges en ménage + nombreux patchs de roche nue";26;9; +373596;"BDD_IVR&QECB_Locmariaquer_20151030_VImport.xlsx";2;47.54783;-2.95083;;;;;;;"DSCN2979.JPG";"1";"Zone de blocs mobiles de taille moyenne sur sable grossier, cailloux et cailloutis. Blocs dont les faces supérieures sont dominées par des algues vertes opportunistes, des algues rouges en ménage + nombreux patchs de roche nue";10;8; +373596;"BDD_IVR&QECB_Locmariaquer_20151030_VImport.xlsx";1;47.54812;-2.95133;;;;;;;"DSCN2970.JPG";"2";"Zone de blocs mobiles sur platier rocher qui s'ensable, dominés par le Fucus serratus, blocs de taille moyenne.";24;15; +373663;"BDD_IVR&QECB_Locmariaquer_20160309_VImport.xlsx";5;47.54803;-2.95118;;;;;;;"DSCN3297.JPG";"2";"Blocs mobiles de taille moyenne sur platier rocheux qui s'ensable, dominés par le Fucus serratu";6;1; +373663;"BDD_IVR&QECB_Locmariaquer_20160309_VImport.xlsx";4;47.5477;-2.95079;;;;;;;"DSCN3199.JPG";"1";"Blocs mobiles de taille moyenne sur sable grossier, cailloux et cailloutis. Blocs dont les faces supérieures sont dominées par des algues vertes opportunistes, des algues rouges en mélange et de nombreuses zones de roche nue";4;1; +373663;"BDD_IVR&QECB_Locmariaquer_20160309_VImport.xlsx";3;47.54779;-2.95073;;;;;;;"DSCN3193.JPG";"1";"Blocs mobiles de taille moyenne sur sable grossier, cailloux et cailloutis. Blocs dont les faces supérieures sont dominées par des algues vertes opportunistes, des algues rouges en mélange et de nombreuses zones de roche nue";5;1; +373663;"BDD_IVR&QECB_Locmariaquer_20160309_VImport.xlsx";2;47.54783;-2.95083;;;;;;;"DSCN3189.JPG";"1";"Blocs mobiles de taille moyenne sur sable grossier, cailloux et cailloutis. Blocs dont les faces supérieures sont dominées par des algues vertes opportunistes, des algues rouges en mélange et de nombreuses zones de roche nue";8;2; +373663;"BDD_IVR&QECB_Locmariaquer_20160309_VImport.xlsx";1;47.54812;-2.95133;;;;;;;"DSCN3214.JPG";"2";"Blocs mobiles sur platier rocheux qui s'ensable, dominés par du Fucus serratus, blocs de taille moyenne.";10;2; +373730;"BDD_IVR&QECB_Locmariaquer_20160917_VImport.xlsx";5;47.54803;-2.95118;;;;;;;"DSCN5108.JPG";"2";"Blocs de taille moyenne à élevée, assez ensablés, sur sable grossier et débris coquilliers. Blocs dominés par du Fucus seratus, des algues rouges et des Ulves";3;0; +373730;"BDD_IVR&QECB_Locmariaquer_20160917_VImport.xlsx";4;47.5477;-2.95079;;;;;;;"DSCN5098.JPG";"1";"Blocs de petite taille sur sable grossier et débris coquillers, dominés à la fois par des algues vertes opportunistes et des algues brunes. Présence forte de sédiment sablo-vaseux à la surface des blocs";3;0; +373730;"BDD_IVR&QECB_Locmariaquer_20160917_VImport.xlsx";3;47.54779;-2.95073;;;;;;;"DSCN5089.JPG";"1";"Blocs de petite taille sur sable grossier et débris coquilliers, mais également sur du platier rocheux. Blocs dominés par des algues vertes opportunistes et des algues rouges (50/50)";3;3; +373730;"BDD_IVR&QECB_Locmariaquer_20160917_VImport.xlsx";2;47.54783;-2.95083;;;;;;;"DSCN5081.JPG";"1";"Blocs de taille moyenne et petits sur sable grossier et débris coquilliers, dominés par des algues rouges et vertes opportunistes (50/50) + quelques algues brunes";6;3; +373730;"BDD_IVR&QECB_Locmariaquer_20160917_VImport.xlsx";1;47.54812;-2.95133;;;;;;;"DSCN5075.JPG";"2";"Blocs de taille moyenne et petits sur sable grossier et débris coquilliers, dominés par du Fucus serratus, des algues rouges en mélange et quelques algues vertes opportunistes";16;2; +373797;"BDD_IVR_Locmariaquer_20161020_VImport.xlsx";5;47.54803;-2.95118;;;;;;;"DSC08969.JPG";"2";"Rares blocs mobiles et nombreuses zones de platier rocheux qui s'ensable. Nombreux débris coquilliers. Blocs dominés par le Fucus serratus";2;0; +373797;"BDD_IVR_Locmariaquer_20161020_VImport.xlsx";4;47.5477;-2.95079;;;;;;;"DSC08968.JPG";"1";"Petits blocs sur débris coquilliers et sable grossier, dominés par les algues rouges; présence de quelques algues brunes et vertes opportunistes";8;3; +373797;"BDD_IVR_Locmariaquer_20161020_VImport.xlsx";3;47.54779;-2.95073;;;;;;;"DSC08967.JPG";"1";"Blocs de petite taille sur platier rocheux, sable grossier et débris coquilliers. Blocs dominés par les algues brunes et vertes opportunistes";3;3; +373797;"BDD_IVR_Locmariaquer_20161020_VImport.xlsx";2;47.54783;-2.95083;;;;;;;"DSC08966.JPG";"1";"Blocs de taille moyenne à petite sur sable grossier et débrits coquilliers. Dominance d'algues brunes et rouges en mélange et quelques algues vertes opportunistes";8;4; +373797;"BDD_IVR_Locmariaquer_20161020_VImport.xlsx";1;47.54812;-2.95133;;;;;;;"DSC08964.JPG";"2";"Blocs de taille moyenne à petite, sur débris coquilliers et sable grossier. Blocs dominés par le Fucus serratus et quelques algues vertes opportunistes";9;2; +373804;"BDD_IVR&QECB_BegLann_20141106_VImport.xlsx";5;47.50112;-2.73803;;;;;;;;"1";"Blocs mobiles de taille petite à moyenne, dominés par les algues vertes opportunistes, de la roche nue, des algues rouges en mélange, quelques patchs de Fucus serratus Blocs sur sédiments grossiers, cailloutis, petits blocs, sable vaseux.";20;5; +373804;"BDD_IVR&QECB_BegLann_20141106_VImport.xlsx";4;47.50053;-2.73799;;;;;;;;"1";"Blocs mobiles de taille petite à moyenne, dominés par les algues vertes opportunistes, de la roche nue, des algues rouges en mélange, quelques patchs de Fucus serratus Blocs sur sédiments grossiers, cailloutis, petits blocs, sable vaseux.";15;3; +373804;"BDD_IVR&QECB_BegLann_20141106_VImport.xlsx";3;47.50063;-2.73841;;;;;;;;"2";"Blocs mobiles sur platier rocheux qui s'ensable progressivement en limite basse, de taille petite à moyenne, dominés par mélange d'algues rouges et larges zones de roche nue. ";20;35; +373804;"BDD_IVR&QECB_BegLann_20141106_VImport.xlsx";2;47.50067;-2.7382;;;;;;;"Q2.JPG";"2";"Blocs mobiles sur platier rocheux qui s'ensable progressivement en limite basse, de taille petite à moyenne, dominés par mélange d'algues rouges et larges zones de roche nue. ";17;8; +373804;"BDD_IVR&QECB_BegLann_20141106_VImport.xlsx";1;47.50053;-2.73847;;;;;;;"Q1.JPG";"2";"Blocs mobiles sur platier rocheux qui s'ensable progressivement en limite basse, de taille petite à moyenne, dominés par mélange d'algues rouges et larges zones de roche nue. ";26;10; +373871;"BDD_IVR&QECB_BegLann_20150323_VImport.xlsx";5;47.50112;-2.73803;;;;;;;"DSCN1197.JPG";"2";"Zone de blocs mobiles sur platier rocher qui s'ensable, dominés par le Fucus serratus, blocs de taille moyenne.";52;6; +373871;"BDD_IVR&QECB_BegLann_20150323_VImport.xlsx";4;47.50053;-2.73799;;;;;;;"DSCN1204.JPG";"1";"Zone de blocs mobiles de taille moyenne sur sable grossier, cailloux et cailloutis. Blocs dont les faces supérieures sont dominées par des algues vertes opportunistes, des algues rouges en ménage + nombreux patchs de roche nue";12;7; +373871;"BDD_IVR&QECB_BegLann_20150323_VImport.xlsx";3;47.50063;-2.73841;;;;;;;"DSCN1183.JPG";"1";"Zone de blocs mobiles de taille moyenne sur sable grossier, cailloux et cailloutis. Blocs dont les faces supérieures sont dominées par des algues vertes opportunistes, des algues rouges en ménage + nombreux patchs de roche nue";37;5; +373871;"BDD_IVR&QECB_BegLann_20150323_VImport.xlsx";2;47.50067;-2.7382;;;;;;;"DSCN1190.JPG";"1";"Zone de blocs mobiles de taille moyenne sur sable grossier, cailloux et cailloutis. Blocs dont les faces supérieures sont dominées par des algues vertes opportunistes, des algues rouges en ménage + nombreux patchs de roche nue";26;55; +373871;"BDD_IVR&QECB_BegLann_20150323_VImport.xlsx";1;47.50053;-2.73847;;;;;;;"DSCN1210.JPG";"2";"Zone de blocs mobiles sur platier rocher qui s'ensable, dominés par le Fucus serratus, blocs de taille moyenne.";43;6; +373938;"BDD_IVR&QECB_BegLann_20151031_VImport.xlsx";5;47.50112;-2.73803;;;;;;;"DSCN3025.JPG";"2";"Zone de blocs mobiles sur platier rocher qui s'ensable, dominés par le Fucus serratus, blocs de taille moyenne.";10;14; +373938;"BDD_IVR&QECB_BegLann_20151031_VImport.xlsx";4;47.50053;-2.73799;;;;;;;"DSCN3019.JPG";"1";"Zone de blocs mobiles de taille moyenne sur sable grossier, cailloux et cailloutis. Blocs dont les faces supérieures sont dominées par des algues vertes opportunistes, des algues rouges en ménage + nombreux patchs de roche nue";6;12; +373938;"BDD_IVR&QECB_BegLann_20151031_VImport.xlsx";3;47.50063;-2.73841;;;;;;;"DSCN3004.JPG";"1";"Zone de blocs mobiles de taille moyenne sur sable grossier, cailloux et cailloutis. Blocs dont les faces supérieures sont dominées par des algues vertes opportunistes, des algues rouges en ménage + nombreux patchs de roche nue";8;38; +373938;"BDD_IVR&QECB_BegLann_20151031_VImport.xlsx";2;47.50067;-2.7382;;;;;;;"DSCN3011.JPG";"1";"Zone de blocs mobiles de taille moyenne sur sable grossier, cailloux et cailloutis. Blocs dont les faces supérieures sont dominées par des algues vertes opportunistes, des algues rouges en ménage + nombreux patchs de roche nue";6;21; +373938;"BDD_IVR&QECB_BegLann_20151031_VImport.xlsx";1;47.50053;-2.73847;;;;;;;"DSCN3031.JPG";"2";"Zone de blocs mobiles sur platier rocher qui s'ensable, dominés par le Fucus serratus, blocs de taille moyenne.";3;14; +374005;"BDD_IVR&QECB_BegLann_20160310_VImport.xlsx";5;47.50112;-2.73803;;;;;;;"DSCN3250.JPG";"2";"Blocs mobiles de taille moyenne sur platier rocheux qui s'ensable, dominés par du Fucus serratus";15;9; +374005;"BDD_IVR&QECB_BegLann_20160310_VImport.xlsx";4;47.50053;-2.73799;;;;;;;"DSCN3242.JPG";"1";"Blocs mobiles de taille moyenne sur sable grossier, cailloux et cailloutis. Faces supérieures dominées par des algues vertes opportunistes, des algues rouges en mélange et de nombreuses zones de roche nue";25;26; +374005;"BDD_IVR&QECB_BegLann_20160310_VImport.xlsx";3;47.50063;-2.73841;;;;;;;"DSCN3221.JPG";"1";"Blocs mobiles de taille moyenne sur sable grossier, cailloux et cailloutis. Faces supérieures dominées par des algues vertes opportunistes, des algues rouges en mélange et de nombreuses zones de roche nue";15;25; +374005;"BDD_IVR&QECB_BegLann_20160310_VImport.xlsx";2;47.50067;-2.7382;;;;;;;"DSCN3228.JPG";"1";"Blocs mobiles de taille moyenne sur sable grossier, cailloux et cailloutis. Faces supérieures dominées par des algues vertes opportunistes, des algues rouges en mélange et de nombreuses zones de roche nue";7;17; +374005;"BDD_IVR&QECB_BegLann_20160310_VImport.xlsx";1;47.50053;-2.73847;;;;;;;"DSCN3257.JPG";"2";"Blocs mobiles de taille moyenne sur platier rocheux qui s'ensable, dominés par du Fucus serratus";7;11; +374072;"BDD_IVR&QECB_BegLann_20160918_VImport.xlsx";5;47.50112;-2.73803;;;;;;;"Q5.JPG";"2";"Blocs de taille moyenne sur substrat sablo-vaseux, dominés par de la roche nue, quelques algues rouges dressées et algues vertes opportunistes";5;16; +374072;"BDD_IVR&QECB_BegLann_20160918_VImport.xlsx";4;47.50053;-2.73799;;;;;;;"Q4.JPG";"1";"Blocs mobiles sur substrat sablo-vaseux et petits cailloux, dominés majoritairement pas les algues rouges dressées et les algues vertes opportunistes ";9;16; +374072;"BDD_IVR&QECB_BegLann_20160918_VImport.xlsx";3;47.50063;-2.73841;;;;;;;"Q3.JPG";"1";"Blocs de taille moyenne sur sable grossier et débris coquillers, présence de nombreuses nasses. Mélange d'algues vertes opportunistes et d'algues rouges dressées";1;20; +374072;"BDD_IVR&QECB_BegLann_20160918_VImport.xlsx";2;47.50067;-2.7382;;;;;;;"Q2.JPG";"1";"Blocs de taille moyenne à élevée sur substrat sablo-vaseux et débris coquillers, dominés par de la roche nue et des algues rouges en mélange. Quelques algues vertes opportunistes, Fucus serratus et rares Laminaires";3;13; +374072;"BDD_IVR&QECB_BegLann_20160918_VImport.xlsx";1;47.50053;-2.73847;;;;;;;"Q1.JPG";"2";"Blocs de taille moyenne à élevée sur substrat sablo-vaseux, dominés par de la roche nue, quelques algues rouges dressées et du Fucus serratus + des algues vertes; Nombreuses nasses";1;5; +374139;"BDD_IVR&QECB_PFour_20150319&20_VImport.xlsx";5;;;;;;;;;;"Pas de stratification";;;; +374139;"BDD_IVR&QECB_PFour_20150319&20_VImport.xlsx";4;;;;;;;;;;"Pas de stratification";;;; +374139;"BDD_IVR&QECB_PFour_20150319&20_VImport.xlsx";3;47.2957222;-2.6311667;;;;;;;"P1230459.JPG";"1";"Strate composée de blocs hétérogènes, dominance de gros blocs, sur sédiments grossiers et petits blocs. Certains bloc sont recouverts de paquets de moules, Quadrat positionné en milieu d'estran";6;21; +374139;"BDD_IVR&QECB_PFour_20150319&20_VImport.xlsx";2;47.2957778;-2.6338056;;;;;;;"P1230483.JPG";"1";"Strate composée de blocs hétérogènes, moyens à gros, sur sédiments grossiers, galets et petits blocs. Les faces supérieures sont dominées par les algues vertes opportunistes. Présence de f.serratus, Quadrat positionné en haut d'estran";6;9; +374139;"BDD_IVR&QECB_PFour_20150319&20_VImport.xlsx";1;47.2950556;-2.63425;;;;;;;"DSC00108.JPG";"2";"Strate composée de blocs hétérogènes, dominance de gros blocs, sur sédiments grossiers et petits blocs. Les faces supérieures sont dominées par les algues vertes opportunistes. Quadrat positionné en bas d'estran";5;10; +374194;"BDD_IVR&QECB_PFour_20150420&21_VImport.xlsx";5;47.2955556;-2.6343056;;;;;;;"DSC01725.JPG";"1";"Blocs de taille hétérogène (petits à moyens sur moulière et sédiment vaseux, les algues vertes (ulves et cladophora) prédominent largement le quadrat.";0;6; +374194;"BDD_IVR&QECB_PFour_20150420&21_VImport.xlsx";4;47.2959167;-2.63325;;;;;;;"DSC01681.JPG";"1";"Blocs de taille hétérogène (petits à moyens) sur petits galets et sables grossiers coquillers. Blocs recouverts d'un mélange d'algues brunes et rouges. Des laminaires sont présentes.";26;0; +374194;"BDD_IVR&QECB_PFour_20150420&21_VImport.xlsx";3;47.2957222;-2.6311667;;;;;;;"DSC00516.JPG";"1";"Blocs de taille petite à moyenne, dominés par algues rouges, brunes et vertes opportunites sur sédiments grossiers et petits blocs. Certains bloc sont recouverts de paquets de moules, Quadrat positionné en milieu d'estran. ";16;10; +374194;"BDD_IVR&QECB_PFour_20150420&21_VImport.xlsx";2;47.2957778;-2.6338056;;;;;;;"P1230507.JPG";"1";"Blocs de taille petite à moyenne, dominés par algues rouges, brunes et vertes opportunites sur sédiments grossiers, galets et petits blocs. Les faces supérieures sont dominées par les algues vertes opportunistes. Présence de f.serratus, Quadrat position";18;2; +374194;"BDD_IVR&QECB_PFour_20150420&21_VImport.xlsx";1;47.2950556;-2.63425;;;;;;;"DSCF3208.JPG";"2";"Blocs de taille élevée, dominés par algues rouges, brunes et vertes opportunites sur sédiments grossiers, galets et petits blocs. Les faces supérieures sont dominées par un mélange d'algues vertes opportunistes et non opportunistes et de fucus. Quadra";21;0; +374261;"BDD_IVR_PlateauFour_20150803_VImport.xlsx";5;47.2955556;-2.6343056;;;;;;;"P1230565.JPG";"1";"Strate composée de blocs hétérogènes (moyens à gros) sur moulière et sédiment vaseux+coquillier, les algues vertes (ulves et cladophora) prédominent largement le quadrat à 80%.";21;0; +374261;"BDD_IVR_PlateauFour_20150803_VImport.xlsx";4;47.2959167;-2.63325;;;;;;;"P1230559.JPG";"1";"Strate composée de blocs hétérogènes de taille moyenne sur petits galets et sables grossiers coquillers. Blocs recouverts à 60% d'un mélange d'algues brunes -rouges (2/3) et verte (1/3). Des laminaires sont présentes (env.30%).";38;0; +374261;"BDD_IVR_PlateauFour_20150803_VImport.xlsx";3;47.2957222;-2.6311667;;;;;;;"P1230578.JPG";"1";"Strate composée de blocs hétérogènesde taille moyenne, sur sédiments grossiers coquillier et petits blocs. Recouverts à 60% d'algues vertes et 40% de laminaires. Quadrat positionné en milieu d'estran";59;0; +374261;"BDD_IVR_PlateauFour_20150803_VImport.xlsx";2;47.2957778;-2.6338056;;;;;;;"DSCN0207.JPG";"1";"Strate composée de blocs hétérogènes, équivalence de petits et moyens blocs, certains sont épais, sur sédiments grossiers coquillier et petits blocs. Présence de f.serratus, Quadrat positionné en haut d'estran";26;8; +374261;"BDD_IVR_PlateauFour_20150803_VImport.xlsx";1;47.2950556;-2.63425;;;;;;;"DSCN0215.JPG";"2";"Strate composée de blocs hétérogènes, dominance de gros blocs plutôt agrégés, sur sédiments grossiers coquillier et petits blocs. Les faces supérieures sont dominées du fucus. Quadrat positionné en bas d'estran";12;9; +374268;"BDD_IVR&QECB_PFour_20150928_VImport.xlsx";5;47.2955556;-2.6343056;;;;;;;"DSC03528.JPG";"1";"Strate composée de blocs hétérogènes (moyens à gros) sur moulière et sédiment vaseux. Blocs peu mobiles. Les algues vertes (ulves et cladophora) prédominent largement le quadrat. Qq algues brunes.";0;6; +374268;"BDD_IVR&QECB_PFour_20150928_VImport.xlsx";4;47.2959167;-2.63325;;;;;;;"DSCN3873.JPG";"1";"Strate composée de blocs hétérogènes (petits à moyens) sur petits galets et sables grossiers coquillers. Blocs recouverts d'un mélange d'algues brunes et rouges. Des laminaires sont présentes.";18;5; +374268;"BDD_IVR&QECB_PFour_20150928_VImport.xlsx";3;47.2957222;-2.6311667;;;;;;;;"1";"Strate composée de blocs hétérogènes, dominance de gros blocs, sur sédiments grossiers et petits blocs. Certains bloc sont recouverts de paquets de moules, Quadrat positionné en milieu d'estran";;; +374268;"BDD_IVR&QECB_PFour_20150928_VImport.xlsx";2;47.2957778;-2.6338056;;;;;;;"DSCN3920.JPG";"1";"Strate composée de blocs hétérogènes, moyens à gros, sur sédiments grossiers, galets et petits blocs. Les faces supérieures sont dominées par un mélange d'algues vertes opportunistes (50%) et d'algues brunes (50%). Présence de f.serratus, Quadrat positio";9;10; +374268;"BDD_IVR&QECB_PFour_20150928_VImport.xlsx";1;47.2950556;-2.63425;;;;;;;"DSC03504.JPG";"2";"Strate composée de blocs hétérogènes (petits à moyens), dominance de gros blocs, sur sédiments grossiers et petits blocs. Les faces supérieures sont dominées par un mélange d'algues brunes et quelques ulves (+qq laminaires). Quadrat positionné en bas d'e";12;5; +374335;"BDD_IVR&QECB_PFour_20160407&08_VImport.xlsx";5;47.2955556;-2.6343056;;;;;;;"P1400729.JPG";"1";"Strate composée de blocs hétérogènes (moyens à gros). Blocs peu mobiles. Les algues vertes (ulves et cladophora) dominent largement le quadrat";6;13; +374335;"BDD_IVR&QECB_PFour_20160407&08_VImport.xlsx";4;47.2959167;-2.63325;;;;;;;"DSC_0006.JPG";"1";"Strate composée de blocs hétérogènes (petits à moyens) sur petits galets et sables grossiers coquillers. Blocs recouverts d'algues rouges surtout, quelques blocs avec des algues vertes";14;4; +374335;"BDD_IVR&QECB_PFour_20160407&08_VImport.xlsx";3;47.2957222;-2.6311667;;;;;;;"DSC_0039.JPG";"1";"Strate composée de blocs homogènes, dominance de blocs moyens, sur sédiments grossiers et petits blocs. Mélange de blocs recouverts d'algues rouges (++), d'algues vertes non opportunistes (++), et blocs nus.";7;2; +374335;"BDD_IVR&QECB_PFour_20160407&08_VImport.xlsx";2;47.2957778;-2.6338056;;;;;;;"P1230674.JPG";"1";"Strate composée de blocs hétérogènes, moyens à gros, sur sédiments grossiers, galets et petits blocs. Les faces supérieures sont dominées par les algues rouges . Présence éparse d'algues vertes et d'ulves. Quadrat positionné en haut d'estran";11;6; +374335;"BDD_IVR&QECB_PFour_20160407&08_VImport.xlsx";1;47.2950556;-2.63425;;;;;;;"P1230629.JPG";"2";"Strate composée de blocs hétérogènes (petits à moyens), sur sédiments grossiers et petits blocs. Les faces supérieures sont dominées les algues brunes et rouges, quelques ulves. Quadrat positionné en bas d'estran";12;6; +374402;"BDD_IVR&QECB_PFour_20160606&07_VImport.xlsx";5;47.2955556;-2.6343056;;;;;;;"DSC_0211.JPG";"1";"Blocs moyens, dominance d'algues vertes. Substrat de sable grossier coquillier et petits blocs";3;20; +374402;"BDD_IVR&QECB_PFour_20160606&07_VImport.xlsx";4;47.2959167;-2.63325;;;;;;;"P1230759.JPG";"1";"Blocs éparses dansle quadrat; Hétérogènes petit à moyen-gros ; Présence algues vertes et rouges surtout";23;6; +374402;"BDD_IVR&QECB_PFour_20160606&07_VImport.xlsx";3;47.2957222;-2.6311667;;;;;;;"DSC_0146.JPG";"1";"Blocs petits à moyens, dominace algues vertes avec quelques blocs dominance algues rouges";14;10; +374402;"BDD_IVR&QECB_PFour_20160606&07_VImport.xlsx";2;47.2957778;-2.6338056;;;;;;;"DSC_0178.JPG";"1";"Blocs petits à moyens, mélange algues rouges et vertes opportunistes, présence de Fucus serratus; substrat sableux/platier rocheux";8;8; +374402;"BDD_IVR&QECB_PFour_20160606&07_VImport.xlsx";1;47.2950556;-2.63425;;;;;;;"P1230704.JPG";"2";"Blocs hétérogènes, petits à gros. Mélange d'algues vertes opportunistes et d'algues rouges";12;4; +374469;"BDD_IVR&QECB_PFour_20160919&20_VImport.xlsx";5;47.2955556;-2.6343056;;;;;;;"DSC_0325.JPG";"1";"Blocs moyens, dominance d'algues vertes. Substrat de sable grossier coquillier et petits blocs";1;17; +374469;"BDD_IVR&QECB_PFour_20160919&20_VImport.xlsx";4;47.2959167;-2.63325;;;;;;;"DSC_0366.JPG";"1";"Blocs éparses dans le quadrat ; Hétérogènes petit à moyen-gros ; Présence algues vertes (40%) et rouges (60%) + présence laminaires. Nessain de moules très présent";7;1; +374469;"BDD_IVR&QECB_PFour_20160919&20_VImport.xlsx";3;47.2957222;-2.6311667;;;;;;;"P1230883.JPG";"1";"Blocs petits à moyens, dominance algues vertes avec quelques blocs dominance algues rouges. Peu de gros blocs mobiles, surtout petits blocs. Nessain de moules très présent";0;21; +374469;"BDD_IVR&QECB_PFour_20160919&20_VImport.xlsx";2;47.2957778;-2.6338056;;;;;;;"P1230920.JPG";"1";"Blocs petits à moyens, mélange algues rouges et vertes opportunistes, présence fucus; substrat sableux/platier rocheux";8;9; +374469;"BDD_IVR&QECB_PFour_20160919&20_VImport.xlsx";1;47.2950556;-2.63425;;;;;;;"P1230847.JPG";"2";"Blocs hétérogènes, petits à gros. Mélange d'algues vertes opportunistes (20%) et d'algues rouges (80%). Sable coquilier. Peu de blocs mobiles";6;4; +374536;"BDD_IVR&QECB_Groinducou_20141008_VImport.xlsx";5;46.3416111;-1.47575;;;;;;;;"4";"Strate située en limite haute du champ de blocs, composée de petits blocs dont les faces supérieures sont dominées par les balanes et la roche nue. Substrat sous-jacent : petits blocs, cailloux et présence de platier rocheux +";40;11; +374536;"BDD_IVR&QECB_Groinducou_20141008_VImport.xlsx";4;46.3410278;-1.4760556;;;;;;;;"3";"Strate composée de blocs de petite taille dont les faces supérieures sont dominées par la présence de moulières. Substrat sous-jacent : petits blocs, quelques zones de platier rocheux et sédiments grossiers +";11;35; +374536;"BDD_IVR&QECB_Groinducou_20141008_VImport.xlsx";3;46.3404722;-1.4766944;;;;;;;;"2";"Strate située dans la limite basse à moyenne du champ de blocs, dominée par des blocs de taille élevée, présence de nombreuses algues vertes opportunistes sur les faces supérieures. Substrat sous-jacent : petits blocs, cailloux et présence de platier roc";81;17; +374536;"BDD_IVR&QECB_Groinducou_20141008_VImport.xlsx";2;46.34075;-1.4754444;;;;;;;;"2";"Strate située dans la limite basse à moyenne du champ de blocs, dominée par des blocs de taille élevée, présence de nombreuses algues vertes opportunistes sur les faces supérieures. Substrat sous-jacent : petits blocs, cailloux et présence de platier roc";0;69; +374536;"BDD_IVR&QECB_Groinducou_20141008_VImport.xlsx";1;46.3410556;-1.4773611;;;;;;;;"1";"Strate située dans la limite inférieure du champ de blocs, orientation ouest, présentée en arc de cercle, composée de blocs de taille très diverse avec une dominance de moulières sur les faces supérieures. Substrat sous-jacent composé de petits blocs et ";48;19; +374599;"BDD_IVR&QECB_GroinduCou_20150319&20_VImport.xlsx";5;46.3416111;-1.47575;;;;;;;"Q5.JPG";"4";"Strate composée de blocs de taille moyenne à élevée, sur sédiments grossiers et petits blocs, dont les faces supérieures. ";13;18; +374599;"BDD_IVR&QECB_GroinduCou_20150319&20_VImport.xlsx";4;46.3410278;-1.4760556;;;;;;;"Q4.JPG";"3";"Strate composée de blocs de taille moyenne à élevée, sur sédiments grossiers et petits blocs, dont les faces supérieures. ";17;30; +374599;"BDD_IVR&QECB_GroinduCou_20150319&20_VImport.xlsx";3;46.3404722;-1.4766944;;;;;;;"Q3.JPG";"2";"Strate composée de blocs de taille moyenne à élevée, sur sédiments grossiers et petits blocs, dont les faces supérieures. ";21;4; +374599;"BDD_IVR&QECB_GroinduCou_20150319&20_VImport.xlsx";2;46.34075;-1.4754444;;;;;;;"Q2.JPG";"2";"Strate composée de blocs de taille moyenne à élevée, sur sédiments grossiers et petits blocs, dont les faces supérieures. ";14;16; +374599;"BDD_IVR&QECB_GroinduCou_20150319&20_VImport.xlsx";1;46.3410556;-1.4773611;;;;;;;"Q1.JPG";"1";"Strate composée de blocs de taille moyenne à élevée, sur sédiments grossiers et petits blocs, dont les faces supérieures. ";23;27; +374666;"BDD_IVR&QECB_GroinduCou_20150929_VImport.xlsx";5;46.3416111;-1.47575;;;;;;;"Tranche_01102015_Q5.JPG";"4";;17;40; +374666;"BDD_IVR&QECB_GroinduCou_20150929_VImport.xlsx";4;46.3410278;-1.4760556;;;;;;;"Tranche_02102015_Q4.JPG";"3";;20;20; +374666;"BDD_IVR&QECB_GroinduCou_20150929_VImport.xlsx";3;46.3404722;-1.4766944;;;;;;;"Tranche_01102015_Q3.JPG";"2";;13;49; +374666;"BDD_IVR&QECB_GroinduCou_20150929_VImport.xlsx";2;46.34075;-1.4754444;;;;;;;"Tranche_01102015_Q2.JPG";"2";;11;24; +374666;"BDD_IVR&QECB_GroinduCou_20150929_VImport.xlsx";1;46.3410556;-1.4773611;;;;;;;"Tranche_30092015_Q1.JPG";"1";;45;79; +374733;"BDD_IVR&QECB_GrouinDuCou_20160406_VImport.xlsx";5;46.3416111;-1.47575;;;;;;;"P1010378.JPG";"4";"Blocs mobiles dominés par des balanes et/ou de la roche nue";9;22; +374733;"BDD_IVR&QECB_GrouinDuCou_20160406_VImport.xlsx";4;46.3410278;-1.4760556;;;;;;;"P1010369.JPG";"3";"Blocs mobiles dominés par les moulières";11;11; +374733;"BDD_IVR&QECB_GrouinDuCou_20160406_VImport.xlsx";3;46.3404722;-1.4766944;;;;;;;"DSCF7947.JPG";"2";"Blocs mobiles dominés par des algues vertes opportunistes et/ou de la roche nue";24;18; +374733;"BDD_IVR&QECB_GrouinDuCou_20160406_VImport.xlsx";2;46.34075;-1.4754444;;;;;;;"P1010376.JPG";"2";"Blocs mobiles dominés par des algues vertes opportunistes et/ou de la roche nue";2;23; +374733;"BDD_IVR&QECB_GrouinDuCou_20160406_VImport.xlsx";1;46.3410556;-1.4773611;;;;;;;"P1010363.JPG";"1";"Blocs mobiles dominés par les moulières";11;38; +374800;"BDD_IVR&QECB_GrouinDuCou_20161016&18_VImport.xlsx";5;46.3416111;-1.47575;;;;;;;"Q5.JPG";"4";"Blocs de taille moyenne à élevée";9;5; +374800;"BDD_IVR&QECB_GrouinDuCou_20161016&18_VImport.xlsx";4;46.3410278;-1.4760556;;;;;;;"Q4.JPG";"3";"Blocs de taille petite à moyenne, présence d'algues vertes opportunistes";2;12; +374800;"BDD_IVR&QECB_GrouinDuCou_20161016&18_VImport.xlsx";3;46.3404722;-1.4766944;;;;;;;"Q3.JPG";"2";"Blocs de taille moyenne à élevée, avec présence d'algues vertes opportunistes";1;25; +374800;"BDD_IVR&QECB_GrouinDuCou_20161016&18_VImport.xlsx";2;46.34075;-1.4754444;;;;;;;"Q2.JPG";"2";"Blocs de taille petite à moyenne, dominés par des algues vertes opportunistes";2;20; +374800;"BDD_IVR&QECB_GrouinDuCou_20161016&18_VImport.xlsx";1;46.3410556;-1.4773611;;;;;;;"Q1.JPG";"1";"Blocs de taille petite à moyenne,dont les faces supérieures sont dominées par des algues vertes opportunistes";1;26; +374867;"BDD_IVR&QECB_Ensembert_20151029&30_VImport.xlsx";5;33.9417993;10.0386781;;;;;;;;"Pas de stratification";;30;4; +374867;"BDD_IVR&QECB_Ensembert_20151029&30_VImport.xlsx";4;33.941837;10.0384992;;;;;;;;"2";;36;7; +374867;"BDD_IVR&QECB_Ensembert_20151029&30_VImport.xlsx";3;33.942117;10.0383335;;;;;;;;"2";;27;5; +374867;"BDD_IVR&QECB_Ensembert_20151029&30_VImport.xlsx";2;33.9414627;10.0383245;;;;;;;;"2";;50;10; +374867;"BDD_IVR&QECB_Ensembert_20151029&30_VImport.xlsx";1;33.9428247;10.037875;;;;;;;;"1";;26;6; +374934;"BDD_IVR&QECB_Ensembert_20160310_VImport.xlsx";5;33.9417993;10.0386781;;;;;;;"DSC02677.JPG";"2";"Blocs de petite taille sur gravier, galets, rares algues rouges";37;20; +374934;"BDD_IVR&QECB_Ensembert_20160310_VImport.xlsx";4;33.941837;10.0384992;;;;;;;"DSC06094.JPG";"2";"Blocs de petite taille sur sable grossier, cailloux/cailloutis et galets, dominés par quelques algues brunes";25;20; +374934;"BDD_IVR&QECB_Ensembert_20160310_VImport.xlsx";3;33.942117;10.0383335;;;;;;;"DSC02668.JPG";"2";"Blocs de petite taille sur gravier et galets, dominés par les algues rouges";19;36; +374934;"BDD_IVR&QECB_Ensembert_20160310_VImport.xlsx";2;33.9414627;10.0383245;;;;;;;"DSC06101.JPG";"2";"Blocs sur galets et cailloux/cailloutis, dominés par quelques algues rouges";17;11; +374934;"BDD_IVR&QECB_Ensembert_20160310_VImport.xlsx";1;33.9428247;10.037875;;;;;;;"DSC06108.JPG";"1";"Blocs sur galets et cailloux de taille moyenne, dominés par quelques algues rouges et vertes opportunistes";15;9; +375001;"BDD_IVR&QECB_Ensembert_20161018_VImport.xlsx";5;33.9418504;10.038701;;;;;;;"DSC06342.JPG";"2";"Blocs de petite taille dominés par les algues rouges et vertes opportunistes";31;0; +375001;"BDD_IVR&QECB_Ensembert_20161018_VImport.xlsx";4;33.9415846;10.0384542;;;;;;;"DSC06353.JPG";"2";"Blocs de petite taille dominés par les algues rouges et vertes opportunistes";35;0; +375001;"BDD_IVR&QECB_Ensembert_20161018_VImport.xlsx";3;33.9421266;10.038329;;;;;;;"DSC02985.JPG";"2";"Blocs de taille variable mais majoritairement petits, dominés à la fois par des algues vertes opportunistes et des algues rouges";22;8; +375001;"BDD_IVR&QECB_Ensembert_20161018_VImport.xlsx";2;33.9414631;10.0382968;;;;;;;"DSC02986.JPG";"2";"Blocs sur blocs de petite taille, dominés par les algues vertes opportunistes et quelques algues rouges";20;0; +375001;"BDD_IVR&QECB_Ensembert_20161018_VImport.xlsx";1;33.9428177;10.0379141;;;;;;;"DSC02992.JPG";"1";"Blocs de petite taille dominés par algues rouges et vertes opportunistes";11;5; +375068;"BDD_IVR_LaBree_08092014_VImport.xlsx";5;46.027437;-1.346964;;;;;;;"Strate 5 (9).JPG";"5";"Strate située en limite basse (découvre à partit d'un coefficient de 90 et plus). Strate peu large et longue (tombant de banche en escalier), blocs moyens. Fréquentation pour la pêche à pied: ++";30;14; +375068;"BDD_IVR_LaBree_08092014_VImport.xlsx";4;46.028231;-1.345188;;;;;;;"Strate 4 (5).JPG";"4";"Strate qui constitue la limite basse du champ de blocs (découvre à partir d'un coefficient de 95 et plus). Strate peu large et longue (tombant de banche en escalier), gros blocs récents. Fréquentation pour la pêche à pied: +";19;5; +375068;"BDD_IVR_LaBree_08092014_VImport.xlsx";3;46.027453;-1.344091;;;;;;;"Strate 3 (1).JPG";"3";"Strate située en limite basse du champ de blocs (découvre à partit d'un coefficient de 90 et plus). Forte densité de blocs de toutes tailles, blocs assez gros, flaques, strate étendue sur le bas d'estran. Fréquentation pour la pêche à pied: ++";24;13; +375068;"BDD_IVR_LaBree_08092014_VImport.xlsx";2;46.02652;-1.346628;;;;;;;"Strate 2 (13).JPG";"2";"Strate située en limite haute du champ de blocs (découvre à partir d'un coeff icient de 85 et plus). Blocs dispersés, présence de blocs assez gros, substrat composé d'ulves en dépôts, eau et gravier. Fréquentation pour la pêche à pied: -";24;9; +375068;"BDD_IVR_LaBree_08092014_VImport.xlsx";1;46.025006;-1.347422;;;;;;;"Strate 1 (15).JPG";"1";"Strate située en limite haute (découvre à partir de oefficient de 80 et plus). Blocs moyens et dispersés, substrat ulves et eau, légèrement ensablé. Fréquentation pour la pêche à pied: -";22;11; +375075;"BDD_IVR_LaBree_11092014_Vmport.xlsx";5;46.027437;-1.346964;;;;;;;"Strate 5 (5).JPG";"5";"Strate située en limite basse (découvre à partit d'un coefficient de 90 et plus). Strate peu large et longue (tombant de banche en escalier), blocs moyens. Fréquentation pour la pêche à pied: ++";31;9; +375075;"BDD_IVR_LaBree_11092014_Vmport.xlsx";4;46.028231;-1.345188;;;;;;;"Strate 4 (7).JPG";"4";"Strate qui constitue la limite basse du champ de blocs (découvre à partir d'un coefficient de 95 et plus). Strate peu large et longue (tombant de banche en escalier), gros blocs récents. Fréquentation pour la pêche à pied: +";42;7; +375075;"BDD_IVR_LaBree_11092014_Vmport.xlsx";3;46.027453;-1.344091;;;;;;;"Strate 3 (6).JPG";"3";"Strate située en limite basse du champ de blocs (découvre à partit d'un coefficient de 90 et plus). Forte densité de blocs de toutes tailles, blocs assez gros, flaques, strate étendue sur le bas d'estran. Fréquentation pour la pêche à pied: ++";32;5; +375075;"BDD_IVR_LaBree_11092014_Vmport.xlsx";2;46.02652;-1.346628;;;;;;;"Strate 2 (8).JPG";"2";"Strate située en limite haute du champ de blocs (découvre à partir d'un coeff icient de 85 et plus). Blocs dispersés, présence de blocs assez gros, substrat composé d'ulves en dépôts, eau et gravier. Fréquentation pour la pêche à pied: -";21;10; +375075;"BDD_IVR_LaBree_11092014_Vmport.xlsx";1;46.025006;-1.347422;;;;;;;"Strate 1 (5).JPG";"1";"Strate située en limite haute (découvre à partir de oefficient de 80 et plus). Blocs moyens et dispersés, substrat ulves et eau, légèrement ensablé. Fréquentation pour la pêche à pied: -";26;6; +375082;"BDD_IVR&QECB_LaBree_oct2014_VImport.xlsx";5;46.027483;-1.34703;;;;;;;"oct14-BreeQ5.JPG";"2";"Blocs sur blocs avec cailloutis.";26;7; +375082;"BDD_IVR&QECB_LaBree_oct2014_VImport.xlsx";4;46.02823;-1.34573;;;;;;;;"1";"Blocs sur blocs. +Bonne proportion. +Blocs sur banche. +Sable.";12;8; +375082;"BDD_IVR&QECB_LaBree_oct2014_VImport.xlsx";3;46.027283;-1.34385;;;;;;;;"3";"Blocs petits à gros. +Matrice caillouteuse et petits blocs. +Sédiment avec débris coquilliers. +Zone très pêchée avec présence d'eau.";33;5; +375082;"BDD_IVR&QECB_LaBree_oct2014_VImport.xlsx";2;46.0266;-1.346567;;;;;;;"oct14-BreeQ2.JPG";"4";"Blocs sur blocs. +Cailloutis. +Petits blocs en général.";25;1; +375082;"BDD_IVR&QECB_LaBree_oct2014_VImport.xlsx";1;46.025283;-1.347267;;;;;;;"oct14-BreeQ1.JPG";"1";"Matrice cailloux type calé. +Blocs petits à moyens. +Zone peu pêchée, point de passage.";4;4; +375149;"BDD_QECB&IVR_LaBree_23032015_VImport.xlsx";5;46.027483;-1.34703;;;;;;;;"2";"Blocs sur blocs avec cailloutis.";23;3; +375149;"BDD_QECB&IVR_LaBree_23032015_VImport.xlsx";4;46.02823;-1.34573;;;;;;;;"1";"Blocs sur blocs. +Bonne proportion. +Blocs sur banche. +Sable.";8;4; +375149;"BDD_QECB&IVR_LaBree_23032015_VImport.xlsx";3;46.027283;-1.34385;;;;;;;;"3";"Blocs petits à gros. +Matrice caillouteuse et petits blocs. +Sédiment avec débris coquilliers. +Zone très pêchée avec présence d'eau.";10;3; +375149;"BDD_QECB&IVR_LaBree_23032015_VImport.xlsx";2;46.0266;-1.346567;;;;;;;;"4";"Blocs épars gros à petits sur une matrice de petits blocs. Dominance de blocs rouges.";13;6; +375149;"BDD_QECB&IVR_LaBree_23032015_VImport.xlsx";1;46.025283;-1.347267;;;;;;;;"1";"Matrice cailloux type calé. +Blocs petits à moyens. +Zone peu pêchée, point de passage.";7;3; +375216;"BDD_QECB&IVR_LaBree_26&27102015_VImport.xlsx";5;46.027483;-1.34703;;;;;;;"La Bree Oct 2015 Q5 (2).JPG";"2";"Petits blocs sur petits blocs (juste qq moyens), peu exondé";27;1; +375216;"BDD_QECB&IVR_LaBree_26&27102015_VImport.xlsx";4;46.02823;-1.34573;;;;;;;"La Bree Oct 2015 Q4 (2).JPG";"1";"Quadrat en bord de faille, nombreux blocs fixes de tailles variables";10;7; +375216;"BDD_QECB&IVR_LaBree_26&27102015_VImport.xlsx";3;46.027283;-1.34385;;;;;;;"La Bree Oct 2015 Q3 (2).JPG";"3";"Blocs sur blocs de tailles moyennes à petites, majoritairement exondé";12;1; +375216;"BDD_QECB&IVR_LaBree_26&27102015_VImport.xlsx";2;46.0266;-1.346567;;;;;;;"La Bree Oct 2015 Q2 (3).JPG";"4";"Blocs de tailles moyennes à petites sur platier et galets, exondé pour la moitié du quadrat";10;1; +375216;"BDD_QECB&IVR_LaBree_26&27102015_VImport.xlsx";1;46.025283;-1.347267;;;;;;;"La Bree Oct 2015 Q1 (2).JPG";"1";"Blocs de petites tailles sur platier et galets, majoritairement exondé";8;1; +375283;"BDD_IVR&QECB_La Bree_20160406_VImport.xlsx";5;46.027483;-1.34703;;;;;;;"La brée Avr16 Q5 vue générale.JPG";"2";"Blocs de toutes tailles";22;6; +375283;"BDD_IVR&QECB_La Bree_20160406_VImport.xlsx";4;46.02823;-1.34573;;;;;;;"La brée Avr16 Q4 Vue Générale.JPG";"1";"Gros blocs ensablés sur petits blocs";6;9; +375283;"BDD_IVR&QECB_La Bree_20160406_VImport.xlsx";3;46.027283;-1.34385;;;;;;;"La brée Avr16 Q3 Vue Générale.JPG";"3";"Blocs sur banche et galets";16;0; +375283;"BDD_IVR&QECB_La Bree_20160406_VImport.xlsx";2;46.0266;-1.346567;;;;;;;"La brée Avr16 Q2 Vue Générale.JPG";"4";"Blocs mobiles de taille petite à moyenne sur cailloux, galets";10;1; +375283;"BDD_IVR&QECB_La Bree_20160406_VImport.xlsx";1;46.025283;-1.347267;;;;;;;"La brée Avr16 Q1 vue générale.JPG";"1";"Petits blocs sur cailloutis, épars, dominés par de la roche nue et des algues rouges";6;0; +375350;"BDD_IVR&QECB_LaBree_20161016_VImport.xlsx";3;46.027283;-1.34385;;;;;;;"La brée Oct16 Q3 vuegen.JPG";"3";"Blocs sur cailloutis et galets";16;2; +375350;"BDD_IVR&QECB_LaBree_20161016_VImport.xlsx";5;46.027483;-1.34703;;;;;;;"La brée Oct16 Q5 vuegen.JPG";"2";"Blocs de taille petite à moyenne sur platier rocheux";20;0; +375350;"BDD_IVR&QECB_LaBree_20161016_VImport.xlsx";4;46.02823;-1.34573;;;;;;;"La brée Oct16 Q4 vueGen.JPG";"1";"Blocs de taille très élevée sur petits blocs et sable grossier";19;2; +375350;"BDD_IVR&QECB_LaBree_20161016_VImport.xlsx";2;46.0266;-1.346567;;;;;;;"La brée Oct16 Q2 vuegen.JPG";"4";"Blocs épars sur cailloutis et galets";9;3; +375350;"BDD_IVR&QECB_LaBree_20161016_VImport.xlsx";1;46.028415;-1.3467067;;;;;;;"La brée Oct16 Q1 vuegen.JPG";"1";"Blocs sur petits blocs et sable grossier";14;3; +375417;"BDD_IVR&QECB_PerAnt_20141009_VImport.xlsx";1;46.051354;-1.383463;;;;;;;"Vue_gen_Q1.JPG";"2";"Zone de blocs mobiles située en limite haute du champ de blocs. Les blocs sont éparses, de taille moyenne à petite, sur sédiments grossiers, galets et petits blocs. Les faces supérieures sont majoritairement nues mais quelques blocs présentent une couver";43;2; +375417;"BDD_IVR&QECB_PerAnt_20141009_VImport.xlsx";2;46.051781;-1.383774;;;;;;;"Vue_gen_Q2_1.JPG";"1";"Zone de blocs mobiles située en limite haute du champ de blocs. Présence de nombreuses mares. Les blocs sont éparses, de taille moyenne à petite, sur blocs principalement. Les faces supérieures sont majoritairement nues, quelques blocs présentent une cou";44;3; +375417;"BDD_IVR&QECB_PerAnt_20141009_VImport.xlsx";5;46.050313;-1.3836;;;;;;;"Vue_gen_Q5.JPG";"4";"Zone de blocs mobiles située dans la zone moyenne du champ de blocs, à son extrémité sud. Blocs de taille moyenne à petite, relativement éparses, sur sédiments grossiers, cailloutis et petits blocs. Faces supérieures dominées par des algues rouges en mél";17;5; +375417;"BDD_IVR&QECB_PerAnt_20141009_VImport.xlsx";3;46.051254;-1.38408;;;;;;;"Vue_gen_Q3.JPG";"2";"Zone de blocs mobiles située dans la zone moyenne du champ de blocs, très fréquentée. Les blocs sont de taille élevée à très élevée, sur sédiments grossiers et cailloutis.Faces supérieures dominées par des mosaïques d'espèces : patchs d'algues rouges et ";46;2; +375417;"BDD_IVR&QECB_PerAnt_20141009_VImport.xlsx";4;46.051018;-1.38473;;;;;;;"Vue_gen_Q4_2.JPG";"5";"Zone de blocs mobiles située en limite basse du champ de blocs. Les blocs sont de taille moyenne à élevée, sur petits blocs, cailloux et sable grossier. Faces supérieures dominées par des algues rouges en mélange pour les blocs non retournés et présence ";43;6; +375484;"BDD_QECB&IVR_PerAntiochat_18042015_VImport.xlsx";5;46.050313;-1.3836;;;;;;;;"4";"Blocs avec flaques permanentes";12;1; +375484;"BDD_QECB&IVR_PerAntiochat_18042015_VImport.xlsx";4;46.051018;-1.38473;;;;;;;"Perret Avr15 Q4 vue générale 1.JPG";"5";"Blocs de taille plus importante sur blocs, platiers et galets";32;12; +375484;"BDD_QECB&IVR_PerAntiochat_18042015_VImport.xlsx";3;46.051254;-1.38408;;;;;;;"Perret Avr15 Q3 Vue Global 2.JPG";"2";"Blocs soudés par les hermelles, nombreuses flaques";17;3; +375484;"BDD_QECB&IVR_PerAntiochat_18042015_VImport.xlsx";2;46.051781;-1.383774;;;;;;;;"1";;25;1; +375484;"BDD_QECB&IVR_PerAntiochat_18042015_VImport.xlsx";1;46.051354;-1.383463;;;;;;;"Perret Avr15 Q1 vue générale 1.JPG";"2";"Blocs sur platier";22;2; +375551;"BDD_QECB&IVR_PerAntiochat_20151027&29_VImport.xlsx";5;46.050313;-1.3836;;;;;;;"Antiochat Oct 2015 Q5 (3).JPG";"4";"Blocs petits à moyens, banches et galets. Canopée alguale importante.";27;0; +375551;"BDD_QECB&IVR_PerAntiochat_20151027&29_VImport.xlsx";4;46.051018;-1.38473;;;;;;;"Antiochat Oct 2015 Q4.JPG";"5";"Blocs sur cailloux et sable grossier";21;3; +375551;"BDD_QECB&IVR_PerAntiochat_20151027&29_VImport.xlsx";3;46.051254;-1.38408;;;;;;;"Antiochat Oct 2015 Q3 (1).JPG";"2";"Gros et moyens blocs sur platier ensablé par sable grossier";21;4; +375551;"BDD_QECB&IVR_PerAntiochat_20151027&29_VImport.xlsx";2;46.051781;-1.383774;;;;;;;"Antiochat Oct 2015 Q2.JPG";"1";"Blocs sur blocs et blocs sur banche à 1/4 du quadrat";43;4; +375551;"BDD_QECB&IVR_PerAntiochat_20151027&29_VImport.xlsx";1;46.051354;-1.383463;;;;;;;"Antiochat Oct 2015 Q1 (2).JPG";"2";"Blocs moyens et gros, dans flaques et sur banches. Galets. Présence d'hermelles et algues rouges";36;1; +375618;"BDD_IVR&QECB_PerAnt_20160407_VImport.xlsx";5;46.050313;-1.3836;;;;;;;"Antiochat Avr16 Q5 Vue générale.JPG";"4";"Bloc sur platier rocheux ensablé etgalets";12;2; +375618;"BDD_IVR&QECB_PerAnt_20160407_VImport.xlsx";4;46.051018;-1.38473;;;;;;;"Antiochat Avr16 Q4 Vue générale.JPG";"5";"Blocs sur blocs, sable grossier et galets";38;4; +375618;"BDD_IVR&QECB_PerAnt_20160407_VImport.xlsx";3;46.051254;-1.38408;;;;;;;"Antiochat Avr16 Q3 vue Générale.JPG";"2";"Blocs de taille élevée en bordure de flaques, présence d'hermelles,. Blocs sur sable, cailloutis et banches";29;3; +375618;"BDD_IVR&QECB_PerAnt_20160407_VImport.xlsx";2;46.051781;-1.383774;;;;;;;"Antiochat Avr16 Q2 Vue Générale.JPG";"1";"Blocs sur platier rocheux et sable grossier";19;2; +375618;"BDD_IVR&QECB_PerAnt_20160407_VImport.xlsx";1;46.051354;-1.383463;;;;;;;"Antiochat Avr16 Q1 Vue Générale.JPG";"2";"Blocs de taille moyenne dominés par les hermelles et les algues rouges. Substrat sous-jacent: banches ensablées";23;7; +375685;"BDD_IVR&QECB_PerAnt_20161017_VImport.xlsx";5;46.050313;-1.3836;;;;;;;"Perre Oct 16 Q5 vuegen.JPG";"4";"Blocs de taille élevée à moyenne sur galets et cailloutis";16;4; +375685;"BDD_IVR&QECB_PerAnt_20161017_VImport.xlsx";4;46.051018;-1.38473;;;;;;;"Perre Oct 16 Q4 vue gen.JPG";"5";"Blocs de taille élevée à moyenne sur blocs fixés car très ensablés, galets et sable";23;6; +375685;"BDD_IVR&QECB_PerAnt_20161017_VImport.xlsx";3;46.051254;-1.38408;;;;;;;"Perre Oct 16 Q3 vuegen.JPG";"2";"Blocs sur sable grossier";17;1; +375685;"BDD_IVR&QECB_PerAnt_20161017_VImport.xlsx";2;46.051781;-1.383774;;;;;;;"Perre Oct 16 Q2 vuegen.JPG";"1";"Blocs sur blocs, galets sables";25;1; +375685;"BDD_IVR&QECB_PerAnt_20161017_VImport.xlsx";1;46.051354;-1.383463;;;;;;;"Perre Oct 16 Q1 vuegen.JPG";"2";"Bloc sur platier rocheux qui s'ensable avec présence de nombreuses moules.";24;1; +375752;"BDD_IVR&QECB_Chassiron_20150420_VImport.xlsx";5;46.0451610032;-1.41821312496;;;;;;;"Chassiron Avr15 Q5 Vue Globale.JPG";"3";"Blocs sur sables et galets en haut de champ de blcos";11;8; +375752;"BDD_IVR&QECB_Chassiron_20150420_VImport.xlsx";4;46.045017086;-1.41817366379;;;;;;;"Chassiron Avr15 Q4 Vue Globale.JPG";"3";"Blocs sur blocs et substrat de sables grossier dans flaque";;; +375752;"BDD_IVR&QECB_Chassiron_20150420_VImport.xlsx";3;46.0450641247;-1.41859154763;;;;;;;;"2";"Blocs gros à très gros assez fixés. Dans le milieu du champs de blocs en bordure de flaques. Moules";25;1; +375752;"BDD_IVR&QECB_Chassiron_20150420_VImport.xlsx";2;46.0450286923;-1.41892574726;;;;;;;"Chassiron Avr15 Q2 Vue Globale.JPG";"2";"Blocs gros sur platier rocheux";16;14; +375752;"BDD_IVR&QECB_Chassiron_20150420_VImport.xlsx";1;46.0446735417;-1.41935982014;;;;;;;"Chassiron Avr15 Q1 Vue Globale.JPG";"1";"Bas d'estran. Blocs gros à très gros sur banche. Sédiment";26;1; +375819;"BDD_QECB&IVR_Chassiron_20151028_VImport.xlsx";5;46.0451610032;-1.41821312496;;;;;;;"Chassiron_Oct2015_Q5 (1).JPG";"3";"Haut du champs de blocs, bloc moyen à gros ds sables sur bloc médiolittoral moyen";23;0; +375819;"BDD_QECB&IVR_Chassiron_20151028_VImport.xlsx";4;46.045017086;-1.41817366379;;;;;;;"Chassiron_Oct2015_Q4.JPG";"3";"Haut du champs de blocs, bloc moyen à gros ds sables sur bloc médiolittoral moyen";54;7; +375819;"BDD_QECB&IVR_Chassiron_20151028_VImport.xlsx";3;46.0450641247;-1.41859154763;;;;;;;"Chassiron_Oct2015_Q3 (1).JPG";"2";"Blocs sur Blocs bordure mare médiolittoral bas";65;4; +375819;"BDD_QECB&IVR_Chassiron_20151028_VImport.xlsx";2;46.0450286923;-1.41892574726;;;;;;;"Chassiron_Oct2015_Q2 (2).JPG";"2";"Blocs sur platier bordure mare médiolittoral moyen à inférieur";30;0; +375819;"BDD_QECB&IVR_Chassiron_20151028_VImport.xlsx";1;46.0446735417;-1.41935982014;;;;;;;"Chassiron_Oct2015_Q1 (2).JPG";"1";"Blocs sur platier exondé médiolittoral inférieur";21;0; +375886;"BDD_IVR&QECB_Chassiron_20160309&10_VImport.xlsx";5;46.0451610032;-1.41821312496;;;;;;;"Chassiron Mar16 Q5 VG.JPG";"2";"Blocs de taille moyenne sur blocs (50%) et sable grossier/platier rocheux (50%). Présence de flaques permanentes. Blocs dominés par les algues rouges dressées, de nombreuses algues vertes opportunistes, quelques hermelles et balanes vivantes";56;18; +375886;"BDD_IVR&QECB_Chassiron_20160309&10_VImport.xlsx";4;46.045017086;-1.41817366379;;;;;;;"Chassiron Mar16 Q4 VG.JPG";"2";"Blocs de taille moyenne sur sable grossier et flaque permanente. Blocs dominés par les algues rouges dressées, les hermelles et quelques balanes. Sédiment sableux important à la surface des blocs";9;16; +375886;"BDD_IVR&QECB_Chassiron_20160309&10_VImport.xlsx";3;46.0450641247;-1.41859154763;;;;;;;"Chassiron Mar16 Q3 VG.JPG";"3";"Blocs gros à très gros sur matrice rocheuse et blocs. Quelques blocs moyens. Nombreux blocs fixés, cohésion par hermelles. Bordure de flaques. Algues rouges et brunes dominantes";47;4; +375886;"BDD_IVR&QECB_Chassiron_20160309&10_VImport.xlsx";2;46.0450286923;-1.41892574726;;;;;;;"Chassiron Mar16 Q2 VG.JPG";"3";"Blocs sur platier rocheux, dominés par algues rouges dressées et vertes opportunistes en mélange. Présence importante de sédiment à la surface des blocs";18;9; +375886;"BDD_IVR&QECB_Chassiron_20160309&10_VImport.xlsx";1;46.0446735417;-1.41935982014;;;;;;;"Chassiron Mar16 Q1 VG.JPG";"4";"Amas de blocs en bords de banche sur platier rocheux qui s'ensable. En bord de zone de laminaires et présence de nombreux Codium tomentoseum et Chondrus crispus.";12;11; +375953;"BDD_IVR&QECB_Chassiron_20160919&10_VImport.xlsx";2;46.0450286923;-1.41892574726;;;;;;;"Chassiron Sept16 Q2 Vue Gen.JPG";"3";"Blocs sur platier rocheux, dominés par algues rouges dressées et vertes opportunistes en mélange. Présence importante de sédiment à la surface des blocs";24;2; +375953;"BDD_IVR&QECB_Chassiron_20160919&10_VImport.xlsx";5;46.0451610032;-1.41821312496;;;;;;;"Chassiron Sept16 Q5 vue Gen.JPG";"2";"Blocs de taille moyenne sur blocs (50%) et sable grossier/platier rocheux (50%). Présence de flaques permanentes. Blocs dominés par les algues rouges dressées, de nombreuses algues vertes opportunistes, quelques hermelles et balanes vivantes";56;0; +375953;"BDD_IVR&QECB_Chassiron_20160919&10_VImport.xlsx";3;46.0450641247;-1.41859154763;;;;;;;"Chassiron Sept16 Q3 VueGen.JPG";"3";"Blocs gros à très gros sur matrice rocheuse et blocs. Quelques blocs moyens. Nombreux blocs fixés, cohésion par hermelles. Bordure de flaques. Algues rouges et brunes dominantes";22;1; +375953;"BDD_IVR&QECB_Chassiron_20160919&10_VImport.xlsx";1;46.0446735417;-1.41935982014;;;;;;;"Chassiron Sept 16 Q1 vue Gen1.JPG";"4";"Amas de blocs en bords de banche sur platier rocheux qui s'ensable. En bord de zone de laminaires et présence de nombreux Codium tomentoseum et Chondrus crispus.";11;6; +375953;"BDD_IVR&QECB_Chassiron_20160919&10_VImport.xlsx";4;46.045017086;-1.41817366379;;;;;;;"Chassiron Sept16 Q4 vue Gen.JPG";"2";"Blocs de taille moyenne sur sable grossier et flaque permanente. Blocs dominés par les algues rouges dressées, les hermelles et quelques balanes. Sédiment sableux important à la surface des blocs";27;0; +376020;"BDD_IVR&QECB_FB_ZP_20141124&25_VImport.xlsx";5;43.398283;-1.6648;;;;;;;"Q5.JPG";"1";"Blocs de taille moyenne à élevée sur blocs et sédiments grossiers, dominance d'algues rouges. ";35;0; +376020;"BDD_IVR&QECB_FB_ZP_20141124&25_VImport.xlsx";4;43.398916;-1.6654;;;;;;;"Q4.JPG";"2";"Blocs de taille moyenne à élevée sur mélange de petits blocs, cailloux, sable grossier et débris coquillers. Présence d'une zone de cuvette. Blocs dominés par algues rouges en dehors de la cuvette et par algues vertes dans la cuvette.";21;7; +376020;"BDD_IVR&QECB_FB_ZP_20141124&25_VImport.xlsx";3;43.398667;-1.6651;;;;;;;"Q3.JPG";"2";"Blocs de taille moyenne à élevée sur mélange de petits blocs, cailloux, sable grossier et débris coquillers. Mélange de blocs mobiles dominés par algues rouges, roche nue et/ou algues vertes. ";14;8; +376020;"BDD_IVR&QECB_FB_ZP_20141124&25_VImport.xlsx";2;43.398516;-1.66555;;;;;;;"Q2.JPG";"1";"Blocs de taille moyenne à élevée sur blocs, dominance d'algues rouges. ";17;5; +376020;"BDD_IVR&QECB_FB_ZP_20141124&25_VImport.xlsx";1;43.398583;-1.665183;;;;;;;"Q1.JPG";"2";"Blocs de taille moyenne à élevée sur petits blocs, de cailloux, de sable grossier et débris coquillers. Dominance d'algues rouges mais encorede nombreux blocs mobiles présentant de fins tapis d'algues vertes sur leurs faces supérieures";31;12; +376087;"BDD_IVR&QECB_FlotsBleus_ZP_20150319&21_VFinale.xlsx";5;43.398283;-1.6648;;;;;;;;"1";"Blocs sur blocs et galets.";38;6; +376087;"BDD_IVR&QECB_FlotsBleus_ZP_20150319&21_VFinale.xlsx";4;43.398916;-1.6654;;;;;;;"Q4 DSC_2990.JPG";"2";"Blocs sur blocs et galets.";19;8; +376087;"BDD_IVR&QECB_FlotsBleus_ZP_20150319&21_VFinale.xlsx";3;43.398667;-1.6651;;;;;;;;"2";"Strate sur blocs et galets";6;31; +376087;"BDD_IVR&QECB_FlotsBleus_ZP_20150319&21_VFinale.xlsx";2;43.398516;-1.66555;;;;;;;"Q2 DSC_2724.JPG";"1";"Très ensablé. Blocs sur blocs, strates et galets. ";16;3; +376087;"BDD_IVR&QECB_FlotsBleus_ZP_20150319&21_VFinale.xlsx";1;43.398583;-1.665183;;;;;;;"Q1 DSC_2666.JPG";"2";"Strate rouge/vert, blocs et galets. ";17;8; +376154;"BDD_IVR&QECB_FlotsBleus_ZP_20150518&19_VImport.xlsx";5;43.398283;-1.6648;;;;;;;"Q5 DSC_3495.JPG";"1";"Blocs sur blocs et dalles. Quadrat très ensablé.";11;11; +376154;"BDD_IVR&QECB_FlotsBleus_ZP_20150518&19_VImport.xlsx";4;43.398916;-1.6654;;;;;;;"Q4 DSC_3480.JPG";"2";"Blocs sur blocs et galets. Vert, brun et nu. Petite cuvette moins du quart du quadrat.";6;13; +376154;"BDD_IVR&QECB_FlotsBleus_ZP_20150518&19_VImport.xlsx";3;43.398667;-1.6651;;;;;;;"Q3 DSC_3430.JPG";"2";"Dominante verte/nue. Petits blocs sur gros galets, quelques grands blocs.";4;24; +376154;"BDD_IVR&QECB_FlotsBleus_ZP_20150518&19_VImport.xlsx";2;43.398516;-1.66555;;;;;;;"Q2 DSC_3410.JPG";"1";"Dominance dalgues rouges. Nombreux gros blocs et galets.";13;6; +376154;"BDD_IVR&QECB_FlotsBleus_ZP_20150518&19_VImport.xlsx";1;43.398583;-1.665183;;;;;;;"Q1 DSC_3391.JPG";"2";"Dominante verte, plusieurs cuvettes. Blocs verts sur blocs et galets/graviers.";7;25; +376221;"BDD_IVR&QECB_FlotsBleus_ZP_20150803_VImport.xlsx";3;43.398667;-1.6651;;;;;;;"Q3 DSC_3578.JPG";"2";"Dominante rouge. Blocs de bonne taille + petis blocs sur dalles, cuvettes.";13;5; +376221;"BDD_IVR&QECB_FlotsBleus_ZP_20150803_VImport.xlsx";2;43.398516;-1.66555;;;;;;;"Q2 DSC_3661.JPG";"1";"Dominance d'algues vertes + roche nue. Plutôt des petits blocs, Blocs sur blocs et galets.";4;16; +376221;"BDD_IVR&QECB_FlotsBleus_ZP_20150803_VImport.xlsx";1;43.398583;-1.665183;;;;;;;"Q1 DSC_3643.JPG";"2";"Dominante verte. Blocs verts sur strates et galets/graviers.";13;11; +376221;"BDD_IVR&QECB_FlotsBleus_ZP_20150803_VImport.xlsx";5;43.398283;-1.6648;;;;;;;"Q5 DSC_3604.JPG";"1";"Blocs sur blocs et strate. Quadrat très ensablé.";30;1; +376221;"BDD_IVR&QECB_FlotsBleus_ZP_20150803_VImport.xlsx";4;43.398916;-1.6654;;;;;;;"Q4 DSC_3555.JPG";"2";"Blocs sur blocs et galets. Vert, brun et nu. Petite cuvette moins du quart du quadrat.";12;16; +376288;"BDD_IVR&QECB_FlotsBleus_ZP_20150928&30_VImport.xlsx";1;43.398583;-1.665183;;;;;;;"Q1 DSC_4055.JPG";"2";"blocs sur blocs galets et graviers, dominante verte colorée";8;13; +376288;"BDD_IVR&QECB_FlotsBleus_ZP_20150928&30_VImport.xlsx";2;43.398516;-1.66555;;;;;;;;"1";"blocs sur blocs et galets, en partie immergée, Dominante rouge";8;3; +376288;"BDD_IVR&QECB_FlotsBleus_ZP_20150928&30_VImport.xlsx";3;43.398667;-1.6651;;;;;;;;"2";"Blocs sur blocs, galets et sable, Dominante rouge + roche nue";8;11; +376288;"BDD_IVR&QECB_FlotsBleus_ZP_20150928&30_VImport.xlsx";4;43.398916;-1.6654;;;;;;;;"2";"Blocs de taille hétérogènes sur sable et galets. Dominante rouge";2;9; +376288;"BDD_IVR&QECB_FlotsBleus_ZP_20150928&30_VImport.xlsx";5;43.398283;-1.6648;;;;;;;"Q5 DSC_4012.JPG";"1";"Blocs sur strates et blocs + sable, Dominante rouge,";18;6; +376355;"BDD_IVR&QECB_FB_ZP_20160310_VImport.xlsx";5;43.398283;-1.6648;;;;;;;"Q5 DSC_4507.JPG";"3";"Blocs sur blocs et platier rocheux qui s'ensable, dominés par les algues rouges";17;0; +376355;"BDD_IVR&QECB_FB_ZP_20160310_VImport.xlsx";4;43.398916;-1.6654;;;;;;;"Q4 DSC_4493.JPG";"3";"Blocs de taille très élevée sur blocs et galets, dominés par les algues rouges. Strate en partie immergée. ";12;6; +376355;"BDD_IVR&QECB_FB_ZP_20160310_VImport.xlsx";3;43.398667;-1.6651;;;;;;;"Q3 DSC_4544.JPG";"2";"Blocs sur blocs et galets. Dominance d'algues vertes et très nombreuses balanes. Présence de très gros blocs.";10;3; +376355;"BDD_IVR&QECB_FB_ZP_20160310_VImport.xlsx";2;43.398516;-1.66555;;;;;;;"Q2 DSC_4468.JPG";"3";"Blocs sur blocs, galets et graviers. Dominance d'algues rouge.";8;2; +376355;"BDD_IVR&QECB_FB_ZP_20160310_VImport.xlsx";1;43.398583;-1.665183;;;;;;;"Q1 DSC_4532.JPG";"2";"Bloc sur blocs et galets, légèrement dominés par algues rouges. Strate en partie immergée";21;3; +376422;"BDD_IVR&QECB_FB_ZP_20160509_VImport.xlsx";5;43.398283;-1.6648;;;;;;;"Q5 DSC_4667.JPG";"3";"Blocs sur blocs, platier rocheux , galets et graviers, dominés par les algues rouges.";23;2; +376422;"BDD_IVR&QECB_FB_ZP_20160509_VImport.xlsx";1;43.398583;-1.665183;;;;;;;"Q1 DSC_4678.JPG";"2";"Bloc sur petits blocs et galets, dominés par les algues vertes. Strate en partie immergée";25;1; +376422;"BDD_IVR&QECB_FB_ZP_20160509_VImport.xlsx";2;43.398516;-1.66555;;;;;;;"Q2 DSC_4653.JPG";"3";"Blocs sur blocs, galets et graviers, dominés par les algues rouges. Partie immergée = flaque + codium adhérent + stipocolon + cystocères";9;1; +376422;"BDD_IVR&QECB_FB_ZP_20160509_VImport.xlsx";3;43.398667;-1.6651;;;;;;;"Q3 DSC_4600.JPG";"2";"Blocs de taille variable sur blocs et graviers, dominés par les algues vertes.";11;10; +376422;"BDD_IVR&QECB_FB_ZP_20160509_VImport.xlsx";4;43.398916;-1.6654;;;;;;;"Q4 DSC_4637.JPG";"2";"Blocs de taille variable sur galets, dominés par les algues vertes et rouges dressées (strate 2).";12;5; +376489;"BDD_IVR&QECB_FB_ZP_20160822_VImport.xlsx";5;43.398283;-1.6648;;;;;;;"Q5 DSC_4906.JPG";"3";"Blocs sur blocs, platier rocheux, galets et graviers. Dominés par les algues rouges. Présence de quelques gros blocs retournés.";13;5; +376489;"BDD_IVR&QECB_FB_ZP_20160822_VImport.xlsx";4;43.398916;-1.6654;;;;;;;"Q4 DSC_4854.JPG";"2";"Blocs de taille élevée sur petits blocs et galets, dominés par les algues rouges. Présence de nombreuses cuvettes d'eau permanente";13;2; +376489;"BDD_IVR&QECB_FB_ZP_20160822_VImport.xlsx";3;43.398667;-1.6651;;;;;;;"Q3 DSC_4918.JPG";"2";"Blocs de taille variable sur blocs et graviers. Dominé par la roche nue,les algues rouges ou les algues vertes";11;5; +376489;"BDD_IVR&QECB_FB_ZP_20160822_VImport.xlsx";2;43.398516;-1.66555;;;;;;;"Q2 DSC_4876.JPG";"3";"Blocs sur blocs, galets et graviers, dominés par les algues rouge. Quelques blocs de taille élevée.";9;2; +376489;"BDD_IVR&QECB_FB_ZP_20160822_VImport.xlsx";1;43.398583;-1.665183;;;;;;;"Q1 DSC_4936.JPG";"2";"Blocs sur petits blocs et galets, majoritairement de petite taille et dominés par les algues vertes. ";9;4; +376556;"BDD_IVR&QECB_FB_ZP_20160920_VImport.xlsx";3;43.398667;-1.6651;;;;;;;"Q3 DSC_5139.JPG";"2";"Blocs sur blocs, dalles et graviers, dominés par algues vertes et roche nue";4;10; +376556;"BDD_IVR&QECB_FB_ZP_20160920_VImport.xlsx";2;43.398516;-1.66555;;;;;;;"Q2 DSC_5072.JPG";"3";"Blocs sur blocs et platier rocheux, dominés par les algues rouges. Présence de blocs de taille très élevée";6;2; +376556;"BDD_IVR&QECB_FB_ZP_20160920_VImport.xlsx";1;43.398583;-1.665183;;;;;;;"Q1 DSC_5110.JPG";"2";"Blocs sur blocs et galets, dominés par les algues vertes";8;5; +376556;"BDD_IVR&QECB_FB_ZP_20160920_VImport.xlsx";5;43.398283;-1.6648;;;;;;;"Q5 DSC_5093.JPG";"3";"Blocs sur blocs et strates., en partie ensablés, dominés par les algues rouges";10;1; +376556;"BDD_IVR&QECB_FB_ZP_20160920_VImport.xlsx";4;43.398916;-1.6654;;;;;;;"Q4 DSC_5082.JPG";"2";"Blocs sur blocs et graviers, dominés par les algues rouges. Présence de très gros blocs mobiles";6;11; +376623;"BDD_IVR&QECB_FlotsBleus_ZF_20150324&25_VImport.xlsx";1;43.398583;-1.665183;;;;;;;"Q1 DSC_3109.JPG";"3";"Blocs petits à moyens, nus sur galets et graviers.";5;44; +376623;"BDD_IVR&QECB_FlotsBleus_ZF_20150324&25_VImport.xlsx";5;43.397883;-1.663283;;;;;;;"Q5 DSC_3321.JPG";"1";"Blocs sur galets et graviers. Dominace d'algues vertes mais présence importante de Corallines et de Stypocaulon dans les cuvettes. Algues rouges encroûtantes sur les galets.";6;35; +376623;"BDD_IVR&QECB_FlotsBleus_ZF_20150324&25_VImport.xlsx";4;43.397816;-1.66225;;;;;;;"Q4 DSC_3280.JPG";"3";"Strate à dominanate verte. Blocs sur galets, graviers. Présence de Corallines.";5;22; +376623;"BDD_IVR&QECB_FlotsBleus_ZF_20150324&25_VImport.xlsx";3;43.397133;-1.663283;;;;;;;"Q3 DSC_3197.JPG";"1";"Blocs sur galets et graviers. Gigartinales.";12;11; +376623;"BDD_IVR&QECB_FlotsBleus_ZF_20150324&25_VImport.xlsx";2;43.396983;-1.659616;;;;;;;"Q2 DSC_3158.JPG";"2";"Blocs et galets sur galets. Mix rouge et vert.";8;10; +376690;"BDD_IVR&QECB_FB_ZF_20150817_VImport.xlsx";1;43.398583;-1.665183;;;;;;;"Q1 DSC_3679.JPG";"3";"Petits blocs à moyens sur blocs et galets, gris et colorés";3;49; +376690;"BDD_IVR&QECB_FB_ZF_20150817_VImport.xlsx";2;43.396983;-1.659616;;;;;;;"Q2 DSC_3763.JPG";"2";"blocs et galets assez immergés, vert et rouge. ombreux crustacés, crabes et Pagurus, monodontes et gibules, crevettes.";9;12; +376690;"BDD_IVR&QECB_FB_ZF_20150817_VImport.xlsx";3;43.397133;-1.663283;;;;;;;"Q3 DSC_3776.JPG";"1";"Strate à dominance d'algues rouges. Zone de travail encore immergée à 13h40. Blocs sur dalle, blocs moyens à gros, sable dans les intersitces et pris dans les algues.";20;6; +376690;"BDD_IVR&QECB_FB_ZF_20150817_VImport.xlsx";4;43.397816;-1.66225;;;;;;;"Q4 DSC_3691.JPG";"3";"Gros blocs sur blocs, galets et sable. Domnante verte, corallines et Stypocaulon dans les cuvettes.";2;21; +376690;"BDD_IVR&QECB_FB_ZF_20150817_VImport.xlsx";5;43.397883;-1.663283;;;;;;;"Q5 DSC_3791.JPG";"1";"Semble très ensablé. Dominance d'algues rouges avec de nombreuses algues arbustives, ulves également plus grandes. Strate de petits blocs et galets sur strae, sable et galets. Vers 14h30 est restée immergée en grande partie. Nombreuses nasses, gibbules e";4;5; +376757;"BDD_IVR&QECB_FlotsBleus_ZF_20151026&27_VImport.xlsx";5;43.397883;-1.663283;;;;;;;"Q5 DSC_4271.JPG";"1";"Strate à dominante rouge, bloc sur graviers";8;0; +376757;"BDD_IVR&QECB_FlotsBleus_ZF_20151026&27_VImport.xlsx";4;43.397816;-1.66225;;;;;;;"Q4 DSC_4084.JPG";"3";"Starte de blocs moyens à gros sur galets, blocs et sable, dominante verte, reste en eau";7;16; +376757;"BDD_IVR&QECB_FlotsBleus_ZF_20151026&27_VImport.xlsx";3;43.397133;-1.663283;;;;;;;"Q3 DSC_4177.JPG";"1";"Strate à dominante rouge restant en partie immergée, blocs sur blocs et galets";14;2; +376757;"BDD_IVR&QECB_FlotsBleus_ZF_20151026&27_VImport.xlsx";2;43.396983;-1.659616;;;;;;;;"2";"Strate aà dominante rouge, blocs sur blocs, sur dalles et galets, nombreux blocs non mobiles";21;1; +376757;"BDD_IVR&QECB_FlotsBleus_ZF_20151026&27_VImport.xlsx";1;43.398583;-1.665183;;;;;;;"Q1 DSC_4072.JPG";"3";"Blocs petits à moyens, dominante grise (couleur roche), Pas mal retournés, blocs sur blocs, strates, galets et graviers";1;30; +376824;"BDD_IVR&QECB_FB_ZF_20160308&11_VImport.xlsx";5;43.397883;-1.663283;;;;;;;"Q5 DSC_4297.JPG";"1";"Blocs majoritairement de petite taille sur blocs et galets, dominés par les algues rouges et vertes (en particulier pour les blocs de plus grande taille)";17;6; +376824;"BDD_IVR&QECB_FB_ZF_20160308&11_VImport.xlsx";4;43.397816;-1.66225;;;;;;;"Q4 DSC_4556.JPG";"3";"Blocs moyens sur sable et graviers, dominés par les algues vertes. Strate en parie immergée";14;3; +376824;"BDD_IVR&QECB_FB_ZF_20160308&11_VImport.xlsx";3;43.397133;-1.663283;;;;;;;"Q3 DSC_4346.JPG";"1";"Blocs sur strates, galets et graviers, dominés par des algues rouges (dont Gelidium sp.). Nombreux blocs fixés";7;2; +376824;"BDD_IVR&QECB_FB_ZF_20160308&11_VImport.xlsx";2;43.396983;-1.659616;;;;;;;;"2";"Blocs sur blocs, strates et galets, dominés par des algues rouges dressées";8;8; +376824;"BDD_IVR&QECB_FB_ZF_20160308&11_VImport.xlsx";1;43.398583;-1.665183;;;;;;;;"3";"Blocs de petite taille sur galets, dominés par du biofilm (algues vertes) et de la roche nue ";6;5; +376891;"BDD_IVR&QECB_FB_ZF_20160818_VImport.xlsx";5;43.397883;-1.663283;;;;;;;"Q5 DSC_4794.JPG";"1";"Blocs sur petits blocs, dominés par les algues vertes opportunistes (ulves bien développées). ";11;3; +376891;"BDD_IVR&QECB_FB_ZF_20160818_VImport.xlsx";4;43.397816;-1.66225;;;;;;;"Q4 DSC_4818.JPG";"3";"Blocs de taille variable sur sable et gravier, dominés par les algues vertes.";11;3; +376891;"BDD_IVR&QECB_FB_ZF_20160818_VImport.xlsx";3;43.397133;-1.663283;;;;;;;;"1";"Blocs sur strate. Présence de très gros et petits blocs. Dominante algues rouges.";13;1; +376891;"BDD_IVR&QECB_FB_ZF_20160818_VImport.xlsx";2;43.396983;-1.659616;;;;;;;"Q2 DSC_4759.JPG";"2";"Blocs de taille variable sur strate et petits blocs, dominés par les algues vertes et quelques algues rouges. Nombreuses flaques.";12;0; +376891;"BDD_IVR&QECB_FB_ZF_20160818_VImport.xlsx";1;43.398583;-1.665183;;;;;;;"Q1 DSC_4734.JPG";"3";"Blocs de taille moyenne sur petits blocs, galets et graviers, dominés par de la roche nue et des algues vertes opportunistes. ";9;12; +376958;"BDD_IVR&QECB_FB_ZF_20160915&16_VImport.xlsx";5;43.397883;-1.663283;;;;;;;"Q5 DSC_5045.JPG";"2";"Blocs blocs, galets et strate. Majorité de petits blocs. Dominante algues vertes.";4;5; +376958;"BDD_IVR&QECB_FB_ZF_20160915&16_VImport.xlsx";1;43.398583;-1.665183;;;;;;;"Q1 DSC_4967.JPG";"1";"Bloc sur petits blocs, galets et graviers. Dominante roche nue.";2;7; +376958;"BDD_IVR&QECB_FB_ZF_20160915&16_VImport.xlsx";2;43.396983;-1.659616;;;;;;;"Q2 DSC_5007.JPG";"2";"Bloc sur blocs, galets et graviers. Dominante algues rouges. ";11;6; +376958;"BDD_IVR&QECB_FB_ZF_20160915&16_VImport.xlsx";3;43.397133;-1.663283;;;;;;;"Q3 DSC_5021.JPG";"3";"Blocs sur blocs et galets. Dominante de petits blocs. Dominante algues rouges.";6;4; +376958;"BDD_IVR&QECB_FB_ZF_20160915&16_VImport.xlsx";4;43.397816;-1.66225;;;;;;;"Q4 DSC_4995.JPG";"2";"Blocs sur blocs, sables et galets. Dominante algues vertes.";7;2; +377025;"BDD_IVR&QECB_IlotStMichel_20141008&09_VImport.xlsx";5;48.65561;-2.42785;;;;;;;;"1";"Zone de blocs de taille moyenne dont les faces supérieures sont majoritairement dominées par du Fucus serratus et des algues rouges en mélange. Présence également de plusieurs blocs dominés par les algues vertes opportunistes ou récemment retournés. ";30;23; +377025;"BDD_IVR&QECB_IlotStMichel_20141008&09_VImport.xlsx";4;48.65603;-2.42776;;;;;;;;"2";"Zone de blocs de taille moyenne dont les faces supérieures sont dominées par les algues vertes opportunistes (Enteromorpha spp. et Ulva spp.). La zone se situe dans l’extrémité sud et en limite inférieure du champ de blocs. ";34;25; +377025;"BDD_IVR&QECB_IlotStMichel_20141008&09_VImport.xlsx";3;48.65585;-2.42787;;;;;;;;"2";"Zone de blocs de taille moyenne à élevée, assez éparses et situés dans la limite inférieure du champ de blocs. Leurs faces supérieures sont dominées par des algues rouges, mais plusieurs blocs sont également récemment retournés (faces supérieures dominée";;; +377025;"BDD_IVR&QECB_IlotStMichel_20141008&09_VImport.xlsx";2;48.65598;-2.42807;;;;;;;"PA080156.JPG";"2";"Zone de blocs de taille moyenne à élevée, dont les faces supérieures sont dominées par de la roche nue ou les algues vertes opportunistes. Les blocs sont posés sur du sable très grossier et des débris coquillers. ";38;40; +377025;"BDD_IVR&QECB_IlotStMichel_20141008&09_VImport.xlsx";1;48.656;-2.42824;;;;;;;;"1";"Zone de blocs de taille moyenne sur sédiments grossiers, cailloutis et petits blocs, très accolés les uns aux autres. La couverture algale dominante est le Fucus serratus et les algues rouges en mélange, seuls quelques blocs sont dominés par les algues v";;; +377052;"BDD_IVR&QECB_IlotStMichel_20150418&19_VImport.xlsx";2;48.65598;-2.42807;;;;;;;"Q2.JPG";"2";"Blocs moyens à gros à 60% mobiles. Débris coquillers fins + débris de maërl";27;12; +377052;"BDD_IVR&QECB_IlotStMichel_20150418&19_VImport.xlsx";1;48.656;-2.42824;;;;;;;;"1";"Blocs moyens à gros à 75% mobiles. Dabris coquillers fins (dont du maërl) + cailloux";29;14; +377052;"BDD_IVR&QECB_IlotStMichel_20150418&19_VImport.xlsx";3;48.65585;-2.42787;;;;;;;"Q3.JPG";"2";"Blocs moyens à gros à 60% mobiles. Débris coquillers grossiers + débris de maërl+cailloux ";33;9; +377052;"BDD_IVR&QECB_IlotStMichel_20150418&19_VImport.xlsx";4;48.65603;-2.42776;;;;;;;"Q4.JPG";"2";"Blocs moyens à petits, couverture algues vertes, sable fin + cailloux";51;25; +377052;"BDD_IVR&QECB_IlotStMichel_20150418&19_VImport.xlsx";5;48.65561;-2.42785;;;;;;;"Q5.JPG";"1";"Blocs moyens à gros";36;; +377119;"BDD_IVR&QECB_IlotStMichel_20151028&29_VImport.xlsx";1;48.656;-2.42824;;;;;;;"Q1_IVR.JPG";"1";"Fucus serratus, présence d'algues rouges, sédiments grossiers (+ galets), pas d'eau";21;10; +377119;"BDD_IVR&QECB_IlotStMichel_20151028&29_VImport.xlsx";2;48.65598;-2.42807;;;;;;;"Q2_IVR.JPG";"2";"Fucus serratus, présence d'algues rouges, sédiments fin, peu d'eau";13;11; +377119;"BDD_IVR&QECB_IlotStMichel_20151028&29_VImport.xlsx";5;48.65561;-2.42785;;;;;;;"Q5_IVR.JPG";"1";"Fucus serratus, peu d'eau, algues rouges";21;7; +377119;"BDD_IVR&QECB_IlotStMichel_20151028&29_VImport.xlsx";4;48.65603;-2.42776;;;;;;;"Q4_IVR.JPG";"2";"Fucus serratus, beaucoup de blocs retournés, mélange de petits et moyens blocs, algues vertes (Ulva spp.), Algues rouges, sédiment fin";52;14; +377119;"BDD_IVR&QECB_IlotStMichel_20151028&29_VImport.xlsx";3;48.65585;-2.42787;;;;;;;"Q3_IVR.JPG";"2";"Fucus serratus, présence d'algues rouges, sédiment fin";23;25; +377186;"BDD_IVRQECB_IlotStMichel_20160507&08_VImport.xlsx";2;48.65598;-2.42807;;;;;;;"Q2_104.JPG";"2";"Blocs dominés par du Fucus serratus en mélange avec algues brunes/rouges et quelques enteromorphes";40;20; +377186;"BDD_IVRQECB_IlotStMichel_20160507&08_VImport.xlsx";5;48.65561;-2.42785;;;;;;;"Q5_111.JPG";"1";"Blocs dominés par du Fucus serratus en mélange avec algues brunes/rouges et quelques enteromorphes";26;23; +377186;"BDD_IVRQECB_IlotStMichel_20160507&08_VImport.xlsx";4;48.65603;-2.42776;;;;;;;"Q4_113.JPG";"2";"Blocs dominés par du Fucus serratus en mélange avec algues brunes/rouges et quelques enteromorphes. Quadrat positionné dans mare d'eau permanente (mouvement du banc de sable observé)";53;21; +377186;"BDD_IVRQECB_IlotStMichel_20160507&08_VImport.xlsx";3;48.65585;-2.42787;;;;;;;"Q3_112.JPG";"2";"Blocs dominés par du Fucus serratus en mélange avec algues brunes/rouges et quelques enteromorphes. Nombreuses zones de roche nue. Blocs sur substrat grossier et débris coquillers";36;9; +377186;"BDD_IVRQECB_IlotStMichel_20160507&08_VImport.xlsx";1;48.6560022;-2.42824;;;;;;;"Q1_108.JPG";"1";"Blocs dominés par du Fucus serratusen mélange avec algues brunes/rouges et quelques enteromorphes";28;11; +377253;"BDD_IVRQECB_IlotStMichel_20161017&18_VImport.xlsx";1;48.656;-2.42824;;;;;;;"PA180217.JPG";"1";"Fucus serratus et un peu d'Enteromorphes. Blocs moyens à petits";19;10; +377253;"BDD_IVRQECB_IlotStMichel_20161017&18_VImport.xlsx";5;48.65561;-2.42785;;;;;;;"PA170156.JPG";"1";"Fucus serratus en mélange avec algues rouges, blocs assez gros";42;3; +377253;"BDD_IVRQECB_IlotStMichel_20161017&18_VImport.xlsx";4;48.65603;-2.42776;;;;;;;"PA180103.JPG";"2";"Fucus serratus, un peu d'Enteromorphes";23;12; +377253;"BDD_IVRQECB_IlotStMichel_20161017&18_VImport.xlsx";3;48.65585;-2.42787;;;;;;;"PA170154.JPG";"2";"Blocs moyens, dominés par Fucus serratus et quelques Entéromorphes, sur débris coquillers.";47;9; +377253;"BDD_IVRQECB_IlotStMichel_20161017&18_VImport.xlsx";2;48.65598;-2.42807;;;;;;;"PA170089.JPG";"2";"Algues brunes, rouges et vertes, zones nues avec substrat grossier";32;23; +377320;"BD_ChpsBlocs_20_20150420_Quemenes_VImport.xlsx";5;48.37705;-4.91053333;;;;;;;"P4200033.JPG";"Pas de stratification";"Strate composée de blocs éparses de petite taille, dont les faces supérieures sont dominées par les algues brunes. ";62;0; +377320;"BD_ChpsBlocs_20_20150420_Quemenes_VImport.xlsx";1;48.377567;-4.91018333;;;;;;;"P4200002.JPG";"Pas de stratification";"Strate composée de blocs de taille moyenne à élevée, dont les faces supérieures sont dominées par les algues brunes et rouges. ";75;0; +377320;"BD_ChpsBlocs_20_20150420_Quemenes_VImport.xlsx";2;48.3778;-4.91033333;;;;;;;"P4200012.JPG";"Pas de stratification";"Strate composée de blocs de taille moyenne à élevée, dont les faces supérieures sont dominées par les algues brunes et rouges. ";70;3; +377320;"BD_ChpsBlocs_20_20150420_Quemenes_VImport.xlsx";3;48.3776;-4.9104333;;;;;;;"P4200018.JPG";"Pas de stratification";"Strate composée de blocs de taille moyenne à élevée, sur petits cailloux, dont les faces supérieures sont dominées par les algues brunes et rouges. ";45;0; +377320;"BD_ChpsBlocs_20_20150420_Quemenes_VImport.xlsx";4;48.377317;-4.91038333E8;;;;;;;"P4200024.JPG";"Pas de stratification";"Strate composée de blocs éparses de petite taille, dont les faces supérieures sont dominées par les algues brunes. ";43;4; +377387;"BD_ChpsBlocs_20_20150930_Quemenes_VImport.xlsx";4;48.37855;-4.910933;;;;;;;"P9300037.JPG";"Pas de stratification";"Strate composée de blocs de taille moyenne à élevée, dont les faces supérieures sont dominées par les algues brunes Himanthalia et Saccharina et algue rouges + algues vertes.";61;0; +377387;"BD_ChpsBlocs_20_20150930_Quemenes_VImport.xlsx";5;48.3783;-4.91041667;;;;;;;"P9300044.JPG";"Pas de stratification";"Strate composée de blocs de taille moyenne à élevée, dont les faces supérieures sont dominées par les algues brunes Himanthalia et Saccharina et algue rouges. ";55;0; +377387;"BD_ChpsBlocs_20_20150930_Quemenes_VImport.xlsx";1;48.3784;-4.9097;;;;;;;"P9300005.JPG";"Pas de stratification";"Strate composée de blocs de taille moyenne à élevée, dont les faces supérieures sont dominées par les algues brunes Himanthalia et rouges. ";62;0; +377387;"BD_ChpsBlocs_20_20150930_Quemenes_VImport.xlsx";2;48.378683;-4.9104;;;;;;;"P9300007.JPG";"Pas de stratification";"Strate composée de blocs de taille moyenne à élevée, dont les faces supérieures sont dominées par les algues brunes Himanthalia et Saccharina et algue rouges. ";61;0; +377387;"BD_ChpsBlocs_20_20150930_Quemenes_VImport.xlsx";3;48.378567;-4.910683;;;;;;;"P9300035.JPG";"Pas de stratification";"Strate composée de blocs de taille moyenne à élevée, dont les faces supérieures sont dominées par les algues brunes Himanthalia et Saccharina et algue rouges. ";68;0; +377454;"BD_ChpsBlocs_20_20160407_Quemenes_VImport.xlsx";1;48.3784;-4.9097;;;;;;;"P4070005.JPG";"Pas de stratification";"Strate composée de blocs de taille moyenne à élevée, dont les faces supérieures sont dominées par les algues brunes Himanthalia et rouges. ";62;0; +377454;"BD_ChpsBlocs_20_20160407_Quemenes_VImport.xlsx";5;48.3783;-4.91041667;;;;;;;;"Pas de stratification";"Strate composée de blocs de taille moyenne à élevée, dont les faces supérieures sont dominées par les algues brunes Himanthalia et Saccharina et algue rouges. ";55;0; +377454;"BD_ChpsBlocs_20_20160407_Quemenes_VImport.xlsx";4;48.37855;-4.910933;;;;;;;;"Pas de stratification";"Strate composée de blocs de taille moyenne à élevée, dont les faces supérieures sont dominées par les algues brunes Himanthalia et Saccharina et algue rouges + algues vertes.";61;0; +377454;"BD_ChpsBlocs_20_20160407_Quemenes_VImport.xlsx";3;48.378567;-4.910683;;;;;;;"P4070035.JPG";"Pas de stratification";"Strate composée de blocs de taille moyenne à élevée, dont les faces supérieures sont dominées par les algues brunes Himanthalia et Saccharina et algue rouges. ";68;0; +377454;"BD_ChpsBlocs_20_20160407_Quemenes_VImport.xlsx";2;48.378683;-4.9104;;;;;;;"P4070007.JPG";"Pas de stratification";"Strate composée de blocs de taille moyenne à élevée, dont les faces supérieures sont dominées par les algues brunes Himanthalia et Saccharina et algue rouges. ";61;0; +377521;"BD_ChpsBlocs_20_20160917-Quemenes_VImport.xlsx";1;48.3784;-4.9097;;;;;;;;"Pas de stratification";"Strate composée de blocs de taille moyenne à élevée, dont les faces supérieures sont dominées par les algues brunes Himanthalia et fucus et rouges. ";33;0; +377521;"BD_ChpsBlocs_20_20160917-Quemenes_VImport.xlsx";5;48.3783;-4.91041667;;;;;;;;"Pas de stratification";;;; +377521;"BD_ChpsBlocs_20_20160917-Quemenes_VImport.xlsx";4;48.37855;-4.910933;;;;;;;;"Pas de stratification";"Strate composée de blocs de taille moyenne à élevée, dont les faces supérieures sont dominées par les algues brunes Himanthalia et rouges et verte. ";25;0; +377521;"BD_ChpsBlocs_20_20160917-Quemenes_VImport.xlsx";3;48.378567;-4.910683;;;;;;;;"Pas de stratification";"Strate composée de blocs de taille moyenne à élevée, dont les faces supérieures sont dominées par les algues brunes Himanthalia et rouges. ";37;0; +377521;"BD_ChpsBlocs_20_20160917-Quemenes_VImport.xlsx";2;48.378683;-4.9104;;;;;;;;"Pas de stratification";"Strate composée de blocs de taille moyenne à élevée, dont les faces supérieures sont dominées par les algues brunes Himanthalia et rouges et verte. ";30;0; +377576;"BD_ChpsBlocs_20_20170427_Quemenes_VImport.xlsx";1;48.3784;-4.9097;;;;;;;"P4270015.JPG";"Pas de stratification";"Strate composée de blocs de taille moyenne à élevée, dont les faces supérieures sont dominées par les algues brunes et rouges. ";37;0; +377576;"BD_ChpsBlocs_20_20170427_Quemenes_VImport.xlsx";2;48.378683;-4.9104;;;;;;;"P4270001.JPG";"Pas de stratification";;49;0; +377576;"BD_ChpsBlocs_20_20170427_Quemenes_VImport.xlsx";3;48.378567;-4.910683;;;;;;;"P4270009.JPG";"Pas de stratification";"Strate composée de blocs de taille moyenne à élevée, dont les faces supérieures sont dominées par les algues brunes Himanthalia et algue rouges et algues verte";28;6; +377576;"BD_ChpsBlocs_20_20170427_Quemenes_VImport.xlsx";4;48.37855;-4.910933;;;;;;;"P4270022.JPG";"Pas de stratification";"Strate composée de blocs de taille moyenne à élevée, dont les faces supérieures sont dominées par les algues brunes Himanthalia et Saccharina et algue rouges + algues vertes.";36;0; +377576;"BD_ChpsBlocs_20_20170427_Quemenes_VImport.xlsx";5;48.3783;-4.91041667;;;;;;;"P4270028.JPG";"Pas de stratification";"Strate composée de blocs de taille moyenne à élevée, dont les faces supérieures sont dominées par les algues brunes Himanthalia et algue rouges. ";17;0; +377643;"BD_ChpsBlocs_20_20170920_Quemenes_VImport.xlsx";1;48.377567;-4.91018333;;;;;;;"DSCF0417.JPG";"Pas de stratification";"sable moyen, cailloutis, blocs éparses";29;0; +377643;"BD_ChpsBlocs_20_20170920_Quemenes_VImport.xlsx";2;;;;;;;;;;"Pas de stratification";;;; +377643;"BD_ChpsBlocs_20_20170920_Quemenes_VImport.xlsx";3;;;;;;;;;;"Pas de stratification";;;; +377643;"BD_ChpsBlocs_20_20170920_Quemenes_VImport.xlsx";4;48.377317;-4.91038333E8;;;;;;;"DSCF0423.JPG";"Pas de stratification";"sable moyen, cailloutis, blocs éparses";29;0; +377643;"BD_ChpsBlocs_20_20170920_Quemenes_VImport.xlsx";5;48.37705;-4.91053333;;;;;;;"DSCF0430.JPG";"Pas de stratification";"sable moyen, cailloutis, blocs éparses";35;1; +377686;"BD_ChpsBlocs_20_20171007_Quemenes_VImport.xlsx";1;;;;;;;;;;"Pas de stratification";;;; +377686;"BD_ChpsBlocs_20_20171007_Quemenes_VImport.xlsx";2;48.3778;-4.91033333;;;;;;;"PA070038.JPG";"Pas de stratification";"sable grossier, cailloutis et blocs mobiles éparses";29;8; +377686;"BD_ChpsBlocs_20_20171007_Quemenes_VImport.xlsx";3;48.3776;-4.9104333;;;;;;;"PA070055.JPG";"Pas de stratification";"blocs sur cailloutis et galets";25;2; +377686;"BD_ChpsBlocs_20_20171007_Quemenes_VImport.xlsx";4;;;;;;;;;;"Pas de stratification";;;; +377686;"BD_ChpsBlocs_20_20171007_Quemenes_VImport.xlsx";5;;;;;;;;;;"Pas de stratification";;;; +377717;"BD_ChpsBlocs_21_20160408_Sein_Goulenez_VImport.xlsx";4;48.0435;-4.872233;;;;;;;"P4080019.JPG";"Pas de stratification";"GROS BLOCS QUELQUES VERTS QUELQUES BLANCS";91;7; +377717;"BD_ChpsBlocs_21_20160408_Sein_Goulenez_VImport.xlsx";3;48.043467;-4.872333;;;;;;;"P4080013.JPG";"Pas de stratification";"BLOCS TAILLE MOYENNE QUELQUES BLOCS VERTS";29;1; +377717;"BD_ChpsBlocs_21_20160408_Sein_Goulenez_VImport.xlsx";2;48.043167;-4.873083;;;;;;;"P4080007.JPG";"Pas de stratification";"PETITS BLOCS Kernisi (Keraliou)";;"Keraliou (champ de blocs)";"Suivis comportementaux";"V2014";"SYSTEME";"Brest métropole";;"Pluie continue";;15.0;"6-Vent frais-39 à 49 km/h-Crêtes d'écume blanches.";;;;"Reprise de données Champs Blocs +" +373156;"Suivi des champs de blocs";"2015-02-20 00:00:00.0";;;116;"12:12";;;"févr.-15";"BRES_03_CDB_févr.-15";"Non identifié";"Christian Le Jeune";"Rade de Brest";"BRES_03";"Le Cosquer Passage > Kernisi (Keraliou)";;"Keraliou (champ de blocs)";"Suivis comportementaux";"V2015";"SYSTEME";"Brest métropole";;"Pluies éparses";;8.0;"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";;;;"Reprise de données Champs Blocs +" +373166;"Suivi des champs de blocs";"2015-05-18 00:00:00.0";;;101;"12:10";;;"mai-15";"BRES_03_CDB_mai-15";"Non identifié";"Annick Postollec";"Rade de Brest";"BRES_03";"Le Cosquer Passage > Kernisi (Keraliou)";;"Keraliou (champ de blocs)";"Suivis comportementaux";"V2015";"SYSTEME";"Brest métropole";;"Pluie continue";;13.0;"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";;;;"Reprise de données Champs Blocs +" +373169;"Suivi des champs de blocs";"2015-10-27 00:00:00.0";;;108;"10:41";;;"oct.-15";"BRES_03_CDB_oct.-15";"Non identifié";"Franck Oppermann";"Rade de Brest";"BRES_03";"Le Cosquer Passage > Kernisi (Keraliou)";;"Keraliou (champ de blocs)";"Suivis comportementaux";"V2015";"SYSTEME";"Brest métropole";;"Pluie fine";;14.0;"0-Calme-1 km/h-Mer d'huile";;;;"Reprise de données Champs Blocs +" +373173;"Suivi des champs de blocs";"2016-02-11 00:00:00.0";;;106;"13:11";;"Semaine";"févr.-16";"BRES_03_CDB_févr.-16";"Christian Le Jeune";"Frank Oppermann";"Rade de Brest";"BRES_03";"Le Cosquer Passage > Kernisi (Keraliou)";;"Keraliou (champ de blocs)";"Suivis comportementaux";"V2016";"SYSTEME";"Brest métropole";"25-75% (Nuageux)";"Pas de précipitation";;5.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"Ouest";;"Reprise de données Champs Blocs +" +373177;"Suivi des champs de blocs";"2016-03-09 00:00:00.0";;;112;"11:25";;"Semaine";"mars-16";"BRES_03_CDB_mars-16";"Christian Le Jeune";"Frank Oppermann";"Rade de Brest";"BRES_03";"Le Cosquer Passage > Kernisi (Keraliou)";;"Keraliou (champ de blocs)";"Suivis comportementaux";"V2016";"SYSTEME";"Brest métropole";"75-100% (Très nuageux)";"Pas de précipitation";;8.0;"8-Coup de vent-62 à 74 km/h-Tourbillons d'écumes à la crête des lames";"10-Tempête-89 à 102 km/h-Très grosses lames à longue crête en panache. ";"Nord-Ouest";;"Reprise de données Champs Blocs +" +373179;"Suivi des champs de blocs";"2016-05-06 00:00:00.0";;;105;"11:39";;"Semaine";"mai-16";"BRES_03_CDB_mai-16";"Christian Le Jeune";"Ronan Moine";"Rade de Brest";"BRES_03";"Le Cosquer Passage > Kernisi (Keraliou)";;"Keraliou (champ de blocs)";"Suivis comportementaux";"V2016";"SYSTEME";"Brest métropole";"75-100% (Très nuageux)";"Pas de précipitation";;15.0;"1-Très légère brise-1 à 5 km/h-Mer ridée";"2-Lègère brise-6 à 11 km/h-Vaguelettes";"Nord-Est";;"Reprise de données Champs Blocs +" +373183;"Suivi des champs de blocs";"2016-08-20 00:00:00.0";;;103;"13:24";;"Semaine";"août-16";"BRES_03_CDB_août-16";"Christian Le Jeune";"Ronan Moine";"Rade de Brest";"BRES_03";"Le Cosquer Passage > Kernisi (Keraliou)";;"Keraliou (champ de blocs)";"Suivis comportementaux";"V2016";"SYSTEME";"Brest métropole";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;18.0;"6-Vent frais-39 à 49 km/h-Crêtes d'écume blanches.";"7-Grand frais-50 à 61 km/h- Lames déferlantes";"Sud-Ouest";;"Reprise de données Champs Blocs +" +373185;"Suivi des champs de blocs";"2016-10-16 00:00:00.0";;;108;"12:58";;"Week-end ou Jour Férié";"oct.-16";"BRES_03_CDB_oct.-16";"Christian Le Jeune";"Frank Oppermann";"Rade de Brest";"BRES_03";"Le Cosquer Passage > Kernisi (Keraliou)";;"Keraliou (champ de blocs)";"Suivis comportementaux";"V2016";"SYSTEME";"Brest métropole";"75-100% (Très nuageux)";"Pas de précipitation";;15.0;"5-Bonne brise-29 à 38 km/h-Vagues modérées.";"6-Vent frais-39 à 49 km/h-Crêtes d'écume blanches.";"Sud-Ouest";;"Reprise de données Champs Blocs +" +373193;"Suivi des champs de blocs";"2015-01-22 00:00:00.0";;;109;"11:58";;;"janv.-15";"FINS_02_CDB_janv.-15";"Non identifié";"Pauline POISSON et Agathe LEFRANC";"Finistère Sud";"FINS_02";"Pointe de Mousterlin";;"Pointe de Mousterlin (champ de blocs)";"Suivis comportementaux";"V2015";"SYSTEME";"Agence des aires marines protégées";;"Pas de précipitation";;8.0;;;;;"Reprise de données Champs Blocs +" +373198;"Suivi des champs de blocs";"2015-04-20 00:00:00.0";;;109;"12:41";;;"avr.-15";"FINS_02_CDB_avr.-15";"Non identifié";"Pauline POISSON, Héloïse YOU";"Finistère Sud";"FINS_02";"Pointe de Mousterlin";;"Pointe de Mousterlin (champ de blocs)";"Suivis comportementaux";"V2015";"SYSTEME";"Agence des aires marines protégées";;"Pas de précipitation";;18.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;"Reprise de données Champs Blocs +" +373205;"Suivi des champs de blocs";"2015-09-01 00:00:00.0";;;111;"13:19";;;"sept.-15";"FINS_02_CDB_sept.-15";"Non identifié";"Héloïse YOU";"Finistère Sud";"FINS_02";"Pointe de Mousterlin";;"Pointe de Mousterlin (champ de blocs)";"Suivis comportementaux";"V2015";"SYSTEME";"Agence des aires marines protégées";;"Pas de précipitation";;17.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;;"Reprise de données Champs Blocs +" +373210;"Suivi des champs de blocs";"2015-11-26 00:00:00.0";;;104;"14:40";;;"nov.-15";"FINS_02_CDB_nov.-15";"Non identifié";"Héloïse YOU";"Finistère Sud";"FINS_02";"Pointe de Mousterlin";;"Pointe de Mousterlin (champ de blocs)";"Suivis comportementaux";"V2015";"SYSTEME";"Agence des aires marines protégées";;"Pas de précipitation";;8.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";;;;"Reprise de données Champs Blocs +" +373213;"Suivi des champs de blocs";"2016-10-18 00:00:00.0";;;111;"12:56";;"Semaine";"oct.-16";"FINS_02_CDB_oct.-16";"Mathilde Le Roux";"Héloïse You";"Finistère Sud";"FINS_02";"Pointe de Mousterlin";;"Pointe de Mousterlin (champ de blocs)";"Suivis comportementaux";"V2016";"SYSTEME";"Agence des aires marines protégées";;"Pas de précipitation";;10.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";"Ouest";;"Reprise de données Champs Blocs +" +373215;"Suivi des champs de blocs";"2016-11-15 00:00:00.0";;;112;"10:52";;"Semaine";"nov.-16";"FINS_02_CDB_nov.-16";"Héloïse You";"Héloïse You";"Finistère Sud";"FINS_02";"Pointe de Mousterlin";;"Pointe de Mousterlin (champ de blocs)";"Suivis comportementaux";"V2016";"SYSTEME";"Agence des aires marines protégées";"25-75% (Nuageux)";"Pas de précipitation";;10.0;"0-Calme-1 km/h-Mer d'huile";"1-Très légère brise-1 à 5 km/h-Mer ridée";"Ouest";;"Reprise de données Champs Blocs +" +373217;"Suivi des champs de blocs";"2015-01-23 00:00:00.0";;;107;"12:44";;;"janv.-15";"FINS_12_CDB_janv.-15";"Pauline POISSON";"Pauline POISSON, Agathe LEFRANC";"Finistère Sud";"FINS_12";"Saint Nicolas - Bananec";;"Saint-Nicolas des Glénan (champ de blocs)";"Suivis comportementaux";"V2015";"SYSTEME";"Agence des aires marines protégées";;"Pas de précipitation";;10.0;;;;;"Reprise de données Champs Blocs +" +373221;"Suivi des champs de blocs";"2015-09-01 00:00:00.0";;;111;"13:19";;;"sept.-15";"FINS_12_CDB_sept.-15";"Pauline POISSON";"Pauline POISSON";"Finistère Sud";"FINS_12";"Saint Nicolas - Bananec";;"Saint-Nicolas des Glénan (champ de blocs)";"Suivis comportementaux";"V2015";"SYSTEME";"Agence des aires marines protégées";;"Pas de précipitation";;17.0;;;;;"Reprise de données Champs Blocs +" +373230;"Suivi des champs de blocs";"2015-11-26 00:00:00.0";;;104;"10:40";;;"nov.-15";"FINS_12_CDB_nov.-15";"Héloïse YOU";"Pauline Poisson";"Finistère Sud";"FINS_12";"Saint Nicolas - Bananec";;"Saint-Nicolas des Glénan (champ de blocs)";"Suivis comportementaux";"V2015";"SYSTEME";"Agence des aires marines protégées";;"Pas de précipitation";;10.0;;;;;"Reprise de données Champs Blocs +" +373232;"Suivi des champs de blocs";"2016-10-18 00:00:00.0";;;111;"12:56";;"Semaine";"oct.-16";"FINS_12_CDB_oct.-16";"Mathilde Le Roux";"Véronique (bénévole Bretagne Vivante), Mathilde Le Roux";"Finistère Sud";"FINS_12";"Saint Nicolas - Bananec";;"Saint-Nicolas des Glénan (champ de blocs)";"Suivis comportementaux";"V2016";"SYSTEME";"Agence des aires marines protégées";"25-75% (Nuageux)";"Pas de précipitation";;13.0;"5-Bonne brise-29 à 38 km/h-Vagues modérées.";"5-Bonne brise-29 à 38 km/h-Vagues modérées.";"Ouest";;"Reprise de données Champs Blocs +" +373237;"Suivi des champs de blocs";"2016-11-15 00:00:00.0";;;112;"10:52";;"Semaine";"nov.-16";"FINS_12_CDB_nov.-16";"Mathilde Le Roux";"Bruno Férré, Nathanaël Jeune";"Finistère Sud";"FINS_12";"Saint Nicolas - Bananec";;"Saint-Nicolas des Glénan (champ de blocs)";"Suivis comportementaux";"V2016";"SYSTEME";"Agence des aires marines protégées";"25-75% (Nuageux)";"Pas de précipitation";;14.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"Ouest";;"Reprise de données Champs Blocs +" +373240;"Suivi des champs de blocs";"2015-04-20 00:00:00.0";;;109;"12:59";;;"avr.-15";"GDMO_01_CDB_avr.-15";"Non identifié";"Jonathan Pothier";"Golfe du Morbihan";"GDMO_01";"La falaise";;"Locmariaquer (champ de blocs)";"Suivis comportementaux";"V2015";"SYSTEME";"Parc naturel régional du golfe du Morbihan";;"Pas de précipitation";;17.0;"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";;;;"Reprise de données Champs Blocs +" +373243;"Suivi des champs de blocs";"2015-08-31 00:00:00.0";;;114;"12:52";;;"août-15";"GDMO_01_CDB_août-15";"Non identifié";"Jonathan Pothier";"Golfe du Morbihan";"GDMO_01";"La falaise";;"Locmariaquer (champ de blocs)";"Suivis comportementaux";"V2015";"SYSTEME";"Parc naturel régional du golfe du Morbihan";;"Pas de précipitation";;21.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;;"Reprise de données Champs Blocs +" +373246;"Suivi des champs de blocs";"2015-09-01 00:00:00.0";;;111;"13:38";;;"sept.-15";"GDMO_01_CDB_sept.-15";"Non identifié";"Jonathan Pothier";"Golfe du Morbihan";"GDMO_01";"La falaise";;"Locmariaquer (champ de blocs)";"Suivis comportementaux";"V2015";"SYSTEME";"Parc naturel régional du golfe du Morbihan";;"Pas de précipitation";;18.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;;"Reprise de données Champs Blocs +" +373250;"Suivi des champs de blocs";"2015-09-02 00:00:00.0";;;105;"14:25";;;"sept.-15";"GDMO_01_CDB_sept.-15";"Non identifié";"Jonathan Pothier";"Golfe du Morbihan";"GDMO_01";"La falaise";;"Locmariaquer (champ de blocs)";"Suivis comportementaux";"V2015";"SYSTEME";"Parc naturel régional du golfe du Morbihan";;"Pas de précipitation";;18.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;;"Reprise de données Champs Blocs +" +373256;"Suivi des champs de blocs";"2016-10-18 00:00:00.0";;;111;"13:16";;"Semaine";"oct.-16";"GDMO_01_CDB_oct.-16";"margot le priol";"margot le priol et leslie veron";"Golfe du Morbihan";"GDMO_01";"La falaise";;"Locmariaquer (champ de blocs)";"Suivis comportementaux";"V2016";"SYSTEME";"Parc naturel régional du golfe du Morbihan";"75-100% (Très nuageux)";"Pas de précipitation";;13.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"Ouest";;"Reprise de données Champs Blocs +" +373261;"Suivi des champs de blocs";"2015-02-18 00:00:00.0";;;96;"10:20";;;"févr.-15";"GDMO_04_CDB_févr.-15";"Non identifié";"Jonathan Pothier";"Golfe du Morbihan";"GDMO_04";"Beg Lann";;"Beg Lann (champ de blocs)";"Suivis comportementaux";"V2015";"SYSTEME";"Parc naturel régional du golfe du Morbihan";;"Pas de précipitation";;5.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";;;;"Reprise de données Champs Blocs +" +373264;"Suivi des champs de blocs";"2015-08-31 00:00:00.0";;;114;"12:45";;;"août-15";"GDMO_04_CDB_août-15";"Non identifié";"Margaux Féon";"Golfe du Morbihan";"GDMO_04";"Beg Lann";;"Beg Lann (champ de blocs)";"Suivis comportementaux";"V2015";"SYSTEME";"Parc naturel régional du golfe du Morbihan";;"Pas de précipitation";;21.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;;"Reprise de données Champs Blocs +" +373267;"Suivi des champs de blocs";"2015-10-28 00:00:00.0";;;113;"10:05";;;"oct.-15";"GDMO_04_CDB_oct.-15";"Non identifié";"Jonathan Pothier";"Golfe du Morbihan";"GDMO_04";"Beg Lann";;"Beg Lann (champ de blocs)";"Suivis comportementaux";"V2015";"SYSTEME";"Parc naturel régional du golfe du Morbihan";;"Pas de précipitation";;9.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";;;;"Reprise de données Champs Blocs +" +373270;"Suivi des champs de blocs";"2015-10-29 00:00:00.0";;;112;"11:52";;;"oct.-15";"GDMO_04_CDB_oct.-15";"Non identifié";"Jonathan Pothier";"Golfe du Morbihan";"GDMO_04";"Beg Lann";;"Beg Lann (champ de blocs)";"Suivis comportementaux";"V2015";"SYSTEME";"Parc naturel régional du golfe du Morbihan";;"Pas de précipitation";;11.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;;"Reprise de données Champs Blocs +" +373274;"Suivi des champs de blocs";"2016-09-02 00:00:00.0";;;93;"12:33";;"Semaine";"sept.-16";"GDMO_04_CDB_sept.-16";"margot le priol";"leslie veron";"Golfe du Morbihan";"GDMO_04";"Beg Lann";;"Beg Lann (champ de blocs)";"Suivis comportementaux";"V2016";"SYSTEME";"Parc naturel régional du golfe du Morbihan";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;;"2-Lègère brise-6 à 11 km/h-Vaguelettes";"2-Lègère brise-6 à 11 km/h-Vaguelettes";"Ouest";;"Reprise de données Champs Blocs +" +373279;"Suivi des champs de blocs";"2015-08-03 00:00:00.0";;;104;"13:55";;;"août-15";"FOUR_0_CDB_août-15";"Non identifié";"Estelle BAUDINIERE";"Plateau du Four (territoire)";"FOUR_0";"Plateau du Four";;"Plateau du Four (champ de blocs)";"Suivis comportementaux";"V2015";"SYSTEME";"Comité régional des pêches maritimes et des élevages marins des Pays de Loire";;"Pas de précipitation";;19.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;;"Reprise de données Champs Blocs +" +373283;"Suivi des champs de blocs";"2015-08-04 00:00:00.0";;;97;"14:40";;;"août-15";"FOUR_0_CDB_août-15";"Non identifié";"Mathilde et Patrick MUNIER";"Plateau du Four (territoire)";"FOUR_0";"Plateau du Four";;"Plateau du Four (champ de blocs)";"Suivis comportementaux";"V2015";"SYSTEME";"Comité régional des pêches maritimes et des élevages marins des Pays de Loire";;"Pas de précipitation";;17.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;;"Reprise de données Champs Blocs +" +373288;"Suivi des champs de blocs";"2015-08-31 00:00:00.0";;;114;"13:55";;;"août-15";"FOUR_0_CDB_août-15";"Non identifié";"Ion TILLIER";"Plateau du Four (territoire)";"FOUR_0";"Plateau du Four";;"Plateau du Four (champ de blocs)";"Suivis comportementaux";"V2015";"SYSTEME";"Comité régional des pêches maritimes et des élevages marins des Pays de Loire";;"Pas de précipitation";;17.0;"1-Très légère brise-1 à 5 km/h-Mer ridée";;;;"Reprise de données Champs Blocs +" +373294;"Suivi des champs de blocs";"2016-06-06 00:00:00.0";;;102;"12:37";;"Semaine";"juin-16";"FOUR_0_CDB_juin-16";"Estelle BAUDINIERE";"Matthieu HERVY, Guillaume LECOUTURIER";"Plateau du Four (territoire)";"FOUR_0";"Plateau du Four";;"Plateau du Four (champ de blocs)";"Suivis comportementaux";"V2016";"SYSTEME";"Comité régional des pêches maritimes et des élevages marins des Pays de Loire";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;17.0;"1-Très légère brise-1 à 5 km/h-Mer ridée";"2-Lègère brise-6 à 11 km/h-Vaguelettes";"Est";;"Reprise de données Champs Blocs +" +373298;"Suivi des champs de blocs";"2016-06-07 00:00:00.0";;;97;"13:23";;"Semaine";"juin-16";"FOUR_0_CDB_juin-16";"Estelle BAUDINIERE";"Patrick MONNIER, Jérôme FIEVET";"Plateau du Four (territoire)";"FOUR_0";"Plateau du Four";;"Plateau du Four (champ de blocs)";"Suivis comportementaux";"V2016";"SYSTEME";"Comité régional des pêches maritimes et des élevages marins des Pays de Loire";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;18.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"Sud-Ouest";;"Reprise de données Champs Blocs +" +373303;"Suivi des champs de blocs";"2016-09-19 00:00:00.0";;;111;"13:33";;"Semaine";"sept.-16";"FOUR_0_CDB_sept.-16";"Estelle BAUDINIERE";"Laurie-Anne HENO";"Plateau du Four (territoire)";"FOUR_0";"Plateau du Four";;"Plateau du Four (champ de blocs)";"Suivis comportementaux";"V2016";"SYSTEME";"Comité régional des pêches maritimes et des élevages marins des Pays de Loire";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;17.0;"1-Très légère brise-1 à 5 km/h-Mer ridée";"2-Lègère brise-6 à 11 km/h-Vaguelettes";"Nord-Ouest";;"Reprise de données Champs Blocs +" +373306;"Suivi des champs de blocs";"2016-09-20 00:00:00.0";;;105;"14:20";;"Semaine";"sept.-16";"FOUR_0_CDB_sept.-16";"Estelle BAUDINIERE";"Estelle BAUDINIERE";"Plateau du Four (territoire)";"FOUR_0";"Plateau du Four";;"Plateau du Four (champ de blocs)";"Suivis comportementaux";"V2016";"SYSTEME";"Comité régional des pêches maritimes et des élevages marins des Pays de Loire";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;19.0;"1-Très légère brise-1 à 5 km/h-Mer ridée";"2-Lègère brise-6 à 11 km/h-Vaguelettes";"Nord-Ouest";;"Reprise de données Champs Blocs +" +373310;"Suivi des champs de blocs";"2015-02-04 00:00:00.0";;;84;"11:10";;;"févr.-15";"EGMP_014_CDB_févr.-15";"Non identifié";"Julie Vuilleret et Jérémy Daniel";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_014-015";"Pointes du Grouin du Cou et de la République";;"Pointe du Grouin du cou (champ de blocs)";"Suivis comportementaux";"V2015";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";;"Pluie fine";;3.0;"5-Bonne brise-29 à 38 km/h-Vagues modérées.";;;;"Reprise de données Champs Blocs +" +373313;"Suivi des champs de blocs";"2015-07-16 00:00:00.0";;;84;"11:44";;;"juil.-15";"EGMP_014_CDB_juil.-15";"Non identifié";"Camille Pigot et Justine Vallée";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_014-015";"Pointes du Grouin du Cou et de la République";;"Pointe du Grouin du cou (champ de blocs)";"Suivis comportementaux";"V2015";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";;"Pas de précipitation";;23.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;;"Reprise de données Champs Blocs +" +373317;"Suivi des champs de blocs";"2015-10-14 00:00:00.0";;;85;"12:15";;;"oct.-15";"EGMP_014_CDB_oct.-15";"Non identifié";"Justine Vallée et Julie Vuilleret";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_014-015";"Pointes du Grouin du Cou et de la République";;"Pointe du Grouin du cou (champ de blocs)";"Suivis comportementaux";"V2015";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";;"Pas de précipitation";;11.0;"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";;;;"Reprise de données Champs Blocs +" +373321;"Suivi des champs de blocs";"2016-03-10 00:00:00.0";;;116;"11:34";;"Semaine";"mars-16";"EGMP_014_CDB_mars-16";"Pauline Momot";"Justine Vallée, Pauline Momot";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_014-015";"Pointes du Grouin du Cou et de la République";;"Pointe du Grouin du cou (champ de blocs)";"Suivis comportementaux";"V2016";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";"25-75% (Nuageux)";"Pas de précipitation";;8.0;"5-Bonne brise-29 à 38 km/h-Vagues modérées.";"5-Bonne brise-29 à 38 km/h-Vagues modérées.";"Nord-Est";;"Reprise de données Champs Blocs +" +373325;"Suivi des champs de blocs";"2016-06-08 00:00:00.0";;;92;"13:51";;"Semaine";"juin-16";"EGMP_014_CDB_juin-16";"Manon Charpentier";"Pauline Momot, Manon Charpentier";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_014-015";"Pointes du Grouin du Cou et de la République";;"Pointe du Grouin du cou (champ de blocs)";"Suivis comportementaux";"V2016";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";;"Pas de précipitation";;20.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";"2-Lègère brise-6 à 11 km/h-Vaguelettes";"Nord-Est";;"Reprise de données Champs Blocs +" +373329;"Suivi des champs de blocs";"2016-09-30 00:00:00.0";;;88;"11:11";;"Semaine";"sept.-16";"EGMP_014_CDB_sept.-16";"Justine Vallée";"Pauline Momot, Justine Vallée";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_014-015";"Pointes du Grouin du Cou et de la République";;"Pointe du Grouin du cou (champ de blocs)";"Suivis comportementaux";"V2016";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";"25-75% (Nuageux)";"Pluies éparses";;18.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"Nord-Est";;"Reprise de données Champs Blocs +" +373333;"Suivi des champs de blocs";"2015-05-19 00:00:00.0";;;99;"12:22";;;"mai-15";"EGMP_114_CDB_mai-15";"Non identifié";"Sarah Olivier-Maud Granier";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_114";"La Brée et la Balise";;"La Brée-les-Bains (champ de blocs)";"Suivis comportementaux";"V2015";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";;"Pluie continue";;13.0;"6-Vent frais-39 à 49 km/h-Crêtes d'écume blanches.";;;;"Reprise de données Champs Blocs +" +373338;"Suivi des champs de blocs";"2015-07-03 00:00:00.0";;;94;"12:26";;;"juil.-15";"EGMP_114_CDB_juil.-15";"Non identifié";"Jean-Baptiste Bonnin";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_114";"La Brée et la Balise";;"La Brée-les-Bains (champ de blocs)";"Suivis comportementaux";"V2015";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";;"Pas de précipitation";;27.0;"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";;;;"Reprise de données Champs Blocs +" +373344;"Suivi des champs de blocs";"2015-08-03 00:00:00.0";;;104;"13:39";;;"août-15";"EGMP_114_CDB_août-15";"Non identifié";"Sarah Olivier";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_114";"La Brée et la Balise";;"La Brée-les-Bains (champ de blocs)";"Suivis comportementaux";"V2015";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";;"Pas de précipitation";;21.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;;"Reprise de données Champs Blocs +" +373351;"Suivi des champs de blocs";"2015-09-28 00:00:00.0";;;110;"11:28";;;"sept.-15";"EGMP_114_CDB_sept.-15";"Non identifié";"Loeïza Lancelot";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_114";"La Brée et la Balise";;"La Brée-les-Bains (champ de blocs)";"Suivis comportementaux";"V2015";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";;"Pas de précipitation";;18.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;;"Reprise de données Champs Blocs +" +373358;"Suivi des champs de blocs";"2016-05-09 00:00:00.0";;;106;"13:25";;"Semaine";"mai-16";"EGMP_114_CDB_mai-16";"Sarah Olivier";"Zachary Gaudin, Sarah Olivier";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_114";"La Brée et la Balise";;"La Brée-les-Bains (champ de blocs)";"Suivis comportementaux";"V2016";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";"25-75% (Nuageux)";"Pas de précipitation";;19.0;"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";"Sud-Est";;"Reprise de données Champs Blocs +" +373360;"Suivi des champs de blocs";"2016-06-06 00:00:00.0";;;102;"12:21";;"Semaine";"juin-16";"EGMP_114_CDB_juin-16";"Sarah Olivier";"Sarah Olivier";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_114";"La Brée et la Balise";;"La Brée-les-Bains (champ de blocs)";"Suivis comportementaux";"V2016";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;23.9;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"Est";;"Reprise de données Champs Blocs +" +373365;"Suivi des champs de blocs";"2016-07-05 00:00:00.0";;;96;"12:06";;"Semaine";"juil.-16";"EGMP_114_CDB_juil.-16";"Sarah Olivier";"Sarah Olivier";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_114";"La Brée et la Balise";;"La Brée-les-Bains (champ de blocs)";"Suivis comportementaux";"V2016";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";"25-75% (Nuageux)";"Pas de précipitation";;18.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";"2-Lègère brise-6 à 11 km/h-Vaguelettes";"Nord-Ouest";;"Reprise de données Champs Blocs +" +373372;"Suivi des champs de blocs";"2016-08-20 00:00:00.0";;;102;"12:55";;"Week-end ou Jour Férié";"août-16";"EGMP_114_CDB_août-16";"Sarah Olivier";"Zachary Gaudin";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_114";"La Brée et la Balise";;"La Brée-les-Bains (champ de blocs)";"Suivis comportementaux";"V2016";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";"25-75% (Nuageux)";"Pas de précipitation";;21.0;"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";"Ouest";;"Reprise de données Champs Blocs +" +373379;"Suivi des champs de blocs";"2016-10-15 00:00:00.0";;;96;"10:41";;"Week-end ou Jour Férié";"oct.-16";"EGMP_114_CDB_oct.-16";"Sarah Olivier";"Sarah Olivier";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_114";"La Brée et la Balise";;"La Brée-les-Bains (champ de blocs)";"Suivis comportementaux";"V2016";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";"25-75% (Nuageux)";"Pas de précipitation";;15.7;"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";"Sud-Est";;"Reprise de données Champs Blocs +" +373384;"Suivi des champs de blocs";"2015-05-20 00:00:00.0";;;93;"12:38";;;"mai-15";"EGMP_116_CDB_mai-15";"Non identifié";"Adrien Privat-Jean-Baptiste Bonnin-Maud Granier-Virginie Evenou";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_116";"Perré d'Antiochat";;"Pérré d'Antiochat (champ de blocs)";"Suivis comportementaux";"V2015";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";;"Pas de précipitation";;16.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";;;;"Reprise de données Champs Blocs +" +373386;"Suivi des champs de blocs";"2015-07-03 00:00:00.0";;;94;"12:26";;;"juil.-15";"EGMP_116_CDB_juil.-15";"Non identifié";"Sarah Olivier";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_116";"Perré d'Antiochat";;"Pérré d'Antiochat (champ de blocs)";"Suivis comportementaux";"V2015";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";;"Pas de précipitation";;29.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;;"Reprise de données Champs Blocs +" +373389;"Suivi des champs de blocs";"2015-08-04 00:00:00.0";;;101;"14:26";;;"août-15";"EGMP_116_CDB_août-15";"Non identifié";"Adrien Privat";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_116";"Perré d'Antiochat";;"Pérré d'Antiochat (champ de blocs)";"Suivis comportementaux";"V2015";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";;"Pas de précipitation";;24.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;;"Reprise de données Champs Blocs +" +373393;"Suivi des champs de blocs";"2015-09-30 00:00:00.0";;;113;"12:34";;;"sept.-15";"EGMP_116_CDB_sept.-15";"Non identifié";"Kévin Fournière";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_116";"Perré d'Antiochat";;"Pérré d'Antiochat (champ de blocs)";"Suivis comportementaux";"V2015";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";;"Pas de précipitation";;19.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;;"Reprise de données Champs Blocs +" +373397;"Suivi des champs de blocs";"2016-04-09 00:00:00.0";;;113;"12:58";;"Week-end ou Jour Férié";"avr.-16";"EGMP_116_CDB_avr.-16";"Sarah Olivier";"Richard Coz";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_116";"Perré d'Antiochat";;"Pérré d'Antiochat (champ de blocs)";"Suivis comportementaux";"V2016";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;13.1;"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";"Nord-Ouest";;"Reprise de données Champs Blocs +" +373403;"Suivi des champs de blocs";"2016-07-04 00:00:00.0";;;93;"11:18";;"Semaine";"juil.-16";"EGMP_116_CDB_juil.-16";"Sarah Olivier";"Zachary Gaudin, Adrien Privat, Marion Carsac";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_116";"Perré d'Antiochat";;"Pérré d'Antiochat (champ de blocs)";"Suivis comportementaux";"V2016";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";"25-75% (Nuageux)";"Pas de précipitation";;18.6;"2-Lègère brise-6 à 11 km/h-Vaguelettes";"2-Lègère brise-6 à 11 km/h-Vaguelettes";"Nord-Est";;"Reprise de données Champs Blocs +" +373407;"Suivi des champs de blocs";"2016-08-20 00:00:00.0";;;103;"12:55";;"Week-end ou Jour Férié";"août-16";"EGMP_116_CDB_août-16";"Sarah Olivier";"Sarah Olivier";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_116";"Perré d'Antiochat";;"Pérré d'Antiochat (champ de blocs)";"Suivis comportementaux";"V2016";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";"25-75% (Nuageux)";"Pas de précipitation";;21.0;"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";"Ouest";;"Reprise de données Champs Blocs +" +373413;"Suivi des champs de blocs";"2016-10-19 00:00:00.0";;;101;"13:04";;"Semaine";"oct.-16";"EGMP_116_CDB_oct.-16";"Sarah Olivier";"Sarah Olivier";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_116";"Perré d'Antiochat";;"Pérré d'Antiochat (champ de blocs)";"Suivis comportementaux";"V2016";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;16.0;"1-Très légère brise-1 à 5 km/h-Mer ridée";"1-Très légère brise-1 à 5 km/h-Mer ridée";"Nord";;"Reprise de données Champs Blocs +" +373416;"Suivi des champs de blocs";"2015-09-27 00:00:00.0";;;106;"10:24";;;"sept.-15";"BASQ_01_CDB_ZP_sept.-15";"Non identifié";"Josiane POPOSKY";"Côte basque";"BASQ_01";"Les Flots Bleus";;"Les Flots Bleus (champ de blocs) - zone pàªcheurs";"Suivis comportementaux";"V2015";"SYSTEME";"CPIE littoral basque";;"Pas de précipitation";;20.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";;;;"Reprise de données Champs Blocs +" +373418;"Suivi des champs de blocs";"2016-03-07 00:00:00.0";;;87;"09:09";;"Semaine";"mars-16";"BASQ_01_CDB_ZP_mars-16";"Josiane Popovsky";"Pascale Fossecave";"Côte basque";"BASQ_01";"Les Flots Bleus";;"Les Flots Bleus (champ de blocs) - zone pàªcheurs";"Suivis comportementaux";"V2016";"SYSTEME";"CPIE littoral basque";"25-75% (Nuageux)";"Pluie continue";;7.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"Nord-Ouest";;"Reprise de données Champs Blocs +" +373420;"Suivi des champs de blocs";"2016-05-08 00:00:00.0";;;110;"12:18";;"Week-end ou Jour Férié";"mai-16";"BASQ_01_CDB_ZP_mai-16";"Josiane Popovsky";"Pascale Fossecave";"Côte basque";"BASQ_01";"Les Flots Bleus";;"Les Flots Bleus (champ de blocs) - zone pàªcheurs";"Suivis comportementaux";"V2016";"SYSTEME";"CPIE littoral basque";"75-100% (Très nuageux)";"Pas de précipitation";;;"2-Lègère brise-6 à 11 km/h-Vaguelettes";"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"Nord-Ouest";;"Reprise de données Champs Blocs +" +373422;"Suivi des champs de blocs";"2016-08-21 00:00:00.0";;;101;"13:14";;"Week-end ou Jour Férié";"août-16";"BASQ_01_CDB_ZP_août-16";"Josiane Popovsky";"Josiane Popovsky";"Côte basque";"BASQ_01";"Les Flots Bleus";;"Les Flots Bleus (champ de blocs) - zone pàªcheurs";"Suivis comportementaux";"V2016";"SYSTEME";"CPIE littoral basque";"25-75% (Nuageux)";"Pas de précipitation";;24.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";"2-Lègère brise-6 à 11 km/h-Vaguelettes";"Ouest";;"Reprise de données Champs Blocs +" +373427;"Suivi des champs de blocs";"2016-09-14 00:00:00.0";;;72;"09:27";;"Semaine";"sept.-16";"BASQ_01_CDB_ZP_sept.-16";"Josiane Popovsky";"Josiane Popovsky";"Côte basque";"BASQ_01";"Les Flots Bleus";;"Les Flots Bleus (champ de blocs) - zone pàªcheurs";"Suivis comportementaux";"V2016";"SYSTEME";"CPIE littoral basque";"25-75% (Nuageux)";"Pas de précipitation";;18.0;"1-Très légère brise-1 à 5 km/h-Mer ridée";"1-Très légère brise-1 à 5 km/h-Mer ridée";"Ouest";;"Reprise de données Champs Blocs +" +373429;"Suivi des champs de blocs";"2016-09-19 00:00:00.0";;;108;"12:59";;"Semaine";"sept.-16";"BASQ_01_CDB_ZP_sept.-16";"Josiane Popovsky";"Pascale Fossecave";"Côte basque";"BASQ_01";"Les Flots Bleus";;"Les Flots Bleus (champ de blocs) - zone pàªcheurs";"Suivis comportementaux";"V2016";"SYSTEME";"CPIE littoral basque";;;;;"0-Calme-1 km/h-Mer d'huile";"0-Calme-1 km/h-Mer d'huile";;;"Reprise de données Champs Blocs +" +373433;"Suivi des champs de blocs";"2015-03-20 00:00:00.0";;;110;"10:00";;;"mars-15";"BASQ_01_CDB_ZF_mars-15";"Non identifié";"Josiane POPOSKY";"Côte basque";"BASQ_01";"Les Flots Bleus";;"Les Flots Bleus (champ de blocs) - zone famille";"Suivis comportementaux";"V2015";"SYSTEME";"CPIE littoral basque";;"Pas de précipitation";;10.0;"1-Très légère brise-1 à 5 km/h-Mer ridée";;;;"Reprise de données Champs Blocs +" +373435;"Suivi des champs de blocs";"2015-08-16 00:00:00.0";;;86;"12:19";;;"août-15";"BASQ_01_CDB_ZF_août-15";"Non identifié";"Josiane POPOSKY";"Côte basque";"BASQ_01";"Les Flots Bleus";;"Les Flots Bleus (champ de blocs) - zone famille";"Suivis comportementaux";"V2015";"SYSTEME";"CPIE littoral basque";;"Pas de précipitation";;23.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";;;;"Reprise de données Champs Blocs +" +373442;"Suivi des champs de blocs";"2015-10-25 00:00:00.0";;;89;"08:13";;;"oct.-15";"BASQ_01_CDB_ZF_oct.-15";"Non identifié";"Josiane POPOSKY";"Côte basque";"BASQ_01";"Les Flots Bleus";;"Les Flots Bleus (champ de blocs) - zone famille";"Suivis comportementaux";"V2015";"SYSTEME";"CPIE littoral basque";;"Pas de précipitation";;18.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";;;;"Reprise de données Champs Blocs +" +373444;"Suivi des champs de blocs";"2016-03-07 00:00:00.0";;;87;"09:09";;"Semaine";"mars-16";"BASQ_01_CDB_ZF_mars-16";"Josiane Popovsky";"Pascale Fossecave";"Côte basque";"BASQ_01";"Les Flots Bleus";;"Les Flots Bleus (champ de blocs) - zone famille";"Suivis comportementaux";"V2016";"SYSTEME";"CPIE littoral basque";"25-75% (Nuageux)";"Pluie continue";;7.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"Nord-Ouest";;"Reprise de données Champs Blocs +" +373446;"Suivi des champs de blocs";"2016-05-08 00:00:00.0";;;110;"12:18";;"Week-end ou Jour Férié";"mai-16";"BASQ_01_CDB_ZF_mai-16";"Josiane Popovsky";"Pascale Fossecave";"Côte basque";"BASQ_01";"Les Flots Bleus";;"Les Flots Bleus (champ de blocs) - zone famille";"Suivis comportementaux";"V2016";"SYSTEME";"CPIE littoral basque";"75-100% (Très nuageux)";"Pas de précipitation";;;"2-Lègère brise-6 à 11 km/h-Vaguelettes";"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"Nord-Ouest";;"Reprise de données Champs Blocs +" +373448;"Suivi des champs de blocs";"2016-08-17 00:00:00.0";;;83;"10:34";;"Semaine";"août-16";"BASQ_01_CDB_ZF_août-16";"Josiane Popovsky";"Josiane Popovsky, Pascale Fossecave";"Côte basque";"BASQ_01";"Les Flots Bleus";;"Les Flots Bleus (champ de blocs) - zone famille";"Suivis comportementaux";"V2016";"SYSTEME";"CPIE littoral basque";"75-100% (Très nuageux)";"Pas de précipitation";;;"1-Très légère brise-1 à 5 km/h-Mer ridée";"1-Très légère brise-1 à 5 km/h-Mer ridée";"Ouest";;"Reprise de données Champs Blocs +" +373456;"Suivi des champs de blocs";"2016-09-14 00:00:00.0";;;72;"09:27";;"Semaine";"sept.-16";"BASQ_01_CDB_ZF_sept.-16";"Josiane Popovsky";"Josiane Popovsky";"Côte basque";"BASQ_01";"Les Flots Bleus";;"Les Flots Bleus (champ de blocs) - zone famille";"Suivis comportementaux";"V2016";"SYSTEME";"CPIE littoral basque";"25-75% (Nuageux)";"Pas de précipitation";;18.0;"1-Très légère brise-1 à 5 km/h-Mer ridée";"1-Très légère brise-1 à 5 km/h-Mer ridée";"Ouest";;"Reprise de données Champs Blocs +" +373458;"Suivi des champs de blocs";"2016-10-18 00:00:00.0";;;111;"12:34";;"Semaine";"oct.-16";"BASQ_01_CDB_ZF_oct.-16";"Josiane Popovsky";"Josiane Popovsky";"Côte basque";"BASQ_01";"Les Flots Bleus";;"Les Flots Bleus (champ de blocs) - zone famille";"Suivis comportementaux";"V2016";"SYSTEME";"CPIE littoral basque";"25-75% (Nuageux)";"Pas de précipitation";;17.0;"0-Calme-1 km/h-Mer d'huile";"1-Très légère brise-1 à 5 km/h-Mer ridée";"Sud-Ouest";;"Reprise de données Champs Blocs +" +373462;"Suivi des champs de blocs";"2014-10-10 00:00:00.0";;;106;"13:01";;;"oct.-14";"BDD_IVR&QECB_Locmariaquer_20141010_VImport.xlsx";"Non identifié";"Tiphaine Yvon, JoNCthan pothier";"Golfe du Morbihan";"GDMO_01";"La falaise";;"Locmariaquer (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2014";"SYSTEME";"Parc naturel régional du golfe du Morbihan";;"Pluies éparses";;;;;;;"Reprise de données Champs Blocs +" +373529;"Suivi des champs de blocs";"2015-03-22 00:00:00.0";;;115;"12:21";;;"mars-15";"BDD_IVR&QECB_Locmariaquer_22032015_VImport.xlsx";"Non identifié";"Géraldine Colli, Jonathan pothier";"Golfe du Morbihan";"GDMO_01";"La falaise";;"Locmariaquer (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2015";"SYSTEME";"Parc naturel régional du golfe du Morbihan";;"Pas de précipitation";;18.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;;"Reprise de données Champs Blocs +" +373596;"Suivi des champs de blocs";"2015-10-30 00:00:00.0";;;115;"12:21";;;"oct.-15";"BDD_IVR&QECB_Locmariaquer_20151030_VImport.xlsx";"Non identifié";"Géraldine Colli, Jonathan pothier";"Golfe du Morbihan";"GDMO_01";"La falaise";;"Locmariaquer (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2015";"SYSTEME";"Parc naturel régional du golfe du Morbihan";;"Pas de précipitation";;18.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;;"Reprise de données Champs Blocs +" +373663;"Suivi des champs de blocs";"2016-03-09 00:00:00.0";;;107;"11:20";;"Semaine";"mars-16";"BDD_IVR&QECB_Locmariaquer_20160309_VImport.xlsx";"margot le priol";"Jonathan Pothier, Thomas Cosson";"Golfe du Morbihan";"GDMO_01";"La falaise";;"Locmariaquer (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2016";"SYSTEME";"Parc naturel régional du golfe du Morbihan";"25-75% (Nuageux)";"Pas de précipitation";;7.0;"6-Vent frais-39 à 49 km/h-Crêtes d'écume blanches.";"7-Grand frais-50 à 61 km/h- Lames déferlantes";"Nord-Ouest";;"Reprise de données Champs Blocs +" +373730;"Suivi des champs de blocs";"2016-09-17 00:00:00.0";;;104;"12:09";;"Week-end ou Jour Férié";"sept.-16";"BDD_IVR&QECB_Locmariaquer_20160917_VImport.xlsx";"le priol margot";"Leslie Veron, Thomas Cosson, Le priol margot";"Golfe du Morbihan";"GDMO_01";"La falaise";;"Locmariaquer (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2016";"SYSTEME";"Parc naturel régional du golfe du Morbihan";"25-75% (Nuageux)";"Pas de précipitation";;16.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"Nord-Ouest";;"Reprise de données Champs Blocs +" +373797;"Suivi des champs de blocs";"2016-10-20 00:00:00.0";;;95;"14:53";;"Semaine";"oct.-16";"BDD_IVR_Locmariaquer_20161020_VImport.xlsx";"Leslie Veron";"Leslie Veron";"Golfe du Morbihan";"GDMO_01";"La falaise";;"Locmariaquer (champ de blocs)";"Suivi IVR";"V2016";"SYSTEME";"Parc naturel régional du golfe du Morbihan";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;14.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";"2-Lègère brise-6 à 11 km/h-Vaguelettes";"Nord";;"Reprise de données Champs Blocs +" +373804;"Suivi des champs de blocs";"2014-11-06 00:00:00.0";;;100;"10:12";;;"nov.-14";"BDD_IVR&QECB_BegLann_20141106_VImport.xlsx";"Non identifié";"Kim Van Arkel, Jonathan pothier";"Golfe du Morbihan";"GDMO_04";"Beg Lann";;"Beg Lann (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2014";"SYSTEME";"Parc naturel régional du golfe du Morbihan";;;;;;;;;"Reprise de données Champs Blocs +" +373871;"Suivi des champs de blocs";"2015-03-23 00:00:00.0";;;105;"13:00";;;"mars-15";"BDD_IVR&QECB_BegLann_20150323_VImport.xlsx";"Non identifié";"Jonathan pothier";"Golfe du Morbihan";"GDMO_04";"Beg Lann";;"Beg Lann (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2015";"SYSTEME";"Parc naturel régional du golfe du Morbihan";;"Pas de précipitation";;12.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;;"Reprise de données Champs Blocs +" +373938;"Suivi des champs de blocs";"2015-10-31 00:00:00.0";;;113;"13:10";;;"oct.-15";"BDD_IVR&QECB_BegLann_20151031_VImport.xlsx";"Non identifié";"Jonathan pothier/Margaux Féon";"Golfe du Morbihan";"GDMO_04";"Beg Lann";;"Beg Lann (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2015";"SYSTEME";"Parc naturel régional du golfe du Morbihan";"25-75% (Nuageux)";"Pas de précipitation";;14.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;;"Reprise de données Champs Blocs +" +374005;"Suivi des champs de blocs";"2016-03-10 00:00:00.0";;;115;"11:45";;"Semaine";"mars-16";"BDD_IVR&QECB_BegLann_20160310_VImport.xlsx";"Leslie Veron";"Jonathan Pothier, Thomas Cosson";"Golfe du Morbihan";"GDMO_04";"Beg Lann";;"Beg Lann (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2016";"SYSTEME";"Parc naturel régional du golfe du Morbihan";"25-75% (Nuageux)";"Pluie fine";;7.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";"2-Lègère brise-6 à 11 km/h-Vaguelettes";"Nord";;"Reprise de données Champs Blocs +" +374072;"Suivi des champs de blocs";"2016-09-18 00:00:00.0";;;110;"12:40";;"Week-end ou Jour Férié";"sept.-16";"BDD_IVR&QECB_BegLann_20160918_VImport.xlsx";"margot le priol";"leslie , margot le priol, Thomas Cosson";"Golfe du Morbihan";"GDMO_04";"Beg Lann";;"Beg Lann (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2016";"SYSTEME";"Parc naturel régional du golfe du Morbihan";"25-75% (Nuageux)";"Pas de précipitation";;15.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"Nord";;"Reprise de données Champs Blocs +" +374139;"Suivi des champs de blocs";"2015-03-19 00:00:00.0";;;105;"10:01";;;"mars-15";"BDD_IVR&QECB_PFour_20150319&20_VImport.xlsx";"Non identifié";"Ion TILLIER, Françoise GUIMAS, Adrien LETOURNEAU, Estelle BAUDINIERE";"Plateau du Four (territoire)";"FOUR_0";"Plateau du Four";;"Plateau du Four (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2015";"SYSTEME";"Comité régional des pêches maritimes et des élevages marins des Pays de Loire";;"Pas de précipitation";;10.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;;"Reprise de données Champs Blocs +" +374194;"Suivi des champs de blocs";"2015-04-20 00:00:00.0";;;109;"12:58";;;"avr.-15";"BDD_IVR&QECB_PFour_20150420&21_VImport.xlsx";"Non identifié";"Marie FOUCARD, Jacques AUFFRET, Jérôme FIEVET, Maud BERNARD, Samuel MERMET, Ion TILLIER, Helène LEGRAND, Alexis PENGRECH, Estelle BAUDINIERE";"Plateau du Four (territoire)";"FOUR_0";"Plateau du Four";;"Plateau du Four (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2015";"SYSTEME";"Comité régional des pêches maritimes et des élevages marins des Pays de Loire";;"Pas de précipitation";;14.0;"1-Très légère brise-1 à 5 km/h-Mer ridée";;;;"Reprise de données Champs Blocs +" +374261;"Suivi des champs de blocs";"2015-08-03 00:00:00.0";;;104;"13:55";;;"août-15";"BDD_IVR_PlateauFour_20150803_VImport.xlsx";"Non identifié";"Christine TRIDEAU, Camille DELAGE, Antonin GUIMARD, Helène LEGRAND, Alexis PENGRECH, Estelle BAUDINIERE";"Plateau du Four (territoire)";"FOUR_0";"Plateau du Four";;"Plateau du Four (champ de blocs)";"Suivi IVR";"V2015";"SYSTEME";"Comité régional des pêches maritimes et des élevages marins des Pays de Loire";;"Pas de précipitation";;19.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;;"Reprise de données Champs Blocs +" +374268;"Suivi des champs de blocs";"2015-09-28 00:00:00.0";;;114;"11:47";;;"sept.-15";"BDD_IVR&QECB_PFour_20150928_VImport.xlsx";"Non identifié";"Pauline POISSON, Florence BECK, Benjamin BAUDINIERE, Mélissa OUVRARD, Alexandra COLLIAS, Estelle BAUDINIERE";"Plateau du Four (territoire)";"FOUR_0";"Plateau du Four";;"Plateau du Four (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2015";"SYSTEME";"Comité régional des pêches maritimes et des élevages marins des Pays de Loire";;"Pas de précipitation";;16.0;"6-Vent frais-39 à 49 km/h-Crêtes d'écume blanches.";;;;"Reprise de données Champs Blocs +" +374335;"Suivi des champs de blocs";"2016-04-07 00:00:00.0";;;109;"11:45";;"Semaine";"avr.-16";"BDD_IVR&QECB_PFour_20160407&08_VImport.xlsx";"Estelle BAUDINIERE";"Alexis PENGRECH, Morgane GUENO-BRUNEAU, Françoise GUIMAS";"Plateau du Four (territoire)";"FOUR_0";"Plateau du Four";;"Plateau du Four (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2016";"SYSTEME";"Comité régional des pêches maritimes et des élevages marins des Pays de Loire";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;13.0;"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";"Sud-Ouest";;"Reprise de données Champs Blocs +" +374402;"Suivi des champs de blocs";"2016-06-06 00:00:00.0";;;102;"12:37";;"Semaine";"juin-16";"BDD_IVR&QECB_PFour_20160606&07_VImport.xlsx";"Estelle BAUDINIERE";"Ion TILLIER, Gaëtan MORIN, Morgane GUENO-BRUNEAU, Thomas POPOVIC, Estelle BAUDINIERE";"Plateau du Four (territoire)";"FOUR_0";"Plateau du Four";;"Plateau du Four (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2016";"SYSTEME";"Comité régional des pêches maritimes et des élevages marins des Pays de Loire";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;17.0;"1-Très légère brise-1 à 5 km/h-Mer ridée";"2-Lègère brise-6 à 11 km/h-Vaguelettes";"Est";;"Reprise de données Champs Blocs +" +374469;"Suivi des champs de blocs";"2016-09-19 00:00:00.0";;;111;"13:33";;"Semaine";"sept.-16";"BDD_IVR&QECB_PFour_20160919&20_VImport.xlsx";"Estelle BAUDINIERE";"Gaëtan MORIN, Marine REYNAUD, Hélène LEGRAND, Florent CORBAIN, Estelle BAUDINIERE";"Plateau du Four (territoire)";"FOUR_0";"Plateau du Four";;"Plateau du Four (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2016";"SYSTEME";"Comité régional des pêches maritimes et des élevages marins des Pays de Loire";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;17.0;"1-Très légère brise-1 à 5 km/h-Mer ridée";"2-Lègère brise-6 à 11 km/h-Vaguelettes";"Nord-Ouest";;"Reprise de données Champs Blocs +" +374536;"Suivi des champs de blocs";"2014-10-08 00:00:00.0";;;109;"11:08";;;"oct.-14";"BDD_IVR&QECB_Groinducou_20141008_VImport.xlsx";"Non identifié";"Adrien Privat, Sarah Olivier et Marine Le Feunteun";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_014-015";"Pointes du Grouin du Cou et de la République";;"Pointe du Grouin du cou (champ de blocs)";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2014";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";;"Pluies éparses";;14.0;;;;;"Reprise de données Champs Blocs +" +374599;"Suivi des champs de blocs";"2015-03-20 00:00:00.0";;;110;"10:26";;;"mars-15";"BDD_IVR&QECB_GroinduCou_20150319&20_VImport.xlsx";"Non identifié";"Camille Pigot/ Marinne Leclercq";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_014-015";"Pointes du Grouin du Cou et de la République";;"Pointe du Grouin du cou (champ de blocs)";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2015";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";;"Pluie fine";;6.0;"6-Vent frais-39 à 49 km/h-Crêtes d'écume blanches.";;;;"Reprise de données Champs Blocs +" +374666;"Suivi des champs de blocs";"2015-09-29 00:00:00.0";;;113;"12:55";;;"sept.-15";"BDD_IVR&QECB_GroinduCou_20150929_VImport.xlsx";"Non identifié";"Lise Sannipoli/ Julie Vuilleret/ Maud Bernard/ Justine Vallée";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_014-015";"Pointes du Grouin du Cou et de la République";;"Pointe du Grouin du cou (champ de blocs)";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2015";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";;"Pas de précipitation";;16.0;"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";;;;"Reprise de données Champs Blocs +" +374733;"Suivi des champs de blocs";"2016-04-06 00:00:00.0";;;103;"10:42";;"Semaine";"avr.-16";"BDD_IVR&QECB_GrouinDuCou_20160406_VImport.xlsx";"Pauline Momot";"Sebastien Poiret et Pauline Trecant";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_014-015";"Pointes du Grouin du Cou et de la République";;"Pointe du Grouin du cou (champ de blocs)";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2016";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;0.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";"Ouest";;"Reprise de données Champs Blocs +" +374800;"Suivi des champs de blocs";"2016-10-16 00:00:00.0";;;108;"11:20";;"Week-end ou Jour Férié";"oct.-16";"BDD_IVR&QECB_GrouinDuCou_20161016&18_VImport.xlsx";"Justine Vallée";"Marine Genet et Justine Vallée";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_014-015";"Pointes du Grouin du Cou et de la République";;"Pointe du Grouin du cou (champ de blocs)";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2016";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";"25-75% (Nuageux)";"Pas de précipitation";;17.0;"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";"Sud-Ouest";;"Reprise de données Champs Blocs +" +374867;"Suivi des champs de blocs";"2015-10-30 00:00:00.0";;;98;"12:28";;;"oct.-15";"BDD_IVR&QECB_Ensembert_20151029&30_VImport.xlsx";"Non identifié";"Anaïs Lucas, Romary Berlot et Thierry Fradet";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_199";"Les Grenettes";;"Pas d'Ensembert (champ de blocs)";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2015";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";;"Pas de précipitation";;16.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";;;;"Reprise de données Champs Blocs +" +374934;"Suivi des champs de blocs";"2016-03-10 00:00:00.0";;;115;"11:36";;"Semaine";"mars-16";"BDD_IVR&QECB_Ensembert_20160310_VImport.xlsx";"Michael Gamarde";"Alix Burhendt et Michael Gamarde";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_199";"Les Grenettes";;"Pas d'Ensembert (champ de blocs)";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2016";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";"75-100% (Très nuageux)";"Pas de précipitation";;7.0;"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";"Sud-Ouest";;"Reprise de données Champs Blocs +" +375001;"Suivi des champs de blocs";"2016-10-18 00:00:00.0";;;115;"11:36";;"Semaine";"oct.-16";"BDD_IVR&QECB_Ensembert_20161018_VImport.xlsx";"MG";"Alix Burhendt et MG";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_199";"Les Grenettes";;"Pas d'Ensembert (champ de blocs)";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2016";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";"75-100% (Très nuageux)";"Pas de précipitation";;7.0;"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";"Sud-Ouest";;"Reprise de données Champs Blocs +" +375068;"Suivi des champs de blocs";"2014-09-08 00:00:00.0";;;97;"10:41";;;"sept.-14";"BDD_IVR_LaBree_08092014_VImport.xlsx";"Non identifié";"Adrien Privat";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_114";"La Brée et la Balise";;"La Brée-les-Bains (champ de blocs)";"Suivi IVR";"V2014";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";"75-100% (Très nuageux)";"Pluie continue";;;;;"Ouest";;"Reprise de données Champs Blocs +" +375075;"Suivi des champs de blocs";"2014-09-11 00:00:00.0";;;111;"13:04";;;"sept.-14";"BDD_IVR_LaBree_11092014_Vmport.xlsx";"Non identifié";"Adrien Privat, Pascale Marjana (B), Tom Mothet (Stag), Marine LeFeunteun (B)";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_114";"La Brée et la Balise";;"La Brée-les-Bains (champ de blocs)";"Suivi IVR";"V2014";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";;"Pas de précipitation";;;;;;;"Reprise de données Champs Blocs +" +375082;"Suivi des champs de blocs";"2014-10-10 00:00:00.0";;;109;"11:43";;;"oct.-14";"BDD_IVR&QECB_LaBree_oct2014_VImport.xlsx";"Non identifié";"Adrien Privat, Francine Fevre, Benjamin Jaufry";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_114";"La Brée et la Balise";;"La Brée-les-Bains (champ de blocs)";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2014";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";;;;;;;;;"Reprise de données Champs Blocs +" +375149;"Suivi des champs de blocs";"2015-03-23 00:00:00.0";;;105;"12:48";;;"mars-15";"BDD_QECB&IVR_LaBree_23032015_VImport.xlsx";"Non identifié";"Richard Coz/Jean-Baptiste Bonnin";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_114";"La Brée et la Balise";;"La Brée-les-Bains (champ de blocs)";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2015";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";;"Pas de précipitation";;11.6;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;;"Reprise de données Champs Blocs +" +375216;"Suivi des champs de blocs";"2015-10-27 00:00:00.0";;;108;"10:07";;;"oct.-15";"BDD_QECB&IVR_LaBree_26&27102015_VImport.xlsx";"Non identifié";"RC+JBB";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_114";"La Brée et la Balise";;"La Brée-les-Bains (champ de blocs)";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2015";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";;"Pluie fine";;13.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;;"Reprise de données Champs Blocs +" +375283;"Suivi des champs de blocs";"2016-04-06 00:00:00.0";;;96;"10:42";;"Semaine";"avr.-16";"BDD_IVR&QECB_La Bree_20160406_VImport.xlsx";"Richard Coz, Coline Dumas, Adrien Lowenstein";"Adrien Privat, Marion, Axel Lauray, Jean-Louis le coeur";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_114";"La Brée et la Balise";;"La Brée-les-Bains (champ de blocs)";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2016";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";"25-75% (Nuageux)";"Pas de précipitation";;11.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";"Ouest";;"Reprise de données Champs Blocs +" +375350;"Suivi des champs de blocs";"2016-10-16 00:00:00.0";;;108;"11:36";;"Week-end ou Jour Férié";"oct.-16";"BDD_IVR&QECB_LaBree_20161016_VImport.xlsx";"Sarah Olivier et Adrien Lowenstein";"Richard Coz Sarah Olivier Adrien Lowenstein Coline Dumas";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_114";"La Brée et la Balise";;"La Brée-les-Bains (champ de blocs)";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2016";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";"75-100% (Très nuageux)";"Pas de précipitation";;12.0;"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";"5-Bonne brise-29 à 38 km/h-Vagues modérées.";"Sud-Ouest";;"Reprise de données Champs Blocs +" +375417;"Suivi des champs de blocs";"2014-10-09 00:00:00.0";;;111;"11:59";;;"oct.-14";"BDD_IVR&QECB_PerAnt_20141009_VImport.xlsx";"Non identifié";"Richard Coz, Adrien Privat";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_116";"Perré d'Antiochat";;"Pérré d'Antiochat (champ de blocs)";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2014";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";;"Pas de précipitation";;;"1-Très légère brise-1 à 5 km/h-Mer ridée";;;;"Reprise de données Champs Blocs +" +375484;"Suivi des champs de blocs";"2015-04-18 00:00:00.0";;;106;"11:10";;;"avr.-15";"BDD_QECB&IVR_PerAntiochat_18042015_VImport.xlsx";"Non identifié";"Richard Coz, Adrien Privat";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_116";"Perré d'Antiochat";;"Pérré d'Antiochat (champ de blocs)";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2015";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";;"Pluie fine";;15.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;;"Reprise de données Champs Blocs +" +375551;"Suivi des champs de blocs";"2015-10-27 00:00:00.0";;;108;"10:07";;;"oct.-15";"BDD_QECB&IVR_PerAntiochat_20151027&29_VImport.xlsx";"Non identifié";"AP+LL+PM";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_116";"Perré d'Antiochat";;"Pérré d'Antiochat (champ de blocs)";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2015";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";;"Pluie fine";;13.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;;"Reprise de données Champs Blocs +" +375618;"Suivi des champs de blocs";"2016-04-07 00:00:00.0";;;109;"11:28";;"Semaine";"avr.-16";"BDD_IVR&QECB_PerAnt_20160407_VImport.xlsx";"Adrien Privat, Gwenaëlle Auproux";"Adrien Privat, Axel Auvray, Pascale Marjana";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_116";"Perré d'Antiochat";;"Pérré d'Antiochat (champ de blocs)";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2016";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";"75-100% (Très nuageux)";"Pas de précipitation";;10.0;"5-Bonne brise-29 à 38 km/h-Vagues modérées.";"6-Vent frais-39 à 49 km/h-Crêtes d'écume blanches.";"Nord-Ouest";;"Reprise de données Champs Blocs +" +375685;"Suivi des champs de blocs";"2016-10-17 00:00:00.0";;;114;"12:11";;"Semaine";"oct.-16";"BDD_IVR&QECB_PerAnt_20161017_VImport.xlsx";"Samuel Rinaud et Zachary Gaudin";"Pierre Crepin, Camille Lemasne, Richad Coz, Dominique Moisan et Zachary Gaudin";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_116";"Perré d'Antiochat";;"Pérré d'Antiochat (champ de blocs)";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2016";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";"75-100% (Très nuageux)";"Pluies éparses";;13.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"Nord-Ouest";;"Reprise de données Champs Blocs +" +375752;"Suivi des champs de blocs";"2015-04-20 00:00:00.0";;;112;"12:42";;;"avr.-15";"BDD_IVR&QECB_Chassiron_20150420_VImport.xlsx";"Non identifié";"Tamatoa Laughlin/Virginie Evenou/Morgane JeanJean";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_119";"Concession scientifique";;"Chassiron (champ de blocs)";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2015";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";;"Pas de précipitation";;16.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";;;;"Reprise de données Champs Blocs +" +375819;"Suivi des champs de blocs";"2015-10-28 00:00:00.0";;;113;"10:55";;;"oct.-15";"BDD_QECB&IVR_Chassiron_20151028_VImport.xlsx";"Non identifié";"MMP+JBB+RC";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_119";"Concession scientifique";;"Chassiron (champ de blocs)";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2015";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";;"Pluies éparses";;13.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";;;;"Reprise de données Champs Blocs +" +375886;"Suivi des champs de blocs";"2016-03-10 00:00:00.0";;;115;"11:36";;"Semaine";"mars-16";"BDD_IVR&QECB_Chassiron_20160309&10_VImport.xlsx";"Richard Coz -Adrien Privat - Sarah Olivier";"Richard Coz -Adrien Privat - Marion Carsac";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_119";"Concession scientifique";;"Chassiron (champ de blocs)";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2016";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";"75-100% (Très nuageux)";"Pas de précipitation";;8.0;"5-Bonne brise-29 à 38 km/h-Vagues modérées.";;"Nord-Est";;"Reprise de données Champs Blocs +" +375953;"Suivi des champs de blocs";"2016-09-19 00:00:00.0";;;109;"13:13";;"Semaine";"sept.-16";"BDD_IVR&QECB_Chassiron_20160919&10_VImport.xlsx";"CD/SO";"RC/CB/SO";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_119";"Concession scientifique";;"Chassiron (champ de blocs)";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2016";"SYSTEME";"Parc naturel marin de l'estuaire de la Gironde et de la mer des Pertuis";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;17.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"Nord-Ouest";;"Reprise de données Champs Blocs +" +376020;"Suivi des champs de blocs";"2014-11-24 00:00:00.0";;;93;"11:20";;;"nov.-14";"BDD_IVR&QECB_FB_ZP_20141124&25_VImport.xlsx";"Non identifié";"Josiane POPOVSKY/Pascale FOSSECAVE";"Côte basque";"BASQ_01";"Les Flots Bleus";;"Les Flots Bleus (champ de blocs) - zone pàªcheurs";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2015";"SYSTEME";"CPIE littoral basque";;"Pluies éparses";;;;;;;"Reprise de données Champs Blocs +" +376087;"Suivi des champs de blocs";"2015-03-19 00:00:00.0";;;97;"09:30";;;"mars-15";"BDD_IVR&QECB_FlotsBleus_ZP_20150319&21_VFinale.xlsx";"Non identifié";"Josiane POPOVSKY/Pascale FOSSECAVE";"Côte basque";"BASQ_01";"Les Flots Bleus";;"Les Flots Bleus (champ de blocs) - zone pàªcheurs";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2015";"SYSTEME";"CPIE littoral basque";;"Pluie fine";;9.0;"0-Calme-1 km/h-Mer d'huile";;;;"Reprise de données Champs Blocs +" +376154;"Suivi des champs de blocs";"2015-05-18 00:00:00.0";;;101;"11:20";;;"mai-15";"BDD_IVR&QECB_FlotsBleus_ZP_20150518&19_VImport.xlsx";"Non identifié";"Josiane POPOVSKY";"Côte basque";"BASQ_01";"Les Flots Bleus";;"Les Flots Bleus (champ de blocs) - zone pàªcheurs";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2015";"SYSTEME";"CPIE littoral basque";;"Pas de précipitation";;18.0;"1-Très légère brise-1 à 5 km/h-Mer ridée";;;;"Reprise de données Champs Blocs +" +376221;"Suivi des champs de blocs";"2015-08-03 00:00:00.0";;;104;"13:18";;;"août-15";"BDD_IVR&QECB_FlotsBleus_ZP_20150803_VImport.xlsx";"Non identifié";"Josiane Popovsky / Pascale FOSSECAVE";"Côte basque";"BASQ_01";"Les Flots Bleus";;"Les Flots Bleus (champ de blocs) - zone pàªcheurs";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2015";"SYSTEME";"CPIE littoral basque";;"Pas de précipitation";;23.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";;;;"Reprise de données Champs Blocs +" +376288;"Suivi des champs de blocs";"2015-09-30 00:00:00.0";;;103;"12:38";;;"sept.-15";"BDD_IVR&QECB_FlotsBleus_ZP_20150928&30_VImport.xlsx";"Non identifié";"Josiane Popovsky et Pascale Fossecave";"Côte basque";"BASQ_01";"Les Flots Bleus";;"Les Flots Bleus (champ de blocs) - zone pàªcheurs";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2015";"SYSTEME";"CPIE littoral basque";;"Pas de précipitation";;18.0;"0-Calme-1 km/h-Mer d'huile";;;;"Reprise de données Champs Blocs +" +376355;"Suivi des champs de blocs";"2016-03-10 00:00:00.0";;;116;"11:17";;"Semaine";"mars-16";"BDD_IVR&QECB_FB_ZP_20160310_VImport.xlsx";"Pascale Fossecave";"Josiane Popovsky et Pascale Fossecave";"Côte basque";"BASQ_01";"Les Flots Bleus";;"Les Flots Bleus (champ de blocs) - zone pàªcheurs";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2016";"SYSTEME";"CPIE littoral basque";"25-75% (Nuageux)";"Pas de précipitation";;10.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"Sud-Ouest";;"Reprise de données Champs Blocs +" +376422;"Suivi des champs de blocs";"2016-05-09 00:00:00.0";;;110;"13:05";;"Semaine";"mai-16";"BDD_IVR&QECB_FB_ZP_20160509_VImport.xlsx";"Pascale Fossecave";"Josiane Popovsky et Pascale Fossecave";"Côte basque";"BASQ_01";"Les Flots Bleus";;"Les Flots Bleus (champ de blocs) - zone pàªcheurs";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2016";"SYSTEME";"CPIE littoral basque";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;22.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"Sud-Est";;"Reprise de données Champs Blocs +" +376489;"Suivi des champs de blocs";"2016-08-22 00:00:00.0";;;98;"13:58";;"Semaine";"août-16";"BDD_IVR&QECB_FB_ZP_20160822_VImport.xlsx";"Pascale Fossecave";"Josiane Popovsky et Pascale Fossecave";"Côte basque";"BASQ_01";"Les Flots Bleus";;"Les Flots Bleus (champ de blocs) - zone pàªcheurs";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2016";"SYSTEME";"CPIE littoral basque";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;24.0;"1-Très légère brise-1 à 5 km/h-Mer ridée";"2-Lègère brise-6 à 11 km/h-Vaguelettes";"Sud-Ouest";;"Reprise de données Champs Blocs +" +376556;"Suivi des champs de blocs";"2016-09-20 00:00:00.0";;;100;"13:38";;"Semaine";"sept.-16";"BDD_IVR&QECB_FB_ZP_20160920_VImport.xlsx";"Pascale Fossecave";"Josiane Popovsky et Pascale Fossecave";"Côte basque";"BASQ_01";"Les Flots Bleus";;"Les Flots Bleus (champ de blocs) - zone pàªcheurs";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2016";"SYSTEME";"CPIE littoral basque";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;22.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";"2-Lègère brise-6 à 11 km/h-Vaguelettes";"Sud-Ouest";;"Reprise de données Champs Blocs +" +376623;"Suivi des champs de blocs";"2015-03-24 00:00:00.0";;;91;"13:00";;;"mars-15";"BDD_IVR&QECB_FlotsBleus_ZF_20150324&25_VImport.xlsx";"Non identifié";"Josiane Popovsky / Pascale Fossecave";"Côte basque";"BASQ_01";"Les Flots Bleus";;"Les Flots Bleus (champ de blocs) - zone famille";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2015";"SYSTEME";"CPIE littoral basque";;"Pluie fine";;10.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";;;"Reprise de données Champs Blocs +" +376690;"Suivi des champs de blocs";"2015-08-17 00:00:00.0";;;84;"12:51";;;"août-15";"BDD_IVR&QECB_FB_ZF_20150817_VImport.xlsx";"Non identifié";"Josiane Popovsky";"Côte basque";"BASQ_01";"Les Flots Bleus";;"Les Flots Bleus (champ de blocs) - zone famille";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2015";"SYSTEME";"CPIE littoral basque";;"Pas de précipitation";;23.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;;"Reprise de données Champs Blocs +" +376757;"Suivi des champs de blocs";"2015-10-26 00:00:00.0";;;103;"09:05";;;"oct.-15";"BDD_IVR&QECB_FlotsBleus_ZF_20151026&27_VImport.xlsx";"Non identifié";"Josiane Popovsky / Pascale Fossecave";"Côte basque";"BASQ_01";"Les Flots Bleus";;"Les Flots Bleus (champ de blocs) - zone famille";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2015";"SYSTEME";"CPIE littoral basque";;"Pas de précipitation";;13.0;"0-Calme-1 km/h-Mer d'huile";;;;"Reprise de données Champs Blocs +" +376824;"Suivi des champs de blocs";"2016-03-08 00:00:00.0";;;101;"09:52";;"Semaine";"mars-16";"BDD_IVR&QECB_FB_ZF_20160308&11_VImport.xlsx";"Pascale Fossecave";"Josiane Popovsky et Pascale Fossecave";"Côte basque";"BASQ_01";"Les Flots Bleus";;"Les Flots Bleus (champ de blocs) - zone famille";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2016";"SYSTEME";"CPIE littoral basque";"75-100% (Très nuageux)";"Pluies éparses";;5.0;"1-Très légère brise-1 à 5 km/h-Mer ridée";"1-Très légère brise-1 à 5 km/h-Mer ridée";"Nord-Ouest";;"Reprise de données Champs Blocs +" +376891;"Suivi des champs de blocs";"2016-08-18 00:00:00.0";;;93;"11:13";;"Semaine";"août-16";"BDD_IVR&QECB_FB_ZF_20160818_VImport.xlsx";"Pascale Fossecave";"Josiane Popovsky et Pascale Fossecave";"Côte basque";"BASQ_01";"Les Flots Bleus";;"Les Flots Bleus (champ de blocs) - zone famille";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2016";"SYSTEME";"CPIE littoral basque";"75-100% (Très nuageux)";"Pluies éparses";;23.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"Nord-Ouest";;"Reprise de données Champs Blocs +" +376958;"Suivi des champs de blocs";"2016-09-15 00:00:00.0";;;87;"10:09";;"Semaine";"sept.-16";"BDD_IVR&QECB_FB_ZF_20160915&16_VImport.xlsx";"Pascale Fossecave";"Josiane Popovsky et Pascale Fossecave";"Côte basque";"BASQ_01";"Les Flots Bleus";;"Les Flots Bleus (champ de blocs) - zone famille";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2016";"SYSTEME";"CPIE littoral basque";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;15.0;"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";"5-Bonne brise-29 à 38 km/h-Vagues modérées.";"Sud-Est";;"Reprise de données Champs Blocs +" +377025;"Suivi des champs de blocs";"2014-10-08 00:00:00.0";;;109;"14:21";;;"oct.-14";"BDD_IVR&QECB_IlotStMichel_20141008&09_VImport.xlsx";"Non identifié";"Margaux Pinel, Etienne Rogeau";"Golfe Normand Breton";"GONB_00C";"Pointe de la Fosse Eyrand à la Pointe du Champ du Port";;"Champs de blocs de l'îlot Saint-Michel (périmètre étendu)";"Suivi IVR+QECB - Bretagne";"V2014";"SYSTEME";"Mission d'étude du golfe normand-breton";;"Pas de précipitation";;;;;;;"Reprise de données Champs Blocs +" +377052;"Suivi des champs de blocs";"2015-04-18 00:00:00.0";;;108;"14:20";;;"avr.-15";"BDD_IVR&QECB_IlotStMichel_20150418&19_VImport.xlsx";"Non identifié";"Margaux Pinel, Etienne Rogeau, Maud Bernard";"Golfe Normand Breton";"GONB_00C";"Pointe de la Fosse Eyrand à la Pointe du Champ du Port";;"Champs de blocs de l'îlot Saint-Michel (périmètre étendu)";"Suivi IVR+QECB - Bretagne";"V2015";"SYSTEME";"Mission d'étude du golfe normand-breton";;"Pas de précipitation";;17.0;;;;;"Reprise de données Champs Blocs +" +377119;"Suivi des champs de blocs";"2015-10-29 00:00:00.0";;;109;"14:46";;;"oct.-15";"BDD_IVR&QECB_IlotStMichel_20151028&29_VImport.xlsx";"Non identifié";"Margaux Pinel, Florent Corbain, Anais Perucaud, Olivier Abellard, Anne-Gael Planche";"Golfe Normand Breton";"GONB_00C";"Pointe de la Fosse Eyrand à la Pointe du Champ du Port";;"Champs de blocs de l'îlot Saint-Michel (périmètre étendu)";"Suivi IVR+QECB - Bretagne";"V2015";"SYSTEME";"Mission d'étude du golfe normand-breton";;"Pas de précipitation";;15.0;"1-Très légère brise-1 à 5 km/h-Mer ridée";;;;"Reprise de données Champs Blocs +" +377186;"Suivi des champs de blocs";"2016-05-07 00:00:00.0";;;113;"15:03";;"Week-end ou Jour Férié";"mai-16";"BDD_IVRQECB_IlotStMichel_20160507&08_VImport.xlsx";"Rébecca Bauchet, Marie Motte";"Rébecca Bauchet, Pauline Poisson";"Golfe Normand Breton";"GONB_00C";"Pointe de la Fosse Eyrand à la Pointe du Champ du Port";;"Champs de blocs de l'îlot Saint-Michel (périmètre étendu)";"Suivi IVR+QECB - Bretagne";"V2016";"SYSTEME";"Mission d'étude du golfe normand-breton";"25-75% (Nuageux)";"Pas de précipitation";;25.0;"1-Très légère brise-1 à 5 km/h-Mer ridée";"1-Très légère brise-1 à 5 km/h-Mer ridée";"Sud-Est";;"Reprise de données Champs Blocs +" +377253;"Suivi des champs de blocs";"2016-10-18 00:00:00.0";;;111;"16:02";;"Semaine";"oct.-16";"BDD_IVRQECB_IlotStMichel_20161017&18_VImport.xlsx";"Rébecca Bauchet";"Florent Corbain, Rébecca Bauchet";"Golfe Normand Breton";"GONB_00C";"Pointe de la Fosse Eyrand à la Pointe du Champ du Port";;"Champs de blocs de l'îlot Saint-Michel (périmètre étendu)";"Suivi IVR+QECB - Bretagne";"V2016";"SYSTEME";"Mission d'étude du golfe normand-breton";"75-100% (Très nuageux)";"Pluie fine";;15.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"Sud-Ouest";;"Reprise de données Champs Blocs +" +377320;"Suivi des champs de blocs";"2015-04-20 00:00:00.0";;;110;"12:43";;;"avr.-15";"BD_ChpsBlocs_20_20150420_Quemenes_VImport.xlsx";"Non identifié";"BESNIER MAUGARD Maïna, LEFEUVRE Cécile, DIXNEUF Stéphane, TURPIN Yannis";"Iroise";"PNMI_02";"Archipel de Molène";;"Quéménès (champ blocs)";"Suivi IVR+QECB - Bretagne";"V2015";"SYSTEME";"Parc naturel marin de la mer d'Iroise";;"Pas de précipitation";;;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;;"Reprise de données Champs Blocs +" +377387;"Suivi des champs de blocs";"2015-09-30 00:00:00.0";;;116;"13:32";;;"sept.-15";"BD_ChpsBlocs_20_20150930_Quemenes_VImport.xlsx";"Non identifié";"TURPIN Yannis, Cécile Lefeuvre, Romain Hubert, Philippe Le Niliot";"Iroise";"PNMI_02";"Archipel de Molène";;"Quéménès (champ blocs)";"Suivi IVR+QECB - Bretagne";"V2015";"SYSTEME";"Parc naturel marin de la mer d'Iroise";;"Pas de précipitation";;;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;;"Reprise de données Champs Blocs +" +377454;"Suivi des champs de blocs";"2016-04-07 00:00:00.0";;;114;"12:02";;;"avr.-16";"BD_ChpsBlocs_20_20160407_Quemenes_VImport.xlsx";"Non identifié";"TURPIN Yannis, Cécile Lefeuvre, Stéphane Dixneuf, Tiphaine Deheul";"Iroise";"PNMI_02";"Archipel de Molène";;"Quéménès (champ blocs)";"Suivi IVR+QECB - Bretagne";"V2015";"SYSTEME";"Parc naturel marin de la mer d'Iroise";;"Pluie fine";;8.0;"5-Bonne brise-29 à 38 km/h-Vagues modérées.";;;;"Reprise de données Champs Blocs +" +377521;"Suivi des champs de blocs";"2016-09-17 00:00:00.0";;;108;"12:21";;;"sept.-16";"BD_ChpsBlocs_20_20160917-Quemenes_VImport.xlsx";"Non identifié";"TURPIN Yannis, Jean Philippe Coeffet, Antoine Besnier";"Iroise";"PNMI_02";"Archipel de Molène";;"Quéménès (champ blocs)";"Suivi IVR+QECB - Bretagne";"V2015";"SYSTEME";"Parc naturel marin de la mer d'Iroise";;"Pas de précipitation";;17.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;;"Reprise de données Champs Blocs +" +377576;"Suivi des champs de blocs";"2017-04-27 00:00:00.0";;;109;"12:43";;;"avr.-17";"BD_ChpsBlocs_20_20170427_Quemenes_VImport.xlsx";"Non identifié";"Besnier Antoine,Stéphane dixneuf Cécile Lefeuvre, Coeffet jean Philippe, Manon";"Iroise";"PNMI_02";"Archipel de Molène";;"Quéménès (champ blocs)";"Suivi IVR+QECB - Bretagne";"V2015";"SYSTEME";"Parc naturel marin de la mer d'Iroise";;"Pas de précipitation";;;"0-Calme-1 km/h-Mer d'huile";;;;"Reprise de données Champs Blocs +" +377643;"Suivi des champs de blocs";"2017-09-20 00:00:00.0";;;97;"12:08";;;"sept.-17";"BD_ChpsBlocs_20_20170920_Quemenes_VImport.xlsx";"Non identifié";"Besnier Antoine, Cécile Lefeuvre";"Iroise";"PNMI_02";"Archipel de Molène";;"Quéménès (champ blocs)";"Suivi IVR+QECB - Bretagne";"V2015";"SYSTEME";"Parc naturel marin de la mer d'Iroise";;"Pas de précipitation";;;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;;"Reprise de données Champs Blocs +" +377686;"Suivi des champs de blocs";"2017-10-07 00:00:00.0";;;103;"13:02";;;"oct.-17";"BD_ChpsBlocs_20_20171007_Quemenes_VImport.xlsx";"Non identifié";"Besnier Antoine, Coeffet jean Philippe";"Iroise";"PNMI_02";"Archipel de Molène";;"Quéménès (champ blocs)";"Suivi IVR+QECB - Bretagne";"V2015";"SYSTEME";"Parc naturel marin de la mer d'Iroise";;"Pas de précipitation";;;"1-Très légère brise-1 à 5 km/h-Mer ridée";;;;"Reprise de données Champs Blocs +" +377717;"Suivi des champs de blocs";"2016-04-08 00:00:00.0";;;118;"12:48";;"Semaine";"avr.-16";"BD_ChpsBlocs_21_20160408_Sein_Goulenez_VImport.xlsx";"Non identifié";"OG, CLS, CL";"Iroise";"PNMI_07";"Chaussée de Sein";;"Goulenez (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2016";"SYSTEME";"Parc naturel marin de la mer d'Iroise";;"Pas de précipitation";;10.0;"0-Calme-1 km/h-Mer d'huile";"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";"Nord-Ouest";;"Reprise de données Champs Blocs +" +377784;"Suivi des champs de blocs";"2016-10-18 00:00:00.0";;;113;"13:28";;"Semaine";"oct.-16";"BD_ChpsBlocs_21_20161018_Sein_Goulenez_VImport.xlsx";"Non identifié";"OG, CLS, MB, JAP";"Iroise";"PNMI_07";"Chaussée de Sein";;"Goulenez (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2016";"SYSTEME";"Parc naturel marin de la mer d'Iroise";;"Pas de précipitation";;15.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";"Nord-Ouest";;"Reprise de données Champs Blocs +" +377827;"Suivi des champs de blocs";"2017-04-27 00:00:00.0";;;110;"12:43";;"Semaine";"avr.-17";"BD_ChpsBlocs_21_20170427_Sein_Goulenez_Vimport.xlsx";"Non identifié";"OG, CLS";"Iroise";"PNMI_07";"Chaussée de Sein";;"Goulenez (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2016";"SYSTEME";"Parc naturel marin de la mer d'Iroise";;"Pas de précipitation";;16.0;"0-Calme-1 km/h-Mer d'huile";"2-Lègère brise-6 à 11 km/h-Vaguelettes";;;"Reprise de données Champs Blocs +" +377894;"Suivi des champs de blocs";"2017-11-06 00:00:00.0";;;106;"12:36";;"Semaine";"nov.-17";"BD_ChpsBlocs_21_20171106_Sein_Goulenez_Vimport.xlsx";"Non identifié";"OG SM ER";"Iroise";"PNMI_07";"Chaussée de Sein";;"Goulenez (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2016";"SYSTEME";"Parc naturel marin de la mer d'Iroise";;"Pas de précipitation";;14.0;"0-Calme-1 km/h-Mer d'huile";"2-Lègère brise-6 à 11 km/h-Vaguelettes";"Nord-Ouest";;"Reprise de données Champs Blocs +" +377961;"Suivi des champs de blocs";"2015-04-21 00:00:00.0";;;100;"13:57";;;"avr.-15";"BD_ChpsBlocs_22_20150421_Sein_Vimport.xls.xlsx";"Non identifié";"JAP CLS MB";"Iroise";"PNMI_07";"Chaussée de Sein";;"Kilaourou (champ blocs)";"Suivi IVR+QECB - Bretagne";"V2015";"SYSTEME";"Parc naturel marin de la mer d'Iroise";;"Pas de précipitation";;18.0;;;;;"Reprise de données Champs Blocs +" +378028;"Suivi des champs de blocs";"2015-09-29 00:00:00.0";;;117;"12:32";;;"sept.-15";"BD_ChpsBlocs_22_20150929_Sein_Vimport.xls.xlsx";"Non identifié";"JAP LS FG CLS";"Iroise";"PNMI_07";"Chaussée de Sein";;"Kilaourou (champ blocs)";"Suivi IVR+QECB - Bretagne";"V2015";"SYSTEME";"Parc naturel marin de la mer d'Iroise";;"Pas de précipitation";;18.0;"6-Vent frais-39 à 49 km/h-Crêtes d'écume blanches.";;"Nord-Est";;"Reprise de données Champs Blocs +" +378095;"Suivi des champs de blocs";"2016-04-08 00:00:00.0";;;118;"12:48";;;"avr.-16";"BD_ChpsBlocs_22_20160408_Sein_kilaourou_VImportCorrigé.xlsx";"Non identifié";"MB MBM FG CLS";"Iroise";"PNMI_07";"Chaussée de Sein";;"Kilaourou (champ blocs)";"Suivi IVR+QECB - Bretagne";"V2016";"SYSTEME";"Parc naturel marin de la mer d'Iroise";;"Pas de précipitation";;10.0;"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";;"Nord-Ouest";;"Reprise de données Champs Blocs +" +378162;"Suivi des champs de blocs";"2016-09-19 00:00:00.0";;;111;"13:47";;;"sept.-16";"BD_ChpsBlocs_22_20160919_Sein_kilaourou_VImport.xlsx";"Non identifié";"MB JAP LS FG CLS";"Iroise";"PNMI_07";"Chaussée de Sein";;"Kilaourou (champ blocs)";"Suivi IVR+QECB - Bretagne";"V2016";"SYSTEME";"Parc naturel marin de la mer d'Iroise";;"Pluies éparses";;15.0;"1-Très légère brise-1 à 5 km/h-Mer ridée";;;;"Reprise de données Champs Blocs +" +378229;"Suivi des champs de blocs";"2017-04-27 00:00:00.0";;;110;"12:43";;;"avr.-17";"BD_ChpsBlocs_22_20170427_Sein_kilaourou_VImport.xlsx";"Non identifié";"JAP LS ";"Iroise";"PNMI_07";"Chaussée de Sein";;"Kilaourou (champ blocs)";"Suivi IVR+QECB - Bretagne";"V2016";"SYSTEME";"Parc naturel marin de la mer d'Iroise";;"Pas de précipitation";;12.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";;;;"Reprise de données Champs Blocs +" +378280;"Suivi des champs de blocs";"2017-11-06 00:00:00.0";;;106;"12:26";;;"nov.-17";"BD_ChpsBlocs_22_20171106_Sein_kilaourou_VImport.xlsx";"Non identifié";"MB JAP FG EM";"Iroise";"PNMI_07";"Chaussée de Sein";;"Kilaourou (champ blocs)";"Suivi IVR+QECB - Bretagne";"V2016";"SYSTEME";"Parc naturel marin de la mer d'Iroise";;"Pas de précipitation";;14.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";;"Nord-Ouest";;"Reprise de données Champs Blocs +" +378335;"Suivi des champs de blocs";"2014-05-15 00:00:00.0";;;96;"14:43";;;"mai-14";"BDD_IVR&QECB_Verdelet_20140515&17_VImport.xlsx";"Non identifié";"Franck Delisle, Olivier Massard";"Côtes d'Armor";"ARMO_042-043";"Piégu / Verdelet";;"Ilot de Verdelet (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2014";"SYSTEME";"VivArmor Nature";;"Pas de précipitation";;;;;;;"Reprise de données Champs Blocs +" +378402;"Suivi des champs de blocs";"2014-10-08 00:00:00.0";;;109;"14:18";;;"oct.-14";"BDD_IVR&QECB_Verdelet_20141008&09_VImport.xlsx";"Non identifié";"Franck Delisle, Vincent Trémel";"Côtes d'Armor";"ARMO_042-043";"Piégu / Verdelet";;"Ilot de Verdelet (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2014";"SYSTEME";"VivArmor Nature";;"Pas de précipitation";;;;;;;"Reprise de données Champs Blocs +" +378469;"Suivi des champs de blocs";"2015-03-23 00:00:00.0";;;105;"15:46";;;"mars-15";"BDD_IVR&QECB_Verdelet_20150323_VImport.xlsx";"Non identifié";"Franck Delisle, Vincent Trémel, Stéphanie Plaga Lemanski";"Côtes d'Armor";"ARMO_042-043";"Piégu / Verdelet";;"Ilot de Verdelet (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2015";"SYSTEME";"VivArmor Nature";;"Pas de précipitation";;;"2-Lègère brise-6 à 11 km/h-Vaguelettes";"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;"Reprise de données Champs Blocs +" +378536;"Suivi des champs de blocs";"2015-10-26 00:00:00.0";;;103;"12:17";;;"oct.-15";"BDD_IVR&QECB_Verdelet_20151026&30_VImport.xlsx";"Non identifié";"Franck Delisle, Anne Priac, Léa Mie";"Côtes d'Armor";"ARMO_042-043";"Piégu / Verdelet";;"Ilot de Verdelet (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2015";"SYSTEME";"VivArmor Nature";;"Pas de précipitation";;18.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;;"Reprise de données Champs Blocs +" +378603;"Suivi des champs de blocs";"2016-04-09 00:00:00.0";;;115;"16:02";;"Week-end ou Jour Férié";"avr.-16";"BDD_IVR&QECB_Verdelet_20160410_VImport.xlsx";"Nora Tupinier";"Franck Delisle, Benjamin & Pierline Tournant";"Côtes d'Armor";"ARMO_042-043";"Piégu / Verdelet";;"Ilot de Verdelet (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2016";"SYSTEME";"VivArmor Nature";"25-75% (Nuageux)";"Pas de précipitation";;11.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;"Ouest";;"Reprise de données Champs Blocs +" +378670;"Suivi des champs de blocs";"2016-10-18 00:00:00.0";;;111;"15:58";;"Semaine";"oct.-16";"BDD_IVR&QECB_Verdelet_20161018&19_VImport.xlsx";"Nora Tupinier";"Franck Delisle, Yves Faguet, Bruno Appadoo, Nora Tupinier";"Côtes d'Armor";"ARMO_042-043";"Piégu / Verdelet";;"Ilot de Verdelet (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2016";"SYSTEME";"VivArmor Nature";"25-75% (Nuageux)";"Pluie fine";;12.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";"Nord";;"Reprise de données Champs Blocs +" +378737;"Suivi des champs de blocs";"2014-05-14 00:00:00.0";;;91;"14:01";;;"mai-14";"BDD_IVR&QECB_Piegu_20140514_VImport.xlsx";"Non identifié";"Franck Delisle, Olivier Massard";"Côtes d'Armor";"ARMO_042-043";"Piégu / Verdelet";;"Piégu (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2014";"SYSTEME";"VivArmor Nature";;"Pas de précipitation";;;;;;;"Reprise de données Champs Blocs +" +378804;"Suivi des champs de blocs";"2014-11-05 00:00:00.0";;;94;"12:02";;;"nov.-14";"BDD_IVR&QECB_Piegu_20141105&06_VImport.xlsx";"Non identifié";"Franck Delisle, Vincent Trémel";"Côtes d'Armor";"ARMO_042-043";"Piégu / Verdelet";;"Piégu (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2014";"SYSTEME";"VivArmor Nature";"75-100% (Très nuageux)";"Pluies éparses";;;;;;;"Reprise de données Champs Blocs +" +378871;"Suivi des champs de blocs";"2015-03-19 00:00:00.0";;;105;"12:44";;;"mars-15";"BDD_IVR&QECB_Piegu_20150319_VImport.xlsx";"Non identifié";"Franck Delisle, Stéphanie Plaga Lemanski";"Côtes d'Armor";"ARMO_042-043";"Piégu / Verdelet";;"Piégu (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2015";"SYSTEME";"VivArmor Nature";;"Pas de précipitation";;;"5-Bonne brise-29 à 38 km/h-Vagues modérées.";;;;"Reprise de données Champs Blocs +" +378934;"Suivi des champs de blocs";"2015-10-01 00:00:00.0";;;102;"16:44";;;"oct.-15";"BDD_IVR&QECB_Piegu_20151001_VImport.xlsx";"Non identifié";"Franck Delisle, Léa Mie, Marine Salou";"Côtes d'Armor";"ARMO_042-043";"Piégu / Verdelet";;"Piégu (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2015";"SYSTEME";"VivArmor Nature";;"Pas de précipitation";;;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;;"Reprise de données Champs Blocs +" +379001;"Suivi des champs de blocs";"2016-04-06 00:00:00.0";;;103;"13:40";;"Semaine";"avr.-16";"BDD_IVR&QECB_Piegu_20160406_VImport.xlsx";"Nora Tupinier";"Franck Delisle, Dominique Rault, LM";"Côtes d'Armor";"ARMO_042-043";"Piégu / Verdelet";;"Piégu (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2016";"SYSTEME";"VivArmor Nature";"25-75% (Nuageux)";"Pas de précipitation";;;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";"Nord-Ouest";;"Reprise de données Champs Blocs +" +379068;"Suivi des champs de blocs";"2016-10-16 00:00:00.0";;;111;"14:27";;"Week-end ou Jour Férié";"oct.-16";"BDD_IVR&QECB_Piegu_20161016_VImport.xlsx";"Nora Tupinier";"Franck Delisle & Justine Louis";"Côtes d'Armor";"ARMO_042-043";"Piégu / Verdelet";;"Piégu (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2016";"SYSTEME";"VivArmor Nature";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;12.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";;;"Reprise de données Champs Blocs +" +379135;"Suivi des champs de blocs";"2014-04-28 00:00:00.0";;;97;"13:29";;;"avr.-14";"BDD_IVRQECB_PteBilfot_2014042830_VImport.xlsx";"Franck Delisle";"Franck Delisle, Olivier Massard, Christelle Barth";"Côtes d'Armor";"ARMO_078-082-153";"Port Lazo / Pointe de Bilfot";;"Pointe de Bilfot (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2014";"SYSTEME";"VivArmor Nature";;"Pas de précipitation";;;;;;;"Reprise de données Champs Blocs +" +379202;"Suivi des champs de blocs";"2014-10-10 00:00:00.0";;;102;"13:14";;;"oct.-14";"BDD_IVRQECB_PteBilfot_20141007_VImport.xlsx";"Franck Delisle, Vincent Trémel";"Franck Delisle, Vincent Trémel";"Côtes d'Armor";"ARMO_078-082-153";"Port Lazo / Pointe de Bilfot";;"Pointe de Bilfot (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2014";"SYSTEME";"VivArmor Nature";;;;;;;;;"Reprise de données Champs Blocs +" +379269;"Suivi des champs de blocs";"2015-04-18 00:00:00.0";;;110;"14:04";;;"avr.-15";"BDD_IVR&QECB_PteBilfot_20150418_VImport.xlsx";"Non identifié";"Franck Delisle, Vincent Trémel";"Côtes d'Armor";"ARMO_078-082-153";"Port Lazo / Pointe de Bilfot";;"Pointe de Bilfot (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2015";"SYSTEME";"VivArmor Nature";;"Pas de précipitation";;10.0;"6-Vent frais-39 à 49 km/h-Crêtes d'écume blanches.";;;;"Reprise de données Champs Blocs +" +379336;"Suivi des champs de blocs";"2015-10-27 00:00:00.0";;;111;"12:59";;;"oct.-15";"BDD_IVR&QECB_PteBilfot_20151027_VImport.xlsx";"Non identifié";"Franck Delisle, Léa Mie, Stéphanie Plaga Lemanski";"Côtes d'Armor";"ARMO_078-082-153";"Port Lazo / Pointe de Bilfot";;"Pointe de Bilfot (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2015";"SYSTEME";"VivArmor Nature";;"Pas de précipitation";;;"0-Calme-1 km/h-Mer d'huile";;;;"Reprise de données Champs Blocs +" +379403;"Suivi des champs de blocs";"2016-04-07 00:00:00.0";;;114;"14:22";;"Semaine";"avr.-16";"BDD_IVR&QECB_PteBilfot_20160407_VImport.xlsx";"Nora Tupinier";"Franck Delisle, Nora Tupinier";"Côtes d'Armor";"ARMO_078-082-153";"Port Lazo / Pointe de Bilfot";;"Pointe de Bilfot (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2016";"SYSTEME";"VivArmor Nature";"25-75% (Nuageux)";"Pas de précipitation";;;;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"Nord-Ouest";;"Reprise de données Champs Blocs +" +379470;"Suivi des champs de blocs";"2016-10-15 00:00:00.0";;;102;"13:31";;"Semaine";"oct.-16";"BDD_IVR&QECB_PteBilfot_20161015_VImport.xlsx";"Nora Tupinier";"Franck Delisle, Justine Louis, Nora Tupinier";"Côtes d'Armor";"ARMO_078-082-153";"Port Lazo / Pointe de Bilfot";;"Pointe de Bilfot (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2016";"SYSTEME";"VivArmor Nature";"25-75% (Nuageux)";"Pas de précipitation";;12.0;"1-Très légère brise-1 à 5 km/h-Mer ridée";"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"Sud";;"Reprise de données Champs Blocs +" +379537;"Suivi des champs de blocs";"2014-04-29 00:00:00.0";;;99;"13:45";;;"avr.-14";"BDD_IVR&QECB_IlePlate_20140429_VImport.xlsx";"Non identifié";"Franck Delisle, Olivier Massard, Christelle Barth";"Côtes d'Armor";"ARMO_152";"Sept Îles";;"Ile Plate (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2014";"SYSTEME";"VivArmor Nature";"25-75% (Nuageux)";"Pas de précipitation";;;;;;;"Reprise de données Champs Blocs +" +379604;"Suivi des champs de blocs";"2015-04-17 00:00:00.0";;;101;"12:24";;;"avr.-15";"BDD_IVR&QECB_IlePlate_20150417_VImport.xlsx";"Non identifié";"Franck Delisle, Vincent Trémel";"Côtes d'Armor";"ARMO_152";"Sept Îles";;"Ile Plate (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2015";"SYSTEME";"VivArmor Nature";;"Pas de précipitation";;14.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";;;;"Reprise de données Champs Blocs +" +379671;"Suivi des champs de blocs";"2015-10-29 00:00:00.0";;;109;"13:44";;;"oct.-15";"BDD_IVR&QECB_IlePlate_20151029_VImport.xlsx";"Non identifié";"Franck Delisle, Léa Mie";"Côtes d'Armor";"ARMO_152";"Sept Îles";;"Ile Plate (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2015";"SYSTEME";"VivArmor Nature";;"Pluie fine";;;"2-Lègère brise-6 à 11 km/h-Vaguelettes";;;;"Reprise de données Champs Blocs +" +379738;"Suivi des champs de blocs";"2016-03-24 00:00:00.0";;;89;"13:24";;"Semaine";"mars-16";"BDD_IVR&QECB_IlePlate_20160324_VImport.xlsx";"Nora Tupinier";"Franck Delisle & Léa Mie";"Côtes d'Armor";"ARMO_152";"Sept Îles";;"Ile Plate (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2016";"SYSTEME";"VivArmor Nature";"75-100% (Très nuageux)";"Pas de précipitation";;11.0;"1-Très légère brise-1 à 5 km/h-Mer ridée";"1-Très légère brise-1 à 5 km/h-Mer ridée";"Sud";;"Reprise de données Champs Blocs +" +379805;"Suivi des champs de blocs";"2016-11-16 00:00:00.0";;;109;"14:09";;"Semaine";"nov.-16";"BDD_IVR&QECB_IlePlate_20161116_VImport.xlsx";"Nora Tupinier";"Franck Delisle & Nora Tupinier";"Côtes d'Armor";"ARMO_152";"Sept Îles";;"Ile Plate (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2016";"SYSTEME";"VivArmor Nature";"25-75% (Nuageux)";"Pluie fine";;12.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"Ouest";;"Reprise de données Champs Blocs +" +379872;"Suivi des champs de blocs";"2014-05-17 00:00:00.0";;;;;;;"mai-14";"BD_IVR_Perharidy_17052014_Import.xlsx";;;"Pays de Morlaix";"MORL_03";"Roscoff - Perharidy";;"Perharidy (champ de blocs)";"Suivi IVR";"V2014";"SYSTEME";"CPIE pays de Morlaix-Tregor";;;;;;;;;"Reprise de données Champs Blocs +" +379879;"Suivi des champs de blocs";"2014-09-09 00:00:00.0";;;113;"13:29";;;"sept.-14";"BDD_IVR&QECB_Perharidy_20140909&11_VImport.xlsx";"Non identifié";"Bénédicte Compois, Céline Houbin, Nolwenn Malengrau";"Pays de Morlaix";"MORL_03";"Roscoff - Perharidy";;"Perharidy (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2014";"SYSTEME";"CPIE pays de Morlaix-Tregor";;;;;;;;;"Reprise de données Champs Blocs +" +379934;"Suivi des champs de blocs";"2015-03-19 00:00:00.0";;;97;"11:32";;;"mars-15";"BDD_IVR&QECB_Perharidy_201503&05_VImport.xlsx";"Non identifié";"Christine BLAIZE, Camille Lesann";"Pays de Morlaix";"MORL_03";"Roscoff - Perharidy";;"Perharidy (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2015";"SYSTEME";"CPIE pays de Morlaix-Tregor";;"Pas de précipitation";;15.0;"6-Vent frais-39 à 49 km/h-Crêtes d'écume blanches.";;;;"Reprise de données Champs Blocs +" +380001;"Suivi des champs de blocs";"2015-09-28 00:00:00.0";;;114;"13:15";;;"sept.-15";"BDD_IVR&QECB_Perharidy_20150928_VImport.xlsx";"Non identifié";"Géraldine GABILLET, Andrea LAURO, Michaël TANGHE";"Pays de Morlaix";"MORL_03";"Roscoff - Perharidy";;"Perharidy (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2015";"SYSTEME";"CPIE pays de Morlaix-Tregor";;"Pas de précipitation";;18.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;;;"Reprise de données Champs Blocs +" +380068;"Suivi des champs de blocs";"2016-03-10 00:00:00.0";;;116;"13:23";;"Semaine";"mars-16";"BDD_IVR&QECB_Perharidy_20160310_VImport.xlsx";"Michaël Tanghe";"Michaël Tanghe, Gwladys Daudin, Andrea Lauro";"Pays de Morlaix";"MORL_03";"Roscoff - Perharidy";;"Perharidy (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2016";"SYSTEME";"CPIE pays de Morlaix-Tregor";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;15.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";"Ouest";;"Reprise de données Champs Blocs +" +380135;"Suivi des champs de blocs";"2016-09-19 00:00:00.0";;;116;"13:23";;"Semaine";"sept.-16";"BDD_IVR&QECB_Perharidy_20160919_VImport.xlsx";"Michaël Tanghe";"Bénédicte Compois et Christine Blaize";"Pays de Morlaix";"MORL_03";"Roscoff - Perharidy";;"Perharidy (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2016";"SYSTEME";"CPIE pays de Morlaix-Tregor";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;15.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";"Ouest";;"Reprise de données Champs Blocs +" +380202;"Suivi des champs de blocs";"2014-10-10 00:00:00.0";;;106;"13:13";;;"oct.-14";"BDD_IVR&QECB_Keraliou_20141010&11_VImport.xlsx";"Non identifié";"Florence Sénéchal, Christian Lejeune, Maud Bernard";"Rade de Brest";"BRES_03";"Le Cosquer Passage > Kernisi (Keraliou)";;"Keraliou (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2014";"SYSTEME";"Brest métropole";;;;;;;"Sud-Ouest";;"Reprise de données Champs Blocs +" +380269;"Suivi des champs de blocs";"2015-01-22 00:00:00.0";;;109;"12:30";;;"janv.-15";"BDD_IVR_Keraliou_20150122_Import.xlsx";"Non identifié";"Florence Sénéchal, Christian Le Jeune, Maud Bernard et Elisabeth Merceron";"Rade de Brest";"BRES_03";"Le Cosquer Passage > Kernisi (Keraliou)";;"Keraliou (champ de blocs)";"Suivi IVR";"V2015";"SYSTEME";"Brest métropole";;"Pas de précipitation";;8.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";;;;"Reprise de données Champs Blocs +" +380276;"Suivi des champs de blocs";"2015-03-21 00:00:00.0";;;119;"11:52";;;"mars-15";"BDD_IVR&QECB_Keraliou_20150320&21_VImport.xlsx";"Non identifié";"Peter, Annick, Jean Noël, Christian";"Rade de Brest";"BRES_03";"Le Cosquer Passage > Kernisi (Keraliou)";;"Keraliou (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2015";"SYSTEME";"Brest métropole";;"Pas de précipitation";;11.0;"7-Grand frais-50 à 61 km/h- Lames déferlantes";;;;"Reprise de données Champs Blocs +" +380343;"Suivi des champs de blocs";"2016-03-10 00:00:00.0";;;116;"12:09";;"Semaine";"mars-16";"BDD_IVR&QECB_Keraliou_20160310&11_VImport.xlsx";"Ronan Moine";"Annick,Elisabeth,Florence";"Rade de Brest";"BRES_03";"Le Cosquer Passage > Kernisi (Keraliou)";;"Keraliou (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2016";"SYSTEME";"Brest métropole";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;10.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";;;"Reprise de données Champs Blocs +" +380410;"Suivi des champs de blocs";"2016-10-17 00:00:00.0";;;114;"12:42";;"Semaine";"oct.-16";"BDD_IVR&QECB_Keraliou_20161017&18_VImport.xlsx";"Christian Le Jeune";"Annick Postollec, Jean-Noël Leost";"Rade de Brest";"BRES_03";"Le Cosquer Passage > Kernisi (Keraliou)";;"Keraliou (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2016";"SYSTEME";"Brest métropole";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;16.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";"Sud-Ouest";;"Reprise de données Champs Blocs +" +380477;"Suivi des champs de blocs";"2014-10-09 00:00:00.0";;;111;"11:59";;;"oct.-14";"BDD_IVR&QECB_Mousterlin_20141009&10_VImport.xlsx";"Non identifié";"Agathe Lefranc, Maud Bernard, Géraldine Gaillère";"Finistère Sud";"FINS_02";"Pointe de Mousterlin";;"Pointe de Mousterlin (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2014";"SYSTEME";"Agence des aires marines protégées";;"Pas de précipitation";;;;;;;"Reprise de données Champs Blocs +" +380540;"Suivi des champs de blocs";"2015-03-21 00:00:00.0";;;118;"11:19";;;"mars-15";"BDD_IVR&QECB_Mousterlin_20150320&21_VImport.xlsx";"Non identifié";"Géraldine GAILLERE, Pauline POISSON";"Finistère Sud";"FINS_02";"Pointe de Mousterlin";;"Pointe de Mousterlin (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2015";"SYSTEME";"Agence des aires marines protégées";;"Pas de précipitation";;10.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";;;"Reprise de données Champs Blocs +" +380607;"Suivi des champs de blocs";"2015-10-28 00:00:00.0";;;113;"10:55";;;"oct.-15";"BDD_IVR&QECB_Mousterlin_2015102728&29_VImport.xlsx";"Non identifié";"Héloïse You et Pascal Ragot";"Finistère Sud";"FINS_02";"Pointe de Mousterlin";;"Pointe de Mousterlin (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2015";"SYSTEME";"Agence des aires marines protégées";;"Pas de précipitation";;12.0;"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";;;;"Reprise de données Champs Blocs +" +380670;"Suivi des champs de blocs";"2016-03-10 00:00:00.0";;;115;"11:36";;"Semaine";"mars-16";"BDD_IVR&QECB_Mousterlin_20160310_VImport.xlsx";"Héloïse You";"Pauline Poisson";"Finistère Sud";"FINS_02";"Pointe de Mousterlin";;"Pointe de Mousterlin (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2016";"SYSTEME";"Agence des aires marines protégées";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;11.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"5-Bonne brise-29 à 38 km/h-Vagues modérées.";"Nord-Ouest";;"Reprise de données Champs Blocs +" +380733;"Suivi des champs de blocs";"2016-11-14 00:00:00.0";;;109;"10:05";;"Semaine";"nov.-16";"BDD_IVRQECB_Moust_20161114&15_VImport.xlsx";"Héloïse You";"Héloïse You et Mathilde Le Roux";"Finistère Sud";"FINS_02";"Pointe de Mousterlin";;"Pointe de Mousterlin (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2016";"SYSTEME";"Agence des aires marines protégées";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;13.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"Nord-Ouest";;"Reprise de données Champs Blocs +" +380800;"Suivi des champs de blocs";"2014-10-10 00:00:00.0";;;106;"12:43";;;"oct.-14";"BDD_IVR&QECB_SNG_20141010_VImport.xlsx";"Non identifié";"Pascal Ragot et Marion Diard";"Finistère Sud";"FINS_12";"Saint Nicolas - Bananec";;"Saint-Nicolas des Glénan (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2014";"SYSTEME";"Agence des aires marines protégées";;"Pas de précipitation";;;;;;;"Reprise de données Champs Blocs +" +380859;"Suivi des champs de blocs";"2015-03-22 00:00:00.0";;;115;"12:03";;;"mars-15";"BDD_IVR&QECB_SNG_20150322_VImport.xlsx";"Non identifié";"Pauline POISSON, Bruno FERRE, Tony ROBINET, Pascal RAGOT, Marion DIARD";"Finistère Sud";"FINS_12";"Saint Nicolas - Bananec";;"Saint-Nicolas des Glénan (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2015";"SYSTEME";"Agence des aires marines protégées";;"Pas de précipitation";;13.0;"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";"5-Bonne brise-29 à 38 km/h-Vagues modérées.";;;"Reprise de données Champs Blocs +" +380926;"Suivi des champs de blocs";"2016-04-08 00:00:00.0";;;;;;;"avr.-16";"BDD_IVR&QECB_SNG_20160411_VImport.xlsx";"Héloïse You";;"Finistère Sud";"FINS_12";"Saint Nicolas - Bananec";;"Saint-Nicolas des Glénan (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2016";"SYSTEME";"Agence des aires marines protégées";;;;;;;;;"Reprise de données Champs Blocs +" +380973;"Suivi des champs de blocs";"2016-10-18 00:00:00.0";;;114;"12:10";;"Semaine";"oct.-16";"BDD_IVR&QECB_SNG_20161017_VImport.xlsx";"Héloïse You";"You Héloïse, Le Roux Mathilde";"Finistère Sud";"FINS_12";"Saint Nicolas - Bananec";;"Saint-Nicolas des Glénan (champ de blocs)";"Suivi IVR+QECB - Bretagne";"V2016";"SYSTEME";"Agence des aires marines protégées";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;13.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"Ouest";;"Reprise de données Champs Blocs +" +458304;"Suivi des champs de blocs";"2020-09-19 00:00:00.0";"11:20";"13:30";113;;;"Week-end ou Jour Férié";"sep-20";"2020-09-19-EGMP-CDB-002";"Sarah Olivier";"Sarah Olivier et Nathan Ropers";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_116";"Perré d'Antiochat";;"Pérré d'Antiochat (champ de blocs)";"Suivis comportementaux";"V2016";"OLIVIER";"CPIE Marennes-Oléron";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;24.0;;;"Sud-Est";"3-peu agitée-0,5 à 1,25 m"; +487404;"Suivi des champs de blocs";"2021-03-31 00:00:00.0";"12:30";"13:40";107;"13:47";0.5;"Semaine";"mar-21";"2021-03-31-PNMI-CDB-002";"Jean-André PRAT";"JAP- (Orlane et Etienne, stagiaires)";"Iroise";"PNMI_07";"Chaussée de Sein";;"Goulenez (champ de blocs)";"Suivi IVR";"V2016";"PRAT";"Parc naturel marin de la mer d'Iroise";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;12.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"Sud-Ouest";"2-belle-0,1 à 0,5 m";"Belle journée, un peu de monde à la pêche (12 personnes)" +487405;"Suivi des champs de blocs";"2021-03-31 00:00:00.0";"12:30";"13:30";107;"13:47";0.5;"Semaine";"mar-21";"2021-03-31-PNMI-CDB-003";"Jean-André PRAT";"Livier SCHWEYER / Mickael BUANIC";"Iroise";"PNMI_07";"Chaussée de Sein";;"Kilaourou (champ blocs)";"Suivi IVR";"V2016";"PRAT";"Parc naturel marin de la mer d'Iroise";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;10.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"Sud-Ouest";"3-peu agitée-0,5 à 1,25 m";"Belle journée ; 5 pêcheurs sur zone" +488602;"Suivi des champs de blocs";"2021-03-28 00:00:00.0";"10:15";"12:15";97;"11:30";;"Week-end ou Jour Férié";"mar-21";"2021-03-28-EGMP-CDB-001";"Sarah Olivier";"Sarah Olivier +Vaiana Mocka";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_114";"La Brée et la Balise";;"La Brée-les-Bains (champ de blocs)";"Suivis comportementaux";"V2016";"GUYONNARD";"CPIE Marennes-Oléron";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;7.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;"Sud-Est";;"Suivis comportementaux PNMEGMP 2020-2021" +488609;"Suivi des champs de blocs";"2021-05-27 00:00:00.0";"10:30";"12:40";103;"12:06";;"Semaine";"mai-21";"2021-05-27-EGMP-CDB-001";"Sarah Olivier";"Sarah Olivier +Joachim Jouzeau ";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_114";"La Brée et la Balise";;"La Brée-les-Bains (champ de blocs)";"Suivis comportementaux";"V2016";"GUYONNARD";"CPIE Marennes-Oléron";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;20.0;"2-Lègère brise-6 à 11 km/h-Vaguelettes";;"Nord-Ouest";; +492097;"Suivi des champs de blocs";"2021-03-01 00:00:00.0";"11:10";"11:35";106;"12:16";;"Vacances";"mar-21";"2021-03-01-EGMP-CDB-001";"Sarah Olivier";"Sarah Olivier +Nathan Ropers";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_116";"Perré d'Antiochat";;"Pérré d'Antiochat (champ de blocs)";"Suivis comportementaux";"V2016";"GUYONNARD";"CPIE Marennes-Oléron";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";"Est";;"Suivis comportementaux réalisés pour le PNM EGMP" +492115;"Suivi des champs de blocs";"2021-03-28 00:00:00.0";"10:15";"12:05";97;"11:30";;"Week-end ou Jour Férié";"mar-21";"2021-03-28-EGMP-CDB-002";"Sarah Olivier";"Jean-Baptiste Bonnin +Thierry Duchamp";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_116";"Perré d'Antiochat";;"Pérré d'Antiochat (champ de blocs)";"Suivis comportementaux";"V2016";"GUYONNARD";"CPIE Marennes-Oléron";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;7.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;"Sud-Est";;"Suivis comportementaux réalisés pour le PNM EGMP 2020-2021" +492173;"Suivi des champs de blocs";"2021-04-27 00:00:00.0";"10:25";"12:10";100;"11:43";;"Vacances";"avr-21";"2021-04-27-EGMP-CDB-001";"Sarah Olivier";"Nathan Ropers +Thierry Duchamp";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_116";"Perré d'Antiochat";;"Pérré d'Antiochat (champ de blocs)";"Suivis comportementaux";"V2016";"GUYONNARD";"CPIE Marennes-Oléron";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;18.0;"3-Petite brise-12 à 19 km/h-Très petites vagues. Déferlement.";;"Nord-Est";; +494454;"Suivi des champs de blocs";"2021-07-26 00:00:00.0";;;94;"13:15";;"Vacances";"jul-21";"2021-07-26-EGMP-CDB-001";"Sarah Olivier";;"Estuaire de la Gironde et Mer des Pertuis";"EGMP_116";"Perré d'Antiochat";;"Pérré d'Antiochat (champ de blocs)";"Suivis comportementaux";"V2016";"OLIVIER";"CPIE Marennes-Oléron";"25-75% (Nuageux)";"Pas de précipitation";;19.0;"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";;"Ouest";; +494456;"Suivi des champs de blocs";"2021-07-26 00:00:00.0";;;94;"13:15";;"Vacances";"jul-21";"2021-07-26-EGMP-CDB-002";"Jean-Baptiste Bonnin";"Jean-Baptiste Bonnin, Louise Haran";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_116";"Perré d'Antiochat";;"Pérré d'Antiochat (champ de blocs)";"Suivis comportementaux";"V2016";"OLIVIER";"CPIE Marennes-Oléron";"25-75% (Nuageux)";"Pas de précipitation";;20.0;"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";;"Ouest";; +496643;"Suivi des champs de blocs";"2021-08-23 00:00:00.0";"10:16";"13:00";93;"12:16";;"Vacances";"aou-21";"2021-08-23-EGMP-CDB-001";"Sarah Olivier";"Sarah Olivier et Zachary Gaudin";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_114";"La Brée et la Balise";;"La Brée-les-Bains (champ de blocs)";"Suivis comportementaux";"V2016";"OLIVIER";"CPIE Marennes-Oléron";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;;"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";;"Nord-Est";; +496659;"Suivi des champs de blocs";"2021-08-23 00:00:00.0";"10:16";"13:00";93;"12:16";;"Vacances";"aou-21";"2021-08-23-EGMP-CDB-002";"Vaiana Mocka";"Vaiana Mocka et Thierry Duchamp";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_116";"Perré d'Antiochat";;"Pérré d'Antiochat (champ de blocs)";"Suivis comportementaux";"V2016";"OLIVIER";"CPIE Marennes-Oléron";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;22.0;"4-Jolie brise-20 à 28 km/h-Petites vagues. Moutons.";;"Nord-Est";; +512569;"Suivi des champs de blocs";"2021-10-07 00:00:00.0";"10:00";"13:30";107;"12:03";;"Semaine";"oct-21";"2021-10-07-EGMP-CDB-001";"Perrine Bergossi";"CPIE-IODDE : Léa Camus, Jacques Baudouin. PNM EGMP : Sebastien Meslin, Mickael Fleury, Juliette Frigo. Bénévole : Pierre Lagorce";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_119";"Concession scientifique";;"Chassiron (champ de blocs)";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2016";"BERGOSSI";"CPIE Marennes-Oléron";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;;;;;;"Quadrat 1 pas réalisé car découvert trop peu de temps et pas de blocs !" +513924;"Suivi des champs de blocs";"2021-10-07 00:00:00.0";"10:00";"13:30";107;"12:03";;"Semaine";"oct-21";"2021-10-07-EGMP-CDB-002";"Perrine Bergossi";"CPIE-IODDE : Sarah Olivier, Jean-Baptiste Bonnin, Vaiana Mocka. PNM EGMP : Valentin Guyonnard, Anavel Ravaud. Bénévole : Claire Dufau.";"Estuaire de la Gironde et Mer des Pertuis";"EGMP_114";"La Brée et la Balise";;"La Brée-les-Bains (champ de blocs)";"Suivi IVR+QECB - Pertuis et Pays Basque";"V2016";"BERGOSSI";"CPIE Marennes-Oléron";"0-25% (Peu ou pas nuageux)";"Pas de précipitation";;;;;;;"Quadrat 3 non réalisé par manque de temps !" diff --git a/tools/indic_ivr/test-data/qecbnew.Rdata b/tools/indic_ivr/test-data/qecbnew.Rdata new file mode 100644 index 000000000..3058f216c Binary files /dev/null and b/tools/indic_ivr/test-data/qecbnew.Rdata differ diff --git a/tools/obisindicators/.shed.yml b/tools/obisindicators/.shed.yml new file mode 100644 index 000000000..13aea28ed --- /dev/null +++ b/tools/obisindicators/.shed.yml @@ -0,0 +1,14 @@ +categories: + - Ecology +owner: ecology +remote_repository_url: https://github.com/Marie59/Sentinel_2A/obisindicators +long_description: | + Compute biodiveristy indicators from OBIS +type: unrestricted +auto_tool_repositories: + name_template: "{{ tool_id }}" + description_template: "Wrapper for obisindicators tool: {{ tool_name }}." +suite: + name: "obisindicators" + description: "A tool for generating indicators from OBIS" + type: unrestricted diff --git a/tools/obisindicators/analyze.r b/tools/obisindicators/analyze.r new file mode 100644 index 000000000..95df9284c --- /dev/null +++ b/tools/obisindicators/analyze.r @@ -0,0 +1,72 @@ +#' Calculate Biodiversity Indicators, including ES50 (Hurlbert index) +#' +#' Calculate the expected number of marine species in a random sample of 50 +#' individuals (records) +#' +#' @param df data frame with unique species observations containing columns: +#' `cell`, `species`, `records` +#' @param esn expected number of marine species +#' +#' @return Data frame with the following extra columns: - `n`: number of records +#' - `sp`: species richness - `shannon`: Shannon index - `simpson`: Simpson +#' index - `es`: Hurlbert index (n = 50), i.e. expected species from 50 +#' samples ES(50) - `hill_1`: Hill number `exp(shannon)` - `hill_2`: Hill +#' number `1/simpson` - `hill_inf`: Hill number `1/maxp` +#' +#' @details The expected number of marine species in a random sample of 50 +#' individuals (records) is an indicator on marine biodiversity richness. The +#' ES50 is defined in OBIS as the `sum(esi)` over all species of the following +#' per species calculation: +#' +#' - when `n - ni >= 50 (with n as the total number of records in the cell and +#' ni the total number of records for the ith-species) +#' - `esi = 1 - exp(lngamma(n-ni+1) + lngamma(n-50+1) - lngamma(n-ni-50+1) - lngamma(n+1))` +#' +#' - when `n >= 50` - `esi = 1` +#' +#' - else - `esi = NULL` +#' +#' Warning: ES50 assumes that individuals are randomly distributed, the sample +#' size is sufficiently large, the samples are taxonomically similar, and that +#' all of the samples have been taken in the same manner. +#' +#' @export +#' @concept analyze +#' @examples +#' @importFrom gsl lngamma +calc_indicators <- function(df, esn = 50) { + + stopifnot(is.data.frame(df)) + stopifnot(is.numeric(esn)) + stopifnot(all(c("cell", "species", "records") %in% names(df))) + + df %>% + dplyr::group_by(cell, species) %>% + dplyr::summarize( + ni = sum(records), + .groups = "drop_last") %>% + dplyr::mutate(n = sum(ni)) %>% + dplyr::group_by(cell, species) %>% + dplyr::mutate( + hi = -(ni / n * log(ni / n)), + si = (ni / n)^2, + qi = ni / n, + esi = dplyr::case_when( + n - ni >= esn ~ 1 - exp(gsl::lngamma(n - ni + 1) + gsl::lngamma(n - esn + 1) - gsl::lngamma(n - ni - esn + 1) - gsl::lngamma(n + 1)), + n >= esn ~ 1 + ) + ) %>% + dplyr::group_by(cell) %>% + dplyr::summarize( + n = sum(ni), + sp = dplyr::n(), + shannon = sum(hi), + simpson = sum(si), + maxp = max(qi), + es = sum(esi), + .groups = "drop") %>% + dplyr::mutate( + hill_1 = exp(shannon), + hill_2 = 1 / simpson, + hill_inf = 1 / maxp) +} diff --git a/tools/obisindicators/data.r b/tools/obisindicators/data.r new file mode 100644 index 000000000..db8692d52 --- /dev/null +++ b/tools/obisindicators/data.r @@ -0,0 +1,125 @@ +#' OBIS occurrences, global sample of 1 million records +#' +#' Simple dataset of species and location for example purposes from 2022-04-04. +#' +#' @format A data frame with 1,000,000 rows and 4 variables: +#' - `decimalLongitude`: unique x +#' - `decimalLatitude`: unique y +#' - `species`: unique species +#' - `records`: number of records based on unique values of x, y, species +#' +#' @source \url{https://obis.org/data/access} +#' @concept data +"occ_1M" + +#' OBIS occurrences, South Atlantic full regional sample +#' +#' Simple dataset of species and location for example purposes from 2022-04-04. +#' +#' @format A data frame with 1,014,006 rows and 4 variables: +#' - `decimalLongitude`: unique x +#' - `decimalLatitude`: unique y +#' - `species`: unique species +#' - `records`: number of records based on unique values of x, y, species +#' +#' @source \url{https://obis.org/data/access} +#' @concept data +"occ_SAtlantic" + +#' OBIS occurrences, Florida Keys full regional sample +#' +#' Simple dataset of species and location for example purposes from 2022-04-04. +#' +#' @format A data frame with 1,014,006 rows and 4 variables: +#' - `decimalLongitude`: unique x +#' - `decimalLatitude`: unique y +#' - `species`: unique species +#' - `records`: number of records based on unique values of x, y, species +#' +#' @source \url{https://obis.org/data/access} +#' @concept data +"occ_fk" + +#' OBIS occurrences, temporal sample for the 1960s, limited to 1M records +#' +#' Simple dataset of species and location for example purposes from 2022-04-04. +#' +#' @format A data frame with 1,014,006 rows and 4 variables: +#' - `decimalLongitude`: unique x +#' - `decimalLatitude`: unique y +#' - `species`: unique species +#' - `records`: number of records based on unique values of x, y, species +#' +#' @source \url{https://obis.org/data/access} +#' @concept data +"occ_1960s" + +#' OBIS occurrences, temporal sample for the 1970s, limited to 1M records +#' +#' Simple dataset of species and location for example purposes from 2022-04-04. +#' +#' @format A data frame with 1,014,006 rows and 4 variables: +#' - `decimalLongitude`: unique x +#' - `decimalLatitude`: unique y +#' - `species`: unique species +#' - `records`: number of records based on unique values of x, y, species +#' +#' @source \url{https://obis.org/data/access} +#' @concept data +"occ_1970s" + +#' OBIS occurrences, temporal sample for the 1980s, limited to 1M records +#' +#' Simple dataset of species and location for example purposes from 2022-04-04. +#' +#' @format A data frame with 1,014,006 rows and 4 variables: +#' - `decimalLongitude`: unique x +#' - `decimalLatitude`: unique y +#' - `species`: unique species +#' - `records`: number of records based on unique values of x, y, species +#' +#' @source \url{https://obis.org/data/access} +#' @concept data +"occ_1980s" + +#' OBIS occurrences, temporal sample for the 1990s, limited to 1M records +#' +#' Simple dataset of species and location for example purposes from 2022-04-04. +#' +#' @format A data frame with 1,014,006 rows and 4 variables: +#' - `decimalLongitude`: unique x +#' - `decimalLatitude`: unique y +#' - `species`: unique species +#' - `records`: number of records based on unique values of x, y, species +#' +#' @source \url{https://obis.org/data/access} +#' @concept data +"occ_1990s" + +#' OBIS occurrences, temporal sample for the 2000s, limited to 1M records +#' +#' Simple dataset of species and location for example purposes from 2022-04-04. +#' +#' @format A data frame with 1,014,006 rows and 4 variables: +#' - `decimalLongitude`: unique x +#' - `decimalLatitude`: unique y +#' - `species`: unique species +#' - `records`: number of records based on unique values of x, y, species +#' +#' @source \url{https://obis.org/data/access} +#' @concept data +"occ_2000s" + +#' OBIS occurrences, temporal sample for the 2010s, limited to 1M records +#' +#' Simple dataset of species and location for example purposes from 2022-04-04. +#' +#' @format A data frame with 1,014,006 rows and 4 variables: +#' - `decimalLongitude`: unique x +#' - `decimalLatitude`: unique y +#' - `species`: unique species +#' - `records`: number of records based on unique values of x, y, species +#' +#' @source \url{https://obis.org/data/access} +#' @concept data +"occ_2010s" diff --git a/tools/obisindicators/macro.xml b/tools/obisindicators/macro.xml new file mode 100644 index 000000000..ad7eca0a1 --- /dev/null +++ b/tools/obisindicators/macro.xml @@ -0,0 +1,34 @@ + + 0.0.0 + + + r-ggplot2 + + + + + + + + + + + + @Manual{, + title = {obisindicators: Develop marine biodiversity indicators from OBIS data}, + author = {Ben Ben and Pieter Provoost and Tylar Murray}, + year = {2022}, + note = {https://marinebon.org/obisindicators, + https://github.com/marinebon/obisindicators}, + } + + + + + + topic_0610 + topic_3050 + + + + diff --git a/tools/obisindicators/obisindicators.r b/tools/obisindicators/obisindicators.r new file mode 100644 index 000000000..b3688ca82 --- /dev/null +++ b/tools/obisindicators/obisindicators.r @@ -0,0 +1,113 @@ +#Rscript + +########################################### +## Mapping alpha and beta diversity ## +########################################### + +#####Packages : obisindicators +# dplyr +# sf +# ggplot2 +# rnaturalearth +# rnaturalearthdata +# viridis +# dggridr +library(magrittr) +remotes::install_github("r-barnes/dggridR") +#####Load arguments + +args <- commandArgs(trailingOnly = TRUE) + +# url for the S2 subset + +if (length(args) < 4) { + stop("This tool needs at least 4 argument : longitude, latitude, species and number of records") +}else { + raster <- args[1] + hr <- args[2] + sep <- as.character(args[3]) + longitude <- as.numeric(args[4]) + latitude <- as.numeric(args[5]) + spe <- as.numeric(args[6]) + rec <- as.numeric(args[7]) + crs <- as.numeric(args[8]) + source(args[9]) + source(args[10]) + source(args[11]) +} + +if (hr == "false") { + hr <- FALSE +}else { + hr <- TRUE +} + +if (sep == "t") { + sep <- "\t" +} + +if (crs == "0") { + crs <- "+proj=robin +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs" +} +#####Import data +occ <- read.table(raster, sep = sep, dec = ".", header = hr, fill = TRUE, encoding = "UTF-8") # occ_1M OR occ_SAtlantic +occ <- na.omit(occ) +#Get biological occurrences +#Use the 1 million records subsampled from the full OBIS dataset +colnames(occ)[longitude] <- c("decimalLongitude") +colnames(occ)[latitude] <- c("decimalLatitude") +colnames(occ)[spe] <- c("species") +colnames(occ)[rec] <- c("records") + +#Create a discrete global grid +#Create an ISEA discrete global grid of resolution 9 using the dggridR package: + +dggs <- dggridR::dgconstruct(projection = "ISEA", topology = "HEXAGON", res = 9) + +#Then assign cell numbers to the occurrence data +occ$cell <- dggridR::dgGEO_to_SEQNUM(dggs, occ$decimalLongitude, occ$decimalLatitude)[["seqnum"]] + +#Calculate indicators +#The following function calculates the number of records, species richness, Simpson index, Shannon index, Hurlbert index (n = 50), and Hill numbers for each cell. + +#Perform the calculation on species level data +idx <- calc_indicators(occ) +write.table(idx, file = "Index.csv", sep = ",", dec = ".", na = " ", col.names = TRUE, row.names = FALSE, quote = FALSE) + +#add cell geometries to the indicators table (idx) +grid_idx <- sf::st_wrap_dateline(dggridR::dgcellstogrid(dggs, idx$cell)) +colnames(grid_idx) <- c("cell", "geometry") + +grid <- dplyr::left_join(grid_idx, + idx, + by = "cell") + +#Plot maps of indicators +#Let’s look at the resulting indicators in map form. +#Indice ES(50) +es_50_map <- gmap_indicator(grid, "es", label = "ES(50)", crs = crs) +es_50 <- ggplot2::ggsave("ES_50.png", es_50_map, scale = 0.38, width = 12, height = 7, units = "in", dpi = 300, limitsize = TRUE) + +# Shannon index +shannon_map <- gmap_indicator(grid, "shannon", label = "Shannon index", crs = crs) +shannon <- ggplot2::ggsave("Shannon_index.png", shannon_map, scale = 0.38, width = 12, height = 7, units = "in", dpi = 300, limitsize = TRUE) + + +# Number of records, log10 scale, Geographic projection +records_map <- gmap_indicator(grid, "n", label = "# of records", trans = "log10", crs = crs) +records <- ggplot2::ggsave("Records.png", records_map, scale = 0.38, width = 12, height = 7, units = "in", dpi = 300, limitsize = TRUE) + +# Simpson index +simpson_map <- gmap_indicator(grid, "simpson", label = "Simpson index", crs = crs) +simpson <- ggplot2::ggsave("Simpson_index.png", simpson_map, scale = 0.38, width = 12, height = 7, units = "in", dpi = 300, limitsize = TRUE) + +# maxp +maxp_map <- gmap_indicator(grid, "maxp", label = "maxp index", crs = crs) +maxp <- ggplot2::ggsave("Maxp.png", maxp_map, scale = 0.38, width = 12, height = 7, units = "in", dpi = 300, limitsize = TRUE) + +#Mapping +es_50 +shannon +simpson +maxp +records diff --git a/tools/obisindicators/obisindicators.xml b/tools/obisindicators/obisindicators.xml new file mode 100644 index 000000000..43787f715 --- /dev/null +++ b/tools/obisindicators/obisindicators.xml @@ -0,0 +1,127 @@ + + from OBIS + + macro.xml + + + + + r-magrittr + r-dplyr + r-sf + r-gsl + r-rnaturalearth + r-rnaturalearthdata + r-viridis + r-remotes + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tools/obisindicators/test-data/Index.csv b/tools/obisindicators/test-data/Index.csv new file mode 100644 index 000000000..d7a79965b --- /dev/null +++ b/tools/obisindicators/test-data/Index.csv @@ -0,0 +1,1122 @@ +cell,n,sp,shannon,simpson,maxp,es,hill_1,hill_2,hill_inf +33478,13,1,0,1,1, ,1,1,1 +33890,2,1,0,1,1, ,1,1,1 +33972,2,1,0,1,1, ,1,1,1 +34219,1,1,0,1,1, ,1,1,1 +34792,3,1,0,1,1, ,1,1,1 +34874,2,1,0,1,1, ,1,1,1 +35118,1,1,0,1,1, ,1,1,1 +35119,1,1,0,1,1, ,1,1,1 +35202,1,1,0,1,1, ,1,1,1 +35494,1,1,0,1,1, ,1,1,1 +35931,48,5,1.31474686520139,0.299479166666667,0.375, ,3.72380824048646,3.33913043478261,2.66666666666667 +36263,2,1,0,1,1, ,1,1,1 +36587,1,1,0,1,1, ,1,1,1 +36831,1,1,0,1,1, ,1,1,1 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,1,1,1 +137949,4,1,0,1,1, ,1,1,1 +137953,19,1,0,1,1, ,1,1,1 +137956,8,1,0,1,1, ,1,1,1 +138032,8,1,0,1,1, ,1,1,1 +138033,1,1,0,1,1, ,1,1,1 +138035,4,1,0,1,1, ,1,1,1 +138113,9,1,0,1,1, ,1,1,1 +138115,15,1,0,1,1, ,1,1,1 +138195,11,1,0,1,1, ,1,1,1 +138196,1,1,0,1,1, ,1,1,1 +138277,1,1,0,1,1, ,1,1,1 +138278,1,1,0,1,1, ,1,1,1 +138357,2,1,0,1,1, ,1,1,1 +138439,4,1,0,1,1, ,1,1,1 +138440,1,1,0,1,1, ,1,1,1 diff --git a/tools/obisindicators/test-data/occ_at.tabular b/tools/obisindicators/test-data/occ_at.tabular new file mode 100644 index 000000000..22e09922b --- /dev/null +++ b/tools/obisindicators/test-data/occ_at.tabular @@ -0,0 +1,6930 @@ +decimalLongitude decimalLatitude species date_year records +-66.1350021362305 -50.5466651916504 Andreella uncifera 3 +-62.7333335876465 -43.283332824707 Andreella uncifera 1 +-60.9383316040039 -45.281665802002 Andreella uncifera 1 +-60.4266662597656 -40.281665802002 Andreella uncifera 1 +-59.2483329772949 -40.033332824707 Andreella uncifera 2 +-60.9049987792969 -41.6966667175293 Andreella uncifera 1 +-61.9150009155273 -42.2849998474121 Andreella uncifera 1 +-60.5633316040039 -50.6916656494141 Andreella uncifera 1 +-61.935001373291 -48.5550003051758 Andreella uncifera 1 +-61.6033325195312 -45.1150016784668 Andreella uncifera 1 +-61.5133323669434 -42.6633338928223 Andreella uncifera 1 +-63.6083335876465 -41.4516677856445 Andreella uncifera 1 +-63.5683326721191 -50.8549995422363 Andreella uncifera 1 +-59.2449989318848 -40.3666648864746 Andreella uncifera 1 +-5.9980001449585 -49 Rhizosolenia styliformis 1992 1 +-56.632999420166 -51.9669990539551 Leitoscoloplos kerguelensis 1963 1 +-24.8080005645752 -55.8419990539551 Umbellula magniflora 1963 2 +-38.6669998168945 -53.867000579834 Serolella pagenstecheri 1986 1 +18.5 -12.5 Echinolittorina punctata 1 +18.5833339691162 -34.1333351135254 Zeus faber 1 +-57.3333015441895 -50.5833015441895 Cyamiocardium crassilabrum 1928 1 +-12.2917003631592 -37.0750007629395 Eatoniella trochiformis 1938 3 +13.0500001907349 -30.2483005523682 Pronucula benguelana 1957 1 +-34.9333000183105 -6.81669998168945 Eleotris pisonis 1 +-41.6500015258789 -2.86669993400574 Eleotris pisonis 1 +-43.86669921875 -22.8999996185303 Eleotris pisonis 1 +-52.0833015441895 -32.1666984558105 Eleotris pisonis 1 +-44.4166984558105 -2.53329992294312 Eleotris pisonis 1 +-44 -23.1667003631592 Eleotris pisonis 1 +-50.11669921875 -30 Eleotris pisonis 1 +-47.9166984558105 -25 Eleotris pisonis 1 +-44.4500007629395 -2.5 Eleotris pisonis 1 +-38.7000007629395 -12.6999998092651 Eleotris pisonis 1 +-44.4333000183105 -2.70000004768372 Sciades parkeri 1 +-46.3166999816895 -23.9832992553711 Mycteroperca acutirostris 1 +-48.5499992370605 -25.86669921875 Mycteroperca acutirostris 1 +-52.6666984558105 -33 Mycteroperca acutirostris 1 +-44.4500007629395 -2.5 Sciades parkeri 1 +-48.4000015258789 -25.5 Mycteroperca acutirostris 1 +-50.7999992370605 -0.71670001745224 Sciades parkeri 1 +-35.7832984924316 -9.60000038146973 Sciades parkeri 1 +-38.7000007629395 -12.6999998092651 Sciades parkeri 1 +-44.5 -2.56669998168945 Sciades parkeri 1 +-35.7832984924316 -9.60000038146973 Lutjanus griseus 1 +-43.86669921875 -22.8999996185303 Lutjanus griseus 1 +-48.5499992370605 -25.86669921875 Lutjanus griseus 1 +-34.8166999816895 -7.56669998168945 Sciades parkeri 1 +-37.7667007446289 -4.43330001831055 Lutjanus griseus 1 +-52.0833015441895 -32.1666984558105 Mycteroperca acutirostris 1 +-34.8166999816895 -7.56669998168945 Lutjanus griseus 1 +-48.4000015258789 -25.5 Lutjanus griseus 1 +-40.1623916625977 -20.0403251647949 Codium spongiosum 1982 1 +-40.1623916625977 -20.0403251647949 Codium spongiosum 1973 1 +-40.4356536865234 -20.641393661499 Codium spongiosum 2000 1 +-40.1335868835449 -19.967830657959 Dictyota dichotoma 1986 1 +-65.3333358764648 -55.3333320617676 Austrocidaris spinulosa 2 +-64.8833312988281 -54.9833335876465 Austrocidaris spinulosa 2 +-66.7799987792969 -55.1399993896484 Austrocidaris spinulosa 2 +-56.61669921875 -54.7167015075684 Austrocidaris spinulosa 2 +-39.0099983215332 -55.6749992370605 Antholoba achates 1 +17.4500007629395 -33.2832984924316 Diastobranchus capensis 1997 1 +-41.7999992370605 -22.7667007446289 Octopus hubbsorum 2001 1 +-66.2249984741211 -55.4383316040039 Ctenocidaris (Eurocidaris) nutrix 2 +-36.9160003662109 -54.0830001831055 Ctenocidaris (Eurocidaris) nutrix 2 +-37.25 -54 Ctenocidaris (Eurocidaris) nutrix 2 +-36.5 -54.1669998168945 Ctenocidaris (Eurocidaris) nutrix 1 +-38.9165992736816 -14.8083000183105 Amaryllis atlantica 2001 1 +-67.6500015258789 -52.3300018310547 Antholoba achates 2 +2.79999995231628 -54.5400009155273 Cribrostomoides wiesneri 1991 5 +3.37932991981506 -54.1716995239258 Cribrostomoides wiesneri 1991 1 +-39.7052993774414 -59.826301574707 Cribrostomoides wiesneri 1992 11 +-40.823299407959 -59.5428009033203 Cribrostomoides wiesneri 1992 8 +-41.9874992370605 -59.6394996643066 Cribrostomoides wiesneri 1992 7 +-66.4079971313477 -56.1080017089844 Neosmilaster steineni 1963 1 +-41.3352012634277 -59.4832000732422 Cribrostomoides wiesneri 1992 4 +-68.6669998168945 -55.1669998168945 Leptonotus blainvilleanus 4 +11.3999996185303 -4.18300008773804 Abudefduf taurus 2 +15 -35 Muraenoclinus dorsalis 2 +-38.4830017089844 -12.9670000076294 Eleotris pisonis 2 +13.4707002639771 -41.148998260498 Neogloboquadrina pachyderma 1991 111 +11.7093410491943 -6.70460748672485 Chlorophthalmus agassizi 1986 1 +12.6364002227783 -7.98898696899414 Carcharhinus limbatus 1986 1 +-7.11670017242432 -18.75 Conaea rapax 1973 30 +11.6034507751465 -16.3204536437988 Neomerinthe folgori 1986 1 +12.7852144241333 -13.0726642608643 Neomerinthe folgori 1986 1 +13.4905033111572 -11.4897603988647 Neomerinthe folgori 1986 1 +7.61232995986938 -46.7709999084473 Neogloboquadrina pachyderma 1989 39 +13.6466665267944 -11.0166664123535 Neomerinthe folgori 1986 1 +13.5691270828247 -10.8027153015137 Neomerinthe folgori 1986 1 +13.174916267395 -8.7584171295166 Neomerinthe folgori 1986 1 +12.2597818374634 -7.04792070388794 Neomerinthe folgori 1986 1 +11.8720836639404 -5.14061784744263 Neomerinthe folgori 1986 1 +11.7897167205811 -6.12797689437866 Neomerinthe folgori 1986 1 +13.4812002182007 -41.1352996826172 Neogloboquadrina pachyderma 1991 2 +-36.9500007629395 -15.6499996185303 Farranula carinata 1963 10 +-36.4669990539551 -53.5830001831055 Lissodendoryx (Lissodendoryx) flabellata 1963 1 +-3.20667004585266 -59.4467010498047 Adercotryma glomeratum 1994 8 +1.16666996479034 -55.4583015441895 Adercotryma glomeratum 1989 2 +-0.481667011976242 -56.3199996948242 Adercotryma glomeratum 1989 3 +3.59817004203796 -53.872200012207 Adercotryma glomeratum 1991 3 +-61.380500793457 0 Bitectatodinium tepikiense 1 +-61.801700592041 0 Bitectatodinium tepikiense 1 +-61.8116989135742 0 Bitectatodinium tepikiense 1 +-61.9042015075684 0 Bitectatodinium tepikiense 1 +3.37932991981506 -54.1716995239258 Eggerella bradyi 1991 3 +-40.5094985961914 -59.548698425293 Eggerella bradyi 1992 14 +-39.7052993774414 -59.826301574707 Eggerella bradyi 1992 14 +-40.823299407959 -59.5428009033203 Adercotryma glomeratum 1992 12 +-40.823299407959 -59.5428009033203 Eggerella bradyi 1992 14 +-41.3352012634277 -59.4832000732422 Eggerella bradyi 1992 7 +-35.5772018432617 -59.0587997436523 Eggerella bradyi 1992 15 +-5.99809980392456 -48.0617980957031 Neogloboquadrina pachyderma 1988 1 +-6 -48.5666999816895 Neogloboquadrina pachyderma 1988 1 +7.75670003890991 -48.3193016052246 Neogloboquadrina pachyderma 1988 1 +6.70599985122681 -49.1343002319336 Neogloboquadrina pachyderma 1988 1 +-4.99700021743774 -53.0759010314941 Neogloboquadrina pachyderma 1988 1 +-3.21180009841919 -50.7425003051758 Neogloboquadrina pachyderma 1988 1 +-2.84750008583069 -48.6940002441406 Neogloboquadrina pachyderma 1988 1 +0.0939999967813492 -41.8540000915527 Neogloboquadrina pachyderma 1988 1 +13.8283004760742 -29 Neogloboquadrina pachyderma 1988 1 +16.3551998138428 -35.6100006103516 Neogloboquadrina pachyderma 1988 1 +1.83809995651245 -44.250301361084 Neogloboquadrina pachyderma 1988 1 +1.25119996070862 -45.5028991699219 Neogloboquadrina pachyderma 1988 1 +13.0882997512817 -29.1749992370605 Neogloboquadrina pachyderma 1988 1 +0.289600014686584 -47.4023017883301 Neogloboquadrina pachyderma 1988 1 +-57.9404983520508 -45.0999984741211 Neogloboquadrina pachyderma 1988 1 +-58.8866996765137 -53.7643013000488 Neogloboquadrina pachyderma 1988 1 +-58.9039993286133 -53.7360000610352 Neogloboquadrina pachyderma 1988 1 +14.0050001144409 -27.9549999237061 Neogloboquadrina pachyderma 1988 1 +12.1549997329712 -30.4382991790771 Neogloboquadrina pachyderma 1988 1 +11.7399997711182 -31.0849990844727 Neogloboquadrina pachyderma 1988 1 +10.4809999465942 -44.4982986450195 Neogloboquadrina pachyderma 1988 1 +9.60999965667725 -45.6209983825684 Neogloboquadrina pachyderma 1988 1 +7.5939998626709 -46.7667007446289 Neogloboquadrina pachyderma 1988 1 +-7.50299978256226 -50.9519996643066 Neogloboquadrina pachyderma 1988 1 +-12.7033004760742 -49.0116996765137 Neogloboquadrina pachyderma 1988 1 +11.4517002105713 0 Protoperidinium shanghaiense 1 +-6.59170007705688 0 Protoperidinium shanghaiense 1 +4.9867000579834 0 Protoperidinium shanghaiense 1 +6.68499994277954 0 Protoperidinium shanghaiense 1 +-32.1782989501953 0 Protoperidinium shanghaiense 1 +1.0333000421524 0 Protoperidinium shanghaiense 1 +-0.0166999995708466 0 Protoperidinium shanghaiense 1 +3.21499991416931 0 Protoperidinium shanghaiense 1 +-28.9367008209229 0 Protoperidinium shanghaiense 2 +9.74330043792725 0 Protoperidinium shanghaiense 1 +13.9582996368408 0 Protoperidinium shanghaiense 1 +-1.04670000076294 0 Protoperidinium shanghaiense 1 +-20.4116992950439 0 Protoperidinium shanghaiense 1 +3.94829988479614 0 Protoperidinium shanghaiense 1 +3.89499998092651 0 Protoperidinium shanghaiense 1 +-22.8500003814697 0 Protoperidinium shanghaiense 1 +-10.7166996002197 0 Protoperidinium shanghaiense 1 +-48.3600006103516 0 Protoperidinium shanghaiense 1 +-43.9667015075684 0 Protoperidinium shanghaiense 1 +-45.8699989318848 0 Protoperidinium shanghaiense 1 +-8.32670021057129 0 Protoperidinium shanghaiense 1 +-3.00500011444092 0 Protoperidinium shanghaiense 1 +-4.90000009536743 0 Protoperidinium shanghaiense 1 +1.55330002307892 0 Protoperidinium shanghaiense 1 +2.17499995231628 0 Protoperidinium shanghaiense 1 +1.47500002384186 0 Protoperidinium shanghaiense 1 +7.75829982757568 0 Protoperidinium shanghaiense 1 +1.48500001430511 0 Protoperidinium shanghaiense 1 +-2.6949999332428 0 Protoperidinium shanghaiense 1 +-57.6352996826172 -55.6155014038086 Neogloboquadrina pachyderma 1988 1 +-26.4832992553711 -4.31669998168945 Conaea rapax 1977 15 +11.6983003616333 -23.4316997528076 Eggerella bradyi 1992 17 +7.23999977111816 -24.6900005340576 Eggerella bradyi 1990 53 +-14.4899997711182 -41.2750015258789 Neogloboquadrina pachyderma 1993 91 +15.3000001907349 -29.9832992553711 Holohalaelurus regani 1988 1 +16.25 -30.1667003631592 Holohalaelurus regani 1991 1 +18.8332996368408 -35.63330078125 Holohalaelurus regani 1985 2 +15.4833002090454 -28.8999996185303 Holohalaelurus regani 1987 1 +16.9333000183105 -32.5833015441895 Holohalaelurus regani 1987 1 +14.7449998855591 -28.2332992553711 Holohalaelurus regani 1983 1 +18.9167003631592 -35.3333015441895 Holohalaelurus regani 1991 1 +17.2000007629395 -32.5833015441895 Holohalaelurus regani 1989 2 +16.2999992370605 -31.4500007629395 Holohalaelurus regani 1985 3 +15.2200002670288 -28.9750003814697 Holohalaelurus regani 1983 1 +15.6499996185303 -28.8500003814697 Holohalaelurus regani 1987 1 +16.6667003631592 -30.3332996368408 Holohalaelurus regani 1989 2 +17.7166996002197 -34.1666984558105 Holohalaelurus regani 1989 1 +16.5832996368408 -30.0832996368408 Holohalaelurus regani 1989 1 +18.7000007629395 -34.9500007629395 Holohalaelurus regani 1987 2 +17.0499992370605 -32.7167015075684 Holohalaelurus regani 1986 2 +18.4666996002197 -34.6666984558105 Holohalaelurus regani 1986 2 +17.5832996368408 -33.7167015075684 Holohalaelurus regani 1988 2 +17.8549995422363 -33.7682991027832 Holohalaelurus regani 1 +15.2166996002197 -30.0832996368408 Holohalaelurus regani 1990 1 +17.61669921875 -32.13330078125 Holohalaelurus regani 1985 1 +17.13330078125 -32.11669921875 Holohalaelurus regani 1990 2 +17.3999996185303 -32.6666984558105 Holohalaelurus regani 1987 1 +17.9167003631592 -34.2832984924316 Holohalaelurus regani 1985 2 +17.5832996368408 -33.5833015441895 Holohalaelurus regani 1989 3 +19.6667003631592 -35.4166984558105 Holohalaelurus regani 1991 2 +19.7999992370605 -36.0332984924316 Holohalaelurus regani 1989 1 +16.3332996368408 -31.7332992553711 Holohalaelurus regani 1988 2 +16.9167003631592 -32 Holohalaelurus regani 1989 1 +19.9832992553711 -35.9832992553711 Holohalaelurus regani 1985 1 +17.1667003631592 -32.4166984558105 Holohalaelurus regani 1987 1 +17.8500003814697 -33.3499984741211 Holohalaelurus regani 1986 2 +17.38330078125 -31.0333003997803 Holohalaelurus regani 1985 1 +18.8166999816895 -34.9500007629395 Holohalaelurus regani 1989 1 +18.8332996368408 -35.4000015258789 Holohalaelurus regani 1989 1 +17.25 -31.4167003631592 Holohalaelurus regani 1991 1 +18.5167007446289 -34.61669921875 Holohalaelurus regani 1988 2 +16.0167007446289 -30.3500003814697 Holohalaelurus regani 1986 1 +14.5550003051758 -29.5499992370605 Holohalaelurus regani 1984 1 +15.9333000183105 -30.3500003814697 Holohalaelurus regani 1990 1 +15.0832996368408 -30.1000003814697 Holohalaelurus regani 1987 1 +14.4666996002197 -29.0349998474121 Holohalaelurus regani 1984 1 +18.6833000183105 -34.7000007629395 Holohalaelurus regani 1986 2 +17.0333003997803 -31.0832996368408 Holohalaelurus regani 1991 1 +16.7332992553711 -30.7332992553711 Holohalaelurus regani 1991 1 +17.2667007446289 -31.4167003631592 Holohalaelurus regani 1988 2 +15.6332998275757 -29.7166996002197 Holohalaelurus regani 1986 2 +14.7333002090454 -29.86669921875 Holohalaelurus regani 1986 1 +16.8332996368408 -30.88330078125 Holohalaelurus regani 1989 1 +16.1667003631592 -31.3500003814697 Holohalaelurus regani 1990 1 +17.1499996185303 -31.5666999816895 Holohalaelurus regani 1986 2 +17 -31.7000007629395 Holohalaelurus regani 1987 1 +18.8500003814697 -34.0999984741211 Holohalaelurus regani 1986 1 +15.2833003997803 -29.8999996185303 Holohalaelurus regani 1985 1 +17.11669921875 -31.75 Holohalaelurus regani 1990 2 +14.4799995422363 -28.2332992553711 Holohalaelurus regani 1983 1 +15.7666997909546 -29.8999996185303 Holohalaelurus regani 1990 1 +17.6667003631592 -33.5833015441895 Holohalaelurus regani 1987 1 +14.3983001708984 -28.5417003631592 Holohalaelurus regani 1981 1 +15.4967002868652 -29.1067008972168 Holohalaelurus regani 1983 1 +14.7666997909546 -30.1000003814697 Holohalaelurus regani 1985 3 +19.11669921875 -35.1500015258789 Holohalaelurus regani 1988 1 +16.2166996002197 -31.61669921875 Holohalaelurus regani 1990 2 +14.5 -28.2166996002197 Holohalaelurus regani 1986 2 +17.11669921875 -31.5167007446289 Holohalaelurus regani 1990 2 +17.6833000183105 -33.2332992553711 Holohalaelurus regani 1985 1 +15.7333002090454 -28.8483009338379 Holohalaelurus regani 1984 1 +17.5832996368408 -33.5499992370605 Holohalaelurus regani 1987 2 +14.5100002288818 -28.9883003234863 Holohalaelurus regani 1983 1 +17.0832996368408 -30.6000003814697 Holohalaelurus regani 1990 1 +16.6499996185303 -30.75 Holohalaelurus regani 1991 1 +14.3816995620728 -28.7667007446289 Holohalaelurus regani 1984 1 +16.3332996368408 -31.4333000183105 Holohalaelurus regani 1989 2 +16.7166996002197 -32.4832992553711 Holohalaelurus regani 1986 1 +16.8999996185303 -30.9167003631592 Holohalaelurus regani 1987 1 +15.3999996185303 -28.5666999816895 Holohalaelurus regani 1988 2 +17.25 -31.11669921875 Holohalaelurus regani 1986 2 +17.61669921875 -33.5666999816895 Holohalaelurus regani 1987 1 +17.6000003814697 -33.5833015441895 Holohalaelurus regani 1989 1 +14.5017004013062 -27.6366996765137 Holohalaelurus regani 1984 1 +17.61669921875 -33.5833015441895 Holohalaelurus regani 1989 3 +14.7833003997803 -29.2166996002197 Holohalaelurus regani 1988 2 +17.8166999816895 -33.61669921875 Holohalaelurus regani 1989 2 +18.2999992370605 -34.7332992553711 Holohalaelurus regani 1986 2 +16.4832992553711 -29.7833003997803 Holohalaelurus regani 1990 1 +17.6833000183105 -32.86669921875 Holohalaelurus regani 1988 2 +19.9167003631592 -35.8333015441895 Holohalaelurus regani 1989 2 +17.7667007446289 -32.2999992370605 Holohalaelurus regani 1989 2 +19.13330078125 -35.3499984741211 Holohalaelurus regani 1986 1 +15.5500001907349 -30.6667003631592 Holohalaelurus regani 1986 2 +19.3332996368408 -35.25 Holohalaelurus regani 1986 2 +16.0499992370605 -29.7166996002197 Holohalaelurus regani 1988 2 +17.9666996002197 -34.5999984741211 Holohalaelurus regani 1987 1 +15.2666997909546 -29.1499996185303 Holohalaelurus regani 1987 1 +16.25 -31.3332996368408 Holohalaelurus regani 1986 2 +16.1000003814697 -30.1667003631592 Holohalaelurus regani 1987 1 +15.75 -30.3332996368408 Holohalaelurus regani 1987 1 +15.1667003631592 -29.25 Holohalaelurus regani 1989 1 +17.0333003997803 -31.1667003631592 Holohalaelurus regani 1986 2 +17.7667007446289 -34.0499992370605 Holohalaelurus regani 1985 1 +17.8500003814697 -33.5833015441895 Holohalaelurus regani 1988 2 +17.6000003814697 -33.5 Holohalaelurus regani 1987 1 +17.5 -31.25 Holohalaelurus regani 1989 1 +18.7999992370605 -34.9500007629395 Holohalaelurus regani 1986 2 +16.5832996368408 -32 Holohalaelurus regani 1991 1 +17.3500003814697 -31.0666999816895 Holohalaelurus regani 1987 2 +16.4666996002197 -31.0832996368408 Holohalaelurus regani 1986 2 +15.4167003631592 -30.75 Holohalaelurus regani 1987 1 +16.7667007446289 -32.4667015075684 Holohalaelurus regani 1989 2 +17.9832992553711 -34.0167007446289 Holohalaelurus regani 1988 2 +17.9500007629395 -34.4500007629395 Holohalaelurus regani 1986 2 +15.0832996368408 -29.9666996002197 Holohalaelurus regani 1986 2 +14.4449996948242 -28.3449993133545 Holohalaelurus regani 1984 1 +17.8332996368408 -33.88330078125 Holohalaelurus regani 1989 2 +16.6833000183105 -30.25 Holohalaelurus regani 1989 1 +17.63330078125 -33.61669921875 Holohalaelurus regani 1986 12 +16.5 -32.4832992553711 Holohalaelurus regani 1990 2 +17.25 -31.8999996185303 Holohalaelurus regani 1987 1 +19.5333003997803 -35.2667007446289 Holohalaelurus regani 1985 2 +15.8166999816895 -28.8682994842529 Holohalaelurus regani 1983 1 +18.7332992553711 -35.13330078125 Holohalaelurus regani 1986 1 +15.9499998092651 -29.2667007446289 Holohalaelurus regani 1990 1 +17.5666999816895 -33.4832992553711 Holohalaelurus regani 1986 2 +16.9666996002197 -32.2832984924316 Holohalaelurus regani 1989 2 +16.8166999816895 -32.4832992553711 Holohalaelurus regani 1989 1 +19.8500003814697 -35.9500007629395 Holohalaelurus regani 1986 2 +16.4333000183105 -31.9666996002197 Holohalaelurus regani 1985 2 +17 -31.7667007446289 Holohalaelurus regani 1989 1 +18.7332992553711 -35.0833015441895 Holohalaelurus regani 1987 1 +17.5 -33.75 Holohalaelurus regani 1988 2 +16.4167003631592 -30.1000003814697 Holohalaelurus regani 1990 1 +17.1000003814697 -30.7332992553711 Holohalaelurus regani 1985 1 +17.63330078125 -33.5332984924316 Holohalaelurus regani 1986 2 +15.0666999816895 -29.8332996368408 Holohalaelurus regani 1991 1 +19.6833000183105 -35.8499984741211 Holohalaelurus regani 1986 1 +18.5167007446289 -34.86669921875 Holohalaelurus regani 1988 2 +14.5 -27.5666999816895 Holohalaelurus regani 1975 2 +18.0300006866455 -34.0633010864258 Holohalaelurus regani 1 +18.0333003997803 -34.4667015075684 Holohalaelurus regani 1986 2 +16.9832992553711 -32.7832984924316 Holohalaelurus regani 1986 2 +17.36669921875 -31.25 Holohalaelurus regani 1988 2 +15.0832996368408 -29.63330078125 Holohalaelurus regani 1987 1 +16.88330078125 -32.25 Holohalaelurus regani 1985 2 +15.2833003997803 -29.8332996368408 Holohalaelurus regani 1989 1 +17.88330078125 -34.3166999816895 Holohalaelurus regani 1986 2 +16.6000003814697 -32.2999992370605 Holohalaelurus regani 1989 1 +17.6833000183105 -32 Holohalaelurus regani 1988 2 +17.0832996368408 -31.7166996002197 Holohalaelurus regani 1991 1 +14.4167003631592 -27.4500007629395 Holohalaelurus regani 1988 2 +18.75 -35.2667007446289 Holohalaelurus regani 1989 1 +16.4666996002197 -31.7999992370605 Holohalaelurus regani 1985 2 +19.6667003631592 -35.5666999816895 Holohalaelurus regani 1989 1 +15.3332996368408 -29.75 Holohalaelurus regani 1989 1 +14.5050001144409 -28.2049999237061 Holohalaelurus regani 1981 1 +17.0832996368408 -30.8332996368408 Holohalaelurus regani 1989 1 +16.25 -30.3332996368408 Holohalaelurus regani 1990 1 +17.3332996368408 -31.1667003631592 Holohalaelurus regani 1987 1 +17.7166996002197 -33.9166984558105 Holohalaelurus regani 1987 1 +16.3332996368408 -29.75 Holohalaelurus regani 1990 1 +19.3999996185303 -35.61669921875 Holohalaelurus regani 1987 1 +19.0666999816895 -35.5999984741211 Holohalaelurus regani 1987 1 +17.61669921875 -33.5499992370605 Holohalaelurus regani 1985 2 +15.6833000183105 -29.4333000183105 Holohalaelurus regani 1985 3 +15.75 -29.3999996185303 Holohalaelurus regani 1988 2 +16.6667003631592 -30.3166999816895 Holohalaelurus regani 1988 2 +17.0666999816895 -32.4166984558105 Holohalaelurus regani 1991 1 +19.8500003814697 -36.11669921875 Holohalaelurus regani 1987 1 +15.4167003631592 -30.0666999816895 Holohalaelurus regani 1989 1 +16.7000007629395 -30.3166999816895 Holohalaelurus regani 1986 2 +14.9833002090454 -29.5499992370605 Holohalaelurus regani 1990 1 +16.75 -31.0832996368408 Holohalaelurus regani 1990 1 +17.5666999816895 -32.1500015258789 Holohalaelurus regani 1987 1 +17.1499996185303 -32.63330078125 Holohalaelurus regani 1986 1 +16.8166999816895 -31.9832992553711 Holohalaelurus regani 1985 4 +15.5166997909546 -29.9500007629395 Holohalaelurus regani 1985 1 +12.2333002090454 -6.09999990463257 Aetomylaeus bovinus 1948 1 +16.6833000183105 -32.25 Holohalaelurus regani 1986 2 +14.8999996185303 -30.0832996368408 Holohalaelurus regani 1989 1 +15.7666997909546 -30.36669921875 Holohalaelurus regani 1988 2 +16.25 -30.86669921875 Holohalaelurus regani 1989 1 +17.4167003631592 -32.1666984558105 Holohalaelurus regani 1987 1 +17 -31.7166996002197 Holohalaelurus regani 1985 2 +16.1499996185303 -31.1499996185303 Holohalaelurus regani 1986 2 +15.8999996185303 -29.7833003997803 Holohalaelurus regani 1988 2 +14.3999996185303 -29.2166996002197 Holohalaelurus regani 1987 1 +17.1667003631592 -31.5333003997803 Holohalaelurus regani 1985 2 +16.11669921875 -31.6000003814697 Holohalaelurus regani 1988 2 +18.4167003631592 -34.6666984558105 Holohalaelurus regani 1991 1 +14.8466997146606 -29.2866992950439 Holohalaelurus regani 1984 1 +16.0666999816895 -30 Holohalaelurus regani 1987 1 +19.2667007446289 -35.2667007446289 Holohalaelurus regani 1987 1 +17.5499992370605 -33.5167007446289 Holohalaelurus regani 1989 2 +17.25 -31.8999996185303 Holohalaelurus regani 1991 1 +16.2332992553711 -31.9666996002197 Holohalaelurus regani 1985 2 +14.4333000183105 -29.0666999816895 Holohalaelurus regani 1987 1 +19.2332992553711 -35.4667015075684 Holohalaelurus regani 1986 2 +17.6499996185303 -33.5167007446289 Holohalaelurus regani 1986 2 +18.36669921875 -34.5 Holohalaelurus regani 1988 2 +17 -30.4167003631592 Holohalaelurus regani 1989 1 +16.5 -32.0999984741211 Holohalaelurus regani 1989 1 +17.1833000183105 -33.2832984924316 Holohalaelurus regani 1985 2 +15.3500003814697 -29.9666996002197 Holohalaelurus regani 1985 1 +16.13330078125 -31.2999992370605 Holohalaelurus regani 1989 2 +16.7332992553711 -29.5499992370605 Holohalaelurus regani 1988 1 +15.75 -29.1667003631592 Holohalaelurus regani 1987 1 +16.7332992553711 -30.7833003997803 Holohalaelurus regani 1990 1 +17.3166999816895 -31.3166999816895 Holohalaelurus regani 1985 2 +19.4167003631592 -35.0833015441895 Holohalaelurus regani 1986 2 +17.7000007629395 -33.4667015075684 Holohalaelurus regani 1985 1 +17.3999996185303 -32.6666984558105 Holohalaelurus regani 1985 2 +14.7716999053955 -29.6133003234863 Holohalaelurus regani 1984 1 +18.0333003997803 -34.25 Holohalaelurus regani 1991 1 +19.63330078125 -35.7999992370605 Holohalaelurus regani 1985 1 +14.7166996002197 -27.2350006103516 Holohalaelurus regani 1983 1 +16.5832996368408 -32.1666984558105 Holohalaelurus regani 1991 1 +16.6667003631592 -30.8500003814697 Holohalaelurus regani 1988 2 +-57.1199989318848 0 Protoperidinium shanghaiense 1 +-17.74169921875 0 Protoperidinium shanghaiense 1 +-26.4832992553711 -17 Clausocalanus lividus 1977 25 +-7.11670017242432 -18.75 Clausocalanus lividus 1973 53 +-7.11670017242432 -18.75 Haloptilus angusticeps 1973 15 +-26.5 -10.6833000183105 Conaea rapax 1977 30 +-35.8396987915039 -58.9990005493164 Eggerella bradyi 1992 7 +-14.2283000946045 -44.1533012390137 Neogloboquadrina pachyderma 1993 60 +-35.8396987915039 -58.9990005493164 Adercotryma glomeratum 1989 1 +-23.274299621582 -59.7352981567383 Adercotryma glomeratum 1989 1 +-35.8396987915039 -58.9990005493164 Eggerella bradyi 1989 1 +-35.5772018432617 -59.0587997436523 Adercotryma glomeratum 1992 6 +-35.2849998474121 -30.2765007019043 Globorotalia (Truncorotalia) crassula 1980 6 +-58.5698013305664 0 Bitectatodinium tepikiense 1 +8.71669960021973 0 Bitectatodinium tepikiense 1 +-57.9361991882324 0 Bitectatodinium tepikiense 1 +-12.9833002090454 0 Bitectatodinium tepikiense 1 +1.79999995231628 0 Bitectatodinium tepikiense 1 +0.0154999997466803 0 Bitectatodinium tepikiense 1 +1.73300004005432 0 Bitectatodinium tepikiense 1 +4.23150014877319 0 Bitectatodinium tepikiense 1 +3.08170008659363 0 Bitectatodinium tepikiense 1 +3.21499991416931 0 Bitectatodinium tepikiense 1 +-28.9367008209229 0 Bitectatodinium tepikiense 2 +-62.7891998291016 0 Bitectatodinium tepikiense 1 +3.89499998092651 0 Bitectatodinium tepikiense 1 +-39.6650009155273 0 Bitectatodinium tepikiense 1 +-8.32670021057129 0 Bitectatodinium tepikiense 1 +3.72329998016357 0 Bitectatodinium tepikiense 1 +-10.1833000183105 0 Bitectatodinium tepikiense 1 +-60.1673011779785 0 Bitectatodinium tepikiense 1 +-61.9500007629395 0 Bitectatodinium tepikiense 1 +15.8332996368408 0 Bitectatodinium tepikiense 1 +-69.5770034790039 0 Bitectatodinium tepikiense 1 +-60.2421989440918 0 Bitectatodinium tepikiense 1 +-60.6347999572754 0 Bitectatodinium tepikiense 1 +-62.5434989929199 0 Bitectatodinium tepikiense 1 +-63.5200004577637 0 Bitectatodinium tepikiense 1 +-59.8842010498047 0 Bitectatodinium tepikiense 1 +8.60620021820068 0 Bitectatodinium tepikiense 1 +8.07320022583008 0 Bitectatodinium tepikiense 1 +-33.5297012329102 0 Bitectatodinium tepikiense 1 +-30.2301998138428 0 Bitectatodinium tepikiense 1 +-13.2502002716064 0 Bitectatodinium tepikiense 1 +-46.9716987609863 -51.0046997070312 Eggerella bradyi 1980 2 +-40.8451995849609 -49.869800567627 Eggerella bradyi 1980 14 +-11.4766998291016 0 Bitectatodinium tepikiense 1 +-8.02499961853027 0 Bitectatodinium tepikiense 1 +-4.74170017242432 0 Bitectatodinium tepikiense 1 +13.2333002090454 -25.5167007446289 Eggerella bradyi 1996 1 +-12.4282999038696 -1.66499996185303 Neogloboquadrina pachyderma 1989 64 +-10.75 -5.77832984924316 Neogloboquadrina pachyderma 1989 37 +11.6983003616333 -23.4316997528076 Neogloboquadrina pachyderma 1992 54 +10.5187997817993 -19.7443008422852 Neogloboquadrina pachyderma 1980 21 +-34.3479995727539 -49.7290000915527 Adercotryma glomeratum 1989 1 +4.86299991607666 -51.8315010070801 Eggerella bradyi 1989 3 +6.70599985122681 -49.1343002319336 Eggerella bradyi 1989 1 +-12.7033004760742 -49.0116996765137 Eggerella bradyi 1989 5 +6.23667001724243 -46.935001373291 Eggerella bradyi 1994 11 +10.4809999465942 -44.4982986450195 Adercotryma glomeratum 1989 4 +10.4809999465942 -44.4982986450195 Eggerella bradyi 1989 2 +7.5939998626709 -46.7667007446289 Adercotryma glomeratum 1989 1 +7.5939998626709 -46.7667007446289 Eggerella bradyi 1989 4 +7.10249996185303 -47.7890014648438 Adercotryma glomeratum 1989 9 +4.86299991607666 -51.8315010070801 Adercotryma glomeratum 1989 4 +4.46500015258789 -52.5890007019043 Eggerella bradyi 1989 13 +-12.7033004760742 -49.0116996765137 Adercotryma glomeratum 1989 4 +3.47900009155273 -54.0097007751465 Eggerella bradyi 1991 5 +11.7383003234863 -43.2201995849609 Adercotryma glomeratum 1991 4 +7.03617000579834 -46.3704986572266 Adercotryma glomeratum 1991 1 +6.70599985122681 -49.1343002319336 Adercotryma glomeratum 1991 2 +6.70599985122681 -49.1343002319336 Eggerella bradyi 1991 3 +-33.2442016601562 -50.3712005615234 Adercotryma glomeratum 1992 4 +-23.274299621582 -59.7352981567383 Adercotryma glomeratum 1992 6 +-41.9874992370605 -59.6394996643066 Adercotryma glomeratum 1992 3 +-41.3352012634277 -59.4832000732422 Adercotryma glomeratum 1992 11 +-3.21182990074158 -50.7425003051758 Adercotryma glomeratum 1991 3 +-3.21182990074158 -50.7425003051758 Eggerella bradyi 1991 12 +-40.5094985961914 -59.548698425293 Adercotryma glomeratum 1992 9 +-28.6667003631592 -31.6667003631592 Neogloboquadrina pachyderma 1991 74 +12.3717002868652 -23.3166999816895 Eggerella bradyi 1996 3 +11.5799999237061 -15.6282997131348 Adercotryma glomeratum 1996 2 +9.78499984741211 -3.73000001907349 Adercotryma glomeratum 1996 2 +12.9982995986938 -24.301700592041 Eggerella bradyi 1996 8 +15.5 -31.7849998474121 Eggerella bradyi 1996 6 +13.0733003616333 -11.9033002853394 Adercotryma glomeratum 1998 3 +8.625 -2.0699999332428 Eggerella bradyi 1996 4 +8.37670040130615 -0.689999997615814 Eggerella bradyi 1996 4 +14.0050001144409 -27.9517002105713 Eggerella bradyi 1996 7 +13.8367004394531 -28.9983005523682 Eggerella bradyi 1992 2 +-23.9549999237061 -54.638500213623 Adercotryma glomeratum 1992 5 +-22.3251991271973 -56.8274993896484 Adercotryma glomeratum 1992 2 +-56.810001373291 -44.8543014526367 Adercotryma glomeratum 1992 2 +-28.49880027771 -57.5209999084473 Adercotryma glomeratum 1992 2 +-30.5508003234863 -52.6571998596191 Eggerella bradyi 1992 2 +-28.5415000915527 -53.2812004089355 Eggerella bradyi 1992 2 +-22.3251991271973 -56.8274993896484 Eggerella bradyi 1992 2 +-34.3479995727539 -49.7290000915527 Eggerella bradyi 1992 2 +-29.1865005493164 -57.5172004699707 Eggerella bradyi 1992 2 +-31.5072994232178 -58.2314987182617 Eggerella bradyi 1992 2 +-59.3300018310547 -43.6749992370605 Adercotryma glomeratum 1994 2 +-56.4866981506348 -47.1617012023926 Adercotryma glomeratum 1994 2 +-56.1749992370605 -47.9632987976074 Adercotryma glomeratum 1994 2 +-21.38330078125 -49.4067001342773 Adercotryma glomeratum 1994 2 +-54.3417015075684 -39.2900009155273 Eggerella bradyi 1994 2 +-24.3166999816895 -50.75 Eggerella bradyi 1994 2 +-25.7000007629395 -51.4082984924316 Eggerella bradyi 1994 2 +-26.2000007629395 -51.3732986450195 Eggerella bradyi 1994 2 +-26.5333003997803 -51.6683006286621 Eggerella bradyi 1994 2 +-45.2933006286621 -53.5449981689453 Eggerella bradyi 1994 2 +-51.4550018310547 -53.2517013549805 Eggerella bradyi 1994 2 +-15.3332996368408 -46.5116996765137 Eggerella bradyi 1994 2 +-53.9282989501953 -38.9249992370605 Eggerella bradyi 1994 2 +-53.3633003234863 -39.2433013916016 Eggerella bradyi 1994 2 +-59.3300018310547 -43.6749992370605 Eggerella bradyi 1994 1 +-58.1749992370605 -47.3067016601562 Eggerella bradyi 1994 2 +-29.6567001342773 -31.6616992950439 Neogloboquadrina pachyderma 1991 26 +9.78499984741211 -3.73000001907349 Eggerella bradyi 1998 3 +-34.0217018127441 -8.52832984924316 Neogloboquadrina pachyderma 1993 9 +-30.2345008850098 -57.5092010498047 Eggerella bradyi 1992 2 +-33.005500793457 -58.7267990112305 Eggerella bradyi 1992 2 +-43.7667007446289 -53.6666984558105 Eggerella bradyi 1994 2 +-15.4232997894287 -47.6199989318848 Adercotryma glomeratum 1994 2 +-60.0917015075684 -47.4416999816895 Adercotryma glomeratum 1994 1 +-56.9733009338379 -43.99169921875 Adercotryma glomeratum 1994 2 +-53.2482986450195 -44.4749984741211 Adercotryma glomeratum 1994 2 +-51.4233016967773 -44.2066993713379 Adercotryma glomeratum 1994 2 +-24.3166999816895 -50.75 Adercotryma glomeratum 1994 2 +-31.3717002868652 -51.5317001342773 Adercotryma glomeratum 1992 2 +-25.3360004425049 -57.7560005187988 Adercotryma glomeratum 1992 6 +-27.4559993743896 -57.5351982116699 Adercotryma glomeratum 1992 2 +-30.2345008850098 -57.5092010498047 Adercotryma glomeratum 1992 2 +-31.5072994232178 -58.2314987182617 Adercotryma glomeratum 1992 2 +-33.005500793457 -58.7267990112305 Adercotryma glomeratum 1992 2 +-52.921501159668 -57.5908012390137 Adercotryma glomeratum 1992 2 +-44.4516983032227 -44.5164985656738 Adercotryma glomeratum 1992 2 +-37.005199432373 -48.5023002624512 Adercotryma glomeratum 1992 2 +-34.3479995727539 -49.7290000915527 Adercotryma glomeratum 1992 5 +-57.9404983520508 -45.0999984741211 Eggerella bradyi 1992 1 +-30.4710006713867 -52.0643005371094 Eggerella bradyi 1992 2 +-22.2514991760254 -55.9692993164062 Eggerella bradyi 1992 2 +-23.9549999237061 -54.638500213623 Eggerella bradyi 1989 1 +6.23667001724243 -46.935001373291 Adercotryma glomeratum 1994 8 +17.8617000579834 -34.6383018493652 Protoperidinium shanghaiense 1966 1 +17.7549991607666 -34.7966995239258 Protoperidinium shanghaiense 1966 1 +15.2966995239258 -31.4466991424561 Protoperidinium shanghaiense 1966 1 +9.78499984741211 -3.73000001907349 Eggerella bradyi 1996 3 +8.625 -2.0699999332428 Eggerella bradyi 1998 7 +2.79999995231628 -54.5400009155273 Eggerella bradyi 1991 12 +-39.7052993774414 -59.826301574707 Adercotryma glomeratum 1992 3 +-33.7167015075684 -2.2666699886322 Galeocerdo cuvier 1 +-25.3360004425049 -57.7560005187988 Eggerella bradyi 1992 5 +12.8999996185303 -22.75 Centrophorus uyato 1980 1 +13.1999998092651 -12.6000003814697 Centrophorus uyato 1 +11.6000003814697 -19.4832992553711 Centrophorus uyato 1980 1 +12 -20.0167007446289 Centrophorus uyato 1980 1 +13.1332998275757 -22.6000003814697 Centrophorus uyato 1980 1 +13.2833003997803 -23.4750003814697 Centrophorus uyato 1983 1 +11.3450002670288 -17.5583000183105 Centrophorus uyato 1981 1 +13.1833000183105 -23.7432994842529 Centrophorus uyato 1983 1 +-38.2999992370605 -2.2666699886322 Galeocerdo cuvier 2 +12.6016998291016 -22.7166996002197 Eggerella bradyi 1996 10 +-28.5415000915527 -53.2812004089355 Adercotryma glomeratum 1992 2 +-38.5418014526367 -45.0117988586426 Adercotryma glomeratum 1992 2 +-52.921501159668 -57.5908012390137 Eggerella bradyi 1992 2 +-45.2933006286621 -53.5449981689453 Adercotryma glomeratum 1994 2 +-55.5349998474121 -42.3683013916016 Adercotryma glomeratum 1994 2 +-57.6617012023926 -43.9033012390137 Eggerella bradyi 1994 2 +-20.0333003997803 -2 Oncaea venusta 1963 10 +-29.6000003814697 -54.6300010681152 Adercotryma glomeratum 1994 2 +-57.4566993713379 0 Bitectatodinium tepikiense 1 +-48.3844985961914 0 Bitectatodinium tepikiense 1 +-8.08500003814697 0 Bitectatodinium tepikiense 1 +15.9899997711182 -28.8400001525879 Protoperidinium shanghaiense 1970 1 +18.3549995422363 -34.0527992248535 Brama brama 1993 4 +-49.7649993896484 -31.2819995880127 Umbrina canosai 1996 1 +-48.0449981689453 -30.1630001068115 Brama brama 1996 1 +-16.8500003814697 -14.5333003997803 Oncaea venusta 1963 15 +-36.5 -20.5 Rhabdosoma armatum 1913 1 +-43.0499992370605 -46 Oncaea obscura 1976 5 +13.176905632019 -10.4928302764893 Chlorophthalmus agassizi 1989 1 +12.9176778793335 -8.41565608978271 Chlorophthalmus agassizi 1989 1 +12.7828969955444 -8.27500343322754 Chlorophthalmus agassizi 1989 1 +12.6128759384155 -7.82142925262451 Chlorophthalmus agassizi 1989 1 +12.5716505050659 -7.70416688919067 Chlorophthalmus agassizi 1989 1 +12.849066734314 -8.79723834991455 Chlorophthalmus agassizi 1989 1 +12.1746206283569 -7.22899198532104 Chlorophthalmus agassizi 1989 1 +11.6128759384155 -6.72142934799194 Chlorophthalmus agassizi 1989 1 +11.475193977356 -5.70697116851807 Chlorophthalmus agassizi 1989 1 +-41.61669921875 -22.3799991607666 Warrana besnardi 1993 1 +-41.75830078125 -22.4167003631592 Warrana besnardi 1993 1 +-41.5 -22.3582992553711 Warrana besnardi 1993 1 +12.7887496948242 -8.17395114898682 Neomerinthe folgori 1991 1 +-66.6750030517578 -53.3250007629395 Antholoba achates 1 +-68.0800018310547 -55.5299987792969 Antholoba achates 4 +-66.6999969482422 -46.1100006103516 Antholoba achates 4 +2.8989999294281 -54.5251998901367 Cribrostomoides wiesneri 1991 3 +8.37666988372803 -0.689999997615814 Cribrostomoides wiesneri 1998 6 +18.4500007629395 -33.88330078125 Philorthragoriscus serratus 3 +16.4500007629395 -28.63330078125 Caprella scaura 1 +18 -33.0499000549316 Daphnia magna 2 +18.3666000366211 -33.9165992736816 Philorthragoriscus serratus 1972 1 +19.4167003631592 -35.0867004394531 Lophogaster challengeri 1988 1 +13.2343997955322 -8.83829975128174 Pontonia pinnophylax 1966 1 +17.6263999938965 -34.0913009643555 Sphyrion laevigatum 1 +18.4500007629395 -33.88330078125 Philorthragoriscus serratus 1999 1 +19.4183006286621 -35.0766983032227 Lophogaster challengeri 1988 1 +18.36669921875 -33.9000015258789 Triviella costata 1975 1 +11.9333000183105 -15.6833000183105 Echinolittorina punctata 1969 1 +18.4633007049561 -34.1699981689453 Semicassis zeylanica 1948 1 +-42.8838005065918 -59.8372993469238 Cribrostomoides wiesneri 1992 1 +10.5187997817993 -19.7443008422852 Eggerella bradyi 1980 8 +-35.5597991943359 -30.0041999816895 Globorotalia (Truncorotalia) crassula 1974 5 +-35.5597991943359 -30.0041999816895 Neogloboquadrina pachyderma 1974 8 +3.51230001449585 -29.4841995239258 Pullenia jarvisi 1980 6 +10.5325002670288 -19.7574996948242 Neogloboquadrina pachyderma 1975 29 +-45.11669921875 -23.5 Dictyota dichotoma 1962 4 +-46.3824996948242 -23.9827995300293 Jania adhaerens 1955 6 +-45.11669921875 -23.5 Jania adhaerens 1961 2 +-45.0172004699707 -23.4939002990723 Jania adhaerens 1961 2 +-45.1356010437012 -23.50830078125 Jania adhaerens 1957 2 +-47.86669921875 -25.1217002868652 Jania adhaerens 1966 2 +-45.11669921875 -23.5 Dictyota dichotoma 1961 6 +-46.2881011962891 -24.0305995941162 Jania adhaerens 1955 2 +-60.7099990844727 -51.3600006103516 Bunodactis octoradiata 1 +-67.6699981689453 -55.0699996948242 Bunodactis octoradiata 6 +14.3832998275757 -28.5832996368408 Neoraja stehmanni 1988 1 +-46.3165016174316 -25.3852005004883 Leitoscoloplos kerguelensis 2006 1 +-46.8274993896484 -24.5156993865967 Leitoscoloplos kerguelensis 2006 1 +-46.6529998779297 -24.8099994659424 Leitoscoloplos kerguelensis 2006 1 +-45.2146987915039 -24.6282997131348 Leitoscoloplos kerguelensis 2006 1 +-45.6940002441406 -25.0359992980957 Leitoscoloplos kerguelensis 2006 1 +-45.898998260498 -23.9930000305176 Poecilochaetus australis 2006 1 +-46.0340003967285 -24.4610004425049 Poecilochaetus australis 2005 1 +-45.6199989318848 -23.94700050354 Poecilochaetus australis 2005 1 +-46.6171989440918 -24.1688003540039 Poecilochaetus australis 2006 1 +-46.0340003967285 -24.4610004425049 Cossura delta 2005 1 +8.97329998016357 -42.8732986450195 Neogloboquadrina pachyderma 1993 267 +-68.5699996948242 -54.8300018310547 Bunodactis octoradiata 2 +-10.8482999801636 0 Protoperidinium shanghaiense 1 +-11.4766998291016 0 Protoperidinium shanghaiense 1 +-21.88330078125 0 Protoperidinium shanghaiense 1 +-12.7833003997803 0 Protoperidinium shanghaiense 1 +-17.9167003631592 0 Protoperidinium shanghaiense 1 +7.95919990539551 -47.0245018005371 Neogloboquadrina pachyderma 1988 1 +-3.32349991798401 -51.3302993774414 Neogloboquadrina pachyderma 1988 1 +8.97019958496094 -20.1187992095947 Neogloboquadrina pachyderma 1988 1 +0.628400027751923 -46.7667999267578 Neogloboquadrina pachyderma 1988 1 +-57.5299987792969 -38.0800018310547 Antholoba achates 1 +-57.5499992370605 -38 Antholoba achates 1 +-45.4000015258789 -23.729700088501 Halopteris minuta 1988 1 +-45.4202995300293 -23.8213996887207 Halopteris minuta 1992 1 +13.3550052642822 -11.5102024078369 Neomerinthe folgori 1991 1 +13.3948230743408 -11.531307220459 Neomerinthe folgori 1991 1 +11.6109628677368 -5.56267642974854 Chlorophthalmus agassizi 1991 1 +9.18583011627197 -20.1040000915527 Gephyrocapsa aperta 1988 42 +-12.4282999038696 -1.66499996185303 Bitectatodinium tepikiense 1989 51 +3.78099989891052 -53.7589988708496 Cribrostomoides wiesneri 1989 1 +-3.21182990074158 -50.7425003051758 Cribrostomoides wiesneri 1991 2 +-40.5094985961914 -59.548698425293 Cribrostomoides wiesneri 1992 7 +8.625 -2.0699999332428 Cribrostomoides wiesneri 1998 8 +2.90669989585876 0 Protoperidinium shanghaiense 1 +0.0799999982118607 0 Protoperidinium shanghaiense 1 +11.5799999237061 -15.6282997131348 Lagenammina spiculata 1996 3 +11.5799999237061 -15.6282997131348 Cribrostomoides wiesneri 1996 2 +11.5799999237061 -15.6282997131348 Verneuilinulla propinqua 1996 5 +11.8732995986938 -7.75 Verneuilinulla propinqua 1996 4 +8.37670040130615 -0.689999997615814 Cribrostomoides wiesneri 1996 3 +9.78499984741211 -3.73000001907349 Cribrostomoides wiesneri 1996 4 +11.8732995986938 -7.75 Cribrostomoides wiesneri 1996 4 +14.0050001144409 -27.9517002105713 Cribrostomoides wiesneri 1996 6 +15.5 -31.7849998474121 Cribrostomoides wiesneri 1996 4 +8.625 -2.0699999332428 Cribrostomoides wiesneri 1996 4 +8.37670040130615 -0.689999997615814 Cribrostomoides wiesneri 1998 1 +11.8732995986938 -7.75 Verneuilinulla propinqua 1998 1 +9.78499984741211 -3.73000001907349 Verneuilinulla propinqua 1998 1 +11.8732995986938 -7.75 Cribrostomoides wiesneri 1998 1 +-52.921501159668 -57.5908012390137 Cribrostomoides wiesneri 1992 2 +12.3717002868652 -23.3166999816895 Cribrostomoides wiesneri 1996 2 +7.10249996185303 -47.7890014648438 Cribrostomoides wiesneri 1989 3 +11.0717000961304 -5.50330018997192 Cribrostomoides wiesneri 1996 3 +9.78499984741211 -3.73000001907349 Verneuilinulla propinqua 1996 4 +12.6866998672485 -10.1700000762939 Verneuilinulla propinqua 1996 4 +-34.0217018127441 -8.52832984924316 Bitectatodinium tepikiense 1993 6 +-59.4432983398438 -43.6467018127441 Cribrostomoides wiesneri 1994 1 +-41.3199996948242 -23.6679992675781 Diaphus problematicus 1996 1 +-50.125 -32.0919990539551 Desmodema polystictum 1997 1 +12.5908012390137 -7.79576206207275 Neomerinthe folgori 1991 1 +12.8378639221191 -8.37132549285889 Neomerinthe folgori 1991 1 +11.3614149093628 -5.75947523117065 Neomerinthe folgori 1992 1 +11.5615472793579 -5.49333333969116 Neomerinthe folgori 1992 1 +11.385214805603 -17.139331817627 Chlorophthalmus agassizi 1992 1 +-64.2743988037109 -42.6325340270996 Dictyota dichotoma 2019 15 +-64.245246887207 -42.6408348083496 Dictyota dichotoma 2019 11 +-64.2661361694336 -42.623779296875 Dictyota dichotoma 2019 29 +-26.4832992553711 -17 Farranula carinata 1977 5 +12.2082986831665 -7.1930365562439 Neomerinthe folgori 1995 1 +-64.2602233886719 -42.6240577697754 Dictyota dichotoma 2019 21 +16.86669921875 -29.2332992553711 Paridotea ungulata 1935 1 +15.1377000808716 -26.6086006164551 Lysianassa ceratina 1969 1 +19.1266994476318 -34.9166984558105 Lestrigonus schizogeneios 1962 1 +16.7000007629395 -34.0332984924316 Lestrigonus schizogeneios 1963 2 +18.9832992553711 -35.2332992553711 Lestrigonus schizogeneios 1963 2 +18.0400009155273 -33.1183013916016 Lysianassa ceratina 1946 1 +16.0916996002197 -33.6199989318848 Lestrigonus schizogeneios 1963 1 +17.6200008392334 -34.7332992553711 Lestrigonus schizogeneios 1963 1 +15.3000001907349 -33.5666999816895 Lestrigonus schizogeneios 1963 1 +18.38330078125 -33.88330078125 Ischyromene macrocephala 1913 1 +18 -33.0499000549316 Paridotea ungulata 1900 1 +16.4500007629395 -28.63330078125 Lysianassa ceratina 1 +18.3166999816895 -34.11669921875 Paridotea ungulata 1940 1 +15.1377000808716 -26.6086006164551 Paridotea ungulata 1969 1 +18.4500007629395 -33.88330078125 Paridotea ungulata 1896 1 +18.4167003631592 -33.88330078125 Paridotea ungulata 1896 1 +14.5333003997803 -22.6833000183105 Paridotea ungulata 1964 1 +18.0333003997803 -32.7499008178711 Paridotea ungulata 1939 1 +18.4500007629395 -34.11669921875 Paridotea ungulata 1904 1 +15.0166997909546 -26.5333003997803 Polyspirella trachealis 1969 1 +18.3166999816895 -34.11669921875 Polyspirella trachealis 1971 1 +-37.848331451416 -53.5200004577637 Maurea eltanini 1975 1 +13.6267700195312 -10.6274995803833 Carcharhinus limbatus 1995 1 +-65.7333297729492 -45.0666656494141 Campylonotus vagans 2005 1 +-65.8166656494141 -45.0999984741211 Campylonotus vagans 2005 6 +-65.716667175293 -45.0999984741211 Campylonotus vagans 2005 3 +-65.8166656494141 -45.1166648864746 Campylonotus vagans 2005 4 +-65.8333358764648 -45.1166648864746 Campylonotus vagans 2005 2 +-65.8833312988281 -45.0833320617676 Campylonotus vagans 2005 2 +-65.716667175293 -45.0666656494141 Campylonotus vagans 2005 1 +-65.8166656494141 -45.0833320617676 Campylonotus vagans 2005 14 +-66.1166687011719 -45.1500015258789 Campylonotus vagans 2005 3 +-66.1500015258789 -45.1500015258789 Campylonotus vagans 2005 2 +-64.716667175293 -46.6333351135254 Campylonotus vagans 2004 1 +-65.7333297729492 -45.0999984741211 Campylonotus vagans 2005 4 +-65.7666702270508 -45.0499992370605 Campylonotus vagans 2005 18 +-65.8000030517578 -45.0833320617676 Campylonotus vagans 2005 14 +-65.6833343505859 -45.0833320617676 Campylonotus vagans 2005 6 +-64.9499969482422 -46.5666656494141 Campylonotus vagans 2003 1 +-64.8499984741211 -46.5999984741211 Campylonotus vagans 2003 1 +-65.75 -45.0499992370605 Campylonotus vagans 2005 2 +-65.75 -45.0833320617676 Campylonotus vagans 2005 2 +-65.6666641235352 -45.0833320617676 Campylonotus vagans 2005 2 +-65.7666702270508 -45.0833320617676 Campylonotus vagans 2005 3 +-64.3499984741211 -45.5166664123535 Campylonotus vagans 2003 1 +-64.9166641235352 -45.1833343505859 Campylonotus vagans 2003 1 +-64.966667175293 -46.3833351135254 Campylonotus vagans 2004 2 +-64.9499969482422 -46.3666648864746 Campylonotus vagans 2004 2 +-65.7666702270508 -45.0666656494141 Campylonotus vagans 2005 5 +-65.6333312988281 -45.0833320617676 Campylonotus vagans 2005 1 +-66.0833358764648 -45.1500015258789 Campylonotus vagans 2005 1 +-65.8499984741211 -45.0999984741211 Campylonotus vagans 2005 1 +-65.6999969482422 -45.0833320617676 Campylonotus vagans 2005 4 +-64.6666641235352 -45.8499984741211 Campylonotus vagans 2003 1 +-65.966667175293 -45.5166664123535 Brama brama 2004 1 +-65.8333358764648 -45.0833320617676 Campylonotus vagans 2005 12 +-64.8666687011719 -46.1166648864746 Campylonotus vagans 2003 1 +-64.8666687011719 -46.0499992370605 Campylonotus vagans 2003 1 +-64.8166656494141 -45.9833335876465 Campylonotus vagans 2003 1 +-65.9000015258789 -45.0999984741211 Campylonotus vagans 2005 2 +-65.716667175293 -45.0833320617676 Campylonotus vagans 2005 6 +-65.8833312988281 -45.0999984741211 Campylonotus vagans 2005 2 +-65.716667175293 -45.1333351135254 Brama brama 2005 1 +-65.783332824707 -45.0833320617676 Campylonotus vagans 2005 8 +-65.783332824707 -45.0999984741211 Campylonotus vagans 2005 3 +-66.9166641235352 -45.3333320617676 Campylonotus vagans 2003 1 +-66.6999969482422 -45.3666648864746 Campylonotus vagans 2003 1 +-64.8666687011719 -46.4833335876465 Campylonotus vagans 2004 1 +-64.9333343505859 -46.5166664123535 Campylonotus vagans 2004 1 +-65.6833343505859 -45.1166648864746 Campylonotus vagans 2005 1 +-65.6833343505859 -45.0999984741211 Campylonotus vagans 2005 2 +-64.966667175293 -45.0499992370605 Campylonotus vagans 2003 1 +-65.0833358764648 -45.6166648864746 Campylonotus vagans 2003 1 +-65.1833343505859 -46.5833320617676 Campylonotus vagans 2005 1 +-65.0833358764648 -46.4333343505859 Campylonotus vagans 2004 1 +-64.966667175293 -46 Campylonotus vagans 2003 1 +-65.0999984741211 -45.4333343505859 Campylonotus vagans 2004 1 +-65.1666641235352 -46.0499992370605 Campylonotus vagans 2003 1 +-64.8000030517578 -46.3499984741211 Campylonotus vagans 2004 1 +-65.25 -46.3666648864746 Campylonotus vagans 2004 1 +-65.1166687011719 -46.5166664123535 Campylonotus vagans 2004 1 +-65.1166687011719 -46.4500007629395 Campylonotus vagans 2004 2 +-64.8333358764648 -46.3333320617676 Campylonotus vagans 2004 1 +8.96666622161865 -1.03333330154419 Centrarchops chapini 4 +17.3645992279053 -34.7532005310059 Protoperidinium shanghaiense 1966 1 +-9.96829986572266 0 Protoperidinium shanghaiense 1 +-21.7332992553711 0 Protoperidinium shanghaiense 1 +-45.6857986450195 0 Protoperidinium shanghaiense 1 +0.793299973011017 0 Protoperidinium shanghaiense 1 +-8.83329963684082 0 Protoperidinium shanghaiense 1 +2.65499997138977 0 Protoperidinium shanghaiense 1 +5.86999988555908 0 Protoperidinium shanghaiense 1 +0.663299977779388 0 Protoperidinium shanghaiense 1 +-15.8633003234863 0 Protoperidinium shanghaiense 1 +-33.5821990966797 0 Protoperidinium shanghaiense 1 +-57.4566993713379 0 Protoperidinium shanghaiense 1 +-18.6944999694824 0 Protoperidinium shanghaiense 1 +7.79169988632202 0 Protoperidinium shanghaiense 1 +7.23999977111816 -24.6900005340576 Adercotryma glomeratum 1990 2 +-35.5597991943359 -30.0041999816895 Anomalina parvula 1974 8 +-64.2568817138672 -42.6202583312988 Dictyota dichotoma 2019 44 +-64.2537155151367 -42.6203346252441 Dictyota dichotoma 2019 55 +17.9166660308838 -34.533332824707 Holohalaelurus regani 1984 1 +-7.11670017242432 -18.75 Farranula carinata 1973 10 +-43.5499992370605 -37.0666999816895 Clausocalanus lividus 1976 5 +11.6166667938232 -4.53333330154419 Cyclichthys orbicularis 8 +17.6683330535889 -34.3966674804688 Diastobranchus capensis 1987 1 +-42.0033988952637 -22.9946994781494 Jania adhaerens 1981 2 +18.6683006286621 -34.2633018493652 Ilanga laevissima 1991 2 +-6.20550012588501 0 Bitectatodinium tepikiense 1 +7.75829982757568 0 Bitectatodinium tepikiense 1 +1.47500002384186 0 Bitectatodinium tepikiense 1 +-67.3582992553711 0 Bitectatodinium tepikiense 1 +2.65499997138977 0 Bitectatodinium tepikiense 1 +1.13170003890991 0 Bitectatodinium tepikiense 1 +-7.64669990539551 0 Bitectatodinium tepikiense 1 +-3.00500011444092 0 Bitectatodinium tepikiense 1 +-64.0832977294922 0 Bitectatodinium tepikiense 1 +-48.3652305603027 -1.28319299221039 Awaous flavus 1 +18.6783008575439 -34.1699981689453 Ilanga laevissima 1991 1 +5.86999988555908 0 Bitectatodinium tepikiense 1 +-7.12419986724854 0 Bitectatodinium tepikiense 1 +-6.78999996185303 0 Bitectatodinium tepikiense 1 +2.17499995231628 0 Bitectatodinium tepikiense 1 +-17.74169921875 0 Bitectatodinium tepikiense 1 +2.06669998168945 0 Bitectatodinium tepikiense 1 +-67.9203033447266 0 Bitectatodinium tepikiense 1 +1.0333000421524 0 Bitectatodinium tepikiense 1 +-64 0 Bitectatodinium tepikiense 1 +-63.9928016662598 0 Bitectatodinium tepikiense 1 +-59.7807998657227 0 Bitectatodinium tepikiense 1 +13.1016998291016 0 Bitectatodinium tepikiense 1 +14.4266996383667 0 Bitectatodinium tepikiense 1 +-43.9667015075684 0 Bitectatodinium tepikiense 1 +-20.8332996368408 0 Bitectatodinium tepikiense 1 +8.33380031585693 0 Bitectatodinium tepikiense 1 +0.606500029563904 0 Bitectatodinium tepikiense 1 +-13.9316997528076 0 Bitectatodinium tepikiense 1 +-61.8367004394531 0 Bitectatodinium tepikiense 1 +-45.6857986450195 0 Bitectatodinium tepikiense 1 +-52.0880012512207 -32.0610008239746 Umbrina canosai 1996 1 +-52.1349983215332 -32.015998840332 Umbrina canosai 1996 1 +-52.1020011901855 -32.1510009765625 Umbrina canosai 1998 1 +-52.1349983215332 -32.015998840332 Umbrina canosai 2000 1 +-22.9500007629395 -22.9666996002197 Conaea rapax 1973 15 +-66.3170013427734 -56.0999984741211 Tremaster mirabilis 1963 1 +-2.34200000762939 -25.7427501678467 Vinciguerria poweriae 2009 1 +1.20029997825623 -29.5125503540039 Vinciguerria poweriae 2009 1 +2.60844993591309 -33.6916999816895 Vinciguerria poweriae 2009 1 +3.12979984283447 -3.32964992523193 Benthonella tenella 2009 1 +2.84050011634827 -30.0583992004395 Benthonella tenella 2009 1 +17.5400009155273 -34.4000015258789 Diastobranchus capensis 1989 1 +16.4699993133545 -33.2299995422363 Diastobranchus capensis 1995 1 +12.52184009552 -7.83577632904053 Carcharhinus limbatus 2001 1 +-48.4874992370605 -25.5168991088867 Caprella scaura 2007 7 +-48.4653015136719 -25.5293998718262 Caprella scaura 2007 11 +-48.4310989379883 -25.5468997955322 Caprella scaura 2007 11 +-48.4275016784668 -25.5461006164551 Caprella scaura 2007 5 +-48.4939002990723 -25.5139007568359 Caprella scaura 2007 5 +-48.3107986450195 -25.5520000457764 Caprella scaura 1990 1 +-48.4902992248535 -25.5142002105713 Caprella scaura 2007 10 +-48.4939002990723 -25.5139007568359 Poecilochaetus australis 2007 5 +-48.4995994567871 -25.5161991119385 Poecilochaetus australis 2008 5 +18.6749992370605 -34.2482986450195 Ilanga laevissima 1991 1 +-66.1999969482422 -46.7000007629395 Brama brama 2004 1 +-65.4499969482422 -45.4833335876465 Brama brama 2003 1 +-64.9166641235352 -45.4000015258789 Brama brama 2003 1 +-64.783332824707 -46.0999984741211 Campylonotus vagans 2003 1 +-66.2666702270508 -45.1666679382324 Brama brama 2005 1 +-65.5 -45.4500007629395 Brama brama 2003 1 +-66.1833343505859 -46.6666679382324 Brama brama 2004 1 +-66.716667175293 -45.2666664123535 Brama brama 2005 1 +-66.6999969482422 -45.2666664123535 Brama brama 2005 1 +-66.8166656494141 -45.0833320617676 Brama brama 2005 1 +-65.9000015258789 -45.0833320617676 Brama brama 2005 4 +-64.8833312988281 -46.5666656494141 Campylonotus vagans 2004 2 +-64.9166641235352 -46.5166664123535 Campylonotus vagans 2004 1 +-66.1999969482422 -45.4500007629395 Campylonotus vagans 2004 1 +-66.25 -45.466667175293 Campylonotus vagans 2004 1 +-66.1999969482422 -45.4166679382324 Campylonotus vagans 2004 1 +-66.0833358764648 -45.5166664123535 Campylonotus vagans 2004 1 +-66.1999969482422 -45.5166664123535 Campylonotus vagans 2004 1 +-66.3333358764648 -45.6333351135254 Campylonotus vagans 2004 1 +-66.1999969482422 -45.533332824707 Campylonotus vagans 2004 2 +-66.1999969482422 -45.4833335876465 Campylonotus vagans 2004 1 +-66.1999969482422 -45.5 Campylonotus vagans 2004 1 +-65.9499969482422 -45.6166648864746 Campylonotus vagans 2004 1 +-65.9833297729492 -45.5666656494141 Campylonotus vagans 2004 2 +-66.3833312988281 -45.6333351135254 Campylonotus vagans 2004 4 +-66.3499984741211 -45.6333351135254 Campylonotus vagans 2004 3 +-66.3499984741211 -45.6166648864746 Campylonotus vagans 2004 1 +-66.4000015258789 -45.6333351135254 Campylonotus vagans 2004 3 +-66.3166656494141 -45.6166648864746 Campylonotus vagans 2004 2 +-66.3666687011719 -45.6500015258789 Campylonotus vagans 2004 2 +-65.9166641235352 -46.5833320617676 Campylonotus vagans 2004 1 +-66.3833312988281 -45.6166648864746 Campylonotus vagans 2004 1 +-66.4333343505859 -45.6500015258789 Campylonotus vagans 2004 1 +-66.4166641235352 -45.6333351135254 Campylonotus vagans 2004 1 +-66.4333343505859 -45.6666679382324 Campylonotus vagans 2004 3 +-64.9166641235352 -46.3499984741211 Campylonotus vagans 2004 1 +-64.9333343505859 -46.3666648864746 Campylonotus vagans 2004 1 +-66.3333358764648 -45.466667175293 Campylonotus vagans 2004 2 +-66.1999969482422 -45.6166648864746 Campylonotus vagans 2004 1 +-66.1833343505859 -45.6166648864746 Campylonotus vagans 2004 1 +-66.1666641235352 -45.5999984741211 Campylonotus vagans 2004 1 +-66.1999969482422 -45.5999984741211 Campylonotus vagans 2004 1 +-66.3666687011719 -45.4333343505859 Campylonotus vagans 2004 2 +-66.3666687011719 -45.4833335876465 Campylonotus vagans 2004 2 +-66.3000030517578 -45.4500007629395 Campylonotus vagans 2004 1 +-66.0833358764648 -45.6833343505859 Campylonotus vagans 2004 1 +-66.0166702270508 -45.6833343505859 Campylonotus vagans 2004 1 +-66.0500030517578 -45.5999984741211 Campylonotus vagans 2004 1 +-66.0166702270508 -45.6500015258789 Campylonotus vagans 2004 4 +-66.033332824707 -45.533332824707 Campylonotus vagans 2004 1 +-66.0166702270508 -45.533332824707 Campylonotus vagans 2004 3 +-66.8833312988281 -45.3333320617676 Brama brama 2004 1 +-66.0500030517578 -45.5499992370605 Campylonotus vagans 2004 2 +-66.0666656494141 -45.5999984741211 Campylonotus vagans 2004 2 +-66.1166687011719 -45.6500015258789 Campylonotus vagans 2004 1 +-66.25 -45.533332824707 Campylonotus vagans 2004 1 +-66.283332824707 -45.5666656494141 Campylonotus vagans 2004 1 +-66.2666702270508 -45.533332824707 Campylonotus vagans 2004 2 +-66.7333297729492 -45.6833343505859 Campylonotus vagans 2004 2 +-65.8000030517578 -45.6500015258789 Campylonotus vagans 2004 1 +-65.7333297729492 -45.6333351135254 Campylonotus vagans 2004 1 +-65.8333358764648 -45.6166648864746 Campylonotus vagans 2004 1 +-65.8166656494141 -45.6333351135254 Campylonotus vagans 2004 1 +-66.25 -45.1166648864746 Brama brama 2005 1 +-66.7333297729492 -45.6666679382324 Campylonotus vagans 2004 1 +-66.8333358764648 -45.75 Campylonotus vagans 2004 1 +-66.1333312988281 -45.6500015258789 Campylonotus vagans 2004 1 +-66.1500015258789 -45.6500015258789 Campylonotus vagans 2004 2 +-66.0999984741211 -45.533332824707 Campylonotus vagans 2004 2 +-66.033332824707 -45.5833320617676 Campylonotus vagans 2004 1 +-66.0833358764648 -45.5833320617676 Campylonotus vagans 2004 2 +-65.0666656494141 -46.5666656494141 Campylonotus vagans 2004 1 +-65.0666656494141 -46.5 Campylonotus vagans 2004 1 +-65.0833358764648 -46.533332824707 Campylonotus vagans 2004 1 +-66.4333343505859 -45.25 Brama brama 2005 1 +-66.3000030517578 -45.5499992370605 Campylonotus vagans 2004 1 +-66.283332824707 -45.5999984741211 Campylonotus vagans 2004 1 +-67.0166702270508 -45.8499984741211 Campylonotus vagans 2004 1 +-65.7666702270508 -45.0833320617676 Brama brama 2005 1 +-66.7333297729492 -45.25 Brama brama 2005 2 +-66.1312637329102 -45.1706352233887 Brama brama 2005 1 +-64.966667175293 -46.0833320617676 Campylonotus vagans 2003 1 +-65.033332824707 -46.0833320617676 Campylonotus vagans 2003 1 +-65.3833312988281 -45.7999992370605 Campylonotus vagans 2003 1 +-65.8833312988281 -46.6666679382324 Brama brama 2003 1 +-67.0166702270508 -45.4000015258789 Campylonotus vagans 2003 1 +-66.2666702270508 -45.1833343505859 Campylonotus vagans 2003 1 +-66.25 -45.1500015258789 Campylonotus vagans 2003 1 +-65.216667175293 -45.8666648864746 Campylonotus vagans 2003 1 +-64.95263671875 -42.7851104736328 Dictyota dichotoma 2018 5 +-11.6661996841431 -26.1366996765137 Trilobatus sacculifer 1980 51 +-11.6661996841431 -26.1366996765137 Turborotalita humilis 1980 21 +-11.1857004165649 -25.5233001708984 Trilobatus sacculifer 1980 2 +-10.2644996643066 -26.0737991333008 Trilobatus sacculifer 1980 13 +19.6149997711182 -36.1483001708984 Nematoscelis megalops 1957 1 +4 -50 Pachyptila desolata 2006 1 +8 -45 Pachyptila desolata 2006 1 +-61.2449989318848 -48.5166664123535 Andreella uncifera 1 +-61.4599990844727 -46.6949996948242 Andreella uncifera 1 +-61.6783332824707 -43.098331451416 Andreella uncifera 1 +-60.2533340454102 -45.6316680908203 Andreella uncifera 1 +-57.8816680908203 -52.2616653442383 Andreella uncifera 3 +-57.2449989318848 -40.8600006103516 Andreella uncifera 1 +-40.1269989013672 -19.9358005523682 Jania rubens 2018 9 +-33.6666984558105 -18.8500003814697 Lanceola felina 1913 1 +11.895299911499 -11.6517000198364 Gonyaulax spinifera 1975 2 +12.0124521255493 -15.4322443008423 Chromis cadenati 1989 1 +13.4707002639771 -41.148998260498 Turborotalita humilis 1991 12 +13.4707002639771 -41.148998260498 Trilobatus sacculifer 1991 48 +3.47900009155273 -54.0097007751465 Ammobaculites agglutinans 1991 6 +2.79999995231628 -54.5400009155273 Ammobaculites agglutinans 1991 12 +-35.5772018432617 -59.0587997436523 Ammobaculites agglutinans 1992 13 +-40.5094985961914 -59.548698425293 Ammobaculites agglutinans 1992 2 +-61.7566986083984 0 Gonyaulax spinifera 1 +-8.16499996185303 0 Gonyaulax spinifera 1 +-7.64669990539551 0 Gonyaulax spinifera 1 +0.55019998550415 0 Gonyaulax spinifera 1 +-68.1856994628906 0 Gonyaulax spinifera 1 +-20.8332996368408 0 Gonyaulax spinifera 1 +-59.4617004394531 0 Gonyaulax spinifera 1 +-61.9042015075684 0 Gonyaulax spinifera 1 +-21.61669921875 0 Gonyaulax spinifera 1 +-38.8333015441895 0 Gonyaulax spinifera 1 +2.73099994659424 0 Gonyaulax spinifera 1 +-30.3577003479004 0 Gonyaulax spinifera 1 +-27.9232997894287 0 Gonyaulax spinifera 1 +-8.32670021057129 0 Gonyaulax spinifera 1 +-48.3600006103516 0 Gonyaulax spinifera 1 +-57.4566993713379 0 Gonyaulax spinifera 1 +-68.4613037109375 0 Gonyaulax spinifera 1 +-61.380500793457 0 Gonyaulax spinifera 1 +-61.801700592041 0 Gonyaulax spinifera 1 +-61.600700378418 0 Gonyaulax spinifera 1 +-61.8367004394531 0 Gonyaulax spinifera 1 +-67.7012023925781 0 Gonyaulax spinifera 1 +-26.5167007446289 0 Macrosetella gracilis 1977 5 +-59.076000213623 0 Gonyaulax spinifera 1 +11.6983003616333 -23.4316997528076 Cassidulina laevigata 1992 47 +13.0882997512817 -29.1749992370605 Trilobatus sacculifer 1988 2 +2.40669989585876 -29.8383007049561 Trilobatus sacculifer 1988 2 +1.00170004367828 -28.8917007446289 Trilobatus sacculifer 1988 2 +14.0050001144409 -27.9549999237061 Trilobatus sacculifer 1988 2 +-1.30330002307892 -31.9050006866455 Trilobatus sacculifer 1988 2 +13.7251996994019 -40.8077011108398 Trilobatus sacculifer 1988 2 +13.4812002182007 -41.1352996826172 Trilobatus sacculifer 1988 2 +17.3645992279053 -34.7532005310059 Trilobatus sacculifer 1988 2 +3.26169991493225 -30.2700004577637 Trilobatus sacculifer 1988 2 +2.33299994468689 0 Gonyaulax spinifera 1 +-26.5 -1.9666999578476 Macrosetella gracilis 1977 5 +-22.9500007629395 -22.9666996002197 Scaphocalanus curtus 1973 10 +-26.4832992553711 -17 Macrosetella gracilis 1977 5 +19.7486896514893 -34.7688484191895 Alaginella ochracea 2 +-41.9874992370605 -59.6394996643066 Ammobaculites agglutinans 1992 1 +-3.20667004585266 -59.4467010498047 Ammobaculites agglutinans 1994 13 +17.5433006286621 -35.125 Thoracosphaera heimii 1996 59 +-68.6675033569336 0 Gonyaulax spinifera 1 +-68.2419967651367 0 Gonyaulax spinifera 1 +-9.85000038146973 0 Gonyaulax spinifera 1 +-60.6347999572754 0 Gonyaulax spinifera 1 +-60.0276985168457 0 Gonyaulax spinifera 1 +-56.3692016601562 0 Gonyaulax spinifera 1 +-3.22670006752014 0 Gonyaulax spinifera 1 +-10.4567003250122 0 Gonyaulax spinifera 1 +1.55330002307892 0 Gonyaulax spinifera 1 +2.65499997138977 0 Gonyaulax spinifera 1 +1.13170003890991 0 Gonyaulax spinifera 1 +1.47500002384186 0 Gonyaulax spinifera 1 +7.75829982757568 0 Gonyaulax spinifera 1 +-11.6606998443604 0 Gonyaulax spinifera 1 +-67.760498046875 0 Gonyaulax spinifera 1 +9.26500034332275 0 Gonyaulax spinifera 1 +-2.1282000541687 0 Gonyaulax spinifera 1 +11.4517002105713 0 Gonyaulax spinifera 1 +3.47900009155273 -54.0097007751465 Ammobaculites agglutinans 1989 1 +-27.4559993743896 -57.5351982116699 Ammobaculites agglutinans 1992 2 +-35.2849998474121 -30.2765007019043 Turborotalita humilis 1980 46 +-35.2849998474121 -30.2765007019043 Trilobatus sacculifer 1980 46 +-14.8966999053955 -3.81500005722046 Thoracosphaera heimii 1989 24 +-29.5 -10 Macrosetella gracilis 1977 10 +-35.8396987915039 -58.9990005493164 Ammobaculites agglutinans 1992 7 +-59.0950012207031 -53.2167015075684 Cassidulina laevigata 1994 1 +-38.1352996826172 -29.9736995697021 Trilobatus sacculifer 1980 77 +-38.1352996826172 -29.9736995697021 Turborotalita humilis 1980 76 +11.6983003616333 -23.4316997528076 Thoracosphaera heimii 1992 34 +2.8989999294281 -54.5251998901367 Ammobaculites agglutinans 1991 8 +-34.0217018127441 -8.52832984924316 Thoracosphaera heimii 1993 35 +-34.0217018127441 -8.52832984924316 Gonyaulax spinifera 1993 22 +-12.4282999038696 -1.66499996185303 Thoracosphaera heimii 1989 59 +-12.4282999038696 -1.66499996185303 Gonyaulax spinifera 1989 24 +-36.3466987609863 -4.24499988555908 Thoracosphaera heimii 1996 2 +-36.2700004577637 -3.54830002784729 Thoracosphaera heimii 1996 2 +-6.00299978256226 -59.0029983520508 Symbolophora microtrias 1992 1 +-43.0499992370605 -46 Oncaea notopus 1976 15 +-13.2600002288818 0 Thoracosphaera heimii 1 +-11.5799999237061 0 Thoracosphaera heimii 1 +7.23999977111816 -24.6900005340576 Thoracosphaera heimii 1990 75 +-16.5100002288818 0 Thoracosphaera heimii 1 +-36.6349983215332 -4.61329984664917 Thoracosphaera heimii 1996 2 +9.14999961853027 -20.0499992370605 Coccolithus pelagicus 1989 5 +4.46500015258789 -52.5890007019043 Ammobaculites agglutinans 1989 4 +-0.481667011976242 -56.3199996948242 Ammobaculites agglutinans 1989 4 +-11.0333003997803 -48.2332992553711 Ammobaculites agglutinans 1989 5 +-12.7033004760742 -49.0116996765137 Ammobaculites agglutinans 1989 3 +8.625 -2.0699999332428 Ammobaculites agglutinans 1998 6 +-33.2442016601562 -50.3712005615234 Ammobaculites agglutinans 1992 6 +-23.9549999237061 -54.638500213623 Ammobaculites agglutinans 1992 1 +-25.3360004425049 -57.7560005187988 Ammobaculites agglutinans 1992 2 +-15.0500001907349 -0.033330000936985 Macrosetella gracilis 1963 15 +-10.2517004013062 -4.32667016983032 Trilobatus sacculifer 1991 5 +-10.0666999816895 -2.18166995048523 Trilobatus sacculifer 1991 5 +-10.7417001724243 -0.0783333033323288 Trilobatus sacculifer 1991 5 +15.2966995239258 -31.4466991424561 Coccolithus pelagicus 1989 1 +13.0832996368408 -25.4666996002197 Coccolithus pelagicus 1989 1 +12.2017002105713 -22.3617000579834 Coccolithus pelagicus 1989 1 +12.9982995986938 -24.3117008209229 Coccolithus pelagicus 1989 1 +13.3500003814697 -24.8332996368408 Coccolithus pelagicus 1989 1 +12.8717002868652 -24.9950008392334 Coccolithus pelagicus 1989 1 +12.6682996749878 -25.0683002471924 Coccolithus pelagicus 1989 1 +5.01669979095459 -26.5482997894287 Coccolithus pelagicus 1990 1 +10.6549997329712 -19.6949996948242 Coccolithus pelagicus 1990 1 +10.7582998275757 -23.5883007049561 Coccolithus pelagicus 1990 1 +11.7033004760742 -23.4300003051758 Coccolithus pelagicus 1990 1 +-34.0217018127441 -8.52832984924316 Trilobatus sacculifer 1993 90 +-28.6667003631592 -31.6667003631592 Trilobatus sacculifer 1991 164 +9.78499984741211 -3.73000001907349 Ammobaculites agglutinans 1998 2 +9.78499984741211 -3.73000001907349 Cassidulina laevigata 1998 1 +11.0566997528076 -18.9549999237061 Cassidulina laevigata 1996 9 +11.5799999237061 -15.6282997131348 Ammobaculites agglutinans 1996 3 +11.5799999237061 -15.6282997131348 Cassidulina laevigata 1996 8 +10.7716999053955 -19.8349990844727 Nodosinum gaussicum 1996 2 +15.5 -31.7849998474121 Cassidulina laevigata 1996 5 +8.37670040130615 -0.689999997615814 Cassidulina laevigata 1996 4 +8.625 -2.0699999332428 Cassidulina laevigata 1996 4 +11.0717000961304 -5.50330018997192 Cassidulina laevigata 1996 4 +12.6866998672485 -10.1700000762939 Cassidulina laevigata 1996 4 +13.0733003616333 -11.9033002853394 Cassidulina laevigata 1996 4 +13.4549999237061 -26.7917003631592 Cassidulina laevigata 1996 5 +13.2333002090454 -25.5167007446289 Cassidulina laevigata 1996 6 +12.6016998291016 -22.7166996002197 Cassidulina laevigata 1996 2 +11.8249998092651 -21.0900001525879 Cassidulina laevigata 1996 7 +14.0050001144409 -27.9517002105713 Cassidulina laevigata 1996 6 +13.0733003616333 -11.9033002853394 Ammobaculites agglutinans 1998 1 +11.8732995986938 -7.75 Cassidulina laevigata 1996 4 +12.6967000961304 -12.8400001525879 Ammobaculites agglutinans 1998 1 +11.125 -17.1867008209229 Ammobaculites agglutinans 1996 6 +12.9982995986938 -24.301700592041 Cassidulina laevigata 1996 2 +-26.8866996765137 -52.0699996948242 Ammobaculites agglutinans 1994 2 +-54.2016983032227 -38.5133018493652 Ammobaculites agglutinans 1994 2 +-53.9282989501953 -38.9249992370605 Ammobaculites agglutinans 1994 2 +-53.3633003234863 -39.2433013916016 Ammobaculites agglutinans 1994 2 +-56.323299407959 -41.9449996948242 Ammobaculites agglutinans 1994 2 +-51.4233016967773 -44.2066993713379 Ammobaculites agglutinans 1994 2 +-24.3166999816895 -50.75 Ammobaculites agglutinans 1994 2 +-43.00830078125 -53.7099990844727 Cassidulina laevigata 1994 1 +-43.7667007446289 -53.6666984558105 Cassidulina laevigata 1994 1 +-61.5800018310547 -53.1399993896484 Cassidulina laevigata 1994 2 +-54.2016983032227 -38.5133018493652 Cassidulina laevigata 1994 2 +-53.9282989501953 -38.9249992370605 Cassidulina laevigata 1994 2 +-57.2999992370605 -41.4183006286621 Cassidulina laevigata 1994 2 +-29.6567001342773 -31.6616992950439 Trilobatus sacculifer 1991 81 +13.0733003616333 -11.9033002853394 Cassidulina laevigata 1998 5 +11.8732995986938 -7.75 Nodosinum gaussicum 1998 2 +11.6117000579834 -19.4067001342773 Coccolithus pelagicus 1990 1 +12.3782997131348 -23.3166999816895 Coccolithus pelagicus 1990 1 +11.6366996765137 -26.4766998291016 Coccolithus pelagicus 1990 1 +13.0882997512817 -29.1749992370605 Coccolithus pelagicus 1990 1 +6.85500001907349 -24.5983009338379 Coccolithus pelagicus 1990 1 +14.0050001144409 -27.9517002105713 Coccolithus pelagicus 1990 1 +12.6049995422363 -22.7250003814697 Coccolithus pelagicus 1990 1 +13.1599998474121 -24.8950004577637 Coccolithus pelagicus 1989 1 +11.5267000198364 -25.3932991027832 Coccolithus pelagicus 1989 1 +-48.8182983398438 -31.5186996459961 Coccolithus pelagicus 1989 1 +-20.7840003967285 -42.000301361084 Coccolithus pelagicus 1989 1 +17.7549991607666 -34.7966995239258 Coccolithus pelagicus 1989 1 +14.0050001144409 -27.9517002105713 Coccolithus pelagicus 1989 1 +13.4533004760742 -26.7917003631592 Coccolithus pelagicus 1989 1 +12.3717002868652 -23.3166999816895 Coccolithus pelagicus 1990 1 +8.25 -24.3316993713379 Coccolithus pelagicus 1990 1 +6.78830003738403 -24.9249992370605 Coccolithus pelagicus 1990 1 +-37.005199432373 -48.5023002624512 Ammobaculites agglutinans 1992 2 +-30.4710006713867 -52.0643005371094 Cassidulina laevigata 1992 2 +-59.4432983398438 -43.6467018127441 Cassidulina laevigata 1994 2 +-59.3300018310547 -43.6749992370605 Cassidulina laevigata 1994 2 +-54.3417015075684 -39.2900009155273 Cassidulina laevigata 1994 2 +-25.471700668335 -51.1866989135742 Cassidulina laevigata 1994 2 +-25.7000007629395 -51.4082984924316 Cassidulina laevigata 1994 1 +15.2966995239258 -31.4466991424561 Gonyaulax spinifera 1966 1 +-34.3479995727539 -49.7290000915527 Ammobaculites agglutinans 1989 1 +11.125 -17.1867008209229 Cassidulina laevigata 1996 9 +10.7716999053955 -19.8349990844727 Cassidulina laevigata 1996 6 +13.8367004394531 -28.9983005523682 Cassidulina laevigata 1992 2 +-34.3479995727539 -49.7290000915527 Ammobaculites agglutinans 1992 6 +6.70599985122681 -49.1343002319336 Ammobaculites agglutinans 1991 3 +-38.2282981872559 -2.72499990463257 Thoracosphaera heimii 1996 1 +-23.4283008575439 -0.00669999979436398 Thoracosphaera heimii 1996 2 +12.6866998672485 -10.1700000762939 Cassidulina laevigata 1998 2 +-30.3999996185303 -55.3316993713379 Ammobaculites agglutinans 1994 2 +-48.0917015075684 -53.9682998657227 Ammobaculites agglutinans 1994 2 +11.7383003234863 -43.2201995849609 Gonyaulax spinifera 1991 13 +10.5187997817993 -19.7443008422852 Trilobatus sacculifer 1980 17 +-30.2345008850098 -57.5092010498047 Cassidulina laevigata 1992 1 +-22.2514991760254 -55.9692993164062 Ammobaculites agglutinans 1992 1 +-56.810001373291 -44.8543014526367 Ammobaculites agglutinans 1992 2 +13.7200002670288 -27.0300006866455 Gonyaulax spinifera 1970 1 +14.1000003814697 -27.0300006866455 Gonyaulax spinifera 1970 1 +13.7799997329712 -26.2199993133545 Gonyaulax spinifera 1970 1 +17.1000003814697 -32.4900016784668 Gonyaulax spinifera 1970 1 +17.0100002288818 -29.6900005340576 Gonyaulax spinifera 1970 1 +14.3900003433228 -22.9400005340576 Gonyaulax spinifera 1970 1 +14.3999996185303 -23.1000003814697 Gonyaulax spinifera 1970 1 +11.3900003433228 -17.2600002288818 Gonyaulax spinifera 1970 1 +11.6999998092651 -17.9400005340576 Gonyaulax spinifera 1970 1 +11.6300001144409 -20.9200000762939 Gonyaulax spinifera 1970 1 +12.2299995422363 -20.5799999237061 Gonyaulax spinifera 1970 1 +18.2099990844727 -31.9300003051758 Gonyaulax spinifera 1970 1 +11.1199998855591 -17.5699996948242 Gonyaulax spinifera 1970 1 +11.2799997329712 -17.5699996948242 Gonyaulax spinifera 1970 1 +11.4499998092651 -17.5699996948242 Gonyaulax spinifera 1970 1 +14.6000003814697 -30.1000003814697 Gonyaulax spinifera 1970 1 +15.8299999237061 -29.7800006866455 Gonyaulax spinifera 1970 1 +13.3999996185303 -27.2000007629395 Gonyaulax spinifera 1970 1 +14.6700000762939 -26.9500007629395 Gonyaulax spinifera 1970 1 +-47.1319999694824 -27.0790004730225 Bellator brachychir 1997 1 +17.3332996368408 -33.1666984558105 Nematoscelis megalops 1963 2 +18.4666004180908 -34.3166007995605 Ampithoe falsa 1915 1 +18.9249992370605 -34.8479995727539 Gnathophausia zoea 1 +14.6167001724243 -26.6000003814697 Achelous tuberculatus 1948 2 +18.4500007629395 -34.11669921875 Caprella equilibra 1914 1 +18 -33.0499000549316 Caprella equilibra 1 +15.1377000808716 -26.6086006164551 Ampithoe falsa 1969 1 +12.6999998092651 -19.38330078125 Apohyale hirtipalma 1 +17.9085006713867 -34.4429016113281 Platytyphlops peringueyi 1 +18.4666004180908 -34.3166007995605 Ampithoe falsa 1913 1 +18.7467002868652 -34.2550010681152 Liljeborgia dubia 1953 1 +18.0249996185303 -33.0932998657227 Radsia nigrovirescens 1961 1 +18.9167003631592 -34.7999992370605 Daption capense 1985 1 +-69.4666976928711 -56.5833015441895 Daption capense 1991 1 +-68.7667007446289 -56.5833015441895 Daption capense 1991 2 +-66.2332992553711 -55.7000007629395 Daption capense 1991 1 +-64.4332962036133 -55.2167015075684 Daption capense 1991 2 +18.5167007446289 -37.3333015441895 Daption capense 1984 1 +18.7332992553711 -37.63330078125 Daption capense 1984 2 +18.9500007629395 -37.9667015075684 Daption capense 1984 1 +19.1499996185303 -38.13330078125 Daption capense 1984 1 +19.36669921875 -38.4500007629395 Daption capense 1984 1 +-39.2400016784668 -0.516700029373169 Turborotalita humilis 1969 2 +3.13800001144409 -30.1226997375488 Trilobatus sacculifer 1980 9 +3.13800001144409 -30.1226997375488 Globoturborotalita ouachitaensis 1980 6 +-35.5597991943359 -30.0041999816895 Trilobatus sacculifer 1974 35 +-35.5597991943359 -30.0041999816895 Globoturborotalita ouachitaensis 1974 5 +10.5325002670288 -19.7574996948242 Cassidulina laevigata 1975 3 +18.0965003967285 -35.8457984924316 Cassidulina laevigata 1974 2 +10.5325002670288 -19.7574996948242 Globigerinatella insueta 1975 1 +10.5325002670288 -19.7574996948242 Globoconella conoidea 1975 12 +18.0965003967285 -35.8457984924316 Trilobatus sacculifer 1974 15 +10.5325002670288 -19.7574996948242 Trilobatus sacculifer 1975 8 +-45.2792015075684 -23.5860996246338 Jania rubens 1963 2 +-45.3507995605469 -23.7733001708984 Acanthophora spicifera 1949 2 +-45.11669921875 -23.5 Acanthophora spicifera 1964 2 +-45.3842010498047 -23.6282997131348 Jania rubens 1953 2 +-45.0675010681152 -23.565299987793 Jania rubens 1963 2 +-45.0741996765137 -23.5375003814697 Jania rubens 1963 2 +-43 -59.2299995422363 Oncaea notopus 1979 4 +-60.6349983215332 0 Gonyaulax spinifera 1 +-7.01719999313354 0 Gonyaulax spinifera 1 +-59.5299987792969 0 Gonyaulax spinifera 1 +14.4266996383667 0 Gonyaulax spinifera 1 +-6.55170011520386 -34.6699981689453 Trilobatus sacculifer 1988 1 +13.8283004760742 -29 Trilobatus sacculifer 1988 1 +13.0469999313354 -41.7169990539551 Trilobatus sacculifer 1988 1 +-62.624698638916 0 Gonyaulax spinifera 1 +-67.4369964599609 0 Gonyaulax spinifera 1 +-10.1833000183105 0 Gonyaulax spinifera 1 +-9.96829986572266 0 Gonyaulax spinifera 1 +-45.2641983032227 0 Gonyaulax spinifera 1 +-21.0832996368408 0 Gonyaulax spinifera 1 +-12.7833003997803 0 Gonyaulax spinifera 1 +-10.8482999801636 0 Gonyaulax spinifera 1 +-61.3217010498047 0 Gonyaulax spinifera 1 +-61.872501373291 0 Gonyaulax spinifera 1 +-64.0832977294922 0 Gonyaulax spinifera 1 +-64.07080078125 0 Gonyaulax spinifera 1 +-11.568699836731 0 Gonyaulax spinifera 1 +-61.9724998474121 0 Gonyaulax spinifera 1 +15.4799995422363 -31.4867000579834 Trilobatus sacculifer 1988 1 +-64.2667007446289 0 Gonyaulax spinifera 1 +13.1016998291016 0 Gonyaulax spinifera 1 +8.96666622161865 -1.03333330154419 Ilisha africana 12 +-45.0200004577637 -55.5499992370605 Gonyaulax spinifera 1966 1 +17.3645992279053 -34.7532005310059 Gonyaulax spinifera 1966 1 +-61.8116989135742 0 Gonyaulax spinifera 1 +3.08170008659363 0 Gonyaulax spinifera 1 +3.21499991416931 0 Gonyaulax spinifera 1 +-28.9367008209229 0 Gonyaulax spinifera 1 +-10.3249998092651 0 Gonyaulax spinifera 1 +-10.0017004013062 0 Gonyaulax spinifera 1 +-66.6849975585938 0 Gonyaulax spinifera 1 +-14.4899997711182 -41.2750015258789 Trilobatus sacculifer 1993 1 +-42.9166984558105 -27.6667003631592 Macrosetella gracilis 1976 10 +-67.0670013427734 -53.0999984741211 Clathria (Microciona) antarctica 3 +-38.7000007629395 -18.1000003814697 Ophionereis reticulata 1961 1 +19.1866664886475 -34.4280548095703 Gonyaulax spinifera 2017 1 +14.4691667556763 -22.9330558776855 Gonyaulax spinifera 2011 1 +-48.4653015136719 -25.5293998718262 Mactrotoma janeiroensis 2007 1 +-48.4653015136719 -25.5293998718262 Nereis broa 2007 2 +-48.4289016723633 -25.5471992492676 Nereis broa 2007 1 +-48.3107986450195 -25.5520000457764 Caprella equilibra 1990 8 +-48.4976005554199 -25.4738998413086 Chione cancellata 1996 1 +-48.3107986450195 -25.5520000457764 Prionospio steenstrupi 1990 1 +-38.5 -18.1499996185303 Ophionereis reticulata 1961 1 +-22.9500007629395 -22.9666996002197 Pontoeciella abyssicola 1973 15 +-37.5 -50.25 Cunina duplicata 20 +-37.5 -50.25 Diphyes antarctica 20 +-37.5 -50.25 Corymorpha gigantea 20 +-45.375 -23.6291675567627 Caprella equilibra 2001 8 +-44.8993988037109 -23.3747005462646 Caprella equilibra 2001 21 +-44.8388900756836 -23.3738899230957 Caprella equilibra 2001 9 +-44.9324989318848 -23.3569431304932 Caprella equilibra 2001 4 +-45.3138885498047 -23.5844440460205 Caprella equilibra 2001 2 +-45.2835998535156 -23.9368991851807 Nereis broa 2002 1 +-45.5111122131348 -23.836389541626 Caprella equilibra 2001 2 +-44.8388900756836 -23.3741664886475 Caprella equilibra 2001 3 +-45.7249984741211 -23.7917003631592 Caprella equilibra 2001 3 +-45.3138999938965 -23.584400177002 Caprella equilibra 2001 11 +-45.1305999755859 -23.7928009033203 Nereis broa 2001 1 +-44.1949996948242 -23.6907997131348 Nereis broa 2001 7 +-45.1638984680176 -23.5205993652344 Chione cancellata 2002 3 +-45.0553016662598 -23.4382991790771 Chione cancellata 2002 3 +-44.7886123657227 -23.5400009155273 Ampelisca cristata 2001 1 +-44.8658332824707 -23.3569431304932 Caprella equilibra 2001 1 +-45.3752784729004 -23.6258335113525 Caprella equilibra 2001 3 +-44.8488998413086 -23.3644008636475 Nereis broa 2001 1 +-45.4094009399414 -23.9664001464844 Nereis broa 2001 1 +-45.6644439697266 -23.7811107635498 Caprella equilibra 2001 1 +-44.8366012573242 -23.3741664886475 Caprella equilibra 2001 1 +-44.1949996948242 -23.6907997131348 Turritellopsis marplatensis 2001 1 +-45.1730880737305 -23.8015289306641 Ampelisca cristata 2002 1 +-45.3805541992188 -23.6288890838623 Caprella equilibra 2001 5 +-45.6644439697266 -23.7808322906494 Caprella equilibra 2001 2 +8.83666706085205 -0.0366670005023479 Lophius vaillanti 1963 1 +8 -1 Oxyporhamphus similis 1961 1 +5.13033008575439 -53.1804008483887 Fissurina staphyllearia 1998 44 +5.91907978057861 -47.094799041748 Fissurina staphyllearia 1998 85 +7.0789999961853 -46.4109992980957 Fissurina staphyllearia 1998 27 +5.8655800819397 -49.9765014648438 Fissurina staphyllearia 1998 117 +-45.5666007995605 -0.275000005960464 Ogcocephalus nasutus 1979 1 +-51.935001373291 -38.281665802002 Axinulus subequatorius 1 +-52.3216667175293 -37.6816673278809 Axinulus subequatorius 1 +-22.9500007629395 -22.9666996002197 Oncaea notopus 1973 15 +12.7373914718628 -7.25662231445312 Chromis cadenati 1995 1 +-9.71669960021973 -1.89999997615814 Priacanthus hamrur 1 +-44.5579986572266 -23.2210006713867 Prionospio steenstrupi 2003 1 +-44.689998626709 -23.2140007019043 Mussismilia hispida 2003 1 +-44.3629989624023 -23.0709991455078 Mussismilia hispida 2003 1 +-44.326000213623 -23.136999130249 Mussismilia hispida 2003 1 +-44.5960006713867 -23.2029991149902 Spyridia filamentosa 2004 1 +-44.689998626709 -23.2140007019043 Ampelisca cristata 2004 1 +-44.3300018310547 -22.9729995727539 Ampelisca cristata 2003 1 +-44.2970008850098 -23.113000869751 Mussismilia hispida 2003 1 +-44.3730010986328 -23.1669998168945 Mussismilia hispida 2003 1 +-44.689998626709 -23.2140007019043 Spyridia filamentosa 2004 1 +-44.681999206543 -23.1569995880127 Mussismilia hispida 2003 1 +-44.2249984741211 -23.0790004730225 Mussismilia hispida 2004 1 +-44.6440010070801 -23.193000793457 Mussismilia hispida 2003 1 +-44.1500015258789 -23.1679992675781 Mussismilia hispida 2003 1 +-44.1910018920898 -23.1019992828369 Mussismilia hispida 2003 1 +-44.5369987487793 -23.1520004272461 Mussismilia hispida 2003 1 +-44.5779991149902 -23.1849994659424 Mussismilia hispida 2003 1 +-44.601001739502 -23.0629997253418 Mussismilia hispida 2004 1 +-44.3790016174316 -23.0319995880127 Mussismilia hispida 2003 1 +-44.5579986572266 -23.2210006713867 Mussismilia hispida 2003 1 +-44.310001373291 -23.0849990844727 Mussismilia hispida 2003 1 +-44.5960006713867 -23.2029991149902 Mussismilia hispida 2004 1 +-44.1479988098145 -23.125 Prionospio steenstrupi 2003 1 +-44.2970008850098 -23.113000869751 Macromphalina palmalitoris 2003 1 +-44.689998626709 -23.2140007019043 Spyridia filamentosa 2003 1 +-44.5410003662109 -23.2679996490479 Mussismilia hispida 2003 1 +-44.5060005187988 -23.2910003662109 Mussismilia hispida 2003 1 +-44.3730010986328 -23.1669998168945 Ophionereis reticulata 2004 1 +-44.3730010986328 -23.1669998168945 Ophionereis reticulata 2003 1 +-44.6310005187988 -23.2719993591309 Laurencia obtusa 2003 1 +-44.3730010986328 -23.1669998168945 Laurencia obtusa 2004 1 +-44.1500015258789 -23.1679992675781 Ophionereis reticulata 2003 1 +-44.4739990234375 -23.0090007781982 Ophionereis reticulata 2003 1 +-44.5460014343262 -23.0480003356934 Laurencia obtusa 2004 1 +-44.314998626709 -23.193000793457 Ophionereis reticulata 2003 1 +-44.326000213623 -23.136999130249 Ophionereis reticulata 2004 1 +-44.1500015258789 -23.2210006713867 Laurencia obtusa 2004 1 +-44.4070014953613 -23.0979995727539 Laurencia obtusa 2003 1 +-44.1399993896484 -23.0429992675781 Laurencia obtusa 2003 1 +-44.2529983520508 -23.0699996948242 Laurencia obtusa 2004 1 +-44.367000579834 -22.9619998931885 Ophionereis reticulata 2003 1 +-44.2970008850098 -23.113000869751 Ophionereis reticulata 2003 1 +-44.310001373291 -23.0849990844727 Ophionereis reticulata 2003 1 +-44.2970008850098 -23.113000869751 Laurencia obtusa 2003 1 +-44.4210014343262 -23.0599994659424 Laurencia obtusa 2003 1 +-44.326000213623 -23.136999130249 Prionospio steenstrupi 2004 1 +-44.5960006713867 -23.2029991149902 Prionospio steenstrupi 2003 1 +-44.310001373291 -23.0849990844727 Ampelisca cristata 2003 1 +-44.367000579834 -22.9619998931885 Mussismilia hispida 2003 1 +-38.6788902282715 -18.0480556488037 Prionospio steenstrupi 2000 1 +-38.9786109924316 -17.7519435882568 Prionospio steenstrupi 2000 1 +-38.6394424438477 -18.0127773284912 Mussismilia hispida 2000 1 +-39.0277786254883 -17.4783325195312 Mussismilia hispida 2000 1 +-56.6419982910156 -51.9420013427734 Doloria levinsoni 1963 1 +-58.9000015258789 -55.0250015258789 Doloria levinsoni 1962 1 +-38.9663887023926 -17.682222366333 Mussismilia hispida 2000 1 +-38.6902770996094 -17.9069442749023 Spyridia filamentosa 2000 1 +-39.1352767944336 -17.4219436645508 Mussismilia hispida 2000 1 +-39.1683311462402 -17.0983333587646 Laurencia obtusa 2000 1 +-39.0913887023926 -16.8958320617676 Mussismilia hispida 2000 1 +-38.6597213745117 -17.9808330535889 Mussismilia hispida 2000 1 +-38.8738899230957 -17.3502788543701 Mussismilia hispida 2000 1 +-38.6008338928223 -17.7152786254883 Mussismilia hispida 2000 1 +-38.5905570983887 -18.1019439697266 Synopia ultramarina 2000 1 +-39.272777557373 -17.9961109161377 Mussismilia hispida 2000 2 +-39.2138900756836 -17.9611110687256 Synopia ultramarina 2000 1 +-38.6597213745117 -17.9808330535889 Chione cancellata 2000 1 +-38.9247207641602 -17.8191661834717 Chione cancellata 2000 1 +-39.1133346557617 -16.9513893127441 Lutjanus apodus 2000 1 +-39.2186126708984 -17.9549999237061 Laurencia obtusa 2000 1 +-38.9791679382324 -17.875 Laurencia obtusa 2000 2 +-39 -18.0283336639404 Mussismilia hispida 2000 1 +-39.0849990844727 -16.8863887786865 Mussismilia hispida 2000 1 +-38.9247207641602 -17.8191661834717 Crassinella marplatensis 2000 1 +-38.9786109924316 -17.7519435882568 Laurencia obtusa 2000 1 +-38.9661102294922 -17.9886112213135 Crassinella marplatensis 2000 1 +-39.0786094665527 -16.897777557373 Laurencia obtusa 2000 1 +19.0832996368408 -35.0833015441895 Chelidonichthys queketti 1984 1 +15.3000001907349 -29.88330078125 Chelidonichthys queketti 1985 1 +17.3166999816895 -31.3166999816895 Chelidonichthys queketti 1985 1 +17.11669921875 -33.5999984741211 Himantolophus appelii 1997 1 +18 -34 Chelidonichthys queketti 1984 1 +15.6833000183105 -29.4333000183105 Chelidonichthys queketti 1985 1 +14.9666996002197 -30.5333003997803 Gadomus capensis 2001 1 +12.7666997909546 -8.43332958221436 Diceratias pileatus 2002 1 +17.6366996765137 -33.6833000183105 Ophichthus serpentinus 1985 1 +17.9821186065674 -33.1428756713867 Radsia nigrovirescens 2008 2 +18.6644306182861 -34.1728591918945 Radsia nigrovirescens 1989 1 +-69.2421798706055 -55.3251914978027 Monetaria caputdraconis 1902 1 +-38.0183296203613 -53.6366691589355 Stibarobdella macrothela 1975 1 +-37.9916687011719 -53.7366714477539 Stibarobdella macrothela 1975 1 +-26.4832992553711 -17 Monothula subtilis 1977 15 +-26.4832992553711 -4.31669998168945 Monothula subtilis 1977 5 +-64.2568817138672 -42.6202583312988 Lomentaria clavellosa 2019 2 +-29.2700004577637 -8.5600004196167 Remora osteochir 2002 1 +-29.2800006866455 -9.15999984741211 Remora osteochir 2002 4 +-21.1559715270996 -7.84194421768188 Remora osteochir 2002 5 +-21.7915287017822 -8.5105562210083 Remora osteochir 2002 2 +-42.86669921875 -32.9000015258789 Oncaea notopus 1976 5 +-41.9581985473633 -22.7341003417969 Jania rubens 1981 2 +18.4666996002197 -34.1417007446289 Prunum capense 1991 1 +18.4832992553711 -34.2249984741211 Prunum capense 1991 1 +18.7266998291016 -34.1199989318848 Prunum capense 1991 1 +-22.2986106872559 -7.73513889312744 Remora osteochir 2002 7 +-24.4691677093506 -8.29374980926514 Remora osteochir 2002 6 +-21.0168056488037 -8.09083271026611 Remora osteochir 2002 7 +-29.5100002288818 -9.5 Remora osteochir 2002 6 +-29.3199996948242 -8.64999961853027 Remora osteochir 2002 4 +-21.0575008392334 -7.97208309173584 Remora osteochir 2002 3 +-23.9850006103516 -7.6712498664856 Remora osteochir 2002 2 +-2 -21.6000003814697 Scopeloberyx robustus 1971 1 +-29.2999992370605 -8.6899995803833 Remora osteochir 2002 14 +1.20029997825623 -29.5125503540039 Scopeloberyx robustus 2009 1 +1.20029997825623 -29.5125503540039 Lampanyctus intricarius 2009 1 +-12.40505027771 -4.15779972076416 Acanthephyra pelagica 2009 3 +-26.5 -1.9666999578476 Oncaea notopus 1977 10 +13.6332998275757 -25.13330078125 Centrophorus uyato 1984 1 +14.5832996368408 -27.61669921875 Centrophorus uyato 1984 1 +14.6833000183105 -29.38330078125 Loligo reynaudii 1986 1 +14.8667001724243 -28.0333003997803 Loligo reynaudii 1985 1 +6.40000009536743 -24.7166996002197 Coryphaenoides striaturus 1982 1 +14.3500003814697 -28.2000007629395 Coryphaenoides striaturus 1982 1 +13.9333000183105 -27.1499996185303 Coryphaenoides striaturus 1982 2 +14.75 -28.1833000183105 Loligo reynaudii 1989 1 +8.47500038146973 -28.4200000762939 Torometopa aequalis 1 +-65.466667175293 -54.3333282470703 Exoediceropsis lobata 2 +-38.3274993896484 -13.3325004577637 Diaphus problematicus 2000 1 +13.033332824707 -9.66666698455811 Monodaeus rouxi 1948 2 +-64.0666656494141 -54.783332824707 Laophontodes whitsoni 1 +-66.2383346557617 -55.4416656494141 Laophontodes whitsoni 1 +-36.533332824707 -54.2666664123535 Amphiporus moseleyi 1 +-36.5 -54.283332824707 Amphiporus moseleyi 1 +-38.1699981689453 -16.117000579834 Eulithidium affine 2001 1 +-36.7519989013672 -17.0979995727539 Conus regius 1997 1 +-38.6870002746582 -16.7870006561279 Phosinella fenestrata 1997 1 +-38.5180015563965 -15.8970003128052 Phosinella fenestrata 1996 1 +-38.6870002746582 -16.7870006561279 Eulithidium affine 1997 1 +-31.8279991149902 -20.738000869751 Eulithidium affine 2001 1 +-38.1360015869141 -19.673999786377 Eulithidium affine 1996 1 +-33.6360015869141 -20.8570003509521 Eulithidium affine 2001 1 +-38.0190010070801 -19.2719993591309 Eulithidium affine 2001 1 +-40.0989990234375 -20.3400001525879 Eulithidium affine 1996 1 +-38.5180015563965 -15.8970003128052 Eulithidium affine 1996 1 +12.7022495269775 -7.29475021362305 Chromis cadenati 2009 1 +-36.1030006408691 -20.4869995117188 Eulithidium affine 2001 1 +-40.4329986572266 -21.1669998168945 Eulithidium affine 1996 1 +-38.375 -19.4740009307861 Phosinella fenestrata 1996 1 +-38.1699981689453 -16.117000579834 Phosinella fenestrata 2001 1 +12.9168939590454 -10.4129981994629 Alepocephalus rostratus 2006 1 +12.6168851852417 -9.09583187103271 Alepocephalus rostratus 2006 1 +-45.9020004272461 -26.2409992218018 Urophycis cirrata 1996 1 +-35 -15 Mycteroperca venenosa 18 +-53.0167007446289 -42.2999992370605 Pontoeciella abyssicola 1976 5 +-58.8170013427734 -54.0830001831055 Limopsis hirtella 1962 2 +-66.4079971313477 -56.1080017089844 Cavineptunea insularis 1963 1 +-65.0749969482422 -54.9580001831055 Limopsis hirtella 1964 3 +7.36700010299683 -35.3330001831055 Acanthephyra pelagica 1971 1 +12.3170003890991 -23.0330009460449 Benthopecten simplex 1968 1 +18.0965003967285 -35.8457984924316 Tenuitella munda 1974 3 +-24.6399993896484 -47.5831985473633 Tenuitella munda 1980 12 +-7.11670017242432 -18.75 Pontoeciella abyssicola 1973 10 +-5.12970018386841 -26.1140003204346 Tenuitella munda 1980 12 +-40.011100769043 -21.1821994781494 Trischizostoma costai 2000 1 +-23.13330078125 -24 Oncaea notopus 1973 5 +12.1000003814697 -22.36669921875 Macrosetella gracilis 1973 15 +-58 -51.5499992370605 Clathria (Microciona) antarctica 1902 1 +-42.2482986450195 -53.6450004577637 Alcyonium paucilobulatum 1986 1 +-9.94169998168945 -40.3333015441895 Clathria (Microciona) antarctica 1904 1 +-56.5 -37.7667007446289 Clathria (Microciona) antarctica 1926 1 +-35.2136611938477 -5.78436899185181 Lutjanus apodus 1975 1 +12.1166667938232 -5.86805582046509 Olindias muelleri 1955 1 +-9.94169998168945 -40.3333015441895 Clathria (Microciona) antarctica 1 +-60.5999984741211 -52.4832992553711 Clathria (Microciona) antarctica 1 +12 -5.93333292007446 Olindias muelleri 1948 1 +-48.4916648864746 -1.45833337306976 Lutjanus apodus 1 +-39.8916664123535 -18.7416667938232 Lutjanus apodus 1 +-43.2311477661133 -23.0780353546143 Lutjanus apodus 1 +-38.4482650756836 -16.4495029449463 Lutjanus apodus 2 +-38.4916648864746 -12.9750003814697 Lutjanus apodus 1 +-37.396671295166 -54.8433303833008 Clathria (Microciona) antarctica 8 +-27.4099998474121 -56.396671295166 Clathria (Microciona) antarctica 2 +-59.5169982910156 -53.0830001831055 Clathria (Microciona) antarctica 4 +-65.1330032348633 -52.5830001831055 Clathria (Microciona) antarctica 2 +-67.4666976928711 -45.86669921875 Halichondria (Halichondria) prostrata 1916 2 +10.9250001907349 -9.07499980926514 Oxyporhamphus similis 1 +-43.2311477661133 -23.0780353546143 Eques acuminatus 2 +-37.6749992370605 0.025000000372529 Oxyporhamphus similis 2 +-24.24169921875 -8.35309982299805 Oxyporhamphus similis 2002 1 +2.76799988746643 -28.9305000305176 Globoturborotalita ouachitaensis 1980 1 +-36.502799987793 -26.2385005950928 Turborotalita humilis 1980 3 +-5.12970018386841 -26.1140003204346 Globoturborotalita ouachitaensis 1980 19 +-35.2851982116699 -30.2763004302979 Trilobatus sacculifer 1980 37 +-38.0411987304688 -30.9468002319336 Trilobatus sacculifer 1980 77 +-35.2853012084961 -30.2765007019043 Trilobatus sacculifer 1980 43 +10.5187997817993 -19.7434997558594 Trilobatus sacculifer 1980 54 +-9.44332981109619 -0.971666991710663 Trilobatus sacculifer 1988 29 +-66 -59 Daption capense 2019 1 +5 0 Ilisha africana 4 +-45.367000579834 -24.2999992370605 Bellator brachychir 2 +12.4499998092651 -5.96700000762939 Ilisha africana 4 +-5 0 Ilisha africana 4 +-43.0830001831055 -23.0170001983643 Bellator brachychir 2 +-14.8966999053955 -3.81500005722046 Trilobatus sacculifer 1957 2 +5.01669979095459 -26.5482997894287 Trilobatus sacculifer 1957 2 +-12.7700004577637 -25.1499996185303 Trilobatus sacculifer 1957 2 +7.11329984664917 -24.49169921875 Trilobatus sacculifer 1957 2 +6.72499990463257 -24.9449996948242 Trilobatus sacculifer 1957 2 +5.91830015182495 -25.1683006286621 Trilobatus sacculifer 1957 2 +5.30670022964478 -24.0333003997803 Trilobatus sacculifer 1957 2 +6.01830005645752 -0.228300005197525 Trilobatus sacculifer 1957 2 +10.5494003295898 -11.7406997680664 Trilobatus sacculifer 1957 2 +-8.65170001983643 -5.84329986572266 Trilobatus sacculifer 1957 2 +7.77860021591187 -21.0767993927002 Trilobatus sacculifer 1957 2 +13.2829999923706 -30.5799999237061 Trilobatus sacculifer 1957 2 +-15.1000003814697 -17.6700000762939 Trilobatus sacculifer 1957 2 +-34.6300010681152 -4.23299980163574 Trilobatus sacculifer 1957 2 +5.53299999237061 -1.28299999237061 Trilobatus sacculifer 1957 2 +-15.8000001907349 -30.3500003814697 Trilobatus sacculifer 1957 2 +-37.9500007629395 -23.9699993133545 Trilobatus sacculifer 1957 2 +-35.2299995422363 -3.54999995231628 Turborotalita humilis 1989 4 +5.49830007553101 -25.0450000762939 Trilobatus sacculifer 1957 2 +-9.45670032501221 -15.6750001907349 Trilobatus sacculifer 1957 2 +-11.7117004394531 -1.19830000400543 Trilobatus sacculifer 1957 2 +-27.75 -32.7867012023926 Trilobatus sacculifer 1957 2 +-28.6650009155273 -31.6667003631592 Trilobatus sacculifer 1957 2 +7.10169982910156 -21.882999420166 Trilobatus sacculifer 1957 2 +9.17770004272461 -20.0993003845215 Trilobatus sacculifer 1957 2 +-34.5666999816895 -29.1331996917725 Trilobatus sacculifer 1957 2 +8.88300037384033 -19.7000007629395 Trilobatus sacculifer 1957 2 +-11.9732999801636 -1.3400000333786 Trilobatus sacculifer 1957 2 +1.48300004005432 -33.0999984741211 Trilobatus sacculifer 1957 2 +-13.1499996185303 -28.3299999237061 Trilobatus sacculifer 1957 2 +8.10000038146973 -36.5 Trilobatus sacculifer 1957 2 +7.01700019836426 -22.9799995422363 Trilobatus sacculifer 1957 2 +-11.4300003051758 -19.3700008392334 Trilobatus sacculifer 1957 2 +-30.0699996948242 -28.5799999237061 Trilobatus sacculifer 1957 2 +6.85500001907349 -24.5983009338379 Trilobatus sacculifer 1957 2 +7.43830013275146 -24.4850006103516 Trilobatus sacculifer 1957 2 +4.68300008773804 -26.2000007629395 Trilobatus sacculifer 1957 2 +-10.6099996566772 -9.14200019836426 Trilobatus sacculifer 1957 2 +-13.2700004577637 -24.9699993133545 Trilobatus sacculifer 1957 2 +-15.1199998855591 -24.0699996948242 Trilobatus sacculifer 1957 2 +10.3166999816895 -6.56669998168945 Trilobatus sacculifer 1957 2 +13.8283004760742 -29 Trilobatus sacculifer 1957 1 +7.41699981689453 -24.2199993133545 Trilobatus sacculifer 1957 2 +-25.5 0.0670000016689301 Trilobatus sacculifer 1957 2 +-35.5299987792969 -31.9799995422363 Trilobatus sacculifer 1957 2 +-49.0299987792969 -33.5699996948242 Trilobatus sacculifer 1957 2 +-26.0333003997803 -0.766700029373169 Trilobatus sacculifer 1957 2 +-40.5800018310547 -24.7000007629395 Trilobatus sacculifer 1957 2 +-23.1499996185303 -0.200000002980232 Turborotalita humilis 1959 2 +-35.2299995422363 -3.54999995231628 Turborotalita humilis 1959 3 +3.08330011367798 -30.1732997894287 Trilobatus sacculifer 1957 2 +-6.18499994277954 -12.8183002471924 Trilobatus sacculifer 1957 2 +-11.1199998855591 -8.27999973297119 Trilobatus sacculifer 1957 2 +-12.4282999038696 -1.66499996185303 Trilobatus sacculifer 1957 2 +-12.5570001602173 -1.75999999046326 Trilobatus sacculifer 1957 2 +15.0170001983643 -32.3300018310547 Trilobatus sacculifer 1957 2 +-36.8199996948242 -31.3500003814697 Trilobatus sacculifer 1957 2 +4.83300018310547 -24.5699996948242 Trilobatus sacculifer 1957 2 +-42.4500007629395 -27.9500007629395 Trilobatus sacculifer 1957 2 +-8.94999980926514 -21.3299999237061 Trilobatus sacculifer 1957 2 +16.4330005645752 -33.3499984741211 Trilobatus sacculifer 1957 2 +-15.5699996948242 -11.6000003814697 Trilobatus sacculifer 1957 2 +-7.66666698455811 -24.4916667938232 Coryphaenoides ariommus 1 +-8.26666736602783 -2.33333301544189 Coryphaenoides ariommus 1 +-37.5213012695312 -11.7525997161865 Macrosetella gracilis 1 +3.25500011444092 -25.0833320617676 Uranoscopus archionema 1 +15.3909502029419 -27.4072170257568 Trochia cingulata 2010 2 +15.3909502029419 -27.4072170257568 Trochia cingulata 2007 3 +15.3909502029419 -27.4072170257568 Trochia cingulata 2006 3 +15.3909502029419 -27.4072170257568 Trochia cingulata 2005 2 +15.3909502029419 -27.4072170257568 Trochia cingulata 2004 2 +15.3909502029419 -27.4072170257568 Trochia cingulata 2012 5 +15.5308504104614 -27.7101497650146 Trochia cingulata 2010 4 +15.5308504104614 -27.7101497650146 Trochia cingulata 2009 4 +15.5308504104614 -27.7101497650146 Trochia cingulata 2011 4 +15.5308504104614 -27.7101497650146 Trochia cingulata 2012 5 +15.6843662261963 -27.9153175354004 Trochia cingulata 2010 5 +15.6843662261963 -27.9153175354004 Trochia cingulata 2009 4 +15.6843662261963 -27.9153175354004 Trochia cingulata 2008 5 +15.6843662261963 -27.9153175354004 Trochia cingulata 2007 3 +15.6843662261963 -27.9153175354004 Trochia cingulata 2011 5 +15.6843662261963 -27.9153175354004 Trochia cingulata 2012 5 +15.6490831375122 -27.8592662811279 Trochia cingulata 2006 2 +15.6490831375122 -27.8592662811279 Trochia cingulata 2005 3 +15.6490831375122 -27.8592662811279 Trochia cingulata 2004 1 +15.6273336410522 -27.8319835662842 Trochia cingulata 2006 3 +15.6273336410522 -27.8319835662842 Trochia cingulata 2005 1 +15.6273336410522 -27.8319835662842 Trochia cingulata 2004 1 +15.6273336410522 -27.8319835662842 Trochia cingulata 2011 5 +15.6273336410522 -27.8319835662842 Trochia cingulata 2012 5 +15.1918830871582 -26.9241504669189 Trochia cingulata 2009 3 +15.1629829406738 -26.9078502655029 Trochia cingulata 2009 1 +15.1607503890991 -26.9089336395264 Trochia cingulata 2009 3 +16.106616973877 -28.3389663696289 Trochia cingulata 2008 5 +15.9834833145142 -28.2313175201416 Trochia cingulata 2008 5 +16.0459175109863 -28.2795162200928 Trochia cingulata 2008 5 +15.849949836731 -28.1270999908447 Trochia cingulata 2008 3 +16.106616973877 -28.3389663696289 Trochia cingulata 2007 5 +15.9834833145142 -28.2313175201416 Trochia cingulata 2007 5 +16.0459175109863 -28.2795162200928 Trochia cingulata 2007 5 +15.849949836731 -28.1270999908447 Trochia cingulata 2007 4 +16.106616973877 -28.3389663696289 Trochia cingulata 2009 5 +15.7654666900635 -28.0471839904785 Trochia cingulata 2009 1 +15.9834833145142 -28.2313175201416 Trochia cingulata 2009 4 +16 -33 Lampanyctus intricarius 1 +-64.4866027832031 -54.8465995788574 Pycnogonum platylophum 1 +-67.2332992553711 -52.5 Pycnogonum platylophum 1 +-64 -54.9165992736816 Pycnogonum platylophum 1 +-64.9499969482422 -53.5332984924316 Pycnogonum platylophum 1 +-52.3166999816895 -34.1833000183105 Liljeborgia dubia 1972 1 +-52.0900001525879 -32.078800201416 Ulva linza 1 +-52.5453987121582 -33.7389984130859 Liljeborgia dubia 1968 1 +-52.3376007080078 -34.3233985900879 Liljeborgia dubia 1968 1 +-50.5666999816895 -33.2832984924316 Liljeborgia dubia 1972 1 +-36.3083343505859 -13.3666667938232 Krohnitta subtilis 2000 1 +-35.4583320617676 -13.3666667938232 Krohnitta subtilis 2000 1 +-36.0666656494141 -18.1666660308838 Krohnitta subtilis 2000 1 +-34.75 -20.4166660308838 Krohnitta subtilis 2000 1 +-38.8055572509766 -16.3666667938232 Krohnitta subtilis 2000 1 +-38.6833343505859 -15.3833332061768 Krohnitta subtilis 2000 1 +-38.2916679382324 -14.375 Krohnitta subtilis 2000 1 +-38.7900009155273 -14.375 Krohnitta subtilis 2000 1 +-38.5833320617676 -21.3833332061768 Krohnitta subtilis 2000 1 +-39.666389465332 -19.9033336639404 Krohnitta subtilis 2000 1 +-37.6666679382324 -18.8833332061768 Krohnitta subtilis 2000 1 +-38.5833320617676 -17.8847217559814 Krohnitta subtilis 2000 1 +-37.2736129760742 -17.8902778625488 Krohnitta subtilis 2000 1 +-37.9333343505859 -17.3833332061768 Krohnitta subtilis 2000 1 +-39.966667175293 -23.0499992370605 Krohnitta subtilis 2000 1 +-37.7000007629395 -19.3972225189209 Krohnitta subtilis 2000 1 +-38.6916656494141 -13.3666667938232 Krohnitta subtilis 2000 1 +-47 -24.3166999816895 Puffinus puffinus 1994 3 +-46.4000015258789 -24.0165996551514 Puffinus puffinus 1995 1 +-45.4166984558105 -23.7999992370605 Puffinus puffinus 1991 1 +-46.3333015441895 -23.9500007629395 Puffinus puffinus 2 +-47.9166984558105 -25 Hippocampus reidi 1 +8.96700000762939 -2.61700010299683 Lophius vaillanti 2 +-36.502799987793 -26.2388000488281 Turborotalita humilis 1980 1 +-46.5999984741211 -52.5900001525879 Rhizosolenia styliformis 2005 1 +-42.7999992370605 -52.9300003051758 Rhizosolenia styliformis 2005 1 +-49 -22 Mussismilia hispida 1963 1 +18.3194446563721 -34.1416664123535 Ulva linza 1 +-46.1533012390137 -23.9018993377686 Ulva linza 1976 2 +-46.2952995300293 -24.023099899292 Ulva linza 1976 8 +-47.0102996826172 -24.3708000183105 Ulva linza 1981 2 +-46.8264007568359 -24.2061004638672 Ulva linza 1967 2 +-47.0102996826172 -24.3708000183105 Ulva linza 1977 4 +-46.2708015441895 -24.0146999359131 Ulva linza 1976 2 +-46.7963981628418 -24.1907997131348 Ulva linza 1979 2 +-46.1366996765137 -23.8652992248535 Ulva linza 1976 2 +-45.0661010742188 -23.4311008453369 Ulva linza 1953 2 +-47.8894004821777 -25.0533008575439 Ulva linza 1965 2 +-46.8264007568359 -24.2061004638672 Ulva linza 1965 2 +-46.190299987793 -23.9652996063232 Ulva linza 1976 2 +-45.5381011962891 -23.8103008270264 Ulva linza 1976 2 +-45.4077987670898 -23.8146991729736 Ulva linza 1976 2 +-45.11669921875 -23.5 Spyridia filamentosa 1961 8 +-45.4077987670898 -23.8146991729736 Spyridia filamentosa 1983 4 +-47.0013999938965 -24.3332996368408 Ulva linza 1976 2 +-47 -24.3483009338379 Ulva linza 1976 2 +-47.9742012023926 -25.2056007385254 Ulva linza 1976 4 +-47.0102996826172 -24.3708000183105 Ulva linza 1951 2 +-46.3092002868652 -23.9953002929688 Ulva linza 1976 2 +-47.9035987854004 -25.0139007568359 Ulva linza 1977 2 +-46.8264007568359 -24.2061004638672 Ulva linza 1977 2 +-44.7896995544434 -23.3700008392334 Ulva linza 1978 2 +-45.1464004516602 -23.4978008270264 Ulva linza 1976 4 +-45.0332984924316 -23.4167003631592 Ulva linza 1977 2 +-46.7963981628418 -24.1907997131348 Ulva linza 1977 2 +-46.3897018432617 -23.9778003692627 Ulva linza 1968 2 +-46.3824996948242 -23.9827995300293 Ulva linza 1968 2 +-45.3507995605469 -23.7733001708984 Spyridia filamentosa 1949 2 +-45.4077987670898 -23.8146991729736 Spyridia filamentosa 1982 2 +-45.11669921875 -23.5 Spyridia filamentosa 1962 4 +-47.0102996826172 -24.3708000183105 Spyridia filamentosa 1966 2 +-45.11669921875 -23.5 Spyridia filamentosa 1964 2 +-65.9197998046875 -47.7510986328125 Thalassomonhystera parva 1975 7 +-65.9329528808594 -47.7507514953613 Sphaerolaimus pacificus 1979 1 +-66.0504531860352 -47.7478981018066 Sphaerolaimus pacificus 1975 1 +-66.1222991943359 -47.8074989318848 Sphaerolaimus pacificus 1976 1 +-36.75 -54.25 Daptonema frigidum 1 +-65.9329528808594 -47.7507514953613 Sphaerolaimus pacificus 1978 1 +-65.9329528808594 -47.7507514953613 Thalassomonhystera parva 1978 1 +-65.9329528808594 -47.7507514953613 Thalassomonhystera parva 1976 2 +18.3999996185303 -33.9000015258789 Ulva linza 2 +-47.0102996826172 -24.3708000183105 Ulva linza 1965 2 +-5.99200010299683 -49.5029983520508 Rhizosolenia styliformis 1992 1 +-50.5999984741211 -31.3999996185303 Urophycis cirrata 4 +-56.1669998168945 -35 Mycteroperca venenosa 2 +-38.4830017089844 -12.9670000076294 Mycteroperca venenosa 6 +5 0 Cyclothone acclinidens 2 +-42.0999984741211 -23.6830005645752 Urophycis cirrata 1987 2 +9.19999980926514 -3.59999990463257 Cyclothone acclinidens 1960 4 +5 0 Chromis cadenati 2 +5.80000019073486 -1.33299994468689 Cyclothone acclinidens 1960 2 +18.4169998168945 -34.3499984741211 Batrichthys apiatus 4 +-44.2943000793457 -23.1149005889893 Hippocampus reidi 2012 2 +-43.0800018310547 -59.8499984741211 Oncaea notopus 1979 10 +-29.3476390838623 -20.5054664611816 Acanthurus bahianus 2009 4 +-29.3472919464111 -20.5064506530762 Acanthurus bahianus 2009 20 +-29.3016204833984 -20.5287609100342 Acanthurus bahianus 2009 10 +-29.3027095794678 -20.5148944854736 Acanthurus bahianus 2009 26 +-29.3282279968262 -20.52467918396 Acanthurus bahianus 2007 15 +-29.3282279968262 -20.52467918396 Diplodus argenteus 2007 5 +-29.3440208435059 -20.492883682251 Acanthurus bahianus 2012 7 +-29.3313522338867 -20.5224227905273 Acanthurus bahianus 2012 8 +-29.3452224731445 -20.5003204345703 Acanthurus bahianus 2012 10 +-29.3379287719727 -20.5156173706055 Acanthurus bahianus 2012 12 +-29.3476390838623 -20.5054664611816 Acanthurus bahianus 2012 14 +-29.3214473724365 -20.4980297088623 Acanthurus bahianus 2012 4 +-29.3392238616943 -20.489200592041 Acanthurus bahianus 2012 18 +-29.3282279968262 -20.52467918396 Acanthurus bahianus 2012 2 +-29.3027095794678 -20.5148944854736 Acanthurus bahianus 2012 8 +-29.3344421386719 -20.4881477355957 Acanthurus bahianus 2012 16 +-29.3124294281006 -20.5065116882324 Acanthurus bahianus 2007 12 +-29.3027095794678 -20.5148944854736 Diplodus argenteus 2007 3 +-29.3027095794678 -20.5148944854736 Acanthurus bahianus 2007 15 +-29.3321285247803 -20.4903926849365 Acanthurus bahianus 2007 6 +-29.3472919464111 -20.5064506530762 Acanthurus bahianus 2007 11 +-29.3472919464111 -20.5064506530762 Diplodus argenteus 2007 3 +-29.3392238616943 -20.489200592041 Acanthurus bahianus 2013 9 +-29.3452224731445 -20.5003204345703 Acanthurus bahianus 2013 9 +-29.3050975799561 -20.5271701812744 Acanthurus bahianus 2013 4 +-29.3440208435059 -20.492883682251 Acanthurus bahianus 2013 10 +-29.3214473724365 -20.4980297088623 Acanthurus bahianus 2013 10 +-29.3282279968262 -20.52467918396 Diplodus argenteus 2013 16 +-29.3282279968262 -20.52467918396 Acanthurus bahianus 2013 7 +-29.3027095794678 -20.5148944854736 Acanthurus bahianus 2013 21 +-29.3392238616943 -20.489200592041 Diplodus argenteus 2009 2 +-29.3392238616943 -20.489200592041 Acanthurus bahianus 2009 11 +-29.3440208435059 -20.492883682251 Acanthurus bahianus 2009 3 +-29.3321285247803 -20.4903926849365 Acanthurus bahianus 2009 18 +-29.3124294281006 -20.5065116882324 Acanthurus bahianus 2012 13 +-29.3452224731445 -20.5003204345703 Diplodus argenteus 2012 3 +-29.3313522338867 -20.5224227905273 Acanthurus bahianus 2013 1 +-29.3379287719727 -20.5156173706055 Acanthurus bahianus 2009 12 +-29.3124294281006 -20.5065116882324 Acanthurus bahianus 2009 21 +-29.3472900390625 -20.5064506530762 Acanthurus bahianus 2009 3 +-29.3440208435059 -20.4928798675537 Acanthurus bahianus 2009 2 +-29.3321304321289 -20.4903907775879 Acanthurus bahianus 2009 3 +-29.3321285247803 -20.4903926849365 Acanthurus bahianus 2011 4 +-29.3282279968262 -20.52467918396 Acanthurus bahianus 2011 4 +-29.3379287719727 -20.5156173706055 Acanthurus bahianus 2011 21 +-29.3392238616943 -20.489200592041 Acanthurus bahianus 2011 4 +-29.3392200469971 -20.489200592041 Acanthurus bahianus 2011 3 +-29.3124294281006 -20.5065116882324 Acanthurus bahianus 2013 8 +-29.3124294281006 -20.5065116882324 Acanthurus bahianus 2014 2 +-29.3313522338867 -20.5224227905273 Diplodus argenteus 2014 3 +-29.3476390838623 -20.5054664611816 Acanthurus bahianus 2014 8 +-29.3050975799561 -20.5271701812744 Acanthurus bahianus 2014 2 +-29.3392238616943 -20.489200592041 Diplodus argenteus 2014 2 +-29.3313522338867 -20.5224227905273 Acanthurus bahianus 2014 5 +-29.3214473724365 -20.4980297088623 Acanthurus bahianus 2014 2 +-29.3440208435059 -20.492883682251 Acanthurus bahianus 2014 7 +-29.3321285247803 -20.4903926849365 Acanthurus bahianus 2014 6 +-29.3392238616943 -20.489200592041 Acanthurus bahianus 2014 8 +-29.3379287719727 -20.5156173706055 Acanthurus bahianus 2014 10 +-29.3282279968262 -20.52467918396 Diplodus argenteus 2009 5 +-29.3282279968262 -20.52467918396 Acanthurus bahianus 2009 17 +-29.3214473724365 -20.4980297088623 Acanthurus bahianus 2009 7 +-29.3214473724365 -20.4980297088623 Diplodus argenteus 2009 1 +-14.3867454528809 -7.89248514175415 Acanthurus bahianus 2015 18 +-14.3323526382446 -7.91145181655884 Acanthurus bahianus 2015 23 +-14.4042339324951 -7.91147518157959 Acanthurus bahianus 2015 31 +-29.3379306793213 -20.5156192779541 Acanthurus bahianus 2015 9 +-29.3282299041748 -20.52467918396 Acanthurus bahianus 2015 16 +-29.3321304321289 -20.4903907775879 Acanthurus bahianus 2015 15 +-29.3124294281006 -20.5065097808838 Acanthurus bahianus 2015 7 +-32.4743194580078 -3.87479209899902 Acanthurus bahianus 2015 1 +-33.8088493347168 -3.87272310256958 Acanthurus bahianus 2015 1 +-29.3392200469971 -20.489200592041 Acanthurus bahianus 2016 5 +-29.3313503265381 -20.5224208831787 Acanthurus bahianus 2016 6 +-29.3392200469971 -20.489200592041 Acanthurus bahianus 2017 10 +-29.3124294281006 -20.5065097808838 Acanthurus bahianus 2017 4 +-29.3313503265381 -20.5224208831787 Acanthurus bahianus 2017 2 +-29.3313503265381 -20.5224208831787 Diplodus argenteus 2017 1 +-29.3027095794678 -20.5148906707764 Acanthurus bahianus 2017 22 +-29.3214492797852 -20.4980297088623 Acanthurus bahianus 2018 16 +-32.4743194580078 -3.87479209899902 Acanthurus bahianus 2017 1 +-29.3392200469971 -20.489200592041 Acanthurus bahianus 2018 10 +-29.3027095794678 -20.5148906707764 Acanthurus bahianus 2018 30 +-29.3124294281006 -20.5065097808838 Acanthurus bahianus 2018 7 +-29.3313503265381 -20.5224208831787 Diplodus argenteus 2018 2 +-33.8166313171387 -3.86771202087402 Acanthurus bahianus 2019 2 +-29.3313503265381 -20.5224208831787 Acanthurus bahianus 2019 6 +-29.3392200469971 -20.489200592041 Acanthurus bahianus 2019 7 +-29.3027095794678 -20.5148906707764 Acanthurus bahianus 2019 16 +-29.3379306793213 -20.5156192779541 Acanthurus bahianus 2019 19 +-29.3124294281006 -20.5065097808838 Acanthurus bahianus 2019 8 +-48.5400009155273 -26.3199996948242 Poecilochaetus australis 2003 1 +-38.3106727600098 -13.194676399231 Conus riosi 2001 1 +-35.3810157775879 -5.27213716506958 Conus riosi 2001 1 +-39.2170104980469 -17.5186634063721 Conus riosi 2000 1 +-42.0143737792969 -22.9140377044678 Fissurella nimbosa 1947 1 +-39.5870018005371 -18.8759994506836 Puncturella pauper 1996 2 +-32.3967552185059 -3.8266339302063 Lottia leucopleura 1999 1 +-39.9230003356934 -20.4020004272461 Conus riosi 2001 1 +-40.1710357666016 -22.6128921508789 Pronucula benguelana 2001 1 +-38.8176956176758 -13.5955266952515 Epitonium nautlae 2003 3 +-38.8120956420898 -13.471287727356 Epitonium nautlae 2007 2 +-44.2694435119629 -23.0542507171631 Puncturella pauper 2008 1 +-44.3092231750488 -23.026554107666 Puncturella pauper 2008 1 +-44.3410263061523 -22.9794445037842 Puncturella pauper 2008 1 +-44.4145545959473 -22.967887878418 Puncturella pauper 2008 1 +-44.4833335876465 -0.133332997560501 Conus riosi 2008 1 +-41.0149955749512 -23.1904945373535 Puncturella pauper 2009 1 +-44.3910026550293 -23.1024627685547 Puncturella pauper 2009 1 +-44.0925903320312 -23.1734046936035 Puncturella pauper 2009 1 +-38.6431922912598 -12.9468669891357 Lottia leucopleura 1876 1 +-39.3727760314941 -19.426944732666 Spirolaxis centrifuga 2003 1 +-39.25 -19.4166660308838 Epitonium nautlae 2003 5 +-39.3672218322754 -19.4424991607666 Epitonium nautlae 2003 3 +-39.3333320617676 -19.4333324432373 Epitonium nautlae 2003 6 +-39.4161109924316 -19.4316654205322 Epitonium nautlae 2003 6 +-39.4833335876465 -19.4166660308838 Epitonium nautlae 2003 1 +-39.3666648864746 -19.4166660308838 Epitonium nautlae 2003 2 +-39.3727760314941 -19.4255542755127 Epitonium nautlae 2003 2 +-39.3763885498047 -19.4341659545898 Epitonium nautlae 2003 4 +-39.375 -19.4333324432373 Epitonium nautlae 2003 1 +-39.6572227478027 -19.3794441223145 Epitonium nautlae 2003 1 +-39.3666648864746 -19.4144439697266 Syntomodrillia portoricana 2003 1 +-39.375 -19.4333324432373 Syntomodrillia portoricana 2003 2 +-39.3711128234863 -19.4261112213135 Epitonium nautlae 2003 2 +-39.4161109924316 -19.4616661071777 Epitonium nautlae 2003 2 +-38.8026962280273 -13.4748754501343 Epitonium nautlae 2006 1 +-38.9186096191406 -13.2667360305786 Epitonium nautlae 2006 1 +-50.1599349975586 -30.7271671295166 Mactra isabelleana 1 +-38.8115425109863 -13.4749193191528 Epitonium nautlae 1 +-40.2819671630859 -20.2995738983154 Chthamalus bisinuatus 1 +-43.0455627441406 -22.9729518890381 Chthamalus bisinuatus 1 +-41.9905776977539 -22.5977897644043 Chthamalus bisinuatus 3 +-43.5046730041504 -23.0407695770264 Chthamalus bisinuatus 1 +-43.3307609558105 -22.9965915679932 Chthamalus bisinuatus 1 +-43.165699005127 -22.9634075164795 Chthamalus bisinuatus 1 +-41.9829406738281 -22.8688430786133 Chthamalus bisinuatus 1985 1 +-42.0091514587402 -22.9692115783691 Chthamalus bisinuatus 1 +-43.2124366760254 -22.8160839080811 Chthamalus bisinuatus 1 +-43.1230354309082 -22.9300689697266 Chthamalus bisinuatus 1 +-39.2076110839844 -17.9517250061035 Chthamalus bisinuatus 2 +-38.984676361084 -14.2900896072388 Chthamalus bisinuatus 1 +-28.8514785766602 -20.4751873016357 Glyptoxanthus vermiculatus 1993 1 +-29.301061630249 -20.5190410614014 Chthamalus bisinuatus 1 +-41.9046096801758 -22.7569541931152 Chthamalus bisinuatus 4 +-42.0253295898438 -22.9583930969238 Caprella scaura 1 +-43.1978988647461 -23.034236907959 Caprella scaura 1 +-45.4231338500977 -23.8291568756104 Chthamalus bisinuatus 1 +-38.890998840332 -7.64266681671143 Dendrocephalus (Dendrocephalus) brasiliensis 1 +-40.0247230529785 -13.0716667175293 Odontodactylus havanensis 1 +-41.75 -19.8047218322754 Amaryllis atlantica 1 +-38.4786491394043 -3.81175303459167 Chthamalus bisinuatus 1 +-58.4219436645508 -29.7000007629395 Amigdoscalpellum rigidum 1 +-41.9829406738281 -22.8688430786133 Caprella scaura 2 +-48.3593597412109 -27.2856540679932 Caprella scaura 1 +13.6569633483887 -11.4917011260986 Lagocephalus lunaris 1986 1 +-41.9046096801758 -22.7569541931152 Caprella scaura 2 +-41.8761215209961 -22.7680606842041 Caprella scaura 2 +-43.194522857666 -23.0676555633545 Caprella scaura 1 +-41.8753471374512 -22.7420654296875 Caprella scaura 1 +-48.7448081970215 -28.4090385437012 Microcheus nortoni 2005 1 +-38.3916664123535 -15.0842504501343 Dendrocephalus (Dendrocephalus) brasiliensis 1 +-52.1512222290039 -32.1867179870605 Microcheus nortoni 2 +-43.1612586975098 -22.7891750335693 Caprella scaura 2005 1 +-2.83200001716614 -4.08400011062622 Hymenopenaeus laevis 1 +-2.58533334732056 -29.4978332519531 Hymenopenaeus laevis 1 +-43.2048873901367 -23.037670135498 Caprella scaura 3 +-43.2048873901367 -23.0680809020996 Caprella scaura 1 +3.86601662635803 -19.1152324676514 Sphaerodoropsis chardyi 2000 1 +11.1750001907349 -19.5617008209229 Neogloboquadrina pachyderma 1992 220 +-14.8966999053955 -36.8333015441895 Neogloboquadrina pachyderma 1988 2 +-33.0149993896484 -36.8333015441895 Neogloboquadrina pachyderma 1988 2 +-45.8816986083984 -36.8333015441895 Neogloboquadrina pachyderma 1988 1 +12.0216999053955 -36.8333015441895 Neogloboquadrina pachyderma 1988 8 +11.1750001907349 -36.8333015441895 Neogloboquadrina pachyderma 1988 6 +-10.1916999816895 -36.8333015441895 Neogloboquadrina pachyderma 1988 1 +-16.6133003234863 -36.8333015441895 Neogloboquadrina pachyderma 1988 4 +-14.8966999053955 -3.81500005722046 Neogloboquadrina pachyderma 1989 130 +-38.3432998657227 -36.8333015441895 Neogloboquadrina pachyderma 1988 1 +-23.4950008392334 -36.8333015441895 Neogloboquadrina pachyderma 1988 2 +-14.7117004394531 -36.8333015441895 Neogloboquadrina pachyderma 1988 3 +-12.0216999053955 -36.8333015441895 Neogloboquadrina pachyderma 1988 2 +-13.9867000579834 -36.8333015441895 Neogloboquadrina pachyderma 1988 2 +-46.4410018920898 -26.6700000762939 Calciodinellum elongatum 1989 1 +-12.3680000305176 -9.76000022888184 Calciodinellum elongatum 1989 2 +-15.6850004196167 -12.9300003051758 Calciodinellum elongatum 1989 2 +-13.9379997253418 -12.3929996490479 Calciodinellum elongatum 1989 1 +-9.72000026702881 -19.125 Calciodinellum elongatum 1989 1 +12.3780002593994 -23.3169994354248 Calciodinellum elongatum 1989 1 +-12.6700000762939 -19.3700008392334 Calciodinellum elongatum 1989 1 +-48.148998260498 -31.757999420166 Calciodinellum elongatum 1989 1 +-38.4949989318848 -16.511999130249 Calciodinellum elongatum 1 +-39.7379989624023 -20.0179996490479 Calciodinellum elongatum 1 +-38.9099998474121 -14.3129997253418 Calciodinellum elongatum 1 +-41.1080017089844 -23.4820003509521 Calciodinellum elongatum 1 +5.42170000076294 -36.8333015441895 Neogloboquadrina pachyderma 1988 1 +6.0366997718811 -36.8333015441895 Neogloboquadrina pachyderma 1988 1 +12.3767004013062 -36.8333015441895 Neogloboquadrina pachyderma 1988 6 +10.3185997009277 -36.8333015441895 Neogloboquadrina pachyderma 1988 1 +12.3767004013062 -23.3150005340576 Neogloboquadrina pachyderma 1992 208 +17.8617000579834 -34.6383018493652 Protoperidinium shanghaiense 1992 1 +17.7549991607666 -34.7966995239258 Protoperidinium shanghaiense 1992 1 +15.5 -31.7849998474121 Protoperidinium shanghaiense 1992 1 +14.3317003250122 -23.8882999420166 Protoperidinium shanghaiense 1992 1 +13.3500003814697 -24.8332996368408 Protoperidinium shanghaiense 1992 1 +12.8717002868652 -24.9950008392334 Protoperidinium shanghaiense 1992 1 +10.6549997329712 -19.6949996948242 Protoperidinium shanghaiense 1992 1 +13.0116996765137 -23.2166996002197 Protoperidinium shanghaiense 1992 1 +8.37666988372803 -0.689999997615814 Eggerella bradyi 1998 7 +13.5417003631592 -23.1366996765137 Protoperidinium shanghaiense 1992 1 +14.4232997894287 -28.2082996368408 Protoperidinium shanghaiense 1992 1 +14.1732997894287 -28.926700592041 Protoperidinium shanghaiense 1992 1 +13.8283004760742 -29 Protoperidinium shanghaiense 1992 1 +8.04170036315918 -29.971700668335 Protoperidinium shanghaiense 1992 1 +12.8050003051758 -23.2532997131348 Protoperidinium shanghaiense 1992 1 +2 -54 Pachyptila desolata 2006 1 +6 -47 Pachyptila desolata 2006 1 +12.7166662216187 -13.2166662216187 Cheilopogon milleri 1937 1 +12.783332824707 -7.26666688919067 Cheilopogon milleri 1948 1 +-59 -52 Aruga falklandica 1 +-61.966667175293 -49 Aruga falklandica 1 +-36.5 -54.2832984924316 Prothaumatelson nasutum 1 +-36.4333000183105 -45.2832984924316 Prothaumatelson nasutum 2 +-48.466667175293 -27.1333332061768 Mycteroperca microlepis 1986 1 +-42 -22.966667175293 Diplodus argenteus 1984 1 +12.0124521255493 -15.4322443008423 Chromis limbata 1989 1 +15.3108997344971 -31.4647006988525 Neogloboquadrina pachyderma 1997 192 +-40.276668548584 -20.3266658782959 Poecilochaetus australis 1 +-48.341667175293 -25.5416660308838 Dipolydora barbilla 1 +-62 -41.9500007629395 Coenophthalmus tridentatus 1 +-41.7299995422363 -22.3999996185303 Eulithidium affine 1993 1 +18.1669998168945 -34.8849983215332 Acanthephyra pelagica 1988 1 +5.13033008575439 -53.1804008483887 Neogloboquadrina pachyderma 1998 39 +5.8655800819397 -49.9765014648438 Neogloboquadrina pachyderma 1998 86 +7.0789999961853 -46.4109992980957 Neogloboquadrina pachyderma 1998 19 +9.89398002624512 -40.9362983703613 Neogloboquadrina pachyderma 1997 63 +5.91907978057861 -47.094799041748 Neogloboquadrina pachyderma 1998 80 +8.89982032775879 -42.9136009216309 Neogloboquadrina pachyderma 1997 296 +-46.8202781677246 -41.5999984741211 Lampanyctus intricarius 1970 2 +-11.7391996383667 -1.39020001888275 Neogloboquadrina pachyderma 1986 2 +-11.7391996383667 -1.39020001888275 Globorotalia (Truncorotalia) crassula 1986 6 +1.57700002193451 -27.1858005523682 Cibicidoides hyphalus 2003 22 +1.57700002193451 -27.1858005523682 Pullenia jarvisi 2003 24 +9.89410018920898 -40.9364013671875 Neogloboquadrina pachyderma 1997 35 +11.2179002761841 -20.8946990966797 Neogloboquadrina pachyderma 1997 22 +13.309100151062 -11.9295997619629 Neogloboquadrina pachyderma 1997 6 +10.4365997314453 -5.17994022369385 Neogloboquadrina pachyderma 1997 28 +13.4003000259399 -11.9204998016357 Neogloboquadrina pachyderma 1997 2 +13.4003000259399 -11.9208002090454 Neogloboquadrina pachyderma 1997 6 +11.1014995574951 -5.06883001327515 Neogloboquadrina pachyderma 1997 7 +10.0749998092651 -4.78530979156494 Neogloboquadrina pachyderma 1997 18 +10.0749998092651 -4.78530979156494 Eggerella bradyi 1997 2 +13.5628004074097 -41.136100769043 Eggerella bradyi 1997 1 +13.5628004074097 -41.136100769043 Globorotalia (Truncorotalia) crassula 1997 14 +-46.8264007568359 -24.2061004638672 Jania rubens 1966 2 +-45.4091987609863 -23.7591991424561 Acanthophora spicifera 1985 2 +11.1014995574951 -5.06883001327515 Eggerella bradyi 1997 2 +9.89369964599609 -40.9364013671875 Neogloboquadrina pachyderma 1997 11 +13.0277996063232 -25.5139007568359 Neogloboquadrina pachyderma 1997 9 +10.8199996948242 -16.559700012207 Neogloboquadrina pachyderma 1997 1 +-26.4832992553711 -4.31669998168945 Oncaea notopus 1977 20 +15.3667001724243 -28.6000003814697 Holohalaelurus regani 1986 4 +14.5166997909546 -28.1667003631592 Holohalaelurus regani 1981 4 +6.11670017242432 -25.4832992553711 Sternoptyx diaphana 1982 1 +14.3500003814697 -27.63330078125 Bathothauma lyromma 1989 1 +13.75 -23.1000003814697 Coelorinchus parallelus 1984 5 +-14.2283000946045 -44.1533012390137 Neogloboquadrina pachyderma 1994 5 +15.5166997909546 -31.74169921875 Neogloboquadrina pachyderma 1996 296 +19.4750003814697 -36.2667007446289 Neogloboquadrina pachyderma 1996 124 +-50.1666717529297 -30.0833301544189 Microcheus nortoni 2003 2 +-40.1725006103516 -22.7513885498047 Sternoptyx diaphana 2003 1 +-47.1666679382324 -25.2999992370605 Umbrina canosai 1 +-41.75 -21.6666660308838 Anchoviella lepidentostole 1990 1 +-41.75 -21.6666660308838 Anchoviella lepidentostole 1989 4 +-43.783332824707 -1.53333330154419 Acanthurus bahianus 1 +-29.3333339691162 -20.5 Scartella poiti 1999 3 +-38.587776184082 -15.8083333969116 Hymenocephalus billsam 2000 1 +-38.6216659545898 -15.7113885879517 Hymenocephalus billsam 2000 1 +-40.1883316040039 -21.3991661071777 Hymenocephalus billsam 2000 1 +-38.4127769470215 -13.1374998092651 Hymenocephalus billsam 2000 1 +-38.6258316040039 -13.3972225189209 Hymenocephalus billsam 2000 1 +-38.6497230529785 -13.5077781677246 Hymenocephalus billsam 2000 1 +-38.6497230529785 -13.5077781677246 Sternoptyx diaphana 2000 1 +-39.6441650390625 -19.9822216033936 Sternoptyx diaphana 2000 1 +-39.6716651916504 -21.4769439697266 Sternoptyx diaphana 2000 1 +-40.0191650390625 -22.6613883972168 Sternoptyx diaphana 2003 1 +-40.1197204589844 -22.6977787017822 Sternoptyx diaphana 2003 1 +-39.9213905334473 -22.4074993133545 Sternoptyx diaphana 2003 1 +-25.0167007446289 -9.64999961853027 Oncaea notopus 1964 5 +12.1599998474121 -6.07000017166138 Scyllarus caparti 1948 1 +8.66666698455811 -0.883333027362823 Scyllarus caparti 1949 1 +11.8500003814697 -5.16666698455811 Scyllarus caparti 1949 1 +-36.5194396972656 -4.69582986831665 Chrysogorgia multiflora 2011 1 +-48.4916648864746 -1.45833337306976 Chelon dumerili 1 +-39.8250007629395 -19.625 Eleotris pisonis 1 +-42.3916664123535 -21.1416664123535 Chelon dumerili 1 +18.4974994659424 -35 Tethya magna 1 +16.1916675567627 -34.4416656494141 Gigantactis elsmani 1 +-43.2311477661133 -23.0780353546143 Mycteroperca acutirostris 3 +-16.2083339691162 -36.0416679382324 Vinciguerria poweriae 1 +-16.125 -36.0250015258789 Vinciguerria poweriae 1 +-2.54166674613953 -35.841667175293 Vinciguerria poweriae 1 +-2.49166655540466 -35.841667175293 Vinciguerria poweriae 1 +-2.34166669845581 -35.8250007629395 Vinciguerria poweriae 1 +2.79166674613953 -28.9916667938232 Vinciguerria poweriae 1 +2.70833325386047 -28.9416675567627 Vinciguerria poweriae 1 +-0.308333337306976 -27.5583324432373 Vinciguerria poweriae 2 +-3.45833325386047 -26.0083332061768 Vinciguerria poweriae 1 +-8.82499980926514 -23.8916664123535 Vinciguerria poweriae 2 +-7.09166669845581 -20.0750007629395 Vinciguerria poweriae 2 +-6.99166679382324 -20.0416660308838 Vinciguerria poweriae 1 +-4.00833320617676 -18.5583324432373 Vinciguerria poweriae 1 +-3.92499995231628 -18.5249996185303 Vinciguerria poweriae 1 +2.04166674613953 -15.9916667938232 Vinciguerria poweriae 1 +11.0416669845581 -11.3083333969116 Vinciguerria poweriae 1 +11.6750001907349 -7.65833330154419 Vinciguerria poweriae 1 +8.10833358764648 -2.90833330154419 Vinciguerria poweriae 1 +-38.3083343505859 -27.8250007629395 Vinciguerria poweriae 1 +-48.6416664123535 -35.0083351135254 Vinciguerria poweriae 2 +-42.8250007629395 -34.1416664123535 Vinciguerria poweriae 1 +-39.1749992370605 -33.2249984741211 Vinciguerria poweriae 2 +-39.0750007629395 -33.2583351135254 Vinciguerria poweriae 2 +-26.9416675567627 -33.0083351135254 Vinciguerria poweriae 1 +-26.4416675567627 -32.8250007629395 Vinciguerria poweriae 1 +-22.7583332061768 -34.0250015258789 Vinciguerria poweriae 1 +4.65833330154419 -35.5083351135254 Vinciguerria poweriae 1 +10.5500001907349 -3.34999990463257 Carcharhinus limbatus 1960 1 +-30.5974998474121 -28.5849990844727 Racemiguembelina fructicosa 1969 5 +2.98530006408691 -29.0706996917725 Globoconella miozea 1980 24 +3.13800001144409 -30.1226997375488 Globoconella miozea 1980 12 +3.13800001144409 -30.1226997375488 Globoturborotalita brazieri 1980 9 +-5.12970018386841 -26.1140003204346 Cassidulina havanensis 1980 8 +-38.1352996826172 -29.9736995697021 Globoconella miozea 1980 10 +-46.9716987609863 -51.0046997070312 Alisocysta ornata 1980 3 +11.1014995574951 -5.06883001327515 Epistominella exigua 1997 3 +11.1014995574951 -5.06883001327515 Bulimina striata 1997 20 +13.5628004074097 -41.136100769043 Hansenisca soldanii 1997 1 +13.5628004074097 -41.136100769043 Epistominella exigua 1997 1 +19.4178009033203 -34.6828002929688 Sarpa salpa 1987 1 +19.0333003997803 -34.3499984741211 Sarpa salpa 1992 3 +-39.837329864502 -20.3926696777344 Chlorophthalmus agassizi 1998 1 +-15.5333003997803 -8 Farranula carinata 1963 5 +-15.0500001907349 -0.033330000936985 Farranula carinata 1963 10 +2.21700000762939 -43.8330001831055 Acanthoclinus littoreus 2 +5 0 Chelon dumerili 2 +5.61700010299683 -1.39999997615814 Thalassoma pavo 2 +-42.0180015563965 -23.6110000610352 Chlorophthalmus agassizi 1987 2 +-62 -54 Leptonotus blainvilleanus 2 +-34.88330078125 -15.6332998275757 Farranula carinata 1963 5 +-47.3499984741211 -26.75 Lensia campanella 2006 1 +-47.7667007446289 -26.75 Lensia campanella 2006 1 +-46.3333015441895 -23.9500007629395 Pachyptila belcheri 1954 12 +-47.86669921875 -24.8332996368408 Pachyptila belcheri 1996 10 +-47.4000015258789 -24.6667003631592 Pachyptila belcheri 1905 1 +-47.4000015258789 -24.6667003631592 Pachyptila belcheri 1903 1 +-39.966667175293 -23.0499992370605 Dinophysis schuettii 2000 1 +-34.9333000183105 -6.81669998168945 Menticirrhus littoralis 1 +-34.8166999816895 -7.56669998168945 Menticirrhus littoralis 1 +-38.7000007629395 -12.6999998092651 Menticirrhus littoralis 1 +-43.86669921875 -22.8999996185303 Menticirrhus littoralis 1 +-52.6666984558105 -33 Menticirrhus littoralis 1 +-48.4000015258789 -25.5 Menticirrhus littoralis 1 +-44 -23.1667003631592 Menticirrhus littoralis 1 +-44 -23.1667003631592 Guavina guavina 1 +-44.4333000183105 -2.70000004768372 Guavina guavina 1 +-47.9166984558105 -25 Guavina guavina 1 +-26.4832992553711 -17 Neocalanus gracilis 1977 100 +-26.4832992553711 -4.31669998168945 Neocalanus gracilis 1977 79 +-47.2169990539551 -28.6679992675781 Lensia campanella 1975 1 +-47.9519996643066 -28.7169990539551 Lensia campanella 1976 2 +-46.2509994506836 -29.0330009460449 Amphicaryon peltifera 1976 2 +-41.4669990539551 -24.5170001983643 Lensia campanella 1975 1 +-46.701000213623 -25.2180004119873 Lensia campanella 1976 2 +-46.5509986877441 -26.4009990692139 Lensia campanella 1976 2 +12.867790222168 -8.62074661254883 Holthuispenaeopsis atlantica 1985 1 +12.0091066360474 -5.17248106002808 Holthuispenaeopsis atlantica 1985 1 +-43.0499992370605 -46 Neocalanus gracilis 1976 5 +-43.5499992370605 -37.0666999816895 Neocalanus gracilis 1976 193 +-7.11670017242432 -18.75 Neocalanus gracilis 1973 148 +-25 -9.91670036315918 Neocalanus gracilis 1977 43 +-26.5 -10.6833000183105 Neocalanus gracilis 1977 123 +-23.13330078125 -24 Neocalanus gracilis 1973 23 +-27.9666996002197 -10.0500001907349 Neocalanus gracilis 1977 30 +-22.9500007629395 -22.9666996002197 Neocalanus gracilis 1973 120 +-5 0 Caranx senegallus 2 +-40.0670013427734 -21.5830001831055 Acromycter perturbator 1987 2 +-37.7830009460449 -19 Acromycter perturbator 1987 2 +13.1788463592529 -8.71385478973389 Holthuispenaeopsis atlantica 1986 1 +13.1446533203125 -8.78051090240479 Holthuispenaeopsis atlantica 1986 1 +13.0262422561646 -7.69196510314941 Holthuispenaeopsis atlantica 1986 1 +-58 -42 Brachyscelus crusculum 1 +13.1504745483398 -8.27957248687744 Holthuispenaeopsis atlantica 1986 1 +12.1000003814697 -22.36669921875 Neocalanus gracilis 1973 30 +-33.5999984741211 -29.1667003631592 Neocalanus gracilis 1913 1 +-31.75 -18 Neocalanus gracilis 1913 1 +-27.25 -5 Neocalanus gracilis 1913 1 +-15 -6 Neocalanus gracilis 1963 10 +12.0280952453613 -5.14313268661499 Holthuispenaeopsis atlantica 1989 1 +11.5176000595093 -5.45276212692261 Stereomastis sculpta 1989 1 +-41.61669921875 -22.3799991607666 Aequipecten tehuelchus 1993 1 +-41.6916999816895 -22.4750003814697 Aequipecten tehuelchus 1993 1 +12.3073310852051 -7.47025012969971 Stereomastis sculpta 1991 1 +-29.5 -10 Neocalanus gracilis 1977 20 +-20.0499992370605 -0.983330011367798 Neocalanus gracilis 1963 35 +-37.5900001525879 -53.5999984741211 Hormathia lacunifera 1 +-15.0500001907349 -3.04999995231628 Neocalanus gracilis 1963 15 +12.8064384460449 -8.46071434020996 Stereomastis sculpta 1991 1 +-37.4000015258789 -54.8400001525879 Hormathia lacunifera 1 +-35.7900009155273 -55.1199989318848 Hormathia lacunifera 1 +-37.9099998474121 -53.6399993896484 Hormathia lacunifera 1 +-26.7299995422363 -57 Hormathia lacunifera 1 +-37.5099983215332 -53.7200012207031 Hormathia lacunifera 1 +-26.9300003051758 -59.4300003051758 Hormathia lacunifera 1 +-26.5 -1.9666999578476 Neocalanus gracilis 1977 10 +-26.5167007446289 0 Neocalanus gracilis 1977 40 +-38.7999992370605 -13.5 Apiactis tentaculata 2 +-38.0299987792969 -53.6300010681152 Hormathia lacunifera 1 +13.1750001907349 -9.64999961853027 Holthuispenaeopsis atlantica 1991 1 +13.6074991226196 -10.491063117981 Holthuispenaeopsis atlantica 1991 1 +13.5081415176392 -11.9069633483887 Holthuispenaeopsis atlantica 1991 1 +11.3712129592896 -5.5218653678894 Stereomastis sculpta 1992 1 +11.5615472793579 -5.49333333969116 Stereomastis sculpta 1992 1 +11.4311075210571 -5.43854761123657 Stereomastis sculpta 1992 1 +11.4976148605347 -5.42106294631958 Stereomastis sculpta 1992 1 +-45.1699981689453 -28.6700000762939 Isurus oxyrinchus 1 +-47.0699996948242 -30.2000007629395 Isurus oxyrinchus 5 +11.9924392700195 -5.63914775848389 Holthuispenaeopsis atlantica 1992 1 +11.4188823699951 -6.13600778579712 Stereomastis sculpta 1992 1 +11.6068668365479 -6.52809572219849 Stereomastis sculpta 1992 1 +11.8958311080933 -7.16644859313965 Stereomastis sculpta 1992 1 +11.9393424987793 -7.19333362579346 Stereomastis sculpta 1992 1 +13.1596422195435 -7.98850917816162 Holthuispenaeopsis atlantica 1992 2 +13.1064376831055 -10.6107149124146 Stereomastis sculpta 1992 1 +13.2569522857666 -12.4754667282104 Stereomastis sculpta 1992 1 +13.6878938674927 -10.7374706268311 Holthuispenaeopsis atlantica 1994 1 +11.4543962478638 -5.53571844100952 Stereomastis sculpta 1995 1 +11.3625974655151 -5.62848567962646 Stereomastis sculpta 1995 1 +11.2515077590942 -5.73927211761475 Stereomastis sculpta 1995 1 +11.3072967529297 -5.81214332580566 Stereomastis sculpta 1995 1 +11.3799982070923 -5.81643390655518 Stereomastis sculpta 1995 1 +11.4268808364868 -5.80809593200684 Stereomastis sculpta 1995 1 +11.3935241699219 -6.16142225265503 Stereomastis sculpta 1995 1 +11.3134346008301 -6.18916416168213 Stereomastis sculpta 1995 1 +-64.2661361694336 -42.623779296875 Diaulula punctuolata 2019 2 +11.6761064529419 -6.73834276199341 Stereomastis sculpta 1995 1 +11.6276979446411 -6.90379810333252 Stereomastis sculpta 1995 1 +12.0995512008667 -7.55657005310059 Stereomastis sculpta 1995 1 +12.6050310134888 -7.91229295730591 Stereomastis sculpta 1995 1 +12.6043615341187 -8.02336883544922 Stereomastis sculpta 1995 1 +12.7626266479492 -8.42395496368408 Stereomastis sculpta 1995 1 +13.2311067581177 -12.4552145004272 Stereomastis sculpta 1995 1 +13.3534412384033 -11.9884099960327 Stereomastis sculpta 1995 1 +13.3177738189697 -11.4059724807739 Stereomastis sculpta 1995 1 +12.6808490753174 -9.63821697235107 Stereomastis sculpta 1995 1 +12.5902853012085 -9.47546672821045 Stereomastis sculpta 1995 1 +12.5901832580566 -9.36857509613037 Stereomastis sculpta 1995 1 +13.0051069259644 -7.67904806137085 Holthuispenaeopsis atlantica 1996 1 +13.1091928482056 -7.89228677749634 Holthuispenaeopsis atlantica 1997 2 +12.9240751266479 -7.52275276184082 Holthuispenaeopsis atlantica 1997 2 +-64.2602233886719 -42.6240577697754 Diaulula punctuolata 2019 1 +1.82799994945526 -32.8345489501953 Parapagurus pilosimanus 2009 1 +3.12979984283447 -3.32964992523193 Parapagurus pilosimanus 2009 1 +12.9948806762695 -7.64333343505859 Holthuispenaeopsis atlantica 2001 1 +12.1508483886719 -6.08786296844482 Holthuispenaeopsis atlantica 2000 1 +14.3500003814697 -26.4167003631592 Neocalanus gracilis 1973 10 +-43.8499984741211 -42 Neocalanus gracilis 1976 15 +18.6616992950439 -34.2508010864258 Sarpa salpa 1991 18 +-43.2000007629395 -22.9099998474121 Bodo designis 1 +14.5166997909546 -22.6833000183105 Parablennius cornutus 1955 1 +15.2166996002197 -30.6933002471924 Alepocephalus australis 1988 1 +16.7833003997803 -33.38330078125 Alepocephalus australis 1995 2 +14.4666996002197 -22.9167003631592 Parablennius cornutus 1965 2 +16.1499996185303 -32.36669921875 Alepocephalus australis 1995 2 +15.9366998672485 -31.9967002868652 Alepocephalus australis 1986 1 +14.5166997909546 -22.6833000183105 Parablennius cornutus 1991 1 +18.8332996368408 -34.11669921875 Sarpa salpa 1998 10 +18.5256004333496 -34.0666999816895 Sarpa salpa 1997 2 +17.9821186065674 -33.1428756713867 Gibbula multicolor 2008 2 +18.497200012207 -34.3568992614746 Sarpa salpa 2005 3 +19.4571647644043 -34.5848121643066 Gibbula multicolor 2007 11 +18.6382999420166 -34.073299407959 Sarpa salpa 1989 18 +17.9821186065674 -33.1428756713867 Gigartina bracteata 2008 1 +18.86669921875 -34.1582984924316 Sarpa salpa 1993 41 +-38.3740005493164 -19.4780006408691 Xenophora conchyliophora 1996 1 +-39.0999984741211 -18.882999420166 Arcopsis adamsi 1996 1 +-38.8600006103516 -15.5699996948242 Xenophora conchyliophora 1997 1 +-38.0670013427734 -18.5669994354248 Xenophora conchyliophora 1997 1 +-38.6870002746582 -16.7870006561279 Xenophora conchyliophora 1997 2 +-38.3030014038086 -16.4160003662109 Xenophora conchyliophora 1996 1 +-40.451000213623 -21.1830005645752 Acteocina bullata 1996 1 +-39.5270004272461 -19.7600002288818 Acanthogorgia aspera 2001 1 +-38.8300018310547 -15.5679998397827 Ameritella sybaritica 2001 1 +-38.6870002746582 -16.7870006561279 Alpheus amblyonyx 1997 1 +-38.8600006103516 -15.5699996948242 Arcopsis adamsi 1997 1 +-35.8600006103516 -20.6040000915527 Xenophora conchyliophora 1997 1 +-37.4749984741211 -20.5 Xenophora conchyliophora 1996 1 +-54.5499992370605 -37.5830001831055 Loxechinus albus 1 +-68.2480010986328 -54.8050003051758 Loxechinus albus 2 +-67.6833343505859 -54.9166679382324 Loxechinus albus 2 +-64.8600006103516 -54.7299995422363 Loxechinus albus 2 +13.7648334503174 -11.7994165420532 Holthuispenaeopsis atlantica 2010 1 +11.3748083114624 -17.2236309051514 Holthuispenaeopsis atlantica 2006 2 +9.5001392364502 -3.91680574417114 Isurus oxyrinchus 2 +18.5256004333496 -34.0666999816895 Sarpa salpa 1998 7 +18.8477993011475 -34.2332992553711 Sarpa salpa 1992 1 +17.8918991088867 -33.0311012268066 Sarpa salpa 1987 15 +19.4986000061035 -34.6688995361328 Sarpa salpa 1994 2 +18.2313003540039 -31.8155002593994 Nemadactylus vemae 1985 18 +-55.259880065918 -40.9513816833496 Thalassiosira eccentrica 1995 1 +19.3521995544434 -34.5661010742188 Sarpa salpa 1990 4 +18.2313003540039 -31.8155002593994 Nemadactylus vemae 1991 40 +18.8206005096436 -34.2977981567383 Sarpa salpa 1997 5 +-37.9116706848145 -53.6366691589355 Hormathia lacunifera 1975 1 +-63.8666687011719 -54.7316703796387 Loxechinus albus 1971 1 +-36.5433311462402 -53.4449996948242 Hormathia lacunifera 1975 1 +-64.6330032348633 -54.7669982910156 Loxechinus albus 1969 1 +18.0965003967285 -35.8457984924316 Ciperoella angulisuturalis 1974 1 +10.5325002670288 -19.7574996948242 Ciperoella angulisuturalis 1975 4 +-30.5974998474121 -28.5849990844727 Muricohedbergella rosebudensis 1969 20 +-40.8451995849609 -49.869499206543 Zeauvigerina parri 1980 1 +-11.6661996841431 -26.1366996765137 Globorotalia pertenuis 1980 5 +-26.5167007446289 0 Calocalanus plumulosus 1977 10 +19.3549995422363 -34.6164016723633 Sarpa salpa 1998 3 +18.0238990783691 -33.1086006164551 Sarpa salpa 1987 16 +18.7493991851807 -34.0772018432617 Sarpa salpa 1997 3 +18.6616992950439 -34.2508010864258 Sarpa salpa 1987 23 +18.0238990783691 -33.1086006164551 Sarpa salpa 1993 8 +19.4178009033203 -34.6828002929688 Sarpa salpa 1995 8 +-66.3170013427734 -56.0999984741211 Axinella antarctica 1963 1 +-34.4000015258789 -8.05000019073486 Hebefustis vafer 1967 2 +18.2999992370605 -32.0833015441895 Parablennius cornutus 1963 1 +20 -34.8333015441895 Scombrops boops 1977 1 +17.9666996002197 -33 Parablennius cornutus 1927 1 +18.38330078125 -33.9000015258789 Parablennius cornutus 1965 1 +14.4833002090454 -22.88330078125 Parablennius cornutus 1 +0.758300006389618 -32.4500007629395 Melamphaes hubbsi 1990 1 +19.0167007446289 -34.3333015441895 Scombrops boops 1968 1 +17.7595996856689 -34.4067001342773 Alepocephalus australis 1903 1 +13.4167003631592 -20.8500003814697 Parablennius cornutus 1968 1 +18.5832996368408 -34.0833015441895 Sarpa salpa 1963 1 +17.7998008728027 -34.4035987854004 Alepocephalus australis 1903 1 +10.0683002471924 -33.2066993713379 Melamphaes hubbsi 1990 1 +18.4167003631592 -34.1666984558105 Sarpa salpa 1958 1 +18.5499992370605 -34.11669921875 Scombrops boops 1981 1 +14.75 -38.2332992553711 Alepocephalus australis 2000 1 +18.5832996368408 -34.0833015441895 Parablennius cornutus 1964 2 +13.25 -20.2999992370605 Parablennius cornutus 1969 1 +12.6999998092651 -19.38330078125 Parablennius cornutus 1 +18.4333000183105 -34.11669921875 Scombrops boops 1933 1 +18.8500003814697 -34.1500015258789 Scombrops boops 1961 1 +17.7236995697021 -34.440601348877 Alepocephalus australis 1903 2 +18.5832996368408 -34.0833015441895 Parablennius cornutus 1965 1 +17.7516994476318 -34.49169921875 Alepocephalus australis 1993 1 +16.147216796875 -28.6962337493896 Micronephthys sphaerocirrata 2 +16.1472663879395 -28.6960163116455 Micronephthys sphaerocirrata 2 +15.7782335281372 -28.3459491729736 Micronephthys sphaerocirrata 2 +15.7577171325684 -28.3303508758545 Micronephthys sphaerocirrata 2 +15.7577333450317 -28.3304176330566 Micronephthys sphaerocirrata 2 +15.7574834823608 -28.3303165435791 Micronephthys sphaerocirrata 2 +16.1347007751465 -28.7702674865723 Micronephthys sphaerocirrata 2 +15.9987335205078 -28.5503158569336 Micronephthys sphaerocirrata 2 +15.2297496795654 -27.2296504974365 Micronephthys sphaerocirrata 2 +15.2296333312988 -27.2294826507568 Micronephthys sphaerocirrata 1 +18.7493991851807 -34.0772018432617 Sarpa salpa 1988 1 +18.8477993011475 -34.2332992553711 Sarpa salpa 1990 6 +19.2999992370605 -34.63330078125 Sarpa salpa 1994 10 +18.86669921875 -34.1582984924316 Sarpa salpa 1994 16 +18.0238990783691 -33.1086006164551 Sarpa salpa 1988 19 +18.4731006622314 -34.2153015136719 Sarpa salpa 2005 1 +18.8278007507324 -34.3866996765137 Sarpa salpa 1993 3 +18.4731006622314 -34.2153015136719 Sarpa salpa 1998 3 +18.8194007873535 -34.1944007873535 Sarpa salpa 1989 9 +15.1625003814697 -26.6306991577148 Nemadactylus vemae 1990 7 +18.8206005096436 -34.2977981567383 Sarpa salpa 1996 9 +18.851900100708 -34.1455993652344 Sarpa salpa 1995 1 +19.3549995422363 -34.6164016723633 Sarpa salpa 1997 1 +18.8285999298096 -34.3424987792969 Sarpa salpa 1990 3 +18.6730995178223 -34.073299407959 Sarpa salpa 1994 2 +19.4178009033203 -34.6828002929688 Sarpa salpa 1989 1 +19.3253002166748 -34.6094017028809 Sarpa salpa 1986 1 +18.8278007507324 -34.3866996765137 Sarpa salpa 1992 3 +18.5832996368408 -34.1380996704102 Sarpa salpa 1985 1 +19.4178009033203 -34.6828002929688 Sarpa salpa 1990 3 +19.2266998291016 -34.4331016540527 Sarpa salpa 1992 1 +18.7835998535156 -34.0867004394531 Sarpa salpa 1986 11 +18.6177997589111 -34.0755996704102 Sarpa salpa 1993 4 +19.3253002166748 -34.6094017028809 Sarpa salpa 1995 1 +18.8332996368408 -34.11669921875 Sarpa salpa 2000 2 +18.6382999420166 -34.073299407959 Sarpa salpa 1998 3 +18.86669921875 -34.1582984924316 Sarpa salpa 1995 8 +18.8332996368408 -34.11669921875 Sarpa salpa 1986 5 +18.7835998535156 -34.0867004394531 Sarpa salpa 1987 3 +18.6382999420166 -34.073299407959 Sarpa salpa 1990 12 +18.497200012207 -34.3568992614746 Sarpa salpa 2002 2 +18.4610996246338 -34.3182983398438 Sarpa salpa 1994 1 +19.3246994018555 -34.6244010925293 Sarpa salpa 1986 6 +19.3253002166748 -34.6094017028809 Sarpa salpa 1994 2 +18.5832996368408 -34.1380996704102 Sarpa salpa 1986 2 +19.3549995422363 -34.6164016723633 Sarpa salpa 2002 3 +18.0238990783691 -33.1086006164551 Sarpa salpa 1989 4 +-44.8470001220703 -24.6790008544922 Parviturbo weberi 1998 1 +-45.193000793457 -24.7759990692139 Amphiura complanata 1998 1 +-46.3610000610352 -25.4160003662109 Amphiura complanata 1998 1 +18.8332996368408 -34.38330078125 Scombrops boops 1959 1 +19.4447002410889 -34.6478004455566 Sarpa salpa 1995 8 +19.6835994720459 -34.7663993835449 Sarpa salpa 1992 1 +18.8332996368408 -34.11669921875 Sarpa salpa 1989 10 +18.6616992950439 -34.2508010864258 Sarpa salpa 1989 21 +18.5832996368408 -34.1380996704102 Sarpa salpa 2004 4 +18.6177997589111 -34.0755996704102 Sarpa salpa 1985 2 +18.5256004333496 -34.0666999816895 Sarpa salpa 1987 6 +19.3549995422363 -34.6164016723633 Sarpa salpa 1995 5 +18.5256004333496 -34.0666999816895 Sarpa salpa 1986 6 +18.86669921875 -34.1582984924316 Sarpa salpa 1989 2 +19.3397006988525 -34.584400177002 Sarpa salpa 1996 12 +19.2724990844727 -34.4082984924316 Sarpa salpa 1985 1 +18.7964000701904 -34.0928001403809 Sarpa salpa 1988 1 +18.86669921875 -34.1582984924316 Sarpa salpa 1988 11 +18.1499996185303 -32.7667007446289 Sarpa salpa 1988 1 +19.2999992370605 -34.63330078125 Sarpa salpa 1987 1 +18.8206005096436 -34.2977981567383 Sarpa salpa 1998 1 +18.6730995178223 -34.073299407959 Sarpa salpa 1991 4 +19.3932991027832 -34.6039009094238 Sarpa salpa 1996 3 +19.3932991027832 -34.6039009094238 Sarpa salpa 1994 3 +19.2999992370605 -34.63330078125 Sarpa salpa 1986 12 +18.0965003967285 -35.8457984924316 Globoturborotalita euapertura 1974 12 +17.8918991088867 -33.0311012268066 Sarpa salpa 1988 9 +19.3549995422363 -34.6164016723633 Sarpa salpa 1996 1 +19.4986000061035 -34.6688995361328 Sarpa salpa 1989 1 +19.3549995422363 -34.6164016723633 Sarpa salpa 1986 7 +-69 -54 Cythereis (Procythereis) iganderssoni 1896 1 +19.4447002410889 -34.6478004455566 Sarpa salpa 1991 1 +18.4680995941162 -34.2641983032227 Sarpa salpa 1992 8 +18.8194007873535 -34.1944007873535 Sarpa salpa 1995 1 +-52.1759986877441 -32.2060012817383 Subeucalanus pileatus 2011 8 +-52.0499992370605 -32.0167007446289 Menticirrhus littoralis 1979 1 +-60.7993011474609 0 Pentapharsodinium dalei 1 +-64.07080078125 0 Pentapharsodinium dalei 1 +-21.0832996368408 0 Pentapharsodinium dalei 1 +-61.9724998474121 0 Pentapharsodinium dalei 1 +19.3549995422363 -34.6164016723633 Sarpa salpa 1991 2 +19.2999992370605 -34.63330078125 Sarpa salpa 1996 4 +18.448600769043 -34.127799987793 Sarpa salpa 1985 1 +18.8477993011475 -34.2332992553711 Sarpa salpa 1993 6 +-52.7000007629395 -54.75 Racovitzanus antarcticus 1 +-53.9500007629395 -54.2000007629395 Racovitzanus antarcticus 1 +-55.1166648864746 -53.2333335876465 Racovitzanus antarcticus 2 +-56.1833343505859 -52.6833343505859 Racovitzanus antarcticus 1 +-58.75 -48.3333320617676 Racovitzanus antarcticus 1 +-54.3833351135254 -42.8666648864746 Racovitzanus antarcticus 1 +-51.9166679382324 -55.3833351135254 Racovitzanus antarcticus 1 +18.5256004333496 -34.0666999816895 Sarpa salpa 1988 3 +18.7493991851807 -34.0772018432617 Sarpa salpa 1990 4 +19.2999992370605 -34.63330078125 Sarpa salpa 1993 10 +18.8332996368408 -34.11669921875 Sarpa salpa 1991 8 +16.4799995422363 -32.2400016784668 Cranchia scabra 2005 1 +8.33380031585693 0 Pentapharsodinium dalei 1 +-69.1492004394531 0 Pentapharsodinium dalei 1 +-58.9054985046387 0 Pentapharsodinium dalei 1 +-55.5842018127441 0 Pentapharsodinium dalei 1 +-52.8671989440918 0 Pentapharsodinium dalei 1 +-52.1296997070312 0 Pentapharsodinium dalei 1 +-36.5133285522461 -54.0933303833008 Hormathia lacunifera 1975 1 +9.74330043792725 0 Pentapharsodinium dalei 1 +19.3397006988525 -34.584400177002 Sarpa salpa 1998 4 +16.2667007446289 -32.4000015258789 Cranchia scabra 2000 1 +7.48829984664917 0 Pentapharsodinium dalei 1 +15.8332996368408 0 Pentapharsodinium dalei 1 +-55.6188011169434 0 Pentapharsodinium dalei 1 +17.5832996368408 -30.9500007629395 Cranchia scabra 1988 1 +17.2900009155273 -34.1599998474121 Alepocephalus australis 1995 1 +17.4832992553711 -34.2667007446289 Alepocephalus australis 1995 1 +16.1800003051758 -31.2700004577637 Cranchia scabra 2002 1 +19.2600002288818 -35.310001373291 Scombrops boops 1995 1 +16.1499996185303 -31.3899993896484 Scombrops boops 2003 1 +18.4300003051758 -35.1599998474121 Scombrops boops 2006 1 +-17.8449993133545 0 Pentapharsodinium dalei 1 +-18.2082996368408 0 Pentapharsodinium dalei 1 +-0.811699986457825 0 Pentapharsodinium dalei 1 +-5.6121997833252 0 Pentapharsodinium dalei 1 +-9.18330001831055 0 Pentapharsodinium dalei 1 +-10.4750003814697 0 Pentapharsodinium dalei 1 +-38.8333015441895 0 Pentapharsodinium dalei 1 +2.73099994659424 0 Pentapharsodinium dalei 1 +-1.6713000535965 0 Pentapharsodinium dalei 1 +-22.8500003814697 0 Pentapharsodinium dalei 1 +-14.5375003814697 0 Pentapharsodinium dalei 1 +-3.64170002937317 0 Pentapharsodinium dalei 1 +1.48500001430511 0 Pentapharsodinium dalei 1 +-16.8449993133545 0 Pentapharsodinium dalei 1 +-18.0200004577637 0 Pentapharsodinium dalei 1 +-8.83329963684082 0 Pentapharsodinium dalei 1 +-61.7566986083984 0 Pentapharsodinium dalei 1 +-9.53330039978027 0 Pentapharsodinium dalei 1 +3.03500008583069 0 Pentapharsodinium dalei 1 +-17.74169921875 0 Pentapharsodinium dalei 1 +-62.7891998291016 0 Pentapharsodinium dalei 1 +-16.0767002105713 0 Pentapharsodinium dalei 1 +-12.7283000946045 0 Pentapharsodinium dalei 1 +-30.3577003479004 0 Pentapharsodinium dalei 1 +-8.32670021057129 0 Pentapharsodinium dalei 1 +-40.2182998657227 0 Pentapharsodinium dalei 1 +-48.3600006103516 0 Pentapharsodinium dalei 1 +-57.1199989318848 0 Pentapharsodinium dalei 1 +-43.9667015075684 0 Pentapharsodinium dalei 1 +-47.0849990844727 0 Pentapharsodinium dalei 1 +-52.1759986877441 -32.2060012817383 Subeucalanus pileatus 2012 4 +-52.1759986877441 -32.2060012817383 Subeucalanus pileatus 2010 8 +-52.1059989929199 -32.0250015258789 Subeucalanus pileatus 2010 1 +-52.1059989929199 -32.0250015258789 Subeucalanus pileatus 2012 3 +-52.0999984741211 -32.1479988098145 Subeucalanus pileatus 2011 4 +-52.1059989929199 -32.0250015258789 Subeucalanus pileatus 2011 2 +-52.0999984741211 -32.1479988098145 Subeucalanus pileatus 2013 1 +-19.3316993713379 0 Pentapharsodinium dalei 1 +-57.5918006896973 0 Pentapharsodinium dalei 1 +1.00170004367828 0 Pentapharsodinium dalei 1 +2.44670009613037 0 Pentapharsodinium dalei 1 +11.1382999420166 0 Pentapharsodinium dalei 1 +0.824999988079071 0 Pentapharsodinium dalei 1 +-5.57829999923706 0 Pentapharsodinium dalei 1 +-0.394699990749359 0 Pentapharsodinium dalei 1 +-68.2419967651367 0 Pentapharsodinium dalei 1 +-64 0 Pentapharsodinium dalei 1 +-67.7012023925781 0 Pentapharsodinium dalei 1 +-30.9167003631592 -10.0333003997803 Neocalanus gracilis 1977 10 +-38.2999992370605 -2.2666699886322 Isurus oxyrinchus 2 +-33.7167015075684 -4.08333015441895 Isurus oxyrinchus 1 +18.1000003814697 -34.6500015258789 Isurus oxyrinchus 1991 1 +18.0867004394531 -34.3050003051758 Isurus oxyrinchus 1991 1 +17.8166999816895 -34.3499984741211 Isurus oxyrinchus 1 +-33.7167015075684 -2.2666699886322 Isurus oxyrinchus 1 +-26.4832992553711 -4.31669998168945 Calocalanus plumulosus 1977 5 +18.5256004333496 -34.0666999816895 Sarpa salpa 2005 1 +-15.5333003997803 -8 Neocalanus gracilis 1963 5 +-61.9087982177734 0 Pentapharsodinium dalei 1 +-69.5770034790039 0 Pentapharsodinium dalei 1 +-69.2667007446289 0 Pentapharsodinium dalei 1 +-69.3477020263672 0 Pentapharsodinium dalei 1 +-69.4417037963867 0 Pentapharsodinium dalei 1 +-35.8396987915039 -58.9990005493164 Reophax spiculifer 1989 1 +-59.2000007629395 -51.4000015258789 Rossella inermis 2 +-57.7700004577637 -54.0999984741211 Myxilla (Myxilla) nodaspera 2 +-60.3199996948242 -45.2799987792969 Xenanthura bacescui 1 +-66.0333023071289 -47.7667007446289 Phalacrocorax gaimardi 1 +-66.1500015258789 -47.0833015441895 Phalacrocorax gaimardi 1 +-58.8821983337402 -51.1244010925293 Miliolinella circularis 1926 1 +-38.2167015075684 -53.80419921875 Miliolinella circularis 1926 1 +9.44169998168945 -33.8958015441895 Cavolinia gibbosa 1926 1 +-62.0999984741211 -50.4500007629395 Rossella inermis 1927 2 +-62.61669921875 -50.0833015441895 Rossella inermis 1927 2 +-36.4137001037598 -54.2042007446289 Heterolepa praecincta 1926 1 +-36.4416999816895 -54.0167007446289 Heterolepa praecincta 1927 1 +-57.5829010009766 -51.5814018249512 Fissurina toddae 1926 1 +-57.5800018310547 -51.6323013305664 Fissurina toddae 1927 1 +-62.0382995605469 -51 Fissurina toddae 1927 1 +-63.625 -50.9500007629395 Fissurina toddae 1927 1 +-60.1329002380371 -52.4715995788574 Fissurina toddae 1927 1 +-65.0167007446289 -51.9749984741211 Fissurina toddae 1927 1 +-63.625 -50.9500007629395 Nonion sloanii 1927 1 +-60.1329002380371 -52.4715995788574 Nonion sloanii 1927 1 +-59.24169921875 -49.7000007629395 Nonion sloanii 1927 1 +-36.4682006835938 -54.2666015625 Ammothea clausi 1926 1 +-36.4137001037598 -54.2042007446289 Miliolinella circularis 1926 1 +-37.13330078125 -53.9749984741211 Miliolinella circularis 1926 1 +-36.3652992248535 -53.939998626709 Fissurina toddae 1926 1 +-36.507999420166 -54.2270011901855 Fissurina toddae 1926 1 +-37.13330078125 -53.9749984741211 Fissurina toddae 1926 1 +-36.4416999816895 -54.0167007446289 Fissurina toddae 1927 1 +-37.9620018005371 -54.0400009155273 Fissurina toddae 1926 1 +-38.2167015075684 -53.80419921875 Fissurina toddae 1926 1 +-35.4000015258789 -54.9832992553711 Fissurina toddae 1926 1 +11.3166999816895 -5.90000009536743 Stylephorus chordatus 1927 1 +-32.5499992370605 -55.88330078125 Miliolinella circularis 1930 1 +-36.4267997741699 -54.2233009338379 Aplidium fuegiense 1926 1 +-57.8078002929688 -51.6935997009277 Styela paessleri 1926 1 +-61.6666984558105 -54.0666999816895 Aplidium fuegiense 1931 1 +-61.5 -51.875 Aplidium fuegiense 1927 1 +-64.9625015258789 -50.0125007629395 Aplidium fuegiense 1927 1 +-58.5 -52.1666984558105 Aplidium fuegiense 1928 1 +-61.9667015075684 -44.63330078125 Aplidium fuegiense 1931 1 +-67.2707977294922 -51.5875015258789 Aplidium fuegiense 1932 1 +-47.9403991699219 -25.0132999420166 Onychocorycaeus giesbrechti 1 +-47.9403991699219 -25.0132999420166 Murrayella periclados 1 +-46.7832984924316 -24.1833000183105 Feldmannia mitchelliae 1969 1 +-35.2773017883301 -5.39669990539551 Onychocorycaeus giesbrechti 2000 1 +-34.7717018127441 -8.52999973297119 Euthynnus alletteratus 2006 1 +-44.3389015197754 -2.67810010910034 Protoperidinium pyriforme 2002 1 +-35.1036987304688 -8.76609992980957 Microlaimus formosus 1991 1 +-34.8820991516113 -7.769700050354 Streblospio benedicti 2005 12 +-34.8820991516113 -7.769700050354 Streblospio benedicti 2006 2 +-34.9990997314453 -8.51119995117188 Cinetorhynchus rigens 1 +-34.9355010986328 -8.23980045318604 Surirella angustata 1 +-34.9985008239746 -8.50790023803711 Cinetorhynchus rigens 2 +-34.9980010986328 -8.50619983673096 Cinetorhynchus rigens 1 +-34.9986000061035 -8.50619983673096 Cinetorhynchus rigens 1 +-68.6532974243164 -54.9166984558105 Cervonema papillatum 1 +17.7166996002197 -34.6500015258789 Ceratoscopelus warmingii 2001 1 +-42.0262985229492 -22.9559001922607 Feldmannia mitchelliae 1981 2 +-41.8773994445801 -22.7747001647949 Feldmannia mitchelliae 1981 2 +-41.9581985473633 -22.7341003417969 Murrayella periclados 1981 2 +-42.0144996643066 -22.9785995483398 Feldmannia mitchelliae 1981 2 +-41.8773994445801 -22.7747001647949 Levringia brasiliensis 1981 4 +-41.8776016235352 -22.7674999237061 Levringia brasiliensis 1981 2 +-41.9776000976562 -22.8647003173828 Levringia brasiliensis 1979 3 +-41.8693008422852 -22.743200302124 Levringia brasiliensis 1982 1 +-42.0015983581543 -22.8899002075195 Levringia brasiliensis 1981 5 +-41.9581985473633 -22.7341003417969 Levringia brasiliensis 1981 2 +-42.035400390625 -22.9792003631592 Levringia brasiliensis 1982 3 +-42.0144996643066 -22.9785995483398 Levringia brasiliensis 1981 1 +-66.2666702270508 -45.1166648864746 Libidoclaea granaria 2005 10 +-66.25 -45.1166648864746 Libidoclaea granaria 2005 19 +-66.2333297729492 -45.1166648864746 Libidoclaea granaria 2005 34 +-66.1833343505859 -45.1333351135254 Libidoclaea granaria 2005 6 +-66.0166702270508 -45.1666679382324 Libidoclaea granaria 2005 6 +-65.7666702270508 -46.4000015258789 Libidoclaea granaria 2003 1 +-65.7333297729492 -46.3833351135254 Libidoclaea granaria 2003 3 +-66.0666656494141 -46.7000007629395 Libidoclaea granaria 2003 1 +-66.0999984741211 -46.6500015258789 Libidoclaea granaria 2003 2 +-64.8499984741211 -45.6166648864746 Libidoclaea granaria 2003 1 +-64.9000015258789 -45.5666656494141 Libidoclaea granaria 2003 1 +-64.9166641235352 -45.6166648864746 Libidoclaea granaria 2003 1 +-64.9333343505859 -45.6666679382324 Libidoclaea granaria 2003 1 +-65.3833312988281 -45.7000007629395 Libidoclaea granaria 2004 2 +-65.5833358764648 -45.716667175293 Libidoclaea granaria 2004 1 +-66.7333297729492 -45.6166648864746 Libidoclaea granaria 2004 1 +-65.7333297729492 -45.6333351135254 Libidoclaea granaria 2004 2 +-65.75 -45.6166648864746 Libidoclaea granaria 2004 1 +-64.8333358764648 -45.283332824707 Libidoclaea granaria 2003 1 +-64.8333358764648 -45.3833351135254 Libidoclaea granaria 2003 1 +-64.7333297729492 -45.3833351135254 Libidoclaea granaria 2003 1 +-64.9166641235352 -45.4000015258789 Libidoclaea granaria 2003 1 +-64.8499984741211 -45.4000015258789 Libidoclaea granaria 2003 1 +-64.533332824707 -45.9333343505859 Libidoclaea granaria 2003 1 +-64.533332824707 -46 Libidoclaea granaria 2003 1 +-66.25 -46.533332824707 Libidoclaea granaria 2003 1 +-66.1666641235352 -46.533332824707 Libidoclaea granaria 2003 1 +-65.5833358764648 -46.1166648864746 Libidoclaea granaria 2003 4 +-65.5999984741211 -46.1166648864746 Libidoclaea granaria 2003 1 +-65.9333343505859 -46.6833343505859 Libidoclaea granaria 2004 1 +-66.1333312988281 -46.466667175293 Libidoclaea granaria 2003 1 +-65.9833297729492 -46.4500007629395 Libidoclaea granaria 2003 1 +-64.5999984741211 -46.2333335876465 Libidoclaea granaria 2003 1 +-64.6166687011719 -46.25 Libidoclaea granaria 2003 1 +-64.6500015258789 -46.3333320617676 Libidoclaea granaria 2003 1 +-64.6666641235352 -46.2999992370605 Libidoclaea granaria 2003 1 +-65 -46.6166648864746 Libidoclaea granaria 2003 1 +-65.216667175293 -46.283332824707 Libidoclaea granaria 2003 2 +-65.1999969482422 -46.283332824707 Libidoclaea granaria 2003 2 +-65.25 -46.2333335876465 Libidoclaea granaria 2003 1 +-66.1166687011719 -45.5 Libidoclaea granaria 2004 2 +-65.25 -45.2999992370605 Libidoclaea granaria 2003 2 +-65.2333297729492 -45.6833343505859 Libidoclaea granaria 2003 2 +-65.1333312988281 -45.716667175293 Libidoclaea granaria 2003 1 +-64.7333297729492 -46.3833351135254 Libidoclaea granaria 2003 1 +-64.6833343505859 -46.3166656494141 Libidoclaea granaria 2003 1 +-64.6999969482422 -46.3166656494141 Libidoclaea granaria 2003 1 +-65.9833297729492 -46.6333351135254 Libidoclaea granaria 2003 3 +-66.1166687011719 -46.75 Libidoclaea granaria 2003 1 +-66.1166687011719 -46.783332824707 Libidoclaea granaria 2003 3 +-64.5999984741211 -45.5999984741211 Libidoclaea granaria 2003 2 +-64.5833358764648 -45.5833320617676 Libidoclaea granaria 2003 2 +-65.8833312988281 -45.3666648864746 Libidoclaea granaria 2003 1 +-65.9499969482422 -45.4000015258789 Libidoclaea granaria 2003 1 +-65.033332824707 -45.4000015258789 Libidoclaea granaria 2003 1 +-66.1166687011719 -46.7666664123535 Libidoclaea granaria 2003 1 +-66.1166687011719 -46.716667175293 Libidoclaea granaria 2003 2 +-65.533332824707 -46.0499992370605 Libidoclaea granaria 2003 4 +-64.8333358764648 -45.6500015258789 Libidoclaea granaria 2003 1 +-65.6166687011719 -45.2333335876465 Libidoclaea granaria 2004 1 +-65.6333312988281 -45.6166648864746 Libidoclaea granaria 2004 1 +-65.0166702270508 -45.0499992370605 Libidoclaea granaria 2003 1 +-66.0166702270508 -45.7333335876465 Libidoclaea granaria 2004 1 +-65.6666641235352 -45.1666679382324 Libidoclaea granaria 2004 1 +-66.033332824707 -46.7333335876465 Libidoclaea granaria 2003 2 +-66 -46.6666679382324 Libidoclaea granaria 2003 3 +-65.783332824707 -46.3833351135254 Libidoclaea granaria 2003 1 +-64.4000015258789 -45.5833320617676 Libidoclaea granaria 2003 1 +-65 -45.75 Libidoclaea granaria 2003 2 +-68.6532974243164 -54.9166984558105 Paramonohystera geraerti 1 +-65.6833343505859 -46.3166656494141 Libidoclaea granaria 2003 3 +-66.0833358764648 -45.2666664123535 Libidoclaea granaria 2004 1 +-66.1333312988281 -45.3499984741211 Libidoclaea granaria 2004 1 +-66.0999984741211 -45.3333320617676 Libidoclaea granaria 2004 3 +-66.1500015258789 -45.5499992370605 Libidoclaea granaria 2004 2 +-66.7666702270508 -45.7666664123535 Libidoclaea granaria 2004 1 +-64.9166641235352 -45.7999992370605 Libidoclaea granaria 2003 1 +-64.8000030517578 -45.783332824707 Libidoclaea granaria 2003 2 +-64.8000030517578 -45.8333320617676 Libidoclaea granaria 2003 1 +-66.25 -45.0999984741211 Libidoclaea granaria 2005 26 +-66.8833312988281 -45.25 Libidoclaea granaria 2005 2 +-66.2666702270508 -45.1666679382324 Libidoclaea granaria 2005 2 +-66.2333297729492 -45.0666656494141 Libidoclaea granaria 2005 20 +-66.1333312988281 -45.1500015258789 Libidoclaea granaria 2005 4 +18.4719009399414 -34.3580017089844 Gyropora africana 1 +-57 -51.75 Rossella inermis 1927 1 +-63.9500007629395 -41.0666999816895 Suberites tortuosus 2004 1 +-56.63330078125 -51.9667015075684 Caryophyllia (Caryophyllia) squiresi 1963 1 +15.9366998672485 -31.9967002868652 Lycodes terraenovae 1986 1 +-47.8065986633301 -25.1315994262695 Acartia (Acartiura) longiremis 1958 2 +-44.9165992736816 -25.1499996185303 Acartia (Acartiura) longiremis 1954 1 +-57.5332984924316 -54.4166984558105 Myxilla (Myxilla) nodaspera 1903 1 +-66.1333312988281 -45.5166664123535 Libidoclaea granaria 2004 2 +-66.1500015258789 -45.4500007629395 Libidoclaea granaria 2004 4 +-38.6166648864746 -13 Dactyloscopus crossotus 1 +14.1999998092651 -29.0666999816895 Lycodes terraenovae 1987 1 +-65.9833297729492 -45.7000007629395 Libidoclaea granaria 2004 2 +-65.9499969482422 -45.6500015258789 Libidoclaea granaria 2004 2 +-65.8333358764648 -45.0999984741211 Libidoclaea granaria 2003 2 +-65.8833312988281 -45.0999984741211 Libidoclaea granaria 2003 6 +-65.8166656494141 -45.0999984741211 Libidoclaea granaria 2003 5 +-66.8333358764648 -46.5499992370605 Libidoclaea granaria 2004 2 +-66.1666641235352 -45.4000015258789 Libidoclaea granaria 2004 1 +-66.2333297729492 -45.466667175293 Libidoclaea granaria 2004 3 +-65.7666702270508 -45.0666656494141 Libidoclaea granaria 2003 4 +-65.7666702270508 -45.0499992370605 Libidoclaea granaria 2003 4 +-65.8333358764648 -45.0833320617676 Libidoclaea granaria 2003 5 +-65.6833343505859 -45.0833320617676 Libidoclaea granaria 2003 8 +-66.2333297729492 -45.5 Libidoclaea granaria 2004 4 +-66.1333312988281 -45.533332824707 Libidoclaea granaria 2004 7 +-66.0500030517578 -45.6500015258789 Libidoclaea granaria 2004 11 +-65.9166641235352 -45.0833320617676 Libidoclaea granaria 2003 1 +-65.5 -46.0833320617676 Libidoclaea granaria 2003 1 +-65.533332824707 -46.0166664123535 Libidoclaea granaria 2003 5 +-66.0166702270508 -46.6666679382324 Libidoclaea granaria 2004 1 +-66.0500030517578 -46.716667175293 Libidoclaea granaria 2004 2 +-65.8833312988281 -46.6500015258789 Libidoclaea granaria 2004 1 +16.4018669128418 -29.062650680542 Aorcho delgadus 2003 1 +16.4216327667236 -28.9349994659424 Pterygosquilla capensis 2010 4 +16.3898162841797 -28.9242324829102 Pterygosquilla capensis 2010 6 +16.4321842193604 -28.9399337768555 Pterygosquilla capensis 2011 7 +16.3898162841797 -28.9242324829102 Pterygosquilla capensis 2011 4 +16.4216327667236 -28.9349994659424 Pterygosquilla capensis 2011 5 +-65.9333343505859 -45.5666656494141 Libidoclaea granaria 2004 1 +-66.0833358764648 -46.7666664123535 Libidoclaea granaria 2004 1 +11.8999996185303 -4.93333339691162 Monolene mertensae 8 +-66.0833358764648 -46.6333351135254 Libidoclaea granaria 2004 1 +-65.9833297729492 -46.5999984741211 Libidoclaea granaria 2004 1 +-65.8499984741211 -45.1166648864746 Libidoclaea granaria 2003 1 +-7.16669988632202 -19.75 Onychocorycaeus giesbrechti 1973 5 +8.96666622161865 -1.03333330154419 Monolene mertensae 8 +9.21666622161865 -1.93333327770233 Monolene mertensae 7 +-66.3166656494141 -46.5 Libidoclaea granaria 2004 1 +11.6166667938232 -4.53333330154419 Monolene mertensae 8 +11.8999996185303 -4.93333339691162 Cynoglossus senegalensis 11 +11.6166667938232 -4.53333330154419 Sardinella maderensis 11 +11.0500001907349 -3.96666669845581 Monolene mertensae 7 +10.6166667938232 -3.45000004768372 Monolene mertensae 7 +8.68333339691162 -0.684722244739532 Monolene mertensae 6 +8.68333339691162 -0.684722244739532 Sardinella maderensis 9 +10.6166667938232 -3.45000004768372 Sardinella maderensis 9 +12.0833330154419 -5.80000019073486 Sardinella maderensis 11 +-66.1999969482422 -46.5833320617676 Libidoclaea granaria 2004 3 +12.0833330154419 -5.80000019073486 Cynoglossus senegalensis 11 +9.21666622161865 -1.93333327770233 Cynoglossus senegalensis 10 +-66.1999969482422 -46.7000007629395 Libidoclaea granaria 2004 4 +-66.1333312988281 -46.7333335876465 Libidoclaea granaria 2004 2 +10.1999998092651 -3.03333330154419 Monolene mertensae 7 +9.68333339691162 -2.53333330154419 Sardinella maderensis 8 +11.0500001907349 -3.96666669845581 Sardinella maderensis 10 +10.1999998092651 -3.03333330154419 Sardinella maderensis 8 +-65.7666702270508 -46.0499992370605 Libidoclaea granaria 2004 1 +11.8999996185303 -4.93333339691162 Sardinella maderensis 11 +9.23333358764648 -0.233333334326744 Sardinella maderensis 10 +-65.716667175293 -46.033332824707 Libidoclaea granaria 2004 1 +9.21666622161865 -1.93333327770233 Sardinella maderensis 10 +-65.7666702270508 -46.033332824707 Libidoclaea granaria 2004 1 +-66.4166641235352 -45.6666679382324 Libidoclaea granaria 2004 4 +12.0833330154419 -5.80000019073486 Monolene mertensae 8 +10.1999998092651 -3.03333330154419 Cynoglossus senegalensis 8 +-66.3833312988281 -45.5999984741211 Libidoclaea granaria 2004 4 +9.23333358764648 -0.233333334326744 Cynoglossus senegalensis 10 +9.68333339691162 -2.53333330154419 Monolene mertensae 6 +-66.3666687011719 -45.6500015258789 Libidoclaea granaria 2004 3 +10.6166667938232 -3.45000004768372 Cynoglossus senegalensis 9 +-66 -45.6166648864746 Libidoclaea granaria 2004 7 +-65.966667175293 -45.6500015258789 Libidoclaea granaria 2004 4 +-65.9499969482422 -45.716667175293 Libidoclaea granaria 2004 3 +-65.966667175293 -46.5999984741211 Libidoclaea granaria 2003 2 +-65.966667175293 -46.6166648864746 Libidoclaea granaria 2003 4 +-65.966667175293 -46.533332824707 Libidoclaea granaria 2003 2 +-65.9833297729492 -46.533332824707 Libidoclaea granaria 2004 1 +-65.6833343505859 -46.033332824707 Libidoclaea granaria 2004 1 +-65.6999969482422 -46.0499992370605 Libidoclaea granaria 2004 1 +14.3500003814697 -26.4167003631592 Calocalanus longisetosus 1973 5 +-65.783332824707 -46.0666656494141 Libidoclaea granaria 2004 2 +-65.8000030517578 -46.1333351135254 Libidoclaea granaria 2004 1 +-22.9500007629395 -22.9666996002197 Onychocorycaeus giesbrechti 1973 5 +-66.1333312988281 -45.6833343505859 Libidoclaea granaria 2004 2 +-66.1166687011719 -45.6333351135254 Libidoclaea granaria 2004 5 +-66.0999984741211 -45.533332824707 Libidoclaea granaria 2004 3 +-66.1666641235352 -45.5166664123535 Libidoclaea granaria 2004 2 +-65.6999969482422 -45.7000007629395 Libidoclaea granaria 2004 1 +-66.0833358764648 -45.283332824707 Libidoclaea granaria 2004 2 +-66.0500030517578 -46.5166664123535 Libidoclaea granaria 2004 2 +-66.0833358764648 -46.5833320617676 Libidoclaea granaria 2004 2 +-66.0166702270508 -46.5833320617676 Libidoclaea granaria 2004 1 +-65.9499969482422 -46.5166664123535 Libidoclaea granaria 2004 2 +-65.966667175293 -46.4500007629395 Libidoclaea granaria 2004 3 +-65.9000015258789 -46.1666679382324 Libidoclaea granaria 2004 1 +-66.3499984741211 -45.6333351135254 Libidoclaea granaria 2004 5 +-66.4333343505859 -45.6333351135254 Libidoclaea granaria 2004 1 +-66.4166641235352 -45.5999984741211 Libidoclaea granaria 2004 1 +-66 -45.7000007629395 Libidoclaea granaria 2004 2 +-66.0166702270508 -45.6666679382324 Libidoclaea granaria 2004 3 +-65.9833297729492 -45.5999984741211 Libidoclaea granaria 2004 3 +-26.5 -10.6833000183105 Calocalanus longisetosus 1977 5 +-65.9166641235352 -46.5833320617676 Libidoclaea granaria 2004 1 +-65.4499969482422 -45.6500015258789 Libidoclaea granaria 2004 1 +-65.4166641235352 -45.6500015258789 Libidoclaea granaria 2004 1 +-65.4000015258789 -45.6666679382324 Libidoclaea granaria 2004 3 +-65.4166641235352 -45.6333351135254 Libidoclaea granaria 2004 2 +-64.1999969482422 -53.2667007446289 Balanophyllia (Balanophyllia) ukrainensis 1932 1 +-60.0999984741211 -52.5 Balanophyllia (Balanophyllia) ukrainensis 1927 1 +-61 -52.4166984558105 Balanophyllia (Balanophyllia) ukrainensis 1927 1 +-61.2778015136719 -51.8389015197754 Balanophyllia (Balanophyllia) ukrainensis 1927 1 +-38.000301361084 -53.9166984558105 Kirkpatrickia variolosa 1927 1 +-64.8333358764648 -46.5166664123535 Libidoclaea granaria 2004 2 +-54.5843391418457 -2.04553198814392 Anchoviella guianensis 2014 1 +-54.5833892822266 -2.43224096298218 Anchoviella guianensis 2014 1 +-64.8333358764648 -46.5 Libidoclaea granaria 2004 6 +-64.8333358764648 -46.533332824707 Libidoclaea granaria 2004 3 +-66.0999984741211 -45.5833320617676 Libidoclaea granaria 2004 10 +-66.0500030517578 -45.5833320617676 Libidoclaea granaria 2004 11 +-66.0666656494141 -45.5666656494141 Libidoclaea granaria 2004 6 +-66.8666687011719 -45.2666664123535 Libidoclaea granaria 2005 4 +-66.9166641235352 -45.283332824707 Libidoclaea granaria 2005 8 +-65.6999969482422 -46.2666664123535 Libidoclaea granaria 2003 2 +-65.716667175293 -46.216667175293 Libidoclaea granaria 2003 4 +-65.6999969482422 -46.1833343505859 Libidoclaea granaria 2003 2 +-65.6999969482422 -46.216667175293 Libidoclaea granaria 2003 2 +-65.8666687011719 -45.0666656494141 Libidoclaea granaria 2005 3 +-65.9000015258789 -45.0833320617676 Libidoclaea granaria 2005 12 +-35.8766708374023 -54.9300003051758 Kirkpatrickia variolosa 2 +-57.5332984924316 -54.4166984558105 Myxilla (Myxilla) nodaspera 1 +-29.9833335876465 -16.1166667938232 Ceratoscopelus warmingii 1967 1 +-53.5168685913086 -34.0339012145996 Anchoviella guianensis 1973 1 +-54.7462272644043 -2.4036660194397 Euthynnus alletteratus 2 +-62.0999984741211 -50.4500007629395 Rossella inermis 1 +-45.4832992553711 -24.1000003814697 Trachytedania biraphidora 1 +-33.5750007629395 -6.85833311080933 Ceratoscopelus warmingii 2 +-36.5583343505859 -26.5083332061768 Ceratoscopelus warmingii 2 +-34.591667175293 -1.22500002384186 Stylephorus chordatus 1 +-35.2083320617676 -32.9916648864746 Ceratoscopelus warmingii 1 +-35.1416664123535 -32.9916648864746 Ceratoscopelus warmingii 1 +-30.591667175293 -32.9749984741211 Ceratoscopelus warmingii 1 +-30.4416675567627 -32.9583320617676 Ceratoscopelus warmingii 1 +-30.3916664123535 -32.9416656494141 Ceratoscopelus warmingii 1 +-18.5416660308838 -35.2750015258789 Ceratoscopelus warmingii 1 +-4.00833320617676 -18.5583324432373 Ceratoscopelus warmingii 1 +-0.925000011920929 -17.3083324432373 Ceratoscopelus warmingii 1 +2.00833344459534 -16.0249996185303 Ceratoscopelus warmingii 1 +2.04166674613953 -15.9916667938232 Ceratoscopelus warmingii 1 +3.125 -15.4250001907349 Ceratoscopelus warmingii 1 +5.77500009536743 -14.1750001907349 Ceratoscopelus warmingii 1 +8.625 -5.50833320617676 Ceratoscopelus warmingii 1 +5.20833349227905 0.025000000372529 Ceratoscopelus warmingii 1 +-0.308333337306976 -27.5583324432373 Ceratoscopelus warmingii 1 +-3.45833325386047 -26.0083332061768 Ceratoscopelus warmingii 1 +-8.72500038146973 -23.9416675567627 Ceratoscopelus warmingii 1 +-8.82499980926514 -23.8916664123535 Ceratoscopelus warmingii 1 +-7.09166669845581 -20.0750007629395 Ceratoscopelus warmingii 1 +-34.591667175293 -1.22500002384186 Ceratoscopelus warmingii 1 +-31.0750007629395 -12.3583335876465 Ceratoscopelus warmingii 1 +-3.92499995231628 -18.5249996185303 Ceratoscopelus warmingii 1 +-2.54166674613953 -35.841667175293 Ceratoscopelus warmingii 1 +4.65833330154419 -35.5083351135254 Ceratoscopelus warmingii 1 +17.341667175293 -35.9416656494141 Ceratoscopelus warmingii 1 +-65.8000030517578 -45.0833320617676 Libidoclaea granaria 2003 6 +-65.8166656494141 -45.0833320617676 Libidoclaea granaria 2003 6 +-66.1999969482422 -45.4166679382324 Libidoclaea granaria 2004 1 +-66.1833343505859 -45.4500007629395 Libidoclaea granaria 2004 1 +-66.1999969482422 -45.5166664123535 Libidoclaea granaria 2004 2 +-66.1999969482422 -45.533332824707 Libidoclaea granaria 2004 2 +11.9716997146606 -11.5719995498657 Aspidorhabdus stylifer 1975 10 +9.3858003616333 -19.1877002716064 Aspidorhabdus stylifer 1980 2 +9.38619995117188 -19.1877002716064 Aspidorhabdus stylifer 1980 3 +10.5187997817993 -19.7434997558594 Aspidorhabdus stylifer 1980 5 +-66.1333312988281 -45.5 Libidoclaea granaria 2004 3 +-40.8451995849609 -49.869800567627 Impagidinium victorianum 1980 5 +-46.9716987609863 -51.0046997070312 Impagidinium victorianum 1980 11 +-66.3666687011719 -45.6666679382324 Libidoclaea granaria 2004 1 +-65.7666702270508 -45.0499992370605 Libidoclaea granaria 2005 26 +-66.4000015258789 -45.6500015258789 Libidoclaea granaria 2004 3 +-66.3833312988281 -45.6333351135254 Libidoclaea granaria 2004 5 +-65.9499969482422 -46.5833320617676 Libidoclaea granaria 2004 1 +-65.783332824707 -45.0999984741211 Libidoclaea granaria 2005 10 +-65.7666702270508 -45.0666656494141 Libidoclaea granaria 2005 3 +-65.8333358764648 -45.0833320617676 Libidoclaea granaria 2005 13 +-65.7666702270508 -45.0833320617676 Libidoclaea granaria 2005 8 +-64.9499969482422 -46.4000015258789 Libidoclaea granaria 2004 1 +-64.966667175293 -46.3833351135254 Libidoclaea granaria 2004 1 +-64.9333343505859 -46.3666648864746 Libidoclaea granaria 2004 1 +-64.8666687011719 -46.283332824707 Libidoclaea granaria 2004 1 +-65.6970520019531 -45.1023178100586 Libidoclaea granaria 2005 1 +-7.11670017242432 -18.75 Onychocorycaeus giesbrechti 1973 20 +-29.5 -10 Pachos punctatum 1977 5 +18.6846942901611 -34.0745277404785 Chylocladia capensis 1 +5 0 Sardinella maderensis 6 +11.3830003738403 -5 Monolene mertensae 1964 2 +11.7170000076294 -4.76700019836426 Sardinella maderensis 1966 2 +8.5 -1.91700005531311 Nansenia atlantica 1960 2 +9.72200012207031 -8.80200004577637 Stylephorus chordatus 2003 2 +5 0 Cynoglossus senegalensis 2 +-60.2666702270508 -3.41667008399963 Anchoviella guianensis 1977 13 +11.2329998016357 -4.73299980163574 Squilla cadenati 1968 1 +11.5 -4.93300008773804 Squilla cadenati 1966 1 +11.5170001983643 -4.93300008773804 Squilla cadenati 1965 1 +9.55000019073486 -3.41700005531311 Neoraja africana 2 +11.4499998092651 -7.41699981689453 Stylephorus chordatus 2000 2 +-30 -10 Euthynnus alletteratus 2 +11.3830003738403 -5.03299999237061 Squilla cadenati 1965 1 +-27 -56 Ophiolycus nutrix 1 +-60.716667175293 -54.6666679382324 Ophiolycus nutrix 1 +-38.6666679382324 -53.8333320617676 Amphiura (Amphiura) lymani 1 +-35.5 -54.6138877868652 Amphiura (Amphiura) lymani 1 +-38.8055572509766 -53.8833351135254 Amphiura (Amphiura) lymani 1 +-38.7066650390625 -17.9624996185303 Epinephelus marginatus 2000 1 +-38.701831817627 -17.9599990844727 Epinephelus marginatus 2000 1 +-38.6556663513184 -17.9680004119873 Epinephelus marginatus 2000 2 +-27.3783302307129 -56.3783302307129 Cellariaeforma aurorae 1975 1 +-38.6996650695801 -17.9613342285156 Epinephelus marginatus 2000 3 +11.5 -4.96700000762939 Squilla cadenati 1964 2 +18.3916664123535 -34.3499984741211 Ulva hookeriana 2 +11.6666669845581 -57.1916656494141 Gymnodinium gracile 1986 6 +-12.5749998092651 -55.2716674804688 Gymnodinium gracile 1986 1 +-45.4077987670898 -23.815299987793 Upogebia omissa 1995 1 +-45.2108001708984 -23.7432994842529 Sigambra tentaculata 2001 5 +-45.2942008972168 -23.6949996948242 Nematopalaemon schmitti 2002 1 +-44.99169921875 -23.4081001281738 Sigambra tentaculata 2002 1 +-44.0553016662598 -23.4382991790771 Sigambra tentaculata 2001 5 +-45.50830078125 -23.8600006103516 Epialtus brasiliensis 2001 1 +-45.5266990661621 -23.8456001281738 Martesia cuneiformis 2001 1 +-45.1161003112793 -23.7238998413086 Sigambra tentaculata 2001 1 +-45.2285995483398 -23.7577991485596 Sigambra tentaculata 2001 1 +-44.0847015380859 -23.5494003295898 Sigambra tentaculata 2002 1 +-45.3196983337402 -23.7222003936768 Sigambra tentaculata 2001 2 +-38.1333351135254 -4.33333349227905 Pitho lherminieri 1998 1 +-47.25 -0.25 Nematopalaemon schmitti 1965 2 +12.9666662216187 -23 Luzonia simplex 1 +12.5249996185303 -23 Yoldiella curta 1 +10.4833335876465 -9.78333377838135 Yoldiella curta 1 +-43.1468238830566 -22.823802947998 Epinephelus marginatus 1943 1 +-38.4833335876465 -3.71666693687439 Haemulon steindachneri 1945 2 +-38.5400123596191 -13.0121021270752 Haemulon steindachneri 1945 1 +-38.4833335876465 -3.71666693687439 Gobionellus oceanicus 1945 3 +-34.6745948791504 -8.104079246521 Haemulon steindachneri 1944 2 +-38.6958808898926 -12.8959836959839 Haemulon steindachneri 1944 1 +-38.6415023803711 -12.8172750473022 Haemulon steindachneri 1944 2 +-46.1256256103516 -23.8518848419189 Epinephelus marginatus 1945 1 +-44.3175964355469 -22.9776382446289 Epinephelus marginatus 1 +11.5666999816895 -17 Thorogobius angolensis 2005 1 +13.2543296813965 -8.57833290100098 Thorogobius angolensis 2007 1 +13.0034999847412 -8.89166736602783 Thorogobius angolensis 2007 2 +-44.6440010070801 -23.193000793457 Upogebia omissa 2003 1 +-44.6619987487793 -23.0590000152588 Harengula clupeola 2004 1 +-44.4210014343262 -23 Hypnea musciformis 2003 1 +-44.5820007324219 -23.2549991607666 Hypnea musciformis 2004 1 +-44.3120002746582 -23.1359996795654 Chiridota rotifera 2003 1 +-44.1500015258789 -23.2210006713867 Alaba incerta 2004 1 +-44.6619987487793 -23.0590000152588 Harengula clupeola 2003 1 +-44.681999206543 -23.1569995880127 Hypnea musciformis 2003 1 +-44.1500015258789 -23.1679992675781 Alaba incerta 2003 1 +-44.6310005187988 -23.2719993591309 Upogebia omissa 2004 1 +-0.800000011920929 -17.2670001983643 Acanthephyra acanthitelsonis 1971 1 +-39.0319442749023 -17.4599990844727 Alaba incerta 2000 1 +-39.0008316040039 -17.7080554962158 Alaba incerta 2000 1 +-39.1352767944336 -17.4219436645508 Alaba incerta 2000 1 +-39.0008316040039 -17.7080554962158 Gadila dominguensis 2000 1 +-38.6008338928223 -17.7152786254883 Phyllodoce madeirensis 2000 1 +-46.6318016052246 -0.954699993133545 Brachyplatystoma vaillantii 1 +-46.5164985656738 -0.856100022792816 Brachyplatystoma vaillantii 1997 1 +-46.6231002807617 -0.852500021457672 Nematopalaemon schmitti 1 +-48.511100769043 -1.41390001773834 Brachyplatystoma vaillantii 1 +-48.0172004699707 -0.745400011539459 Namanereis pontica 2004 3 +13.75 -11.2666997909546 Drepane africana 2002 1 +13.4666996002197 -11.25 Xenodermichthys copei 2002 1 +19.9750003814697 -35.9832992553711 Rajella barnardi 1985 2 +-67.3000030517578 -46 Doris fontainii 1 +-65 -50 Doris fontainii 2 +-68.5 -52.5 Ophiolycus nutrix 1 +-68.1500015258789 -54.8699989318848 Ophiolycus nutrix 1 +-67 -54 Abietinella operculata 1 +-59.2000007629395 -51.4000015258789 Abietinella operculata 1 +-59 -54.25 Abietinella operculata 1 +-45.11669921875 -23.5 Feldmannia mitchelliae 1962 2 +-46.3824996948242 -23.9827995300293 Alsidium seaforthii 1955 2 +-45.3842010498047 -23.6282997131348 Alsidium seaforthii 1953 6 +-45.1666984558105 -23.5272006988525 Alsidium seaforthii 1976 2 +-46.3824996948242 -23.9827995300293 Alsidium seaforthii 1968 4 +-47.0102996826172 -24.3708000183105 Callithamnion corymbosum 1968 2 +-45.0741996765137 -23.5375003814697 Levringia brasiliensis 1968 2 +-45.0625 -23.4193992614746 Levringia brasiliensis 1953 6 +-45.0638999938965 -23.4533004760742 Alsidium seaforthii 1988 2 +-46.3824996948242 -23.9827995300293 Alsidium seaforthii 1977 2 +-46.3824996948242 -23.9827995300293 Alsidium seaforthii 1953 4 +-45.3842010498047 -23.6282997131348 Alsidium seaforthii 1983 2 +-46.1533012390137 -23.9018993377686 Alsidium seaforthii 1986 2 +-65.8995513916016 -47.7587509155273 Deontostoma duanei 1980 1 +-65.8458023071289 -47.7383003234863 Deontostoma duanei 1979 1 +-65.9399032592773 -47.7501983642578 Deontostoma duanei 1977 9 +-52.1020011901855 -32.1479988098145 Prorocentrum cordatum 2014 1 +1.7632999420166 -28.0415000915527 Gaudryina pyramidata 1980 21 +7.83300018310547 -33 Systellaspis eltanini 1971 1 +18.3999996185303 -33.9000015258789 Ulva hookeriana 1 +18.4833335876465 -33.8902778625488 Ulva hookeriana 2 +18.4167003631592 -34.1833000183105 Haploblepharus fuscus 2 +-45.0671997070312 -23.4722003936768 Murrayella periclados 1963 6 +-45.11669921875 -23.5 Feldmannia mitchelliae 1973 4 +-47.8981018066406 -25.1082992553711 Alsidium seaforthii 1959 2 +-46.3678016662598 -23.9699993133545 Alsidium seaforthii 1953 4 +-46.8264007568359 -24.2061004638672 Alsidium seaforthii 1987 2 +17.4666996002197 -33.7117004394531 Etmopterus compagnoi 1994 1 +18.8166999816895 -34.0999984741211 Chaetodon marleyi 1900 1 +18.4666996002197 -34.0999984741211 Chaetodon marleyi 1 +18.5832996368408 -34.0833015441895 Chaetodon marleyi 1964 2 +16.5499992370605 -32.4500007629395 Lampanyctus isaacsi 1988 1 +18.0167007446289 -33.0833015441895 Chaetodon marleyi 1950 1 +18.4619998931885 -34.1180000305176 Chaetodon marleyi 1974 1 +18.0417003631592 -34.7766990661621 Lampanyctus isaacsi 1988 1 +18.6499996185303 -35.2832984924316 Etmopterus compagnoi 1996 2 +18.5832996368408 -34.0833015441895 Chaetodon marleyi 1966 1 +16.25 -32.2667007446289 Etmopterus compagnoi 2002 1 +18.9300003051758 -34.3600006103516 Chaetodon marleyi 1968 1 +18.4333000183105 -34.11669921875 Chaetodon marleyi 1932 1 +15.0333003997803 -30.5167007446289 Etmopterus compagnoi 1998 1 +-42.0167007446289 -22.9666996002197 Herdmania pallida 2001 2 +-66.3499984741211 -48.3499984741211 Phalacrocorax brasilianus 1 +-64.4832992553711 -43.0666999816895 Phalacrocorax brasilianus 1 +-66.5142974853516 -45.1758003234863 Phalacrocorax brasilianus 1 +15.2297496795654 -27.2296504974365 Hyboscolex longiseta 1 +15.2296333312988 -27.2294826507568 Hyboscolex longiseta 1 +15.2296333312988 -27.2296829223633 Hyboscolex longiseta 1 +16.0666675567627 -28.6499996185303 Petaloproctus terricolus 1 +15.8000001907349 -28.3833332061768 Petaloproctus terricolus 5 +-40.9500007629395 -53.7277984619141 Ophiolycus nutrix 1927 1 +-60.5750007629395 -53.8917007446289 Ophiolycus nutrix 1927 1 +-56.9667015075684 -50.8333015441895 Ophiolycus nutrix 1928 1 +-58.4500007629395 -52.4832992553711 Ophiolycus nutrix 1932 1 +-63.4832992553711 -53.5 Ophiolycus nutrix 1932 1 +-49.2332992553711 -52.6833000183105 Hansenisca soldanii 1929 1 +-47.2332992553711 -52.9333000183105 Hansenisca soldanii 1929 2 +-30.2625007629395 -31.2707996368408 Bacteriastrum furcatum 1931 1 +-29.9249992370605 -21.2166996002197 Bacteriastrum furcatum 1931 1 +-61.5 -51.875 Epistominella exigua 1927 1 +-64.75 -48.9707984924316 Epistominella exigua 1927 1 +-61.9667015075684 -49.00830078125 Epistominella exigua 1927 1 +-60.5917015075684 -49.9042015075684 Epistominella exigua 1927 1 +5.63889980316162 -1.38650000095367 Eunice websteri 1927 1 +-57.8078002929688 -51.6935997009277 Elminius kingii 1926 1 +-59.5833015441895 -45.75 Bathyraja scaphiops 1928 1 +-57 -51.75 Bathyraja scaphiops 1928 1 +-58.4500007629395 -52.4832992553711 Bathyraja scaphiops 1932 1 +-58.8821983337402 -51.1244010925293 Patagonotothen tessellata 1926 1 +-57.8287010192871 -51.6859016418457 Patagonotothen tessellata 1926 1 +-60.5167007446289 -51.0250015258789 Patagonotothen tessellata 1927 1 +-67.5532989501953 -55.8568000793457 Patagonotothen tessellata 1927 1 +-67.49169921875 -55.8541984558105 Patagonotothen tessellata 1927 1 +-57.8333015441895 -51.5833015441895 Patagonotothen tessellata 1931 1 +-50.5499992370605 -32.5330009460449 Saccella larranagai 1998 1 +-50.3709983825684 -32.6110000610352 Saccella larranagai 1998 1 +-49.3129997253418 -30.1000003814697 Saccella larranagai 1998 1 +-51.2509994506836 -33.7540016174316 Saccella larranagai 1998 1 +-50.3800010681152 -33.0040016174316 Saccella larranagai 1998 1 +-51.5470008850098 -33.693000793457 Macoploma tenta 1998 1 +-52.0460014343262 -34.3129997253418 Macoploma tenta 1998 1 +-50.1609992980957 -31.9610004425049 Macoploma tenta 1998 1 +-42.3333015441895 -22.86669921875 Elphidium galvestonense 2 +-42.11669921875 -22.88330078125 Elphidium galvestonense 1 +-47.9403991699219 -25.0132999420166 Chelorchestia darwinii 1 +-47.9403991699219 -25.0132999420166 Anoplostoma subulatum 1 +-43.1263999938965 -22.7805995941162 Linopherus ambigua 2001 1 +-40.3389015197754 -20.2749996185303 Gobionellus oceanicus 1 +18.0965003967285 -35.8457984924316 Trilobatus bisphericus 1974 1 +9.04669952392578 -19.6457996368408 Dentoglobigerina binaiensis 1975 5 +-35.0955009460449 -8.71059989929199 Bacteriastrum furcatum 2006 6 +-34.7388000488281 -8.0753002166748 Lutjanus alexandrei 1 +-39.0043983459473 -16.3645000457764 Eriphia gonagra 2007 1 +-34.7532997131348 -8.069899559021 Lutjanus alexandrei 1 +-35.0965995788574 -8.72760009765625 Bacteriastrum furcatum 2006 6 +-39.0108985900879 -14.941499710083 Eriphia gonagra 1 +-37.1199989318848 -1.75 Shoemakerella cubensis 2000 1 +-35.0503005981445 -8.59080028533936 Denticella turgida 2006 5 +-35.0503005981445 -8.59080028533936 Denticella turgida 2005 6 +-35.0477981567383 -8.60719966888428 Denticella turgida 2006 6 +-35.1030006408691 -8.68939971923828 Odontella regia 2006 2 +-44.3619003295898 -2.75670003890991 Paralia sulcata 2002 4 +-34.8760986328125 -8.08010005950928 Denticella turgida 2005 1 +-35.0970001220703 -8.68789958953857 Denticella turgida 2002 1 +-35.0625991821289 -8.58749961853027 Denticella turgida 2006 3 +-35.0477981567383 -8.60719966888428 Denticella turgida 2005 6 +-35.1030006408691 -8.68939971923828 Paralia sulcata 2005 5 +-35.1030006408691 -8.68939971923828 Paralia sulcata 2006 7 +-38.7099990844727 -1.12000000476837 Shoemakerella cubensis 2000 1 +-35.1030006408691 -8.68939971923828 Denticella turgida 2005 5 +-35.1030006408691 -8.68939971923828 Denticella turgida 2006 7 +-34.8396987915039 -8.3459997177124 Haemulon steindachneri 2008 5 +-34.8760986328125 -8.08010005950928 Paralia sulcata 2005 4 +-34.8992004394531 -8.13169956207275 Paralia sulcata 2005 4 +-35.0503005981445 -8.59080028533936 Paralia sulcata 2006 1 +-35.0965995788574 -8.72760009765625 Paralia sulcata 2005 5 +-35.0965995788574 -8.72760009765625 Paralia sulcata 2006 7 +-35.0625991821289 -8.58749961853027 Denticella turgida 2005 4 +-34.7532997131348 -8.069899559021 Haemulon steindachneri 1 +-35.1235008239746 -8.67119979858398 Gobionellus oceanicus 2006 4 +-44.3389015197754 -2.67810010910034 Denticella turgida 2002 2 +-35.0965995788574 -8.72760009765625 Denticella turgida 2005 4 +-35.0965995788574 -8.72760009765625 Denticella turgida 2006 3 +-44.3630981445312 -2.68029999732971 Odontella regia 2002 4 +-44.2633018493652 -2.7360999584198 Odontella regia 2002 3 +-35.0477981567383 -8.60719966888428 Odontella regia 2005 1 +-34.9164009094238 -8.17969989776611 Paralia sulcata 2005 3 +-44.3389015197754 -2.67810010910034 Paralia sulcata 2002 4 +-44.3389015197754 -2.67810010910034 Odontella regia 2003 1 +-35.1241989135742 -8.66950035095215 Paralia sulcata 2002 1 +-35.0955009460449 -8.71059989929199 Paralia sulcata 2006 7 +-35.1241989135742 -8.66950035095215 Odontella regia 2002 1 +-44.3630981445312 -2.68029999732971 Paralia sulcata 2002 4 +-35.5999984741211 -3.47000002861023 Ampelisca cristianae 1998 3 +-44.3630981445312 -2.68029999732971 Denticella turgida 2003 1 +-44.3619003295898 -2.75670003890991 Odontella regia 2003 2 +-34.8992004394531 -8.13169956207275 Paralia sulcata 2006 1 +-44.2633018493652 -2.7360999584198 Paralia sulcata 2002 4 +-35.1030006408691 -8.68939971923828 Odontella regia 2005 1 +-34.8992004394531 -8.13169956207275 Denticella turgida 2005 1 +-44.3619003295898 -2.75670003890991 Paralia sulcata 2003 2 +-35.0400009155273 -3.46000003814697 Shoemakerella cubensis 1998 1 +-34.7099990844727 -3.82999992370605 Shoemakerella cubensis 1998 1 +-32.3300018310547 -3.75 Shoemakerella cubensis 1998 1 +-33.6699981689453 -3.82999992370605 Shoemakerella cubensis 2000 1 +-37.7700004577637 -1.91999995708466 Shoemakerella cubensis 2000 1 +13.3500003814697 -11.9167003631592 Xenodermichthys copei 2002 1 +-36.5113983154297 -54.2766990661621 Desmodorella tenuispiculum 2 +-57.7832984924316 -51.6666984558105 Desmodorella tenuispiculum 1 +-57.7000007629395 -51.6666984558105 Desmodorella tenuispiculum 1 +-40.1752014160156 -20.0725994110107 Epialtus brasiliensis 2017 3 +-40.1240005493164 -20.1380004882812 Epialtus brasiliensis 2017 8 +-40.1640014648438 -20.0368995666504 Epialtus brasiliensis 2018 3 +16.7166996002197 -32.7832984924316 Chaetodon marleyi 1990 1 +16.7133007049561 -32.4883003234863 Rajella barnardi 1986 1 +19.5666999816895 -35.9667015075684 Chaetodon marleyi 1995 1 +15.3299999237061 -31 Xenodermichthys copei 2006 1 +8.96666622161865 -1.03333330154419 Xenodermichthys copei 3 +11.6166667938232 -4.53333330154419 Xenodermichthys copei 3 +9.21666622161865 -1.93333327770233 Xenodermichthys copei 3 +-56.1344451904297 -52.6666679382324 Pseudotanais nordenskioldi 2 +-24.9338893890381 -56.7847213745117 Pseudotanais nordenskioldi 2 +-26.6499996185303 -55.8183326721191 Pseudotanais nordenskioldi 2 +-26.6666660308838 -57.1177787780762 Pseudotanais nordenskioldi 3 +12.0833330154419 -5.80000019073486 Xenodermichthys copei 3 +-38.2477989196777 -17.6464996337891 Polyipnus laternatus 1 +-38.2477989196777 -17.6464996337891 Eretmichthys pinnatus 1 +10.1999998092651 -3.03333330154419 Xenodermichthys copei 1 +11.8999996185303 -4.93333339691162 Xenodermichthys copei 3 +-40 -2 Bembrops nematopterus 1 +-48.5999984741211 -25.4944000244141 Dormitator maculatus 1 +-48.4146995544434 -25.5400009155273 Harengula clupeola 1998 2 +-48.4146995544434 -25.5400009155273 Harengula clupeola 1999 5 +-40.2906036376953 -20.3214950561523 Sphyrna media 1944 1 +-48.5066795349121 -1.47882997989655 Brachyplatystoma vaillantii 6 +13.3832998275757 -11.9499998092651 Xenodermichthys copei 2005 1 +13.2666997909546 -10.8166999816895 Xenodermichthys copei 2005 1 +9.94050025939941 -41.1165008544922 Cladorhiza tridentata 2005 1 +-57.7700004577637 -54.0999984741211 Clathria (Clathria) toxipraedita 2 +-55.939998626709 -34.3499984741211 Yoldiella curta 1 +-60.3199996948242 -45.2799987792969 Yoldiella curta 1 +-55.939998626709 -34.3499984741211 Katadesmia cuneata 1 +19.9428005218506 -34.8163986206055 Epinephelus marginatus 1987 1 +18.61669921875 -35.0666999816895 Lissodendoryx (Lissodendoryx) digitata 1873 2 +14.4166669845581 -27.4566669464111 Rajella barnardi 3 +10.6166667938232 -3.45000004768372 Thorogobius angolensis 7 +15.1083002090454 -30.0333003997803 Rajella barnardi 1986 1 +14.5216999053955 -27.5266990661621 Rajella barnardi 1988 2 +-57.8499984741211 -51.7000007629395 Patagonotothen tessellata 2004 3 +16.5650005340576 -32.3166999816895 Rajella barnardi 1986 1 +15.2666997909546 -30.1499996185303 Rajella barnardi 1986 3 +13.4833002090454 -12.3667001724243 Epinephelus marginatus 2001 1 +14.1217002868652 -27 Rajella barnardi 1986 3 +14.2266998291016 -28.5683002471924 Rajella barnardi 1986 1 +16.5400009155273 -32.5633010864258 Rajella barnardi 1987 1 +16.4321842193604 -28.9399337768555 Nassarius vinctus 2010 18 +16.4216327667236 -28.9349994659424 Nassarius vinctus 2010 2 +16.4321842193604 -28.9399337768555 Nassarius vinctus 2011 20 +16.3898162841797 -28.9242324829102 Nassarius vinctus 2011 10 +16.4216327667236 -28.9349994659424 Nassarius vinctus 2011 5 +12.0833330154419 -5.80000019073486 Epinephelus marginatus 11 +9.21666622161865 -1.93333327770233 Epinephelus marginatus 10 +9.68333339691162 -2.53333330154419 Epinephelus marginatus 8 +-38.4833335876465 -12.9666662216187 Gobionellus oceanicus 2 +11.6166667938232 -4.53333330154419 Epinephelus marginatus 11 +8.68333339691162 -0.684722244739532 Epinephelus marginatus 9 +8.96666622161865 -1.03333330154419 Epinephelus marginatus 12 +-57.5666656494141 -51.1166648864746 Patagonotothen tessellata 1 +11.8999996185303 -4.93333339691162 Epinephelus marginatus 11 +10.1999998092651 -3.03333330154419 Epinephelus marginatus 8 +9.23333358764648 -0.233333334326744 Epinephelus marginatus 10 +11.0500001907349 -3.96666669845581 Epinephelus marginatus 10 +-57.8188896179199 -51.5833320617676 Patagonotothen tessellata 1 +-48 -27.5 Epinephelus marginatus 1 +-68.6666641235352 -55.1666679382324 Patagonotothen tessellata 9 +10.6166667938232 -3.45000004768372 Epinephelus marginatus 9 +17.703332901001 -32.8166656494141 Rostroraja alba 1 +-43.283332824707 -23 Gobionellus oceanicus 1 +-40.1339988708496 -19.9662990570068 Hypnea musciformis 2018 4 +-24.3672008514404 -58.2512016296387 Chondrocladia (Chondrocladia) grandis 2002 1 +-12.3193998336792 -37.0306015014648 Caryophyllia (Caryophyllia) profunda 1873 1 +-55.5 -54.7167015075684 Lepidopora granulosa 1966 1 +-66.3167037963867 -56.0999984741211 Lepidopora granulosa 1963 2 +-64.1667022705078 -53.86669921875 Abietinella operculata 1956 1 +-56.61669921875 -54.7167015075684 Lepidopora granulosa 1966 1 +3.30329990386963 -54.3748016357422 Abietinella operculata 2003 1 +-63.0666999816895 -54.9500007629395 Lepidopora granulosa 1961 2 +-55.6500015258789 -54.7332992553711 Lepidopora granulosa 1961 1 +-65.8167037963867 -55.3250007629395 Lepidopora granulosa 1961 1 +-36.7832984924316 -54.8624992370605 Ascothorax bulbosus 1927 1 +-57.5666999816895 -51.11669921875 Calliostoma modestulum 1927 1 +12.7082996368408 -9.63329982757568 Phrosina semilunata 1927 1 +1.86670005321503 -2.21670007705688 Phrosina semilunata 1927 1 +-0.941699981689453 -2.72499990463257 Phrosina semilunata 1927 1 +-0.941699981689453 -2.72499990463257 Platyscelus ovoides 1927 1 +-56.38330078125 -51.7332992553711 Pseudonodosinella nodulosa 1929 1 +-36.4267997741699 -54.2233009338379 Ascothorax bulbosus 1926 1 +-53.5750007629395 -48.5666999816895 Platyscelus ovoides 1926 1 +-1.39170002937317 -32.5139007568359 Platyscelus ovoides 1926 1 +4.50559997558594 -33.127799987793 Platyscelus ovoides 1926 1 +16.0125007629395 -34.0875015258789 Platyscelus ovoides 1926 1 +-36.2667007446289 -53.74169921875 Rhizosolenia chunii 1930 1 +-36.1582984924316 -53.5 Rhizosolenia chunii 1930 1 +-37.4958000183105 -53.4042015075684 Rhizosolenia chunii 1930 1 +-37.69580078125 -53.1916999816895 Rhizosolenia chunii 1930 1 +-37.8292007446289 -55.1458015441895 Rhizosolenia chunii 1930 1 +-38.3166999816895 -55.3417015075684 Rhizosolenia chunii 1930 1 +-36.38330078125 -54.9667015075684 Rhizosolenia chunii 1930 1 +-36.1582984924316 -54.2167015075684 Rhizosolenia chunii 1930 1 +-35.6833000183105 -55.6458015441895 Rhizosolenia chunii 1930 1 +-35.5332984924316 -55.8499984741211 Rhizosolenia chunii 1930 1 +-34.9124984741211 -54.1208000183105 Rhizosolenia chunii 1930 1 +-42.5666999816895 -59.5999984741211 Rhizosolenia chunii 1930 1 +-36.4682006835938 -54.2666015625 Achelia communis 1926 1 +-26.1959991455078 -58.4420013427734 Achelia communis 1930 1 +11.3166999816895 -5.90000009536743 Polyipnus laternatus 1927 1 +-57 -56.7000007629395 Pyrgo depressa 1929 1 +-44.7832984924316 -58.5666999816895 Tholosina laevis 1929 1 +-63.7249984741211 -59.6082992553711 Tholosina laevis 1930 1 +-50.2000007629395 -59.9500007629395 Tholosina laevis 1928 1 +-56.88330078125 -55.36669921875 Lagenosolenia seguenziana 1929 1 +-35.4000015258789 -54.9832992553711 Streblosoma bairdi 1926 1 +-36.0806999206543 -54.5182991027832 Oriopsis limbata 1931 1 +10.5832996368408 -15.9167003631592 Vampyroteuthis infernalis 1927 1 +-9.42500019073486 -2.82500004768372 Vampyroteuthis infernalis 1927 1 +-6.92829990386963 -2.19670009613037 Vampyroteuthis infernalis 1937 1 +-4.03329992294312 -10.2117004394531 Vampyroteuthis infernalis 1937 1 +-58.8821983337402 -51.1244010925293 Mysidetes crassa 1926 1 +-60.8499984741211 -47.4667015075684 Mysidetes crassa 1931 1 +-24.49169921875 -58.2809982299805 Chondrocladia (Chondrocladia) grandis 1 +-24.3672008514404 -58.2512016296387 Chondrocladia (Chondrocladia) grandis 1 +-67.6380004882812 -54.3619995117188 Halichondria (Halichondria) membranacea 1 +-43.2311477661133 -23.0780353546143 Haemulon steindachneri 2 +-52.0882568359375 -32.1006736755371 Epinephelus marginatus 1 +-43.2311477661133 -23.0780353546143 Gobionellus oceanicus 5 +-48.4916648864746 -1.45833337306976 Gobionellus oceanicus 1 +-34.8916664123535 -8.10833358764648 Gobionellus oceanicus 1 +-39.0083351135254 -13.0083332061768 Gobionellus oceanicus 1 +-29.658332824707 -18.3583335876465 Howella sherborni 2 +-32.375 -23.1416664123535 Howella sherborni 1 +-46.1083335876465 -32.8916664123535 Howella sherborni 1 +7.50833320617676 -35.3250007629395 Gonichthys cocco 1 +-30.591667175293 -32.9749984741211 Howella sherborni 1 +-49.4749984741211 -34.7249984741211 Howella sherborni 1 +17.341667175293 -35.9416656494141 Howella sherborni 1 +16.2749996185303 -34.5250015258789 Howella sherborni 1 +-32.2583351135254 -23.0416660308838 Gonichthys cocco 1 +-32.375 -23.1416664123535 Gonichthys cocco 1 +-35.2416648864746 -25.4750003814697 Gonichthys cocco 1 +-38.4416656494141 -27.9249992370605 Gonichthys cocco 1 +-52.9916648864746 -36.5750007629395 Gonichthys cocco 1 +-35.2583351135254 -32.9916648864746 Gonichthys cocco 1 +-35.1416664123535 -32.9916648864746 Gonichthys cocco 1 +8.375 -12.7916669845581 Gonichthys cocco 1 +2.04166674613953 -15.9916667938232 Gonichthys cocco 1 +5.82499980926514 -14.1416664123535 Gonichthys cocco 1 +-44.3516273498535 -2.34312391281128 Haemulon steindachneri 3 +-3.92499995231628 -18.5249996185303 Gonichthys cocco 1 +-35.1416664123535 -25.375 Gonichthys cocco 1 +-41.9416656494141 -30.6083335876465 Gonichthys cocco 1 +-59.3666458129883 -52.1236267089844 Patagonotothen tessellata 1 +-63.2332000732422 -17.5121002197266 Halichoeres pictus 2007 2 +-46.9716987609863 -51.0046997070312 Epistominella exigua 1980 3 +-24.6399993896484 -47.5831985473633 Hansenisca soldanii 1980 15 +-2.25130009651184 -28.5522003173828 Epistominella exigua 1980 5 +-2.25130009651184 -28.5522003173828 Gaudryina pyramidata 1980 13 +-38.9786682128906 -17.7521171569824 Leodamas agrestis 2000 1 +-39.0785827636719 -16.8981838226318 Leodamas agrestis 2000 1 +-39.0130653381348 -17.5059337615967 Leodamas agrestis 2000 1 +11.9716997146606 -11.5719995498657 Spiroplectammina spectabilis 1975 2 +18.0965003967285 -35.8457984924316 Gaudryina pyramidata 1974 2 +18.0965003967285 -35.8457984924316 Hansenisca soldanii 1974 9 +-39.7002754211426 -19.5691661834717 Turbonilla rhabdota 1987 1 +-40.451000213623 -21.1830005645752 Crenella gemma 1996 1 +-39.0999984741211 -18.882999420166 Lanayrella vagabunda 1996 1 +-41.9817161560059 -22.8695735931396 Berthella agassizii 2005 5 +-41.9817161560059 -22.8695735931396 Berthella agassizii 2006 2 +-41.0535125732422 -23.1667461395264 Lanayrella vagabunda 2009 1 +-40.8434448242188 -22.956636428833 Lanayrella vagabunda 2009 1 +-40.053524017334 -22.1017417907715 Lanayrella vagabunda 2009 1 +-40.053524017334 -22.1017417907715 Puncturella billsae 2009 1 +-40.6236457824707 -22.324333190918 Puncturella billsae 2009 1 +-41.360034942627 -23.6029014587402 Puncturella billsae 2009 1 +-42.0068244934082 -22.8864555358887 Berthella agassizii 2012 1 +-42.0068244934082 -22.8864555358887 Berthella agassizii 2013 1 +-41.9044036865234 -22.7554817199707 Berthella agassizii 2013 1 +-43.9881401062012 -23.052526473999 Lamelliconcha circinata 1943 1 +-43.9977149963379 -22.9437313079834 Lamelliconcha circinata 1949 1 +-44.0440635681152 -22.9482612609863 Lamelliconcha circinata 1 +-35.7228050231934 -9.67508125305176 Calappa sulcata 2 +-40.2218322753906 -21.4236831665039 Acanthocarpus alexandri 1 +-58.3333320617676 -32.0419425964355 Platorchestia paraplatensis 1 +9.96680545806885 -3.73347234725952 Euthynnus alletteratus 5 +9.60013961791992 -3.5501389503479 Euthynnus alletteratus 20 +10.3501396179199 -4.65013885498047 Euthynnus alletteratus 12 +8.63347244262695 -3.23347234725952 Euthynnus alletteratus 5 +8.35013961791992 -2.58347225189209 Euthynnus alletteratus 21 +10.9001388549805 -5.35013866424561 Euthynnus alletteratus 45 +-35 -15 Epinephelus marginatus 8 +-60.2000007629395 -52.867000579834 Bathyraja scaphiops 2 +-38.4830017089844 -12.9670000076294 Gobionellus oceanicus 4 +-59.9169998168945 -52.867000579834 Bathyraja scaphiops 2 +-30 -10 Gobionellus oceanicus 4 +-38.4830017089844 -12.9670000076294 Haemulon steindachneri 2 +5 0 Thorogobius angolensis 2 +-68.6669998168945 -55.1669998168945 Patagonotothen tessellata 18 +-58.8170013427734 -52.5009994506836 Patagonotothen tessellata 2004 2 +-62 -54 Patagonotothen tessellata 10 +8.31680583953857 -0.600138902664185 Euthynnus alletteratus 20 +5.91559982299805 -25.5002994537354 Ceratoscopelus warmingii 2010 1 +6.47550010681152 -24.9570007324219 Ceratoscopelus warmingii 2008 1 +8.06680583953857 -1.25013887882233 Euthynnus alletteratus 41 +8.51680564880371 -2.38347220420837 Euthynnus alletteratus 2 +-43.1263999938965 -22.7805995941162 Sthenelanella uniformis 2000 1 +-34.8437004089355 -7.66440010070801 Harengula clupeola 2005 1 +-34.1500015258789 -8.45880031585693 Alaba incerta 2005 10 +-44.3619003295898 -2.75670003890991 Odontella aurita 2003 2 +-38.9303016662598 -13.5668001174927 Upogebia omissa 2008 1 +-35.1030006408691 -8.68939971923828 Odontella aurita 2005 1 +-34.9355010986328 -8.23980045318604 Odontella aurita 1 +-34.8992004394531 -8.13169956207275 Odontella aurita 2005 1 +-34.9164009094238 -8.17969989776611 Odontella aurita 2005 1 +-44.3389015197754 -2.67810010910034 Odontella aurita 2002 3 +-44.3630981445312 -2.68029999732971 Odontella aurita 2003 2 +-34.1500015258789 -8.45880031585693 Alaba incerta 2006 3 +-34.8760986328125 -8.08010005950928 Odontella aurita 2005 1 +-44.2633018493652 -2.7360999584198 Odontella aurita 2002 4 +-35.024299621582 -8.61180019378662 Synalpheus fritzmuelleri 2006 1 +-35.0255012512207 -8.61289978027344 Synalpheus fritzmuelleri 2006 1 +-34.8700981140137 -8.06359958648682 Odontella aurita 2007 2 +-44.3619003295898 -2.75670003890991 Odontella aurita 2002 3 +-35.1241989135742 -8.66950035095215 Odontella aurita 2002 1 +-39.3648986816406 -17.8896007537842 Synalpheus fritzmuelleri 2007 1 +-44.2633018493652 -2.7360999584198 Odontella aurita 2003 2 +-35.024299621582 -8.61180019378662 Synalpheus fritzmuelleri 2007 1 +-38.9137001037598 -13.5804004669189 Synalpheus fritzmuelleri 2008 1 +-39.1806983947754 -17.1051998138428 Upogebia omissa 2007 1 +-35.024299621582 -8.61180019378662 Pitho lherminieri 2006 1 +-35.0955009460449 -8.71059989929199 Chaetoceros compressus 2006 7 +-35.0965995788574 -8.72760009765625 Chaetoceros compressus 2005 2 +-34.9985008239746 -8.50790023803711 Pitho lherminieri 2 +-34.9611015319824 -8.36989974975586 Hypnea musciformis 2007 3 +-35.0955009460449 -8.71059989929199 Chaetoceros compressus 2005 3 +-35.1088981628418 -8.68519973754883 Harengula clupeola 2006 4 +-38.9530982971191 -13.8768997192383 Pitho lherminieri 2000 1 +-34.8760986328125 -8.08010005950928 Chaetoceros compressus 2005 2 +-36.5233993530273 -4.84989976882935 Amphiroa rigida 1 +-34.9548988342285 -8.2367000579834 Odontella aurita 1 +-34.9541015625 -8.36579990386963 Hypnea musciformis 2006 3 +-35.0965995788574 -8.72760009765625 Chaetoceros compressus 2006 7 +-35.1119995117188 -8.68190002441406 Chaetoceros compressus 2002 1 +-35.1088981628418 -8.68519973754883 Harengula clupeola 2005 2 +-34.9488983154297 -8.25170040130615 Odontella aurita 1 +-41.033332824707 -21.6079998016357 Dormitator maculatus 1963 1 +-42.0262985229492 -22.9559001922607 Hypnea musciformis 1981 2 +-41.8776016235352 -22.7674999237061 Hypnea musciformis 1981 2 +-42.0103988647461 -23.005199432373 Sonderophycus capensis 1981 2 +-42.0015983581543 -22.8899002075195 Sonderophycus capensis 1981 4 +-42.035400390625 -22.9792003631592 Hypnea musciformis 1979 6 +-42.0033988952637 -22.9946994781494 Hypnea musciformis 1981 2 +-41.9583015441895 -22.7327995300293 Hypnea musciformis 1981 8 +-42.0015983581543 -22.8899002075195 Hypnea musciformis 1981 6 +-41.8876991271973 -22.7705001831055 Hypnea musciformis 1981 3 +-42.035400390625 -22.9792003631592 Hypnea musciformis 1980 3 +-41.9581985473633 -22.7341003417969 Hypnea musciformis 1981 2 +-42.0144996643066 -22.9785995483398 Hypnea musciformis 1981 2 +-41.9776000976562 -22.8647003173828 Hypnea musciformis 1981 2 +-41.8773994445801 -22.7747001647949 Hypnea musciformis 1979 2 +-66.8933029174805 -55.0666999816895 Metalinhomoeus tristis 1 +-66.9233016967773 -55.11669921875 Metalinhomoeus tristis 1 +-36.5113983154297 -54.2766990661621 Halomonhystera disjuncta 2 +-68.3000030517578 -54.8166999816895 Halomonhystera disjuncta 1 +-36.4667015075684 -54.36669921875 Halomonhystera disjuncta 1 +18.466667175293 -34.1138877868652 Hypnea musciformis 1 +-40.9556007385254 -53.7332992553711 Arachnopusia aviculifera 1929 1 +-35.4000015258789 -54.9832992553711 Clathria (Clathria) toxipraedita 1927 1 +-36.75 -54.25 Arachnopusia aviculifera 1929 3 +-40.9500007629395 -53.7277984619141 Arachnopusia aviculifera 1929 1 +-35.4000015258789 -54.9832992553711 Arachnopusia aviculifera 1929 1 +-64.5 -51.0999984741211 Clathria (Clathria) toxipraedita 1927 1 +-35.5800018310547 -54.9933013916016 Arachnopusia aviculifera 1986 1 +-36.310001373291 -53.8432998657227 Clathria (Clathria) toxipraedita 1975 1 +-12.2833003997803 -37.11669921875 Fenestrulina dictyota 1927 1 +-40.1166648864746 -3.20000004768372 Plotosus limbatus 1 +8.68333339691162 -0.684722244739532 Drepane africana 9 +10.6166667938232 -3.45000004768372 Drepane africana 9 +12.0833330154419 -5.80000019073486 Drepane africana 11 +8.96666622161865 -1.03333330154419 Drepane africana 12 +11.0500001907349 -3.96666669845581 Drepane africana 10 +9.23333358764648 -0.233333334326744 Drepane africana 10 +9.21666622161865 -1.93333327770233 Drepane africana 10 +10.1999998092651 -3.03333330154419 Drepane africana 8 +-38.4833335876465 -12.9666662216187 Dormitator maculatus 1 +9.68333339691162 -2.53333330154419 Drepane africana 8 +11.6166667938232 -4.53333330154419 Drepane africana 11 +18.3333339691162 -32.0666656494141 Chaetodon marleyi 1 +11.8999996185303 -4.93333339691162 Drepane africana 11 +13.2321996688843 -8.78670024871826 Chaetodon marleyi 1 +11.9333333969116 -22.8833332061768 Dicrolene pallidus 1977 1 +-52.86669921875 -42.1500015258789 Euaugaptilus palumboii 1976 5 +-36.2999992370605 -54.2249984741211 Arachnopusia aviculifera 1929 1 +-69 -55.25 Aimulosia australis 1888 1 +3.86601662635803 -19.1152324676514 Parexogone wolfi 1 +-48.3236656188965 -25.5696659088135 Hemipodia pustatula 1 +3.86704993247986 -19.1167507171631 Parexogone wolfi 1 +3.86626672744751 -19.11643409729 Parexogone wolfi 1 +3.86616659164429 -19.1172161102295 Parexogone wolfi 1 +3.86695003509521 -19.11643409729 Parexogone wolfi 1 +-56.2836418151855 -46.0079612731934 Prorocentrum cordatum 1995 1 +-46.2665710449219 -24.0475063323975 Harengula clupeola 1964 1 +-52.1020011901855 -32.1479988098145 Paralia sulcata 1995 1 +-52.1020011901855 -32.1479988098145 Paralia sulcata 2001 4 +-52.1059989929199 -32.0250015258789 Paralia sulcata 2002 2 +-52.1020011901855 -32.1479988098145 Paralia sulcata 1999 2 +-52.1020011901855 -32.1479988098145 Paralia sulcata 2005 2 +-52.1020011901855 -32.1479988098145 Paralia sulcata 2006 3 +-52.1020011901855 -32.1479988098145 Paralia sulcata 2003 1 +-52.1059989929199 -32.0250015258789 Paralia sulcata 2007 1 +-52.1020011901855 -32.1479988098145 Paralia sulcata 2012 3 +-52.1059989929199 -32.0250015258789 Paralia sulcata 2012 5 +-52.1020011901855 -32.1479988098145 Paralia sulcata 2011 2 +-52.1020011901855 -32.1479988098145 Paralia sulcata 2008 2 +-52.1059989929199 -32.0250015258789 Paralia sulcata 2008 1 +-52.1059989929199 -32.0250015258789 Paralia sulcata 2009 1 +-52.1020011901855 -32.1479988098145 Paralia sulcata 2009 2 +-36.310001373291 -53.8433303833008 Clathria (Clathria) toxipraedita 1 +-48.4916648864746 -1.45833337306976 Brachyplatystoma vaillantii 6 +-48.4916648864746 -1.45833337306976 Dormitator maculatus 1 +-39.8916664123535 -18.7416667938232 Dormitator maculatus 1 +-43.2311477661133 -23.0780353546143 Harengula clupeola 5 +-36.4832992553711 -54.3166999816895 Iophon aceratum 1 +-52.1749992370605 -1.75833332538605 Brachyplatystoma vaillantii 1 +-51.6083335876465 -1.42499995231628 Dormitator maculatus 1 +-51.8140983581543 -1.4722900390625 Brachyplatystoma vaillantii 1994 1 +2.32399988174438 -28.5247993469238 Turborotalia praecentralis 1980 3 +-35.2849998474121 -30.2765007019043 Dentoglobigerina binaiensis 1980 11 +10.5325002670288 -19.7574996948242 Trilobatus bisphericus 1975 4 +7.894700050354 -47.0509986877441 Isthmolithus recurvus 1987 8 +-30.6770000457764 -51.5419998168945 Isthmolithus recurvus 1987 13 +-43.2830009460449 -23 Brachyplatystoma vaillantii 2 +5 0 Drepane africana 2 +10.4670000076294 -4.78299999237061 Xenodermichthys copei 1960 2 +-43.2830009460449 -23 Harengula clupeola 2 +-38.4830017089844 -12.9670000076294 Dormitator maculatus 2 +-40.0999984741211 -21.5170001983643 Xenodermichthys copei 1987 2 +-37.4742012023926 -11.5423002243042 Synalpheus fritzmuelleri 1 +-37.5213012695312 -11.7525997161865 Synalpheus fritzmuelleri 1 +-35.1053009033203 -8.68669986724854 Harengula clupeola 2005 2 +-35.1053009033203 -8.68669986724854 Harengula clupeola 2006 4 +-39.5521011352539 -18.0960006713867 Dormitator maculatus 2 +-42 -12 Tadarida brasiliensis 1866 3 +18.0699996948242 -33.1500015258789 Pelecanus onocrotalus 1995 2 +18.0699996948242 -33.1500015258789 Pelecanus onocrotalus 1994 2 +16.6499996185303 -28.5 Pelecanus onocrotalus 1999 1 +18.7199993133545 -33.6300010681152 Pelecanus onocrotalus 1999 2 +18.1499996185303 -32.7799987792969 Pelecanus onocrotalus 1996 2 +18.0699996948242 -33.1500015258789 Pelecanus onocrotalus 1993 2 +18.4699993133545 -34.1199989318848 Pelecanus onocrotalus 2003 2 +18.25 -32.8699989318848 Pelecanus onocrotalus 1995 1 +16.4699993133545 -28.6299991607666 Pelecanus onocrotalus 1999 2 +18.5 -33.8800010681152 Pelecanus onocrotalus 1986 5 +18.5100002288818 -34.0900001525879 Pelecanus onocrotalus 2005 9 +19.1000003814697 -34.3499984741211 Pelecanus onocrotalus 2000 2 +18.5 -34.0699996948242 Pelecanus onocrotalus 1998 2 +18.7199993133545 -33.6300010681152 Pelecanus onocrotalus 2003 2 +18.75 -33.2999992370605 Pelecanus onocrotalus 2003 1 +18.4699993133545 -33.8499984741211 Pelecanus onocrotalus 2006 2 +18.4699993133545 -33.8699989318848 Pelecanus onocrotalus 2004 4 +18.5200004577637 -34.0800018310547 Pelecanus onocrotalus 1985 10 +18.4699993133545 -34.0999984741211 Pelecanus onocrotalus 2002 1 +19.0900001525879 -34.3600006103516 Pelecanus onocrotalus 2006 4 +18.1800003051758 -32.7799987792969 Pelecanus onocrotalus 2001 2 +18.5200004577637 -34.0800018310547 Pelecanus onocrotalus 2004 8 +18.4799995422363 -33.8499984741211 Pelecanus onocrotalus 2003 4 +18.5200004577637 -34.0800018310547 Pelecanus onocrotalus 2002 10 +18.5200004577637 -34.0800018310547 Pelecanus onocrotalus 1988 11 +19.1000003814697 -34.3499984741211 Pelecanus onocrotalus 1996 1 +18.9699993133545 -33.6800003051758 Pelecanus onocrotalus 1996 8 +18.9699993133545 -33.6800003051758 Pelecanus onocrotalus 2003 5 +18.5200004577637 -34.0800018310547 Pelecanus onocrotalus 1996 11 +18.4699993133545 -33.8499984741211 Pelecanus onocrotalus 2004 3 +19.1000003814697 -34.3499984741211 Pelecanus onocrotalus 2003 5 +18.1499996185303 -32.7799987792969 Pelecanus onocrotalus 2001 2 +18.5200004577637 -34.0800018310547 Pelecanus onocrotalus 1998 10 +18.7199993133545 -33.6300010681152 Pelecanus onocrotalus 1997 2 +18.5200004577637 -34.0800018310547 Pelecanus onocrotalus 1992 11 +18.9699993133545 -33.6800003051758 Pelecanus onocrotalus 1997 8 +19.1100006103516 -34.3600006103516 Pelecanus onocrotalus 2005 3 +18.5200004577637 -34.0800018310547 Pelecanus onocrotalus 1989 11 +19.0900001525879 -34.3600006103516 Pelecanus onocrotalus 2005 6 +18.4699993133545 -34.0999984741211 Pelecanus onocrotalus 1993 2 +19.1299991607666 -34.3300018310547 Pelecanus onocrotalus 2005 3 +18.5200004577637 -34.0800018310547 Pelecanus onocrotalus 2005 5 +18.5200004577637 -34.0800018310547 Pelecanus onocrotalus 1991 12 +18.2000007629395 -32.8300018310547 Pelecanus onocrotalus 2006 1 +18.0699996948242 -33.1500015258789 Pelecanus onocrotalus 2003 2 +18.4200000762939 -32.3300018310547 Pelecanus onocrotalus 1998 2 +19.1200008392334 -34.3300018310547 Pelecanus onocrotalus 2006 4 +18.3199996948242 -32.8800010681152 Pelecanus onocrotalus 1996 1 +18.5200004577637 -34.0800018310547 Pelecanus onocrotalus 1987 11 +18.5200004577637 -34.0800018310547 Pelecanus onocrotalus 1997 9 +18.9699993133545 -33.6800003051758 Pelecanus onocrotalus 2002 3 +18.5200004577637 -34.0800018310547 Pelecanus onocrotalus 2001 8 +18.4799995422363 -33.8499984741211 Pelecanus onocrotalus 2005 4 +18.5 -33.8800010681152 Pelecanus onocrotalus 1996 1 +18.1499996185303 -32.7799987792969 Pelecanus onocrotalus 2005 5 +18.5 -33.8800010681152 Pelecanus onocrotalus 1993 3 +18.5 -34.0699996948242 Pelecanus onocrotalus 1992 1 +18.8199996948242 -33.6300010681152 Pelecanus onocrotalus 2004 2 +19.1000003814697 -34.3499984741211 Pelecanus onocrotalus 2002 1 +18.4699993133545 -33.8699989318848 Pelecanus onocrotalus 2006 2 +18.1800003051758 -32.7999992370605 Pelecanus onocrotalus 1998 2 +18.1800003051758 -32.7799987792969 Pelecanus onocrotalus 1999 2 +18.3199996948242 -32.8800010681152 Pelecanus onocrotalus 1998 1 +19.1200008392334 -34.3499984741211 Pelecanus onocrotalus 2006 4 +16.5799999237061 -28.5699996948242 Pelecanus onocrotalus 1998 1 +18.1800003051758 -32.7999992370605 Pelecanus onocrotalus 2002 2 +18.5200004577637 -34.0800018310547 Pelecanus onocrotalus 1984 12 +18.4699993133545 -34.1199989318848 Pelecanus onocrotalus 2004 2 +18.4200000762939 -32.3300018310547 Pelecanus onocrotalus 1992 2 +18.2999992370605 -32.5999984741211 Pelecanus onocrotalus 1998 1 +18.1800003051758 -32.7799987792969 Pelecanus onocrotalus 2000 1 +18.5 -34.0699996948242 Pelecanus onocrotalus 2004 5 +18.1800003051758 -32.7799987792969 Pelecanus onocrotalus 1996 2 +18.2299995422363 -32.8499984741211 Pelecanus onocrotalus 2001 1 +18.0699996948242 -33.1500015258789 Pelecanus onocrotalus 2002 2 +18.2000007629395 -32.7999992370605 Pelecanus onocrotalus 1997 2 +18.5 -33.8800010681152 Pelecanus onocrotalus 1994 3 +18.75 -33.2999992370605 Pelecanus onocrotalus 1996 1 +19.1100006103516 -34.3600006103516 Pelecanus onocrotalus 2006 5 +18.5 -33.8300018310547 Pelecanus onocrotalus 2001 1 +18.7700004577637 -34.0800018310547 Pelecanus onocrotalus 2005 1 +18.5200004577637 -34.0800018310547 Pelecanus onocrotalus 1990 10 +18.9699993133545 -33.6800003051758 Pelecanus onocrotalus 2000 3 +18.75 -33.7700004577637 Pelecanus onocrotalus 2004 1 +18.5200004577637 -34.0800018310547 Pelecanus onocrotalus 1983 6 +18.5300006866455 -34.0900001525879 Pelecanus onocrotalus 2006 3 +18.2800006866455 -32.8699989318848 Pelecanus onocrotalus 1996 2 +19.1000003814697 -34.3499984741211 Pelecanus onocrotalus 1993 1 +18.5200004577637 -33.8300018310547 Pelecanus onocrotalus 2003 2 +18.4200000762939 -32.3300018310547 Pelecanus onocrotalus 1994 2 +18.5200004577637 -34.0800018310547 Pelecanus onocrotalus 1999 7 +18.0699996948242 -33.1500015258789 Pelecanus onocrotalus 1996 2 +18.4699993133545 -33.8699989318848 Pelecanus onocrotalus 2005 3 +18.75 -33.7700004577637 Pelecanus onocrotalus 2005 1 +18.1700000762939 -32.7999992370605 Pelecanus onocrotalus 1999 1 +18.9699993133545 -33.6699981689453 Pelecanus onocrotalus 2001 2 +18.5200004577637 -34.0800018310547 Pelecanus onocrotalus 2006 4 +18.3199996948242 -32.8699989318848 Pelecanus onocrotalus 2005 3 +18.25 -32.8699989318848 Pelecanus onocrotalus 2005 3 +18.5 -33.8800010681152 Pelecanus onocrotalus 1997 3 +18.8199996948242 -33.6300010681152 Pelecanus onocrotalus 2000 2 +18.4699993133545 -33.8699989318848 Pelecanus onocrotalus 2002 1 +18.4200000762939 -32.3300018310547 Pelecanus onocrotalus 2000 2 +18.25 -32.8699989318848 Pelecanus onocrotalus 2003 2 +18.4699993133545 -34.0999984741211 Pelecanus onocrotalus 2005 2 +19.2199993133545 -34.0299987792969 Pelecanus onocrotalus 1996 1 +19.1299991607666 -34.3300018310547 Pelecanus onocrotalus 2006 2 +18.5200004577637 -34.0800018310547 Pelecanus onocrotalus 1994 12 +18.4799995422363 -33.8499984741211 Pelecanus onocrotalus 2002 1 +18.1499996185303 -32.7799987792969 Pelecanus onocrotalus 1998 2 +18.1100006103516 -33.189998626709 Pelecanus onocrotalus 2005 1 +18.1800003051758 -32.7999992370605 Pelecanus onocrotalus 2001 2 +18.4699993133545 -34.0999984741211 Pelecanus onocrotalus 2001 1 +18.5100002288818 -34.0900001525879 Pelecanus onocrotalus 2006 7 +18.5 -33.8600006103516 Pelecanus onocrotalus 1997 1 +18.5200004577637 -34.0800018310547 Pelecanus onocrotalus 1986 10 +19.1000003814697 -34.3499984741211 Pelecanus onocrotalus 1999 1 +18.1800003051758 -32.7999992370605 Pelecanus onocrotalus 2005 5 +18.8199996948242 -33.6300010681152 Pelecanus onocrotalus 2003 2 +18.75 -33.2999992370605 Pelecanus onocrotalus 1999 1 +18.1499996185303 -32.7799987792969 Pelecanus onocrotalus 1999 2 +18.4200000762939 -32.3300018310547 Pelecanus onocrotalus 1993 2 +18.2000007629395 -32.7999992370605 Pelecanus onocrotalus 2000 2 +18.2000007629395 -32.8300018310547 Pelecanus onocrotalus 2003 2 +18.0699996948242 -33.1500015258789 Pelecanus onocrotalus 2004 2 +16.4699993133545 -28.6299991607666 Pelecanus onocrotalus 2003 2 +18.1700000762939 -32.7999992370605 Pelecanus onocrotalus 2006 1 +18.5200004577637 -34.0200004577637 Pelecanus onocrotalus 2002 1 +18.9699993133545 -33.6699981689453 Pelecanus onocrotalus 2004 1 +18.2199993133545 -32.8199996948242 Pelecanus onocrotalus 1999 1 +18.2800006866455 -32.8699989318848 Pelecanus onocrotalus 1998 2 +18.5 -33.8600006103516 Pelecanus onocrotalus 1995 2 +18.5200004577637 -34.0900001525879 Pelecanus onocrotalus 2005 9 +19.1000003814697 -34.3499984741211 Pelecanus onocrotalus 2004 4 +18.1800003051758 -32.7999992370605 Pelecanus onocrotalus 2000 2 +18.9699993133545 -33.6699981689453 Pelecanus onocrotalus 2003 1 +18.3199996948242 -32.8699989318848 Pelecanus onocrotalus 2003 2 +19.1000003814697 -34.3499984741211 Pelecanus onocrotalus 1998 2 +18.5200004577637 -34.0800018310547 Pelecanus onocrotalus 1993 10 +18.9699993133545 -33.6800003051758 Pelecanus onocrotalus 1998 5 +18.8199996948242 -33.6300010681152 Pelecanus onocrotalus 2002 2 +18.25 -32.8699989318848 Pelecanus onocrotalus 2000 1 +18.2000007629395 -31.7000007629395 Pelecanus onocrotalus 1999 1 +18.2000007629395 -32.7999992370605 Pelecanus onocrotalus 1995 2 +18.25 -32.8699989318848 Pelecanus onocrotalus 2004 1 +18.2199993133545 -32.7999992370605 Pelecanus onocrotalus 2005 1 +18.5 -33.8300018310547 Pelecanus onocrotalus 2003 2 +18.2800006866455 -32.8699989318848 Pelecanus onocrotalus 2002 2 +18.5 -33.8800010681152 Pelecanus onocrotalus 1987 4 +18.5200004577637 -34.0800018310547 Pelecanus onocrotalus 1995 11 +18.1800003051758 -32.7799987792969 Pelecanus onocrotalus 1998 2 +18.4200000762939 -32.3300018310547 Pelecanus onocrotalus 1997 2 +18.5 -33.8600006103516 Pelecanus onocrotalus 2001 3 +19.0599994659424 -34.3400001525879 Pelecanus onocrotalus 2003 1 +18.75 -34.0299987792969 Pelecanus onocrotalus 2000 2 +18.1800003051758 -32.7799987792969 Pelecanus onocrotalus 1997 2 +18.4699993133545 -34.0999984741211 Pelecanus onocrotalus 2006 2 +18.4699993133545 -34.0999984741211 Pelecanus onocrotalus 1998 1 +18.5 -33.8800010681152 Pelecanus onocrotalus 1991 1 +18.3199996948242 -32.8800010681152 Pelecanus onocrotalus 1997 1 +18.7199993133545 -33.6300010681152 Pelecanus onocrotalus 1996 2 +18.2000007629395 -32.8300018310547 Pelecanus onocrotalus 2005 4 +18.4699993133545 -33.8499984741211 Pelecanus onocrotalus 2003 3 +18.5 -34.0699996948242 Pelecanus onocrotalus 2006 4 +18.8199996948242 -33.6300010681152 Pelecanus onocrotalus 2001 2 +18.7199993133545 -33.6300010681152 Pelecanus onocrotalus 2002 2 +18.5200004577637 -34.0800018310547 Pelecanus onocrotalus 2000 7 +18.2000007629395 -32.7999992370605 Pelecanus onocrotalus 2005 5 +18.0699996948242 -33.1500015258789 Pelecanus onocrotalus 2001 2 +19.1200008392334 -34.3499984741211 Pelecanus onocrotalus 2005 3 +18.4200000762939 -32.3300018310547 Pelecanus onocrotalus 2002 2 +18.7199993133545 -33.6300010681152 Pelecanus onocrotalus 1998 2 +18.1000003814697 -33.1599998474121 Pelecanus onocrotalus 2005 1 +18.2299995422363 -32.8499984741211 Pelecanus onocrotalus 2005 4 +18.7199993133545 -33.6300010681152 Pelecanus onocrotalus 2004 1 +18.4500007629395 -34.1199989318848 Pelecanus onocrotalus 2002 1 +18.5 -33.8600006103516 Pelecanus onocrotalus 1994 2 +18.2199993133545 -32.8199996948242 Pelecanus onocrotalus 1998 2 +18.1499996185303 -32.7799987792969 Pelecanus onocrotalus 1995 1 +16.5799999237061 -28.5699996948242 Pelecanus onocrotalus 1999 2 +18.2199993133545 -32.8199996948242 Pelecanus onocrotalus 2005 4 +-27.3983001708984 -56.2000007629395 Achelia communis 1 +-27.5065994262695 -56.3366012573242 Achelia communis 1 +-37.6683006286621 -54.206600189209 Achelia communis 1 +-27.4099998474121 -56.396598815918 Achelia communis 1 +-27.0450000762939 -59.4616012573242 Achelia communis 1 +18.3199996948242 -32.8800010681152 Pelecanus onocrotalus 1994 2 +18.1700000762939 -32.7999992370605 Pelecanus onocrotalus 1997 2 +18.2000007629395 -32.8300018310547 Pelecanus onocrotalus 1995 1 +18.2299995422363 -32.8499984741211 Pelecanus onocrotalus 1999 2 +18.4699993133545 -33.9300003051758 Pelecanus onocrotalus 2005 2 +18.1800003051758 -32.7799987792969 Pelecanus onocrotalus 2002 2 +18.3199996948242 -32.8699989318848 Pelecanus onocrotalus 2006 1 +18.5 -33.8800010681152 Pelecanus onocrotalus 1999 2 +18.2000007629395 -31.7000007629395 Pelecanus onocrotalus 1997 1 +18.1499996185303 -32.7799987792969 Pelecanus onocrotalus 1997 1 +18.5 -34.0699996948242 Pelecanus onocrotalus 1995 1 +18.4200000762939 -32.3300018310547 Pelecanus onocrotalus 1999 2 +3.51230001449585 -29.4841995239258 Globigerina simplicissima 1980 4 +-66.3170013427734 -56.0999984741211 Ophiolycus nutrix 1963 1 +13.5333003997803 -26.61669921875 Lycodes terraenovae 1998 1 +14.2266998291016 -28.5683002471924 Lycodes terraenovae 1986 1 +18.7166996002197 -34.6500015258789 Sepia hieronis 1988 1 +15.8999996185303 -30.4832992553711 Sepia hieronis 1993 1 +17.36669921875 -31.25 Sepia hieronis 1988 1 +17.36669921875 -32.4500007629395 Sepia hieronis 1994 1 +17.5832996368408 -31.3332996368408 Sepia hieronis 1992 1 +16.1499996185303 -29.4666996002197 Sepia hieronis 1987 1 +-32.445987701416 -3.85437607765198 Gymnothorax funebris 2007 1 +-32.3902130126953 -3.80894207954407 Gymnothorax funebris 2013 1 +-32.390209197998 -3.80894207954407 Gymnothorax funebris 2016 1 +-36.5666656494141 -54.4000015258789 Ulva hookeriana 1 +-40 -23.5 Syringolaimus annae 2 +-5.71666669845581 -15.9666662216187 Platybelone argalus 1968 1 +17.6833000183105 -31.5666999816895 Pterygosquilla capensis 1990 1 +17.1499996185303 -30.2667007446289 Pterygosquilla capensis 1987 1 +13.6499996185303 -24.4167003631592 Pterygosquilla capensis 2000 4 +17.2833003997803 -31.6833000183105 Pterygosquilla capensis 2000 1 +16.9333000183105 -30.9666996002197 Pterygosquilla capensis 2002 1 +16.5167007446289 -29.11669921875 Sepia hieronis 1993 1 +16.0599994659424 -29.1399993896484 Sepia hieronis 1986 1 +17.3600006103516 -33.4000015258789 Sepia hieronis 1989 1 +15.4200000762939 -29.2299995422363 Sepia hieronis 2005 1 +16.4666996002197 -31.0167007446289 Sepia hieronis 1995 1 +17.1200008392334 -32.1699981689453 Sepia hieronis 1995 1 +18.2999992370605 -34.4000015258789 Sepia hieronis 1995 1 +17.5499992370605 -32.7167015075684 Sepia hieronis 1992 1 +16.25 -30.5 Sepia hieronis 1995 1 +17.3999996185303 -31.2399997711182 Sepia hieronis 1990 1 +16.3500003814697 -30.4099998474121 Sepia hieronis 1995 1 +16.1599998474121 -29.0499992370605 Sepia hieronis 1992 1 +17.4099998474121 -33.060001373291 Sepia hieronis 1991 1 +16.3600006103516 -30.5599994659424 Sepia hieronis 1995 1 +16.25 -28.8999996185303 Sepia hieronis 1996 5 +19.0666999816895 -35.3166999816895 Sepia hieronis 1992 1 +17.8166999816895 -33.3166999816895 Sepia hieronis 1990 1 +16.3500003814697 -29.2800006866455 Sepia hieronis 1995 1 +17 -30.3999996185303 Sepia hieronis 1990 1 +16.3700008392334 -31.5699996948242 Sepia hieronis 2002 1 +17.1499996185303 -30.2667007446289 Sepia hieronis 1987 1 +15.1800003051758 -29.5799999237061 Sepia hieronis 1988 1 +17.3799991607666 -33.2700004577637 Sepia hieronis 2001 1 +17.2099990844727 -31.0400009155273 Sepia hieronis 1987 1 +15.3800001144409 -30.1299991607666 Sepia hieronis 2005 1 +17.5499992370605 -31.5333003997803 Sepia hieronis 1989 1 +16.1599998474121 -29.4599990844727 Sepia hieronis 1987 1 +16.63330078125 -30.0499992370605 Sepia hieronis 1991 1 +17.3332996368408 -30.9666996002197 Sepia hieronis 1987 1 +17.1499996185303 -31.3899993896484 Sepia hieronis 1992 1 +16.0300006866455 -29.3999996185303 Sepia hieronis 2006 1 +17.4167003631592 -31.4666996002197 Sepia hieronis 1987 1 +16 -29.5666999816895 Sepia hieronis 2000 1 +16.3700008392334 -29.3700008392334 Sepia hieronis 2005 1 +16.9333000183105 -30.2833003997803 Sepia hieronis 1988 1 +16.5200004577637 -30.4899997711182 Sepia hieronis 2005 1 +17.1299991607666 -32.1199989318848 Sepia hieronis 2004 1 +16.1499996185303 -30.2833003997803 Sepia hieronis 2002 1 +15.9833002090454 -28.7332992553711 Sepia hieronis 1987 1 +17.5 -33.3899993896484 Sepia hieronis 2003 1 +17.0832996368408 -31.63330078125 Sepia hieronis 2002 1 +16.5499992370605 -31.5499992370605 Sepia hieronis 1989 1 +17.1499996185303 -30.4500007629395 Sepia hieronis 2003 1 +17.2000007629395 -30.7000007629395 Sepia hieronis 1986 1 +17.4333000183105 -32.4667015075684 Sepia hieronis 2002 1 +17.7166996002197 -32.7832984924316 Sepia hieronis 1991 1 +17.5499992370605 -33.439998626709 Sepia hieronis 1992 1 +16.7166996002197 -30.0333003997803 Sepia hieronis 2000 1 +17.0599994659424 -31.5699996948242 Sepia hieronis 1986 1 +16.1700000762939 -28.5799999237061 Sepia hieronis 1991 1 +16.3099994659424 -29.3600006103516 Sepia hieronis 1995 1 +18.11669921875 -34.2000007629395 Sepia hieronis 1988 1 +17.8500003814697 -33.9832992553711 Sepia hieronis 1993 1 +17.5799999237061 -33.439998626709 Sepia hieronis 1986 1 +16.2999992370605 -29.7000007629395 Sepia hieronis 1988 1 +17.0333003997803 -30.5167007446289 Sepia hieronis 2002 1 +16.2600002288818 -30.5200004577637 Sepia hieronis 1992 1 +17.3166999816895 -31.2667007446289 Sepia hieronis 1987 1 +15.3000001907349 -29.2700004577637 Sepia hieronis 1989 1 +17.8166999816895 -32.4667015075684 Sepia hieronis 1990 1 +17.8332996368408 -33.3166999816895 Sepia hieronis 1993 1 +15.2666997909546 -30.1000003814697 Sepia hieronis 1990 1 +20 -35.7000007629395 Sepia hieronis 1993 1 +17.63330078125 -32.5 Sepia hieronis 1989 1 +17.0167007446289 -31.0333003997803 Sepia hieronis 1991 1 +16.1000003814697 -29.0666999816895 Sepia hieronis 1999 1 +16.1833000183105 -29.9832992553711 Sepia hieronis 1996 1 +15.8500003814697 -29.5 Sepia hieronis 1996 1 +18.5400009155273 -35.0499992370605 Sepia hieronis 1992 1 +16.7166996002197 -31.0167007446289 Sepia hieronis 2000 1 +16.0333003997803 -29.5499992370605 Sepia hieronis 1996 1 +17.0799999237061 -32.0699996948242 Sepia hieronis 1990 1 +16.1399993896484 -30.5900001525879 Sepia hieronis 1992 1 +18.4099998474121 -34.3899993896484 Sepia hieronis 1992 1 +17.1499996185303 -31.9832992553711 Sepia hieronis 1990 1 +19.8500003814697 -35.3333015441895 Sepia hieronis 1991 1 +15.8500003814697 -29.0666999816895 Sepia hieronis 1993 1 +16.8999996185303 -30.9333000183105 Sepia hieronis 1997 1 +16.0699996948242 -30.4899997711182 Sepia hieronis 1986 1 +15.6833000183105 -29.5167007446289 Sepia hieronis 1990 1 +15.5 -30.2600002288818 Sepia hieronis 1994 1 +16.2199993133545 -30.1399993896484 Sepia hieronis 1995 1 +18.2999992370605 -34.3499984741211 Sepia hieronis 2006 1 +15.75 -29.9167003631592 Sepia hieronis 1990 1 +17.5832996368408 -33.5999984741211 Sepia hieronis 1990 1 +16.7667007446289 -31.86669921875 Sepia hieronis 1996 1 +17.0699996948242 -31.4400005340576 Sepia hieronis 2004 1 +17.0100002288818 -32 Sepia hieronis 2006 1 +17.2000007629395 -30.5799999237061 Sepia hieronis 1987 1 +16.11669921875 -29.0333003997803 Sepia hieronis 2002 1 +18.2199993133545 -34.4199981689453 Sepia hieronis 2004 1 +17.7833003997803 -32.0499992370605 Sepia hieronis 1988 1 +16.3600006103516 -32.2400016784668 Sepia hieronis 2006 1 +17.8999996185303 -33.9500007629395 Sepia hieronis 2001 1 +17.5 -31.3999996185303 Sepia hieronis 1994 1 +17.4300003051758 -32.4700012207031 Sepia hieronis 1991 1 +19.3500003814697 -35.3400001525879 Sepia hieronis 1988 1 +19.2800006866455 -35.0900001525879 Sepia hieronis 1986 1 +17.5 -33.189998626709 Sepia hieronis 1993 1 +16.0599994659424 -29.1399993896484 Sepia hieronis 1993 1 +17.6000003814697 -32.4333000183105 Sepia hieronis 1997 1 +16.4333000183105 -29.7999992370605 Sepia hieronis 1994 1 +15.5799999237061 -28.2600002288818 Sepia hieronis 1987 1 +17.1000003814697 -31.88330078125 Sepia hieronis 1995 1 +16.9666996002197 -31.7166996002197 Sepia hieronis 1987 1 +17.2000007629395 -32.5200004577637 Sepia hieronis 2006 1 +16.0699996948242 -29.0400009155273 Sepia hieronis 1987 1 +18.5900001525879 -35.189998626709 Sepia hieronis 1995 1 +19.86669921875 -35.36669921875 Sepia hieronis 1992 1 +16.5300006866455 -30.5699996948242 Sepia hieronis 2004 1 +17.25 -32.4832992553711 Sepia hieronis 1996 1 +16.5 -32.2599983215332 Sepia hieronis 2004 1 +17.1000003814697 -31.8166999816895 Sepia hieronis 1988 1 +15.4700002670288 -29.3600006103516 Sepia hieronis 1987 1 +16.4899997711182 -30.25 Sepia hieronis 1989 1 +16.5333003997803 -32.2999992370605 Sepia hieronis 2000 1 +17.0599994659424 -32.1599998474121 Sepia hieronis 2004 1 +16.88330078125 -31.1499996185303 Sepia hieronis 1990 1 +17.5167007446289 -31.25 Sepia hieronis 1988 1 +15.1700000762939 -30.3400001525879 Sepia hieronis 1995 1 +15.6499996185303 -30.3999996185303 Sepia hieronis 1990 1 +15.5799999237061 -30.1800003051758 Sepia hieronis 2003 1 +15.3999996185303 -30.1000003814697 Sepia hieronis 1988 1 +17.3600006103516 -33.3600006103516 Sepia hieronis 1988 1 +15.3166999816895 -29.8500003814697 Sepia hieronis 1988 1 +17.3099994659424 -32.3600006103516 Sepia hieronis 2004 1 +15.75 -30.25 Sepia hieronis 1992 1 +15.4399995803833 -30.2199993133545 Sepia hieronis 2003 1 +16.8166999816895 -30.4167003631592 Sepia hieronis 1989 1 +17.38330078125 -31.5333003997803 Sepia hieronis 1995 1 +16.0300006866455 -29.3799991607666 Sepia hieronis 1993 1 +17.25 -31.4500007629395 Sepia hieronis 2005 1 +15.3100004196167 -30.1100006103516 Sepia hieronis 2006 1 +17.6667003631592 -33.4166984558105 Sepia hieronis 2001 1 +16.2900009155273 -30.1100006103516 Sepia hieronis 1991 1 +17.4400005340576 -32.3300018310547 Sepia hieronis 1989 1 +18.2199993133545 -34.2999992370605 Sepia hieronis 1988 1 +15.2833003997803 -30.11669921875 Sepia hieronis 1996 1 +17.5666999816895 -31.1499996185303 Sepia hieronis 1990 1 +16.2833003997803 -29.3332996368408 Sepia hieronis 1997 1 +17.6667003631592 -32.3166999816895 Sepia hieronis 1991 1 +16.2332992553711 -30.2667007446289 Sepia hieronis 1990 1 +16.6667003631592 -30.8500003814697 Sepia hieronis 1988 1 +19.0900001525879 -35.25 Sepia hieronis 1992 1 +17.3332996368408 -30.7833003997803 Sepia hieronis 1991 1 +16.2667007446289 -29.36669921875 Sepia hieronis 1991 1 +16.9500007629395 -30.9666996002197 Sepia hieronis 2000 1 +17.0799999237061 -31.5300006866455 Sepia hieronis 2003 1 +20 -36.13330078125 Sepia hieronis 1989 1 +15.3999996185303 -28.5666999816895 Sepia hieronis 1988 1 +17.1200008392334 -31.1700000762939 Sepia hieronis 2004 1 +15.0799999237061 -29.4699993133545 Sepia hieronis 2003 1 +17.75 -32.38330078125 Sepia hieronis 2002 1 +15.1800003051758 -30.1800003051758 Sepia hieronis 2003 1 +16.3999996185303 -29.5400009155273 Sepia hieronis 2003 1 +17.61669921875 -33.6500015258789 Sepia hieronis 1989 1 +19.5499992370605 -35.5 Sepia hieronis 1989 1 +17.2000007629395 -31.1800003051758 Sepia hieronis 1987 1 +17.63330078125 -32.4333000183105 Sepia hieronis 2002 1 +19.5666999816895 -35.7000007629395 Sepia hieronis 1990 1 +15.5666999816895 -30.2332992553711 Sepia hieronis 1999 1 +17.3332996368408 -32.2000007629395 Sepia hieronis 1988 1 +14.5200004577637 -29.1399993896484 Sepia hieronis 1988 1 +18.5167007446289 -34.7000007629395 Sepia hieronis 2002 1 +17.2332992553711 -31.3999996185303 Sepia hieronis 1995 1 +15.1199998855591 -29.5699996948242 Sepia hieronis 1989 1 +17.2700004577637 -32.3400001525879 Sepia hieronis 1986 1 +18.8999996185303 -34.7832984924316 Sepia hieronis 2002 1 +16.2700004577637 -28.4200000762939 Sepia hieronis 1993 1 +17.6833000183105 -33.0999984741211 Sepia hieronis 1991 1 +16.9666996002197 -31.8999996185303 Sepia hieronis 1997 1 +17.3999996185303 -31.7166996002197 Sepia hieronis 1989 1 +17.2600002288818 -31.2999992370605 Sepia hieronis 1992 1 +16.63330078125 -32.25 Sepia hieronis 2002 1 +15.4499998092651 -30.1499996185303 Sepia hieronis 1992 1 +17.3999996185303 -31.0666999816895 Sepia hieronis 1991 1 +16.7667007446289 -30.75 Sepia hieronis 1991 1 +15.460000038147 -29.2600002288818 Sepia hieronis 2004 1 +16.3999996185303 -29.5900001525879 Sepia hieronis 2006 1 +17.8500003814697 -33.3499984741211 Sepia hieronis 1986 1 +16.2833003997803 -28.9666996002197 Sepia hieronis 1991 1 +15.8999996185303 -30.2000007629395 Sepia hieronis 1990 1 +16.0666999816895 -29.2332992553711 Sepia hieronis 1997 1 +15.2333002090454 -30.6000003814697 Sepia hieronis 1997 1 +16.75 -32.36669921875 Sepia hieronis 2002 1 +17.3899993896484 -33.3600006103516 Sepia hieronis 1989 1 +17.2000007629395 -32.2832984924316 Sepia hieronis 1995 1 +18.9167003631592 -35 Sepia hieronis 1990 1 +14.6999998092651 -27.3500003814697 Sepia hieronis 2000 1 +17.8500003814697 -32.0332984924316 Sepia hieronis 1995 1 +17.0499992370605 -30.5832996368408 Sepia hieronis 1994 1 +17.2399997711182 -32.0400009155273 Sepia hieronis 1995 1 +16.5200004577637 -30.3700008392334 Sepia hieronis 2003 1 +15.1999998092651 -29.4400005340576 Sepia hieronis 1995 1 +19.0666999816895 -35.4000015258789 Sepia hieronis 1999 1 +16.3166999816895 -30.4167003631592 Sepia hieronis 1988 1 +17.1800003051758 -31.0599994659424 Sepia hieronis 1991 1 +16.7667007446289 -30.2833003997803 Sepia hieronis 1996 1 +16.0100002288818 -29.2000007629395 Sepia hieronis 1992 1 +16.5300006866455 -30.1100006103516 Sepia hieronis 1994 1 +16.5832996368408 -30.6833000183105 Sepia hieronis 1995 1 +15.0799999237061 -29.3999996185303 Sepia hieronis 1993 1 +17.2000007629395 -32.1199989318848 Sepia hieronis 1988 1 +18.25 -34.2999992370605 Sepia hieronis 1988 1 +16.0599994659424 -29.3999996185303 Sepia hieronis 1987 1 +18.4167003631592 -34.5 Sepia hieronis 1990 1 +17.8332996368408 -33.4333000183105 Sepia hieronis 1992 1 +19.1800003051758 -35.0999984741211 Sepia hieronis 1992 1 +17.1900005340576 -31.4699993133545 Sepia hieronis 1994 1 +17.1000003814697 -30 Sepia hieronis 1995 1 +17.4699993133545 -33.2299995422363 Sepia hieronis 2003 1 +16.3799991607666 -31.5799999237061 Sepia hieronis 2004 1 +16.0400009155273 -30.1399993896484 Sepia hieronis 2005 1 +15.7833003997803 -29.6000003814697 Sepia hieronis 1987 1 +15.1167001724243 -29.7000007629395 Sepia hieronis 1987 1 +19.3166999816895 -35.5167007446289 Sepia hieronis 1996 1 +16.8332996368408 -32.0499992370605 Sepia hieronis 2002 1 +16.2600002288818 -30.5599994659424 Sepia hieronis 2006 1 +16.3166999816895 -29.7166996002197 Sepia hieronis 1990 1 +17.2199993133545 -32.2700004577637 Sepia hieronis 1994 1 +17.25 -31.8166999816895 Sepia hieronis 1991 1 +16.5499992370605 -30.3166999816895 Sepia hieronis 1988 1 +16.2833003997803 -29.4666996002197 Sepia hieronis 1994 1 +17.0100002288818 -31.4300003051758 Sepia hieronis 2005 1 +19.2999992370605 -35.5332984924316 Sepia hieronis 1988 1 +15.2833003997803 -30.5666999816895 Sepia hieronis 1995 1 +15.1000003814697 -29.4899997711182 Sepia hieronis 1992 1 +17.3700008392334 -33.3899993896484 Sepia hieronis 1989 1 +16.5167007446289 -29.6000003814697 Sepia hieronis 1995 1 +16.4167003631592 -30.8332996368408 Sepia hieronis 1995 1 +16.5400009155273 -32.3899993896484 Sepia hieronis 1993 1 +16.5 -32.0400009155273 Sepia hieronis 2002 1 +16.3999996185303 -31.9500007629395 Sepia hieronis 2000 1 +15.0900001525879 -29.5400009155273 Sepia hieronis 1987 1 +17.2700004577637 -32.3499984741211 Sepia hieronis 1994 1 +16.0799999237061 -29.5300006866455 Sepia hieronis 2004 1 +16.9666996002197 -30.0167007446289 Sepia hieronis 1995 1 +17.3700008392334 -32.2000007629395 Sepia hieronis 1992 1 +15.3199996948242 -30.3099994659424 Sepia hieronis 2006 1 +19.4899997711182 -35.4900016784668 Sepia hieronis 2005 1 +14.8166999816895 -28.0666999816895 Sepia hieronis 1989 1 +16.2667007446289 -29.7667007446289 Sepia hieronis 1987 1 +16.3999996185303 -30.3099994659424 Sepia hieronis 1992 1 +17.0333003997803 -32.2000007629395 Sepia hieronis 1988 1 +16.3799991607666 -29.4500007629395 Sepia hieronis 1989 1 +19.13330078125 -34.88330078125 Sepia hieronis 1995 1 +16.25 -30.6833000183105 Sepia hieronis 1997 1 +19.6833000183105 -35.5332984924316 Sepia hieronis 1989 1 +17.2667007446289 -31.9333000183105 Sepia hieronis 1991 1 +15.1000003814697 -30.0333003997803 Sepia hieronis 1993 1 +18.3999996185303 -34.5900001525879 Sepia hieronis 1992 1 +19.2000007629395 -35.13330078125 Sepia hieronis 1990 1 +16.7833003997803 -30.86669921875 Sepia hieronis 1989 1 +17.9666996002197 -33.9500007629395 Sepia hieronis 1996 1 +15.460000038147 -30.0799999237061 Sepia hieronis 2004 1 +15.8166999816895 -28.9333000183105 Sepia hieronis 1987 1 +17 -31.4300003051758 Sepia hieronis 1989 1 +17.25 -31.6499996185303 Sepia hieronis 1992 1 +14.4333000183105 -28.9333000183105 Sepia hieronis 1987 1 +19.1200008392334 -35.0800018310547 Sepia hieronis 1990 1 +17.1399993896484 -30.5699996948242 Sepia hieronis 1988 1 +15.5600004196167 -29.3199996948242 Sepia hieronis 1991 1 +18.4599990844727 -34.4500007629395 Sepia hieronis 1992 1 +16.1100006103516 -30.4500007629395 Sepia hieronis 1995 1 +17.5499992370605 -33.3199996948242 Sepia hieronis 2003 1 +16.1100006103516 -28.5699996948242 Sepia hieronis 2004 1 +16.7667007446289 -30.1000003814697 Sepia hieronis 1995 1 +16.4099998474121 -30.1399993896484 Sepia hieronis 1989 1 +17.9333000183105 -33.9000015258789 Sepia hieronis 1997 1 +17.4167003631592 -30.6667003631592 Sepia hieronis 1986 1 +20 -36.0800018310547 Sepia hieronis 1989 1 +16.0699996948242 -30.4699993133545 Sepia hieronis 2004 1 +17.0499992370605 -31.7667007446289 Sepia hieronis 2002 1 +16.5599994659424 -30.1700000762939 Sepia hieronis 1988 1 +19.4200000762939 -35.5499992370605 Sepia hieronis 1986 1 +17.7332992553711 -33.4500007629395 Sepia hieronis 2001 1 +16.6499996185303 -34.9000015258789 Sepia hieronis 2000 1 +16.4899997711182 -30.4300003051758 Sepia hieronis 2006 1 +16.5200004577637 -31.4899997711182 Sepia hieronis 2005 1 +17.3199996948242 -32.4099998474121 Sepia hieronis 2004 1 +15.5500001907349 -29.0699996948242 Sepia hieronis 1995 1 +16.1499996185303 -30.6667003631592 Sepia hieronis 2002 1 +17.2399997711182 -31.4300003051758 Sepia hieronis 1989 1 +17.13330078125 -32.11669921875 Sepia hieronis 1990 1 +16.88330078125 -30.9666996002197 Sepia hieronis 1995 1 +16.3199996948242 -30.1299991607666 Sepia hieronis 1987 1 +15.4833002090454 -30.38330078125 Sepia hieronis 1992 1 +16.0333003997803 -30.6499996185303 Sepia hieronis 1987 1 +14.8667001724243 -29.7833003997803 Sepia hieronis 1987 1 +16.5200004577637 -30.4899997711182 Sepia hieronis 2003 1 +16.1800003051758 -29.4500007629395 Sepia hieronis 2003 1 +16.5599994659424 -30.2800006866455 Sepia hieronis 2006 1 +17.7166996002197 -32.7999992370605 Sepia hieronis 1995 1 +16.3400001525879 -30.2299995422363 Sepia hieronis 1991 1 +15.9167003631592 -29.11669921875 Sepia hieronis 1995 1 +16.5499992370605 -29.9333000183105 Sepia hieronis 1999 1 +17.2299995422363 -31.3799991607666 Sepia hieronis 2005 1 +17.4899997711182 -33.2599983215332 Sepia hieronis 2004 1 +19.0832996368408 -35.3499984741211 Sepia hieronis 2002 1 +15.1332998275757 -29.9666996002197 Sepia hieronis 1996 1 +16.61669921875 -30.9500007629395 Sepia hieronis 1988 1 +16.3899993896484 -30.4899997711182 Sepia hieronis 2003 1 +17.1499996185303 -32.2099990844727 Sepia hieronis 2004 1 +17.5100002288818 -33.2799987792969 Sepia hieronis 2001 1 +17.75 -35.4832992553711 Sepia hieronis 2001 1 +16.1599998474121 -29.2199993133545 Sepia hieronis 1991 1 +16.0499992370605 -29.63330078125 Sepia hieronis 1997 1 +17.5832996368408 -33.5499992370605 Sepia hieronis 1990 1 +19.2700004577637 -35.1300010681152 Sepia hieronis 2006 1 +16.2900009155273 -31.2900009155273 Sepia hieronis 1995 1 +17.3299999237061 -32.4300003051758 Sepia hieronis 1992 1 +15.3599996566772 -29.1900005340576 Sepia hieronis 1994 1 +17.8166999816895 -33.7832984924316 Sepia hieronis 1999 1 +17.2099990844727 -32.1500015258789 Sepia hieronis 1992 1 +16.0799999237061 -29.2600002288818 Sepia hieronis 2006 2 +16.2800006866455 -31.5100002288818 Sepia hieronis 2006 1 +17.0499992370605 -30.9333000183105 Sepia hieronis 1987 1 +17.0900001525879 -31 Sepia hieronis 1989 1 +17.7667007446289 -32.38330078125 Sepia hieronis 1997 1 +16.0799999237061 -29.4599990844727 Sepia hieronis 1991 1 +17.6000003814697 -33.5833015441895 Sepia hieronis 1987 1 +17.1299991607666 -30.4799995422363 Sepia hieronis 2004 1 +17.13330078125 -31.4167003631592 Sepia hieronis 2002 1 +16.1599998474121 -29.1599998474121 Sepia hieronis 2003 1 +17.5832996368408 -33.5499992370605 Sepia hieronis 1997 1 +15.7333002090454 -30.2999992370605 Sepia hieronis 1987 1 +15.3800001144409 -30.0200004577637 Sepia hieronis 1988 1 +15.5832996368408 -29.3166999816895 Sepia hieronis 1996 1 +19.5 -35.7332992553711 Sepia hieronis 1997 1 +19.2800006866455 -35.3699989318848 Sepia hieronis 1995 1 +17.4500007629395 -31.1299991607666 Sepia hieronis 1995 1 +16.61669921875 -29.61669921875 Sepia hieronis 1997 1 +17.3099994659424 -31.1499996185303 Sepia hieronis 1988 1 +15.5799999237061 -29.4400005340576 Sepia hieronis 1987 1 +15.3699998855591 -30.2900009155273 Sepia hieronis 1995 1 +18.1800003051758 -34.2900009155273 Sepia hieronis 1992 1 +17.5 -33.2599983215332 Sepia hieronis 1992 1 +17.1599998474121 -31.5599994659424 Sepia hieronis 1991 1 +15.1899995803833 -29.5100002288818 Sepia hieronis 1988 1 +15.5600004196167 -30.4500007629395 Sepia hieronis 2006 1 +15.6332998275757 -29.25 Sepia hieronis 1996 1 +16.3400001525879 -29.1399993896484 Sepia hieronis 1988 1 +17.5167007446289 -31.7166996002197 Sepia hieronis 2000 1 +17.25 -31.4699993133545 Sepia hieronis 2004 1 +16.1667003631592 -28.9333000183105 Sepia hieronis 1988 1 +17.25 -31.2700004577637 Sepia hieronis 2006 1 +17 -31.7166996002197 Sepia hieronis 1989 1 +-40.2893943786621 -20.2999820709229 Conus lemniscatus 2002 1 +17.1499996185303 -31.4899997711182 Sepia hieronis 1991 1 +15.2700004577637 -30.0100002288818 Sepia hieronis 1994 1 +16.3799991607666 -32.0200004577637 Sepia hieronis 2002 1 +17.7166996002197 -33.3166999816895 Sepia hieronis 1988 1 +17.5900001525879 -34.0099983215332 Sepia hieronis 1988 1 +16.2667007446289 -29.63330078125 Sepia hieronis 2002 1 +16.0300006866455 -29.3899993896484 Sepia hieronis 2006 1 +16.0333003997803 -29.9167003631592 Sepia hieronis 1999 1 +15.8832998275757 -30.38330078125 Sepia hieronis 1996 1 +16.5300006866455 -30.5799999237061 Sepia hieronis 1995 1 +16.4832992553711 -29.8332996368408 Sepia hieronis 1997 1 +16.3899993896484 -31.5799999237061 Sepia hieronis 2006 1 +17.7000007629395 -32.8333015441895 Sepia hieronis 1990 1 +16.0900001525879 -29.2800006866455 Sepia hieronis 1987 1 +16.6667003631592 -31.1667003631592 Sepia hieronis 1995 1 +-40.6666641235352 -22.7000007629395 Isognomon bicolor 2006 3 +16.1399993896484 -30.5699996948242 Sepia hieronis 2003 1 +16.5400009155273 -32.3800010681152 Sepia hieronis 2002 1 +16.1499996185303 -29.4832992553711 Sepia hieronis 1990 1 +15.3000001907349 -29.1599998474121 Sepia hieronis 1988 1 +17.0832996368408 -30.86669921875 Sepia hieronis 1992 1 +17.3700008392334 -34.060001373291 Sepia hieronis 2001 1 +17.1900005340576 -31.1599998474121 Sepia hieronis 1987 1 +16.13330078125 -29.61669921875 Sepia hieronis 1987 1 +16.1000003814697 -29.0599994659424 Sepia hieronis 1989 1 +17.3999996185303 -32.0666999816895 Sepia hieronis 1995 1 +16.1399993896484 -29.5599994659424 Sepia hieronis 1995 1 +17.25 -31.61669921875 Sepia hieronis 2000 1 +15.2700004577637 -30.1100006103516 Sepia hieronis 2003 1 +16.2000007629395 -30.0599994659424 Sepia hieronis 2006 1 +-41.3833312988281 -22.5 Siratus tenuivaricosus 1963 1 +15.5500001907349 -30.7166996002197 Sepia hieronis 2002 1 +17.0200004577637 -32.1199989318848 Sepia hieronis 1988 1 +16.5900001525879 -31.4300003051758 Sepia hieronis 1994 1 +17.4666996002197 -31.1667003631592 Sepia hieronis 1995 1 +17.4400005340576 -33.2299995422363 Sepia hieronis 2004 1 +20 -35.9333000183105 Sepia hieronis 1994 1 +15.25 -30.5499992370605 Sepia hieronis 1990 2 +19.5599994659424 -35.4300003051758 Sepia hieronis 1992 1 +17.8166999816895 -32.4500007629395 Sepia hieronis 1990 1 +15.1800003051758 -29.5900001525879 Sepia hieronis 1988 1 +16.3099994659424 -29.0699996948242 Sepia hieronis 1993 1 +17.2399997711182 -30.3899993896484 Sepia hieronis 1987 1 +16.1833000183105 -30.75 Sepia hieronis 1995 1 +16.6667003631592 -30.7166996002197 Sepia hieronis 1991 1 +19.5499992370605 -35.7667007446289 Sepia hieronis 1993 1 +18.6499996185303 -34.5999984741211 Sepia hieronis 2000 1 +16.4699993133545 -32.0299987792969 Sepia hieronis 2002 1 +19.8332996368408 -36 Sepia hieronis 1997 1 +16.7667007446289 -32.25 Sepia hieronis 2002 1 +15.75 -30.0666999816895 Sepia hieronis 1992 1 +16.0100002288818 -29.4099998474121 Sepia hieronis 1990 1 +17.8500003814697 -33.4166984558105 Sepia hieronis 1989 1 +16.3999996185303 -30.1499996185303 Sepia hieronis 1988 1 +-48.5628776550293 -26.906343460083 Siratus tenuivaricosus 1977 1 +-39.024356842041 -18.0170059204102 Lucapina sowerbii 1993 1 +-39.9099998474121 -20.0049991607666 Alvania faberi 1996 1 +15.3400001525879 -30.0499992370605 Sepia hieronis 1992 1 +17.7999992370605 -33.4166984558105 Sepia hieronis 2001 1 +19.3500003814697 -35.5666999816895 Sepia hieronis 1991 1 +18.5900001525879 -34.5900001525879 Sepia hieronis 1993 1 +16.7999992370605 -29.9333000183105 Sepia hieronis 1986 1 +16.2999992370605 -31.4832992553711 Sepia hieronis 1999 1 +17.1200008392334 -34.4300003051758 Sepia hieronis 1995 1 +19.4333000183105 -35.36669921875 Sepia hieronis 1999 1 +19.7999992370605 -35.6500015258789 Sepia hieronis 1999 1 +-32.4017639160156 -3.8345320224762 Favartia cellulosa 2000 1 +18.9500007629395 -35.1833000183105 Sepia hieronis 1997 1 +15.8500003814697 -30.4333000183105 Sepia hieronis 1989 1 +16.2332992553711 -30.5 Sepia hieronis 1999 1 +17.0167007446289 -32.0666999816895 Sepia hieronis 2002 1 +15.5699996948242 -29.1800003051758 Sepia hieronis 2006 1 +17.0499992370605 -30.5200004577637 Sepia hieronis 1992 1 +17.3700008392334 -32.3800010681152 Sepia hieronis 2006 1 +-43.1290397644043 -22.9085330963135 Isognomon bicolor 2000 1 +16.8500003814697 -31.8999996185303 Sepia hieronis 1993 1 +16.5 -32.0200004577637 Sepia hieronis 1991 1 +18.86669921875 -34.9667015075684 Sepia hieronis 1997 1 +15.6833000183105 -30 Sepia hieronis 2002 1 +16.2600002288818 -30.5300006866455 Sepia hieronis 2004 1 +19.4666996002197 -35.61669921875 Sepia hieronis 1995 1 +17.36669921875 -32.7667007446289 Sepia hieronis 1995 1 +19.1800003051758 -35.2000007629395 Sepia hieronis 2003 1 +17.4300003051758 -33.25 Sepia hieronis 2004 1 +15.1499996185303 -29.4899997711182 Sepia hieronis 1988 1 +17.2999992370605 -31.3400001525879 Sepia hieronis 1991 1 +16.0200004577637 -29.3700008392334 Sepia hieronis 1988 1 +16.2900009155273 -29.4500007629395 Sepia hieronis 2006 1 +17.2299995422363 -32.2799987792969 Sepia hieronis 2005 1 +19.0300006866455 -34.4900016784668 Sepia hieronis 1995 1 +14.7166996002197 -27.36669921875 Sepia hieronis 2000 2 +16.2667007446289 -30.7000007629395 Sepia hieronis 1999 1 +19.25 -35.1500015258789 Sepia hieronis 1988 1 +17.0100002288818 -31.5200004577637 Sepia hieronis 1988 1 +18.4333000183105 -34.6833000183105 Sepia hieronis 1989 1 +19.9167003631592 -35.7999992370605 Sepia hieronis 1995 1 +17.2399997711182 -32.2000007629395 Sepia hieronis 1992 1 +-42.0237350463867 -22.9596252441406 Isognomon bicolor 2003 1 +17.9167003631592 -33.6666984558105 Sepia hieronis 1989 1 +15.5333003997803 -30.4832992553711 Sepia hieronis 1986 1 +15.5900001525879 -29.1499996185303 Sepia hieronis 1989 1 +16.4200000762939 -30.0699996948242 Sepia hieronis 2005 1 +16.2833003997803 -30.88330078125 Sepia hieronis 1989 1 +15.5500001907349 -30.6667003631592 Sepia hieronis 1986 1 +17.1499996185303 -31 Sepia hieronis 1989 1 +17.1000003814697 -31.3600006103516 Sepia hieronis 1992 1 +19.0599994659424 -35.0099983215332 Sepia hieronis 2006 1 +18.36669921875 -34.5 Sepia hieronis 1995 1 +-40.9833335876465 -23.0666675567627 Caecum metamorphosicum 2004 2 +19.36669921875 -35.13330078125 Sepia hieronis 1997 1 +17.3799991607666 -32.5800018310547 Sepia hieronis 2003 1 +-44.3092231750488 -23.026554107666 Isognomon bicolor 2008 1 +16.4200000762939 -32.3300018310547 Sepia hieronis 2006 1 +16.3999996185303 -28.8999996185303 Sepia hieronis 1995 1 +16.5499992370605 -29.9832992553711 Sepia hieronis 1990 1 +15.5600004196167 -29.0599994659424 Sepia hieronis 1988 1 +17.5100002288818 -34.0200004577637 Sepia hieronis 2004 1 +16.1833000183105 -29.5499992370605 Sepia hieronis 2002 1 +19.75 -35.7167015075684 Sepia hieronis 1994 1 +16.2000007629395 -28.8166999816895 Sepia hieronis 1990 1 +-44.4089164733887 -22.9899158477783 Isognomon bicolor 2008 1 +15.4399995803833 -30.1800003051758 Sepia hieronis 1987 1 +17.11669921875 -31.7166996002197 Sepia hieronis 2002 1 +18.86669921875 -35.0666999816895 Sepia hieronis 1995 1 +15.2700004577637 -30.25 Sepia hieronis 1991 1 +17.2800006866455 -32.0800018310547 Sepia hieronis 2004 1 +19.2999992370605 -35.4099998474121 Sepia hieronis 1991 1 +16.2166996002197 -30.2833003997803 Sepia hieronis 1991 1 +16.2332992553711 -28.8999996185303 Sepia hieronis 2002 1 +16.2999992370605 -29.3500003814697 Sepia hieronis 2003 1 +16.1000003814697 -29.1499996185303 Sepia hieronis 1995 1 +20 -35.4199981689453 Sepia hieronis 1993 1 +17.4500007629395 -30.9666996002197 Sepia hieronis 1996 1 +-44.3410263061523 -22.9794445037842 Isognomon bicolor 2008 1 +16.1100006103516 -29.3899993896484 Sepia hieronis 1990 1 +16.5900001525879 -30.2399997711182 Sepia hieronis 1989 1 +16.7332992553711 -30.6667003631592 Sepia hieronis 1996 1 +17.3500003814697 -31.4832992553711 Sepia hieronis 2000 1 +15.5900001525879 -30.1900005340576 Sepia hieronis 1990 1 +16.0333003997803 -29.61669921875 Sepia hieronis 1988 1 +16.3299999237061 -29.5900001525879 Sepia hieronis 1990 1 +17.0832996368408 -32.0833015441895 Sepia hieronis 2002 1 +17.38330078125 -32.5 Sepia hieronis 1989 1 +16.1499996185303 -30.7166996002197 Sepia hieronis 1994 1 +17.61669921875 -33.4667015075684 Sepia hieronis 1999 1 +17.6667003631592 -33.25 Sepia hieronis 1989 1 +17.5799999237061 -31.5100002288818 Pterygosquilla capensis 1990 1 +19.0300006866455 -34.4199981689453 Pterygosquilla capensis 1995 1 +17.2999992370605 -32.8166999816895 Pterygosquilla capensis 1999 1 +17 -31.8166999816895 Pterygosquilla capensis 1989 1 +17.5666999816895 -32.4832992553711 Pterygosquilla capensis 1999 1 +16.0599994659424 -30.1499996185303 Pterygosquilla capensis 2003 1 +18.0666999816895 -31.9167003631592 Pterygosquilla capensis 1994 1 +16.4400005340576 -30.1900005340576 Pterygosquilla capensis 1990 1 +16.3099994659424 -28.5400009155273 Pterygosquilla capensis 1987 1 +17.4500007629395 -32.5833015441895 Pterygosquilla capensis 1994 1 +16.2700004577637 -28.5200004577637 Pterygosquilla capensis 2005 1 +-44.4145545959473 -22.967887878418 Isognomon bicolor 2008 1 +18.5 -34.5833015441895 Sepia hieronis 1992 1 +17.8332996368408 -33.3166999816895 Sepia hieronis 1997 1 +17.1200008392334 -32.1199989318848 Sepia hieronis 2004 1 +16.61669921875 -32.0332984924316 Sepia hieronis 2002 1 +16.2332992553711 -29.0666999816895 Sepia hieronis 1990 1 +16.8500003814697 -30.6499996185303 Sepia hieronis 1989 1 +16.6667003631592 -30.13330078125 Sepia hieronis 1988 1 +16.2800006866455 -30.1399993896484 Sepia hieronis 2003 1 +16.7999992370605 -30.11669921875 Sepia hieronis 1987 1 +-44.1193237304688 -23.1512222290039 Isognomon bicolor 2009 1 +17.1100006103516 -31.2299995422363 Sepia hieronis 1990 1 +17.2299995422363 -32.2999992370605 Sepia hieronis 1989 1 +17.7667007446289 -32.0666999816895 Sepia hieronis 2002 1 +15.8999996185303 -29.7833003997803 Sepia hieronis 1988 1 +20 -36.0699996948242 Sepia hieronis 1995 1 +19.4333000183105 -35.5833015441895 Sepia hieronis 1996 1 +17.2833003997803 -32.61669921875 Sepia hieronis 2002 1 +17.61669921875 -33.88330078125 Sepia hieronis 1987 1 +17.61669921875 -32.5666999816895 Sepia hieronis 1986 1 +-44.3910026550293 -23.1024627685547 Isognomon bicolor 2009 1 +19.4167003631592 -35.25 Sepia hieronis 1988 1 +16.4200000762939 -29.5599994659424 Sepia hieronis 1992 1 +16.3999996185303 -30.0799999237061 Sepia hieronis 1988 1 +16.5799999237061 -31.5900001525879 Sepia hieronis 1992 1 +17.6000003814697 -31.9666996002197 Sepia hieronis 1995 1 +17.7667007446289 -32.5499992370605 Sepia hieronis 1990 1 +-40.5257034301758 -22.5308322906494 Caecum metamorphosicum 2009 1 +15.1499996185303 -29.75 Sepia hieronis 1989 1 +16.7332992553711 -30.3999996185303 Sepia hieronis 1991 1 +19.3700008392334 -35.1199989318848 Sepia hieronis 2003 1 +17.5100002288818 -33.5900001525879 Sepia hieronis 1993 1 +17.3166999816895 -31.7833003997803 Sepia hieronis 1994 1 +17.38330078125 -31.2667007446289 Sepia hieronis 2002 1 +16.0599994659424 -30 Pterygosquilla capensis 1992 1 +-44.0925903320312 -23.1734046936035 Isognomon bicolor 2009 1 +16.3500003814697 -29.3400001525879 Pterygosquilla capensis 2003 1 +16.1000003814697 -29.6667003631592 Pterygosquilla capensis 1987 1 +17.8332996368408 -33.3166999816895 Pterygosquilla capensis 1991 1 +13.6499996185303 -24.3999996185303 Pterygosquilla capensis 2000 4 +16.1599998474121 -31.3899993896484 Pterygosquilla capensis 2004 1 +17.2600002288818 -32.3400001525879 Pterygosquilla capensis 2006 1 +15.5832996368408 -30.4832992553711 Pterygosquilla capensis 1987 1 +17.1100006103516 -30.3600006103516 Pterygosquilla capensis 1986 1 +17.3500003814697 -31.0499992370605 Pterygosquilla capensis 1987 1 +18.0499992370605 -33.6500015258789 Pterygosquilla capensis 1995 1 +-35.0452575683594 -6.22642183303833 Isognomon bicolor 2009 1 +17.3500003814697 -30.5699996948242 Pterygosquilla capensis 1988 1 +17.25 -31.11669921875 Pterygosquilla capensis 1986 1 +17.9832992553711 -31.7000007629395 Pterygosquilla capensis 2002 1 +16.2099990844727 -29.4099998474121 Pterygosquilla capensis 2006 1 +16.0599994659424 -29.0900001525879 Pterygosquilla capensis 1995 1 +17.7833003997803 -32.0499992370605 Pterygosquilla capensis 1988 1 +15.3199996948242 -30.3099994659424 Pterygosquilla capensis 2006 1 +-44.5542526245117 -23.0374698638916 Isognomon bicolor 2009 1 +16.8166999816895 -29.3166999816895 Pterygosquilla capensis 1996 1 +15.4700002670288 -29.3600006103516 Pterygosquilla capensis 1987 1 +17.63330078125 -32.61669921875 Pterygosquilla capensis 1990 1 +17.1000003814697 -31.3099994659424 Pterygosquilla capensis 1992 1 +16.4666996002197 -29.75 Pterygosquilla capensis 1990 1 +17.5499992370605 -33.2900009155273 Pterygosquilla capensis 2004 1 +13.3332996368408 -23.61669921875 Pterygosquilla capensis 1989 1 +17.63330078125 -32.13330078125 Pterygosquilla capensis 1987 1 +17.4599990844727 -32.1800003051758 Pterygosquilla capensis 1989 1 +19.0900001525879 -34.5499992370605 Sepia hieronis 1994 1 +17.0499992370605 -32.7332992553711 Sepia hieronis 1999 1 +-44.6439552307129 -23.1894721984863 Isognomon bicolor 2009 1 +15.4399995803833 -29.3299999237061 Sepia hieronis 1988 1 +17.88330078125 -33.4000015258789 Sepia hieronis 1990 1 +15.5900001525879 -28.4400005340576 Sepia hieronis 1987 1 +16.5499992370605 -32.2000007629395 Sepia hieronis 2002 1 +16.0100002288818 -29.3299999237061 Sepia hieronis 2006 1 +16.0200004577637 -30.3899993896484 Sepia hieronis 1987 1 +17.6667003631592 -33.0833015441895 Sepia hieronis 1994 1 +-34.8031616210938 -7.21418190002441 Isognomon bicolor 2011 1 +15.4099998474121 -29.3199996948242 Sepia hieronis 2006 1 +16.9666996002197 -31.63330078125 Sepia hieronis 1990 1 +-37.6895141601562 -4.51837778091431 Isognomon bicolor 2011 1 +17.1200008392334 -31.4799995422363 Sepia hieronis 2006 1 +17.0300006866455 -31.4599990844727 Sepia hieronis 2004 1 +13.9167003631592 -25.3500003814697 Pterygosquilla capensis 1989 1 +17.0832996368408 -30.9333000183105 Pterygosquilla capensis 1988 1 +17.75 -31.2166996002197 Pterygosquilla capensis 1995 1 +-43.1920623779297 -23.069393157959 Isognomon bicolor 2012 2 +16.7667007446289 -29.1499996185303 Pterygosquilla capensis 2000 1 +18.0400009155273 -31.5400009155273 Pterygosquilla capensis 1986 1 +19.0499992370605 -34.7000007629395 Pterygosquilla capensis 1995 1 +16.3999996185303 -30.1900005340576 Pterygosquilla capensis 1988 1 +17.1000003814697 -31.3600006103516 Pterygosquilla capensis 1992 1 +16.4832992553711 -29.75 Pterygosquilla capensis 1992 1 +17.0100002288818 -31.4200000762939 Pterygosquilla capensis 1985 1 +17.9832992553711 -31.5167007446289 Pterygosquilla capensis 1988 1 +17.1800003051758 -31.0599994659424 Pterygosquilla capensis 1991 1 +18.13330078125 -31.86669921875 Pterygosquilla capensis 2000 1 +17.9666996002197 -31.5499992370605 Pterygosquilla capensis 1992 1 +17.0400009155273 -30.2399997711182 Pterygosquilla capensis 1986 1 +16.2399997711182 -28.5400009155273 Pterygosquilla capensis 1995 1 +17.2999992370605 -31.1000003814697 Pterygosquilla capensis 1991 1 +-38.4950370788574 -12.911395072937 Favartia cellulosa 1955 1 +17.4400005340576 -32.3300018310547 Pterygosquilla capensis 1989 1 +18.1833000183105 -32.0499992370605 Pterygosquilla capensis 1994 1 +17.3332996368408 -32.2000007629395 Pterygosquilla capensis 1988 1 +13.5500001907349 -25.2099990844727 Pterygosquilla capensis 1989 1 +16.4200000762939 -30.4400005340576 Pterygosquilla capensis 2006 1 +16.2099990844727 -30 Pterygosquilla capensis 1992 1 +17.3099994659424 -33.4500007629395 Pterygosquilla capensis 2004 1 +16.5599994659424 -32.0900001525879 Pterygosquilla capensis 1986 1 +17.4400005340576 -33.25 Pterygosquilla capensis 2001 1 +17.7166996002197 -32.8166999816895 Pterygosquilla capensis 1993 1 +13.460000038147 -25.2299995422363 Pterygosquilla capensis 1986 1 +17.4699993133545 -32.0200004577637 Pterygosquilla capensis 2005 1 +-43.2905082702637 -23.0156059265137 Siratus tenuivaricosus 1964 1 +16.3099994659424 -29.0599994659424 Pterygosquilla capensis 2003 1 +16.4599990844727 -29.2600002288818 Pterygosquilla capensis 2003 1 +17.2999992370605 -32.4166984558105 Pterygosquilla capensis 1991 1 +16.2299995422363 -29.5400009155273 Pterygosquilla capensis 1990 1 +16.0699996948242 -29.0400009155273 Pterygosquilla capensis 1987 1 +16.5900001525879 -30.3299999237061 Pterygosquilla capensis 1995 1 +17.4699993133545 -33.2099990844727 Pterygosquilla capensis 1989 1 +17.3600006103516 -33.3400001525879 Pterygosquilla capensis 1990 1 +18.1000003814697 -31.75 Pterygosquilla capensis 1986 1 +17.2667007446289 -31.9167003631592 Pterygosquilla capensis 1997 1 +17.5832996368408 -30.9500007629395 Pterygosquilla capensis 1988 1 +18.0400009155273 -32.2299995422363 Pterygosquilla capensis 2003 1 +13.8500003814697 -24.7000007629395 Pterygosquilla capensis 1989 1 +-41.9135437011719 -22.7657108306885 Lucapina sowerbii 1976 1 +17.8166999816895 -32.4667015075684 Pterygosquilla capensis 1990 1 +16.5499992370605 -29.4599990844727 Pterygosquilla capensis 2003 1 +17.11669921875 -31.88330078125 Pterygosquilla capensis 1999 1 +16.13330078125 -29.4333000183105 Pterygosquilla capensis 1999 1 +16.5200004577637 -31 Pterygosquilla capensis 2003 1 +17.0300006866455 -30.5300006866455 Pterygosquilla capensis 2003 1 +17.1700000762939 -30.2800006866455 Pterygosquilla capensis 1994 1 +16.0499992370605 -29.5699996948242 Pterygosquilla capensis 1987 1 +17.2000007629395 -34.7167015075684 Pterygosquilla capensis 1995 1 +17.5200004577637 -31.4500007629395 Pterygosquilla capensis 1986 1 +17.2999992370605 -31.5 Pterygosquilla capensis 2006 1 +17.5 -31.3899993896484 Pterygosquilla capensis 2006 1 +18.1000003814697 -31.5200004577637 Pterygosquilla capensis 1987 1 +16.5666999816895 -29.0666999816895 Pterygosquilla capensis 1985 1 +-44.3692970275879 -22.9414005279541 Favartia cellulosa 1986 1 +16.1000003814697 -29.2332992553711 Pterygosquilla capensis 1993 1 +17.0400009155273 -32.439998626709 Pterygosquilla capensis 1993 1 +17.3500003814697 -31.3299999237061 Pterygosquilla capensis 2004 1 +17.3332996368408 -30.7833003997803 Pterygosquilla capensis 1991 1 +17.0167007446289 -29.75 Pterygosquilla capensis 1990 1 +16.5599994659424 -29.3500003814697 Pterygosquilla capensis 1988 1 +17.4300003051758 -32.4799995422363 Pterygosquilla capensis 1995 1 +17.7332992553711 -31.9832992553711 Pterygosquilla capensis 2002 1 +16.1499996185303 -30.0200004577637 Pterygosquilla capensis 2005 1 +16.4832992553711 -28.9666996002197 Pterygosquilla capensis 1999 1 +17.11669921875 -31.7332992553711 Pterygosquilla capensis 1990 1 +16.5666999816895 -30.38330078125 Pterygosquilla capensis 1991 1 +17.8999996185303 -31.9333000183105 Pterygosquilla capensis 1994 1 +17.25 -31.3999996185303 Pterygosquilla capensis 1997 1 +17.4899997711182 -33.189998626709 Pterygosquilla capensis 1990 1 +-39.4161109924316 -19.4316654205322 Alvania faberi 2003 1 +15.3999996185303 -30.0400009155273 Pterygosquilla capensis 2006 2 +16.3500003814697 -29.5100002288818 Pterygosquilla capensis 1995 1 +17.2332992553711 -30.36669921875 Pterygosquilla capensis 2000 1 +-53.062370300293 -35.6665954589844 Lyonsia alvarezii 1 +17.3166999816895 -30.8999996185303 Pterygosquilla capensis 1996 1 +16.1667003631592 -28.8166999816895 Pterygosquilla capensis 1987 1 +16.7999992370605 -30.2166996002197 Pterygosquilla capensis 1997 1 +17.4200000762939 -32.4799995422363 Pterygosquilla capensis 1992 1 +17.8332996368408 -31.9167003631592 Pterygosquilla capensis 1985 1 +16.4599990844727 -29.2199993133545 Pterygosquilla capensis 1993 1 +17.3999996185303 -33.1500015258789 Pterygosquilla capensis 1989 1 +17.2299995422363 -31.3199996948242 Pterygosquilla capensis 1995 1 +17.2332992553711 -30.4832992553711 Pterygosquilla capensis 1985 1 +17.5300006866455 -32.0299987792969 Pterygosquilla capensis 1991 1 +17.2999992370605 -31.3299999237061 Pterygosquilla capensis 1994 1 +16.0400009155273 -30.1399993896484 Pterygosquilla capensis 2005 1 +17.1299991607666 -32 Pterygosquilla capensis 2006 1 +17.5599994659424 -31.2900009155273 Pterygosquilla capensis 2002 1 +17.9333000183105 -32.1500015258789 Pterygosquilla capensis 1985 1 +17.5599994659424 -33.4099998474121 Pterygosquilla capensis 1987 1 +17.5 -31.4099998474121 Pterygosquilla capensis 2003 1 +17.0832996368408 -31.7000007629395 Pterygosquilla capensis 1996 1 +17.5 -31.4400005340576 Pterygosquilla capensis 1992 1 +17.0666999816895 -31.7332992553711 Pterygosquilla capensis 1993 1 +16.63330078125 -29.75 Pterygosquilla capensis 1989 1 +19.7833003997803 -35.5332984924316 Pterygosquilla capensis 1987 1 +14.0799999237061 -25.5599994659424 Pterygosquilla capensis 1986 1 +16.5300006866455 -30.2099990844727 Pterygosquilla capensis 2005 1 +17.7667007446289 -31.4666996002197 Pterygosquilla capensis 1990 1 +16.3099994659424 -28.5400009155273 Pterygosquilla capensis 1995 1 +16.1499996185303 -28.9832992553711 Pterygosquilla capensis 2000 1 +16.63330078125 -32.25 Pterygosquilla capensis 2002 1 +17.4500007629395 -32.5800018310547 Pterygosquilla capensis 2003 1 +15.5832996368408 -29.3166999816895 Pterygosquilla capensis 1996 1 +16.38330078125 -29.3166999816895 Pterygosquilla capensis 1997 1 +17.4599990844727 -32.3199996948242 Pterygosquilla capensis 2003 1 +16.3099994659424 -30 Pterygosquilla capensis 2006 1 +14.6667003631592 -29.5 Pterygosquilla capensis 1990 1 +17.0499992370605 -31.63330078125 Pterygosquilla capensis 1987 1 +16.1299991607666 -29.2099990844727 Pterygosquilla capensis 2006 1 +17.1000003814697 -30.5666999816895 Pterygosquilla capensis 1990 1 +17.3400001525879 -31.3099994659424 Pterygosquilla capensis 2002 1 +17.5200004577637 -33.2400016784668 Pterygosquilla capensis 2004 1 +15.0333003997803 -28.5832996368408 Pterygosquilla capensis 1988 1 +17.1399993896484 -30.1900005340576 Pterygosquilla capensis 1988 1 +17.0300006866455 -31.3799991607666 Pterygosquilla capensis 1987 1 +17.7833003997803 -32.2999992370605 Pterygosquilla capensis 1996 1 +18.1667003631592 -31.86669921875 Pterygosquilla capensis 1987 1 +16.3999996185303 -31.0799999237061 Pterygosquilla capensis 2006 1 +17.9832992553711 -31.5499992370605 Pterygosquilla capensis 1997 1 +16.6499996185303 -29.5666999816895 Pterygosquilla capensis 1990 1 +16.2000007629395 -29.4099998474121 Pterygosquilla capensis 2006 1 +16.3999996185303 -28.8166999816895 Pterygosquilla capensis 1990 1 +16.1667003631592 -29.1000003814697 Pterygosquilla capensis 1989 1 +17.36669921875 -31.25 Pterygosquilla capensis 1988 1 +16.0799999237061 -30.5200004577637 Pterygosquilla capensis 1990 1 +17.7833003997803 -31.6499996185303 Pterygosquilla capensis 1997 1 +16.3332996368408 -28.75 Pterygosquilla capensis 1994 1 +-43.1784553527832 -22.8033504486084 Hepatus pudibundus 1 +16.7166996002197 -31.0167007446289 Pterygosquilla capensis 2000 1 +16.1599998474121 -29.4599990844727 Pterygosquilla capensis 1987 1 +17.4300003051758 -31.3899993896484 Pterygosquilla capensis 1990 1 +17.1499996185303 -30.4500007629395 Pterygosquilla capensis 2003 1 +17.2833003997803 -31.5167007446289 Pterygosquilla capensis 2002 1 +16.4400005340576 -30.5200004577637 Pterygosquilla capensis 2006 2 +17.75 -32.4166984558105 Pterygosquilla capensis 1987 1 +17.5 -31.3999996185303 Pterygosquilla capensis 1994 1 +18.11669921875 -33.36669921875 Pterygosquilla capensis 1996 1 +17.1599998474121 -31.5599994659424 Pterygosquilla capensis 1991 1 +16.4200000762939 -30.0699996948242 Pterygosquilla capensis 2005 1 +16.3799991607666 -29.0599994659424 Pterygosquilla capensis 2003 1 +14.3599996566772 -27.0400009155273 Pterygosquilla capensis 1988 1 +17.4300003051758 -32.4700012207031 Pterygosquilla capensis 1991 1 +17.11669921875 -32.4166984558105 Pterygosquilla capensis 1991 1 +18 -32.2000007629395 Pterygosquilla capensis 1986 1 +17.0832996368408 -30.5832996368408 Pterygosquilla capensis 1992 1 +13.75 -25.2000007629395 Pterygosquilla capensis 1986 1 +19.4699993133545 -35.3199996948242 Pterygosquilla capensis 1987 1 +16.2700004577637 -30.0100002288818 Pterygosquilla capensis 1994 1 +17.1000003814697 -30.3999996185303 Pterygosquilla capensis 1985 1 +15.4200000762939 -29.2299995422363 Pterygosquilla capensis 2005 1 +16.9832992553711 -30.3999996185303 Pterygosquilla capensis 1989 1 +17.4500007629395 -31.4400005340576 Pterygosquilla capensis 1986 1 +17.1000003814697 -30.3899993896484 Pterygosquilla capensis 1990 1 +-44.5032730102539 -23.2943286895752 Hepatus pudibundus 1976 1 +16.7667007446289 -30.2833003997803 Pterygosquilla capensis 1996 1 +16 -30.13330078125 Pterygosquilla capensis 2000 1 +16.38330078125 -28.8999996185303 Pterygosquilla capensis 1991 1 +17.9666996002197 -31.8500003814697 Pterygosquilla capensis 1990 1 +17.8332996368408 -31.7332992553711 Pterygosquilla capensis 1992 1 +17.4500007629395 -31.7833003997803 Pterygosquilla capensis 2000 1 +17.4200000762939 -32.5400009155273 Pterygosquilla capensis 2006 1 +17.2299995422363 -31.2299995422363 Pterygosquilla capensis 2004 1 +17.1499996185303 -30.4200000762939 Pterygosquilla capensis 2004 1 +17.1200008392334 -32.1699981689453 Pterygosquilla capensis 1995 1 +16.9666996002197 -29.7332992553711 Pterygosquilla capensis 1995 1 +16.5 -28.86669921875 Pterygosquilla capensis 2002 1 +16.2999992370605 -29.4666996002197 Pterygosquilla capensis 1995 1 +16.63330078125 -30.0167007446289 Pterygosquilla capensis 1988 1 +17.6000003814697 -33.5666999816895 Pterygosquilla capensis 1990 1 +17.38330078125 -31.0333003997803 Pterygosquilla capensis 1985 1 +17.0832996368408 -30.6667003631592 Pterygosquilla capensis 1992 1 +17.0499992370605 -32.0900001525879 Pterygosquilla capensis 1993 1 +17.6667003631592 -31.3999996185303 Pterygosquilla capensis 1990 1 +17.5499992370605 -32.2200012207031 Pterygosquilla capensis 1991 1 +17 -31.4300003051758 Pterygosquilla capensis 1989 1 +17.2667007446289 -32.5332984924316 Pterygosquilla capensis 1989 1 +16.3700008392334 -29.3700008392334 Pterygosquilla capensis 1994 1 +16.9333000183105 -29.61669921875 Pterygosquilla capensis 1996 1 +18.1200008392334 -32.2099990844727 Pterygosquilla capensis 2003 1 +16.2000007629395 -28.5400009155273 Pterygosquilla capensis 1989 1 +16.2667007446289 -29.36669921875 Pterygosquilla capensis 1991 1 +16.63330078125 -28.9832992553711 Pterygosquilla capensis 1986 1 +15.4399995803833 -29.1100006103516 Pterygosquilla capensis 1988 1 +16.2000007629395 -28.8332996368408 Pterygosquilla capensis 1988 1 +16.4400005340576 -30.5100002288818 Pterygosquilla capensis 2006 1 +17.7332992553711 -31.8999996185303 Pterygosquilla capensis 1996 1 +16.2600002288818 -29.2299995422363 Pterygosquilla capensis 1988 1 +17.1700000762939 -31.2999992370605 Pterygosquilla capensis 1986 1 +15.5799999237061 -30.1800003051758 Pterygosquilla capensis 2003 1 +17.7332992553711 -33.0666999816895 Pterygosquilla capensis 1993 1 +17.0699996948242 -31.1000003814697 Pterygosquilla capensis 1991 1 +16.4099998474121 -30.0400009155273 Pterygosquilla capensis 2003 1 +13.3599996566772 -23.5599994659424 Pterygosquilla capensis 1989 1 +17.0699996948242 -31.4400005340576 Pterygosquilla capensis 2004 1 +16.2800006866455 -29.2299995422363 Pterygosquilla capensis 2003 1 +17 -29.7000007629395 Pterygosquilla capensis 2002 1 +16.6667003631592 -30.8500003814697 Pterygosquilla capensis 1988 1 +14.1667003631592 -26.0499992370605 Pterygosquilla capensis 1986 1 +16.1000003814697 -28.4899997711182 Pterygosquilla capensis 1987 1 +17.2000007629395 -30.4599990844727 Pterygosquilla capensis 1992 1 +17.5100002288818 -32.0200004577637 Pterygosquilla capensis 1995 1 +17.2999992370605 -30.5 Pterygosquilla capensis 2002 1 +16.3099994659424 -29.0599994659424 Pterygosquilla capensis 1986 1 +16.1700000762939 -29.2800006866455 Pterygosquilla capensis 1994 1 +17.0599994659424 -32.1699981689453 Pterygosquilla capensis 2005 1 +17.5900001525879 -31.3600006103516 Pterygosquilla capensis 2003 1 +16.9333000183105 -30.4666996002197 Pterygosquilla capensis 1994 1 +17.3700008392334 -33.3699989318848 Pterygosquilla capensis 1989 1 +18.1499996185303 -32.25 Pterygosquilla capensis 1994 1 +17.5499992370605 -33.3199996948242 Pterygosquilla capensis 2003 1 +17.3166999816895 -31.25 Pterygosquilla capensis 1987 1 +16.4599990844727 -29.1599998474121 Pterygosquilla capensis 2005 1 +17.0499992370605 -31.4099998474121 Pterygosquilla capensis 2004 1 +13.7666997909546 -25.38330078125 Pterygosquilla capensis 1986 1 +16.5300006866455 -30.5400009155273 Pterygosquilla capensis 1984 1 +16.0832996368408 -29.6499996185303 Pterygosquilla capensis 2000 1 +17.6499996185303 -31.63330078125 Pterygosquilla capensis 1996 1 +16.4099998474121 -29.0900001525879 Pterygosquilla capensis 1985 1 +16.2199993133545 -30.1399993896484 Pterygosquilla capensis 1995 1 +17.88330078125 -33.4166984558105 Pterygosquilla capensis 2001 1 +17.1499996185303 -31.2900009155273 Pterygosquilla capensis 1993 1 +17.2833003997803 -30.4666996002197 Pterygosquilla capensis 1994 1 +17.3999996185303 -32.189998626709 Pterygosquilla capensis 1991 1 +17.4699993133545 -33.4700012207031 Pterygosquilla capensis 1991 1 +17.5100002288818 -31.3199996948242 Pterygosquilla capensis 1986 1 +17.9500007629395 -32.4166984558105 Pterygosquilla capensis 1986 1 +17.2000007629395 -31.4899997711182 Pterygosquilla capensis 1988 1 +16.4832992553711 -29.8332996368408 Pterygosquilla capensis 1997 1 +17.4099998474121 -32.5200004577637 Pterygosquilla capensis 1988 1 +16.7999992370605 -29.9333000183105 Pterygosquilla capensis 1986 1 +17.0100002288818 -29.4500007629395 Pterygosquilla capensis 1990 1 +18.0167007446289 -33.3333015441895 Pterygosquilla capensis 1988 1 +16.5900001525879 -30.2399997711182 Pterygosquilla capensis 1984 1 +18.5400009155273 -35.1800003051758 Pterygosquilla capensis 1991 1 +16.13330078125 -29.9167003631592 Pterygosquilla capensis 1994 1 +17.4300003051758 -32.2700004577637 Pterygosquilla capensis 2006 2 +18.6667003631592 -34.4333000183105 Pterygosquilla capensis 1993 1 +16.4832992553711 -28.9167003631592 Pterygosquilla capensis 1995 1 +17.5 -31.5499992370605 Pterygosquilla capensis 1985 1 +17.3799991607666 -32.0800018310547 Pterygosquilla capensis 1987 1 +17.5 -31.2166996002197 Pterygosquilla capensis 1989 1 +16.5 -32.1800003051758 Pterygosquilla capensis 1989 1 +16.4500007629395 -30.4099998474121 Pterygosquilla capensis 1992 1 +16.2000007629395 -29.9666996002197 Pterygosquilla capensis 2000 1 +17.3799991607666 -31.4799995422363 Pterygosquilla capensis 2003 1 +17.1200008392334 -31.4099998474121 Pterygosquilla capensis 1986 1 +17.4899997711182 -31.3500003814697 Pterygosquilla capensis 1988 1 +17.5900001525879 -31.3099994659424 Pterygosquilla capensis 1988 1 +17.0499992370605 -30.5200004577637 Pterygosquilla capensis 1992 1 +17.3799991607666 -32.1300010681152 Pterygosquilla capensis 1988 1 +17.0900001525879 -30.2999992370605 Pterygosquilla capensis 1986 1 +16.4599990844727 -29.2600002288818 Pterygosquilla capensis 2005 1 +17.0167007446289 -32.2832984924316 Pterygosquilla capensis 1997 1 +-43.5352821350098 -23.0508460998535 Hepatus pudibundus 1905 1 +-47.9013900756836 -22.8999996185303 Hepatus pudibundus 1 +-41.7029762268066 -22.405574798584 Hepatus pudibundus 1 +-43.9495086669922 -22.9268188476562 Hepatus pudibundus 1 +-49.9180564880371 -22.4833335876465 Hepatus pudibundus 1 +-46.4427337646484 -24.0204372406006 Hepatus pudibundus 1972 1 +17.2667007446289 -31.4167003631592 Pterygosquilla capensis 1988 1 +-35.7228050231934 -9.67508125305176 Hepatus pudibundus 1 +-40.6457099914551 -20.8085803985596 Hepatus pudibundus 1 +-40.4574394226074 -20.6231594085693 Hepatus pudibundus 1 +17.2600002288818 -32 Pterygosquilla capensis 1993 1 +18.1100006103516 -32.3300018310547 Pterygosquilla capensis 1993 1 +-43.1765823364258 -22.8141975402832 Hepatus pudibundus 1983 1 +-43.1019554138184 -22.9345741271973 Hepatus pudibundus 1 +-44.3414535522461 -23.1815643310547 Hepatus pudibundus 1 +-44.0458145141602 -22.9682483673096 Minyocerus angustus 1965 1 +-48.588737487793 -27.5761947631836 Hepatus pudibundus 2 +-43.0455627441406 -22.9729518890381 Hepatus pudibundus 1 +-52.1059989929199 -32.0250015258789 Onychocorycaeus giesbrechti 2012 2 +-49.9000015258789 -10.3666667938232 Squilla discors 1 +-52.1759986877441 -32.2060012817383 Onychocorycaeus giesbrechti 2011 3 +-39.2673835754395 -17.7314395904541 Hepatus pudibundus 1 +-33.8164405822754 -3.86287188529968 Salmoneus setosus 1 +-39.2079582214355 -17.7569694519043 Minyocerus angustus 2003 1 +-41.709156036377 -22.4082126617432 Holothuria (Thymiosycia) arenicola 1963 1 +-38.700626373291 -17.9608211517334 Echinaster (Othilia) echinophorus 1988 1 +-32.7229995727539 -51.306999206543 Proboscia inermis 2013 1 +-31.6580009460449 -51.3530006408691 Proboscia inermis 2013 1 +-53.8819999694824 -55.0979995727539 Proboscia inermis 2009 1 +-56.0929985046387 -53.1570014953613 Proboscia inermis 2009 1 +-54.2200012207031 -55.0900001525879 Proboscia inermis 2007 1 +-55.6100006103516 -54.0299987792969 Proboscia inermis 2007 1 +18.1000003814697 -31.5499992370605 Pterygosquilla capensis 1992 1 +17.5499992370605 -34.2099990844727 Pterygosquilla capensis 2004 1 +15.5799999237061 -29.4400005340576 Pterygosquilla capensis 1987 1 +17.7999992370605 -31.3166999816895 Pterygosquilla capensis 1990 1 +-39.3624000549316 -3.17195010185242 Eriphia gonagra 2003 1 +-39.0126152038574 -3.39728331565857 Eriphia gonagra 2002 1 +-38.2477989196777 -17.6464996337891 Euthynnus alletteratus 1 +18.4500007629395 -35.7799987792969 Globigerinella adamsi 1967 1 +12.2333335876465 -6.09999990463257 Sardinella maderensis 1948 1 +13.1833333969116 -9.64999961853027 Sardinella maderensis 1968 2 +-33.8202781677246 -3.85555553436279 Platybelone argalus 1981 1 +-38.5400123596191 -13.0121021270752 Euthynnus alletteratus 1 +-38.4833335876465 -3.71666693687439 Euthynnus alletteratus 1945 1 +-38.3738441467285 -13.0796632766724 Sparisoma aurofrenatum 1 +-55.0952224731445 -2.23138809204102 Anchoviella guianensis 1924 2 +-67.0018005371094 -13.9754362106323 Anchoviella guianensis 1921 1 +-55.939998626709 -34.3499984741211 Cavolinia gibbosa 1 +-65.879997253418 -47.75 Deontostoma duanei 1 +-65.879997253418 -47.75 Austroregia regis 1 +-59.2000007629395 -51.4000015258789 Austroregia regis 1 +-68.4000015258789 -52.2000007629395 Austroregia regis 1 +-67 -54 Amphilectus flabellatus 1 +-67.61669921875 -49.1500015258789 Phalacrocorax gaimardi 1 +-65.7332992553711 -47.9166984558105 Phalacrocorax gaimardi 1 +-65.8332977294922 -47.88330078125 Phalacrocorax gaimardi 1 +-65.7166976928711 -47.9000015258789 Phalacrocorax gaimardi 1 +-65.9332962036133 -47.75 Phalacrocorax gaimardi 1 +-58.8821983337402 -51.1244010925293 Aplidium fuegiense 1926 1 +-36.4250984191895 -54.2585983276367 Kirkpatrickia variolosa 1927 1 +-64.9582977294922 -54 Amphilectus flabellatus 1927 2 +-62.5 -50.3333015441895 Rossella inermis 1928 1 +-57 -51.75 Rossella inermis 1928 1 +-64.9582977294922 -54 Miliolinella circularis 1927 1 +-68 -52.2999992370605 Miliolinella circularis 1927 1 +-60.1329002380371 -52.4715995788574 Miliolinella circularis 1927 1 +-58.2667007446289 -54.125 Miliolinella circularis 1927 1 +-64.9625015258789 -50.0125007629395 Miliolinella circularis 1927 1 +-64.75 -48.9707984924316 Miliolinella circularis 1927 1 +-37.8333015441895 -54.36669921875 Nonion sloanii 1927 1 +-36.86669921875 -54 Nonion sloanii 1928 1 +-67.1624984741211 -56.3250007629395 Lingulina quadrata 1930 1 +-61.5 -51.875 Lingulina quadrata 1927 1 +-62.5 -50.3333015441895 Nonion sloanii 1928 1 +-36.4416999816895 -54.0167007446289 Miliolinella circularis 1927 1 +-36.6677017211914 -54.1638984680176 Miliolinella circularis 1927 1 +-38.0167007446289 -53.9166984558105 Miliolinella circularis 1926 1 +-36.7832984924316 -54.8624992370605 Fissurina toddae 1927 1 +-37.5416984558105 -54.3375015258789 Fissurina toddae 1927 1 +-38.1500015258789 -54.4000015258789 Fissurina toddae 1927 1 +-38.9000015258789 -54.2062492370605 Fissurina toddae 1927 1 +-35.5416984558105 -54.38330078125 Fissurina toddae 1929 1 +-49.2332992553711 -52.6833000183105 Fissurina toddae 1929 1 +-65.6999969482422 -57.75830078125 Fissurina toddae 1930 1 +-64.5832977294922 -59.6666984558105 Fissurina toddae 1 +-57 -56.7000007629395 Fissurina toddae 1929 1 +-57.5800018310547 -51.6323013305664 Aplidium fuegiense 1927 1 +-58.9000015258789 -51.0167007446289 Aplidium fuegiense 1927 1 +-62.0382995605469 -51 Aplidium fuegiense 1927 1 +-64.99169921875 -51.0250015258789 Aplidium fuegiense 1927 1 +-63.625 -50.9500007629395 Aplidium fuegiense 1927 1 +-60.1329002380371 -52.4715995788574 Aplidium fuegiense 1927 1 +-60.5750007629395 -53.8917007446289 Aplidium fuegiense 1927 1 +-68 -52.2999992370605 Aplidium fuegiense 1927 1 +-64.625 -52.8958015441895 Aplidium fuegiense 1927 1 +-65.0167007446289 -51.9749984741211 Aplidium fuegiense 1927 1 +-63.5625 -48.5125007629395 Aplidium fuegiense 1927 1 +-62.2000007629395 -47.0999984741211 Aplidium fuegiense 1928 1 +-59.75 -49.4166984558105 Aplidium fuegiense 1928 1 +-63.5499992370605 -46.7458000183105 Aplidium fuegiense 1931 1 +-62.1666984558105 -48.2667007446289 Aplidium fuegiense 1931 1 +-65.6999969482422 -49.4707984924316 Aplidium fuegiense 1932 1 +-66.4666976928711 -52.8166999816895 Aplidium fuegiense 1932 1 +-60.3499984741211 -54.2000007629395 Aplidium fuegiense 1932 1 +-64.1999969482422 -53.2667007446289 Aplidium fuegiense 1932 1 +-36.4382019042969 -54.2633018493652 Aplidium fuegiense 1927 1 +-29.3509998321533 -20.5189990997314 Hemimarginula pumila 2001 1 +-37.8699989318848 -18.6630001068115 Hemimarginula pumila 2001 1 +-38.6870002746582 -16.7870006561279 Hemimarginula pumila 1997 1 +-29.3509998321533 -20.5189990997314 Acteocina lepta 2001 1 +-37.4749984741211 -20.5 Hemimarginula pumila 1996 2 +-40.0830001831055 -21.7670001983643 Benthonella tenella 2001 1 +-37.318000793457 -20.5060005187988 Hemimarginula pumila 1996 1 +-38.1699981689453 -16.117000579834 Aplysina lacunosa 2001 1 +-39.2330017089844 -20.8330001831055 Benthonella tenella 2001 2 +-40.1669998168945 -20.867000579834 Gloiocladia hassleri 2001 1 +-38.3660011291504 -15.7950000762939 Benthonella tenella 2001 1 +-34.5839996337891 -20.67799949646 Lithophaga nigra 2001 1 +-37.943000793457 -19.8129997253418 Lithophaga nigra 1997 1 +-36.8079986572266 -17.0659999847412 Lithophaga nigra 1997 1 +-28.8419990539551 -20.5130004882812 Lithophaga nigra 2001 1 +-40.451000213623 -21.1830005645752 Warrana besnardi 1996 1 +-40.451000213623 -21.1660003662109 Warrana besnardi 1996 1 +-38.0589981079102 -20.5729999542236 Lithophaga nigra 2001 1 +-38.1360015869141 -19.673999786377 Lithophaga nigra 1996 1 +-37.314998626709 -18.0590000152588 Lithophaga nigra 2001 1 +-37.7099990844727 -20.6730003356934 Lithophaga nigra 1997 1 +-28.0333003997803 -15.8000001907349 Farranula carinata 1963 5 +-38.0190010070801 -19.2719993591309 Lithophaga nigra 2001 1 +-48.4014015197754 -25.542200088501 Tagelus divisus 2006 73 +-37.4799995422363 -20.1509990692139 Lithophaga nigra 2001 1 +-36.1030006408691 -20.4869995117188 Lithophaga nigra 2001 1 +-38.6110000610352 -13.3699998855591 Cerithiella amblytera 2001 1 +-40.8499984741211 -21.3339996337891 Codium spongiosum 1999 1 +-36.7519989013672 -17.0979995727539 Lithophaga nigra 1997 1 +-33.6430015563965 -20.8570003509521 Lithophaga nigra 1997 1 +-38.6349983215332 -20.4619998931885 Benthonella tenella 2001 1 +-38.8300018310547 -15.5679998397827 Aplysina lacunosa 2001 1 +-37.9389991760254 -19.8080005645752 Aplysina lacunosa 2001 1 +-37.3289985656738 -18.0450000762939 Hemimarginula pumila 2001 1 +-36.7519989013672 -17.0979995727539 Aplysina lacunosa 1997 2 +-39.2330017089844 -20.8330001831055 Acteocina lepta 2001 2 +-40.0250015258789 -20.632999420166 Aplysina lacunosa 2002 1 +-37.314998626709 -18.0590000152588 Aplysina lacunosa 2001 1 +-36.7519989013672 -17.0979995727539 Hemimarginula pumila 1997 1 +-48.3107986450195 -25.5520000457764 Tagelus divisus 1990 13 +-56.6791801452637 -54.3145217895508 Austrocidaris spinulosa 2 +-56.1370010375977 -45.5540008544922 Austrocidaris spinulosa 1 +-50.9833335876465 -52.0666656494141 Austrocidaris spinulosa 2 +-55.7903327941895 -55.8236656188965 Doloria levis 1986 1 +-56.0646667480469 -56.0201683044434 Pontocypria helenae 1986 1 +-55.7903327941895 -55.8236656188965 Pontocypria helenae 1986 1 +-65.283332824707 -45.1666679382324 Brama brama 2004 1 +-64.1500015258789 -45.5999984741211 Campylonotus vagans 2003 1 +-64.8499984741211 -46.1500015258789 Campylonotus vagans 2003 1 +-45.6644439697266 -23.7808322906494 Chthamalus bisinuatus 2001 26 +-44.7249984741211 -23.7917003631592 Caprella scaura 2001 1 +-45.3138999938965 -23.584400177002 Caprella scaura 2001 11 +-45.3847236633301 -23.6177787780762 Chthamalus bisinuatus 2001 8 +-45.6644439697266 -23.7811107635498 Chthamalus bisinuatus 2001 8 +-45.5111122131348 -23.836389541626 Chthamalus bisinuatus 2001 7 +-44.8658332824707 -23.3569431304932 Chthamalus bisinuatus 2001 4 +-45.3752784729004 -23.6263885498047 Chthamalus bisinuatus 2001 4 +-45.3752784729004 -23.6258335113525 Caprella scaura 2001 2 +-44.8993988037109 -23.3747005462646 Caprella scaura 2001 30 +-45.3847236633301 -23.6269435882568 Chthamalus bisinuatus 2001 4 +-45.5111122131348 -23.8366661071777 Chthamalus bisinuatus 2001 4 +-45.3747215270996 -23.6263885498047 Chthamalus bisinuatus 2001 5 +-45.3802795410156 -23.6291675567627 Chthamalus bisinuatus 2001 3 +-44.9324989318848 -23.3569431304932 Chthamalus bisinuatus 2001 3 +-45.5111122131348 -23.836389541626 Caprella scaura 2001 1 +-44.8993988037109 -23.584400177002 Caprella scaura 2001 7 +-44.8657989501953 -23.3572216033936 Chthamalus bisinuatus 2001 2 +-45.1949996948242 -23.6907997131348 Spinolambrus pourtalesii 2001 1 +-45.2285995483398 -23.7577991485596 Poecilochaetus australis 2001 1 +-45.3747215270996 -23.6261100769043 Chthamalus bisinuatus 2001 3 +-45.3747215270996 -23.6258335113525 Chthamalus bisinuatus 2001 2 +-44.839168548584 -23.3738899230957 Chthamalus bisinuatus 2001 2 +-45.6587333679199 -23.7808322906494 Chthamalus bisinuatus 2001 6 +-44.8367156982422 -23.3710498809814 Chthamalus bisinuatus 2001 1 +-44.1599998474121 -23.5631008148193 Exhippolysmata oplophoroides 2002 1 +-45.1949996948242 -23.6231002807617 Exhippolysmata oplophoroides 2002 1 +-45.5349998474121 -23.8369007110596 Exhippolysmata oplophoroides 2002 1 +-44.1458015441895 -23.5324993133545 Exhippolysmata oplophoroides 2002 1 +-45.1305999755859 -23.7928009033203 Exhippolysmata oplophoroides 2001 1 +-45.0505981445312 -23.7378005981445 Spinolambrus pourtalesii 2001 1 +-45.2407989501953 -23.7007999420166 Podarkeopsis levifuscina 2002 7 +-45.2407989501953 -23.7007999420166 Podarkeopsis levifuscina 2001 5 +-45.0208015441895 -23.7343997955322 Spinolambrus pourtalesii 2001 2 +-45.4956016540527 -23.9694004058838 Spinolambrus pourtalesii 2001 1 +-45.2835998535156 -23.9368991851807 Podarkeopsis levifuscina 2002 1 +-45.2817001342773 -23.6557998657227 Poecilochaetus australis 2001 2 +-45.3805541992188 -23.6288890838623 Caprella scaura 2001 6 +-44.122200012207 -23.5256004333496 Exhippolysmata oplophoroides 2002 1 +-44.7886009216309 -23.5400009155273 Spinolambrus pourtalesii 2001 2 +-45.0374984741211 -23.7383003234863 Ophiothrix rathbuni 2000 1 +11.4219636917114 -6.66036415100098 Diastobranchus capensis 2006 1 +13.0400915145874 -12.575309753418 Diastobranchus capensis 2006 1 +13.3425159454346 -11.9086847305298 Diastobranchus capensis 2006 1 +-45.2238998413086 -23.9269008636475 Amphiodia riisei 2002 3 +-45.2817001342773 -23.6557998657227 Calliostoma adspersum 2001 3 +-45.1161003112793 -23.7238998413086 Drymonema dalmatinum 2001 1 +-45.0882987976074 -23.5021991729736 Sicyonia laevigata 2002 1 +-45.3502998352051 -23.7478008270264 Tagelus divisus 1995 1 +-45.4094009399414 -23.9552993774414 Sicyonia laevigata 2001 2 +-65.7641830444336 -45.0773658752441 Diplodus argenteus 2005 1 +-65.7104034423828 -45.0985679626465 Diplodus argenteus 2005 1 +-65 -46.5 Campylonotus vagans 2003 1 +-46.1669998168945 -33 Paraheterorhabdus robustus 1967 1 +-35.0762977600098 -8.69996070861816 Fissurella nimbosa 1 +-35.0762977600098 -8.69996070861816 Vermetus irregularis 1 +-35.0762977600098 -8.69996070861816 Chthamalus bisinuatus 1 +-34.8666648864746 -7.09999990463257 Chaetomorpha clavata 1977 1 +-34.9007987976074 -8.08329963684082 Chaetoceros teres 2 +-34.9000015258789 -7.55000019073486 Oscillatoria princeps 1 +-34.9000015258789 -7.55000019073486 Chaetoceros teres 1 +-38.5 -18 Chaetoceros teres 1995 1 +-39.5 -19 Chaetoceros teres 1995 1 +-38.5 -18 Triadinium polyedricum 1995 1 +-25 -8.55000019073486 Brama brama 1 +-39.0999984741211 -40.2999992370605 Scopelogadus beanii 1971 1 +-51 -31.9167003631592 Umbrina canosai 1978 1 +-51.5499992370605 -32.6666984558105 Umbrina canosai 1978 1 +-52.4333000183105 -34 Umbrina canosai 1978 1 +-49.5 -30.6833000183105 Umbrina canosai 1978 1 +-51.8333015441895 -33.7000007629395 Umbrina canosai 1978 1 +-40.7332992553711 -21.8999996185303 Umbrina canosai 1973 1 +-47.9667015075684 -27.86669921875 Umbrina canosai 1973 1 +-52.0332984924316 -33.3333015441895 Umbrina canosai 1973 1 +-50.9667015075684 -32.5 Umbrina canosai 1973 1 +-50.9667015075684 -33.5 Umbrina canosai 1973 1 +-51.0999984741211 -33 Umbrina canosai 1973 1 +-51.5332984924316 -33.3333015441895 Umbrina canosai 1974 1 +-51.75 -33.6666984558105 Umbrina canosai 1974 1 +-52.0999984741211 -33.5 Umbrina canosai 1974 1 +-51.6833000183105 -32.9166984558105 Umbrina canosai 1974 2 +-51.3166999816895 -32.9166984558105 Umbrina canosai 1974 1 +-51.25 -32.4166984558105 Umbrina canosai 1974 1 +-50.4500007629395 -32.0833015441895 Umbrina canosai 1974 1 +-52.63330078125 -34 Umbrina canosai 1978 1 +-49.0332984924316 -29.2000007629395 Umbrina canosai 1974 1 +-51.5167007446289 -33 Umbrina canosai 1973 1 +-52.0332984924316 -33.8333015441895 Umbrina canosai 1973 1 +-52.2832984924316 -33.6666984558105 Umbrina canosai 1973 1 +-51.5332984924316 -33 Umbrina canosai 1973 1 +-51.2332992553711 -33 Umbrina canosai 1973 1 +-52.3499984741211 -34 Umbrina canosai 1974 1 +-52.36669921875 -33.8333015441895 Umbrina canosai 1974 1 +-51.8166999816895 -33.6666984558105 Umbrina canosai 1974 1 +-50.86669921875 -32.4166984558105 Umbrina canosai 1974 1 +-51.2332992553711 -32.0833015441895 Umbrina canosai 1974 1 +-52.11669921875 -33.3333015441895 Umbrina canosai 1978 1 +-49.4667015075684 -30.3500003814697 Umbrina canosai 1978 1 +-51.7832984924316 -32.9166984558105 Umbrina canosai 1976 1 +-48.5499992370605 -29.0832996368408 Umbrina canosai 1974 1 +-48.9500007629395 -29.4666996002197 Umbrina canosai 1974 1 +-49.25 -29.63330078125 Umbrina canosai 1974 1 +-52.0167007446289 -34 Umbrina canosai 1973 2 +-50.9500007629395 -33 Umbrina canosai 1973 2 +-51.0833015441895 -32.8333015441895 Umbrina canosai 1973 1 +-51.4667015075684 -32.8333015441895 Umbrina canosai 1973 1 +-52.1500015258789 -34.5 Umbrina canosai 1973 1 +-51.9000015258789 -33.8333015441895 Umbrina canosai 1973 1 +-51.7999992370605 -33 Umbrina canosai 1973 2 +-52.75 -34.1666984558105 Umbrina canosai 1974 1 +-52.2999992370605 -33.6666984558105 Umbrina canosai 1974 2 +-51.7832984924316 -33.3333015441895 Umbrina canosai 1974 1 +-51.38330078125 -32.5833015441895 Umbrina canosai 1974 1 +-50.8333015441895 -32.0833015441895 Umbrina canosai 1974 1 +-51.9500007629395 -32.6666984558105 Umbrina canosai 1978 1 +-51.25 -31.9666996002197 Umbrina canosai 1978 1 +-50.6666984558105 -31.3332996368408 Umbrina canosai 1978 1 +-50.36669921875 -31.0499992370605 Umbrina canosai 1978 1 +-51.7167015075684 -33.5332984924316 Umbrina canosai 1978 1 +-51.8333015441895 -33.8333015441895 Umbrina canosai 1973 1 +-52.11669921875 -34.5 Umbrina canosai 1973 1 +-51.5999984741211 -33.6666984558105 Umbrina canosai 1973 1 +-51.9500007629395 -33.6666984558105 Umbrina canosai 1974 1 +-51.9333000183105 -33.5 Umbrina canosai 1974 1 +-51.3166999816895 -32.7999992370605 Umbrina canosai 1974 1 +-52.3333015441895 -34.25 Umbrina canosai 1974 1 +-51.0499992370605 -32.4166984558105 Umbrina canosai 1974 1 +-51.4333000183105 -32.4166984558105 Umbrina canosai 1974 1 +-51.3499984741211 -32.25 Umbrina canosai 1974 1 +-52.63330078125 -33.2167015075684 Umbrina canosai 1977 1 +-48.9500007629395 -29.0333003997803 Umbrina canosai 1974 1 +-48.8166999816895 -29.3500003814697 Umbrina canosai 1974 1 +-51.1666984558105 -33 Umbrina canosai 1973 1 +-51.75 -33.2332992553711 Umbrina canosai 1974 1 +-51.88330078125 -33.6666984558105 Umbrina canosai 1974 1 +-51.5999984741211 -33.0999984741211 Umbrina canosai 1974 1 +-50.9667015075684 -32.2999992370605 Umbrina canosai 1974 1 +-50.7999992370605 -32.0167007446289 Umbrina canosai 1974 1 +-52.0499992370605 -33.5 Umbrina canosai 1973 1 +-51.5 -32.6666984558105 Umbrina canosai 1973 1 +-50.8333015441895 -32.5 Umbrina canosai 1973 1 +-52 -33.8333015441895 Umbrina canosai 1973 1 +-51.3166999816895 -33.1666984558105 Umbrina canosai 1973 1 +-51.3499984741211 -33.5 Umbrina canosai 1973 1 +-52.2000007629395 -34.1666984558105 Umbrina canosai 1974 1 +-51.6833000183105 -33.5 Umbrina canosai 1974 2 +-52.1666984558105 -33.6666984558105 Umbrina canosai 1974 1 +-51.7667007446289 -33.11669921875 Umbrina canosai 1974 1 +-52.5167007446289 -34.25 Umbrina canosai 1974 1 +-51.2167015075684 -33.0833015441895 Umbrina canosai 1974 1 +-51.5167007446289 -32.9166984558105 Umbrina canosai 1974 1 +-51.5 -32.5833015441895 Umbrina canosai 1974 2 +-50.9000015258789 -32.5833015441895 Umbrina canosai 1974 1 +-51.3499984741211 -32.6666984558105 Umbrina canosai 1978 1 +-51.88330078125 -33.6500015258789 Umbrina canosai 1978 1 +-51.5833015441895 -33.3333015441895 Umbrina canosai 1978 1 +-51.9000015258789 -32.9166984558105 Umbrina canosai 1978 1 +-51.9832992553711 -34.3333015441895 Umbrina canosai 1973 1 +-52.1833000183105 -34 Umbrina canosai 1973 1 +-51.7332992553711 -33.6666984558105 Umbrina canosai 1973 1 +-50.8166999816895 -33 Umbrina canosai 1973 1 +-51.0499992370605 -33.1666984558105 Umbrina canosai 1973 1 +-48.9333000183105 -29.2332992553711 Umbrina canosai 1974 1 +-48.6500015258789 -29.3500003814697 Umbrina canosai 1974 1 +-51.3499984741211 -32.6666984558105 Umbrina canosai 1973 1 +-51.9166984558105 -34.3333015441895 Umbrina canosai 1973 1 +-50.6666984558105 -31.4832992553711 Umbrina canosai 1974 1 +-51.2667007446289 -32.3333015441895 Umbrina canosai 1978 1 +-49.25 -30.38330078125 Umbrina canosai 1978 1 +-51.86669921875 -33.13330078125 Umbrina canosai 1978 1 +-50.5499992370605 -32.9500007629395 Umbrina canosai 1978 1 +-48.75 -29.25 Umbrina canosai 1974 1 +-49.0332984924316 -29.0666999816895 Umbrina canosai 1974 1 +-48.4500007629395 -29.0666999816895 Umbrina canosai 1974 1 +-51.4000015258789 -32.3333015441895 Umbrina canosai 1974 1 +-52.3166999816895 -34.2332992553711 Umbrina canosai 1974 1 +-52.61669921875 -34.1666984558105 Umbrina canosai 1974 2 +-52.4166984558105 -34.3333015441895 Umbrina canosai 1974 1 +-51.7999992370605 -33.25 Umbrina canosai 1974 1 +-48.9500007629395 -29.2000007629395 Umbrina canosai 1974 1 +-48.8333015441895 -29.4666996002197 Umbrina canosai 1974 1 +-51.7999992370605 -34 Umbrina canosai 1973 1 +-52.0167007446289 -33.6666984558105 Umbrina canosai 1973 1 +-51.0999984741211 -33.3333015441895 Umbrina canosai 1973 1 +-52.4166984558105 -33.8333015441895 Umbrina canosai 1974 1 +-51.2332992553711 -32.4000015258789 Umbrina canosai 1974 1 +-51.2999992370605 -32.5833015441895 Umbrina canosai 1974 2 +-51.25 -32.3333015441895 Umbrina canosai 1974 1 +-52.7332992553711 -34.1666984558105 Umbrina canosai 1974 1 +-51.2332992553711 -33.4166984558105 Umbrina canosai 1974 1 +-51.5499992370605 -32.75 Umbrina canosai 1974 1 +-51.6833000183105 -33 Umbrina canosai 1978 2 +-52.7832984924316 -34.1666984558105 Umbrina canosai 1974 1 +-51.5167007446289 -33.6666984558105 Umbrina canosai 1974 1 +-51.7667007446289 -33.3333015441895 Umbrina canosai 1974 2 +-51.2667007446289 -32.8333015441895 Umbrina canosai 1974 1 +-51.1666984558105 -31.9500007629395 Umbrina canosai 1974 1 +-52.0999984741211 -34 Umbrina canosai 1973 2 +-27.7999992370605 -0.0500000007450581 Limopsis galatheae 2 +2.58166670799255 -33.341667175293 Limopsis galatheae 1 +9.05000019073486 -10.4833335876465 Limopsis galatheae 1 +-37.5 -16 Rhabdosphaera clavigera 1995 1 +-38.5 -16 Rhabdosphaera clavigera 1995 1 +-25 -8 Hirundichthys speculiger 1969 2 +-48 -60 Tintinnopsis lata 1 +-65 -54 Tintinnopsis lata 1 +-65 -48 Tintinnopsis lata 1 +-68 -53 Tintinnopsis parva 1 +-68 -52 Tintinnopsis parva 1 +-68.3000030517578 -45.7999992370605 Tintinnopsis parva 1 +-52.5 -32.9667015075684 Squatina punctata 1972 1 +-50.4667015075684 -30.7833003997803 Squatina punctata 1972 1 +-51 -31.5 Squatina punctata 1972 1 +-50.6666984558105 -31.6667003631592 Squatina punctata 1972 1 +-50.1500015258789 -30.25 Squatina punctata 1972 1 +-51.75 -32.6666984558105 Bathytoshia centroura 1978 1 +-51.5 -32.9667015075684 Bathytoshia centroura 1978 1 +-49.7832984924316 -30.2833003997803 Bathytoshia centroura 1978 1 +-47.2000007629395 -28.7000007629395 Bathytoshia centroura 1976 1 +-50.2332992553711 -32.0833015441895 Bathytoshia centroura 1974 1 +-50.5999984741211 -32 Bathytoshia centroura 1978 1 +-49.7167015075684 -30.6667003631592 Bathytoshia centroura 1978 1 +-50.4333000183105 -33 Bathytoshia centroura 1977 1 +-50.9667015075684 -31.6499996185303 Bathytoshia centroura 1978 1 +-51 -31.9167003631592 Bathytoshia centroura 1978 1 +-52.4333000183105 -34 Bathytoshia centroura 1978 1 +-52.0167007446289 -34 Bathytoshia centroura 1978 1 +-51.88330078125 -33.6500015258789 Bathytoshia centroura 1978 1 +-50.1666984558105 -31 Bathytoshia centroura 1978 1 +-50.0999984741211 -30.6667003631592 Bathytoshia centroura 1978 1 +-50.9333000183105 -32.9500007629395 Bathytoshia centroura 1978 1 +-49.61669921875 -29.86669921875 Bathytoshia centroura 1972 1 +-50.9166984558105 -32.9166984558105 Bathytoshia centroura 1974 1 +-52.0499992370605 -32.88330078125 Bathytoshia centroura 1978 1 +-51.2999992370605 -33.3333015441895 Bathytoshia centroura 1978 1 +-50.5833015441895 -33.0167007446289 Bathytoshia centroura 1974 1 +-52.2167015075684 -34 Bathytoshia centroura 1978 1 +-51.3166999816895 -33.3333015441895 Bathytoshia centroura 1978 1 +-52.1833000183105 -34.1666984558105 Galeocerdo cuvier 1974 1 +-50.5167007446289 -32.8333015441895 Bathytoshia centroura 1974 1 +-50.4832992553711 -32.6666984558105 Bathytoshia centroura 1974 1 +-51.4000015258789 -32.9500007629395 Bathytoshia centroura 1978 1 +-51.3499984741211 -32.6666984558105 Bathytoshia centroura 1978 1 +-52.0999984741211 -33.6666984558105 Bathytoshia centroura 1978 1 +-51.9500007629395 -32.6666984558105 Bathytoshia centroura 1978 1 +-52.1500015258789 -33.0833015441895 Bathytoshia centroura 1978 1 +-50.5833015441895 -31.6000003814697 Bathytoshia centroura 1978 1 +-51.6666984558105 -33.7000007629395 Bathytoshia centroura 1978 1 +-50.36669921875 -31.3166999816895 Bathytoshia centroura 1972 1 +-51.2999992370605 -33.4333000183105 Bathytoshia centroura 1978 1 +-58.8170013427734 -54.0830001831055 Eurhomalea exalbida 1962 1 +-44.5820007324219 -23.2549991607666 Diplodus argenteus 2004 1 +-44.0989990234375 -23.1639995574951 Diplodus argenteus 2003 1 +-44.681999206543 -23.1569995880127 Diplodus argenteus 2003 1 +-44.1500015258789 -23.2210006713867 Diplodus argenteus 2004 1 +-44.5960006713867 -23.2029991149902 Diplodus argenteus 2003 1 +-44.1910018920898 -23.1019992828369 Diplodus argenteus 2003 1 +-44.1500015258789 -23.2210006713867 Diplodus argenteus 2003 1 +-44.314998626709 -23.1280002593994 Diplodus argenteus 2004 1 +-44.326000213623 -23.136999130249 Diplodus argenteus 2004 1 +-44.2249984741211 -23.0790004730225 Mycteroperca microlepis 2003 1 +-44.5779991149902 -23.1849994659424 Acanthurus bahianus 2003 1 +-44.5060005187988 -23.2910003662109 Acanthurus bahianus 2003 1 +-44.5960006713867 -23.2029991149902 Acanthurus bahianus 2003 1 +-44.3730010986328 -23.1669998168945 Acanthurus bahianus 2003 1 +-44.5779991149902 -23.1849994659424 Acanthurus bahianus 2004 1 +-44.4210014343262 -23.0599994659424 Acanthurus bahianus 2003 1 +-44.2249984741211 -23.0790004730225 Mycteroperca microlepis 2004 1 +-44.5960006713867 -23.2029991149902 Mycteroperca microlepis 2003 1 +-44.0989990234375 -23.1639995574951 Acanthurus bahianus 2003 1 +-44.5060005187988 -23.2910003662109 Poecilochaetus australis 2004 1 +-44.2140007019043 -23.0470008850098 Puncturella pauper 2003 1 +-44.1479988098145 -23.125 Poecilochaetus australis 2003 1 +-44.5369987487793 -23.1520004272461 Poecilochaetus australis 2004 1 +-44.3730010986328 -23.1669998168945 Poecilochaetus australis 2004 1 +-44.5960006713867 -23.2029991149902 Diplodus argenteus 2004 1 +-46.0955009460449 -50.655200958252 Globoconella miozea 1974 2 +-46.0955009460449 -50.655200958252 Globoturborotalita brazieri 1974 4 +-30.5974998474121 -28.5849990844727 Globotruncanella havanensis 1969 7 +2.98530006408691 -29.0706996917725 Globotruncanella havanensis 1980 32 +2.98530006408691 -29.0706996917725 Racemiguembelina fructicosa 1980 49 +-11.1857004165649 -25.5233001708984 Globoconella miozea 1980 3 +-64.1667022705078 -53.86669921875 Phyllodoce patagonica 1958 1 +-43.1632995605469 -22.9475002288818 Monoliropus enodis 2001 3 +19.9428005218506 -34.8163986206055 Loligo reynaudii 2003 384 +18.6730995178223 -34.073299407959 Loligo reynaudii 1986 3 +18.5256004333496 -34.0666999816895 Loligo reynaudii 1998 8 +18.448600769043 -34.127799987793 Loligo reynaudii 1997 31 +18.4297008514404 -34.1913986206055 Loligo reynaudii 1988 115 +18.4610996246338 -34.3182983398438 Loligo reynaudii 1986 431 +18.448600769043 -34.127799987793 Loligo reynaudii 1986 201 +18.4297008514404 -34.1913986206055 Loligo reynaudii 1986 239 +18.4353008270264 -34.136100769043 Loligo reynaudii 1986 182 +18.5256004333496 -34.0666999816895 Loligo reynaudii 1986 63 +18.4353008270264 -34.136100769043 Loligo reynaudii 1987 263 +18.6616992950439 -34.2508010864258 Loligo reynaudii 1987 144 +18.4297008514404 -34.1913986206055 Loligo reynaudii 1989 96 +18.4353008270264 -34.136100769043 Loligo reynaudii 1989 171 +18.6616992950439 -34.2508010864258 Loligo reynaudii 1988 59 +18.497200012207 -34.3568992614746 Loligo reynaudii 1990 138 +18.86669921875 -34.1582984924316 Loligo reynaudii 2001 20 +18.576099395752 -34.0825004577637 Loligo reynaudii 1991 4 +18.4610996246338 -34.3182983398438 Loligo reynaudii 1989 91 +18.6616992950439 -34.2508010864258 Loligo reynaudii 1991 16 +18.497200012207 -34.3568992614746 Loligo reynaudii 1992 54 +19.9428005218506 -34.8163986206055 Loligo reynaudii 1994 158 +19.9428005218506 -34.8163986206055 Loligo reynaudii 2001 284 +18.497200012207 -34.3568992614746 Loligo reynaudii 2003 19 +19.9428005218506 -34.8163986206055 Loligo reynaudii 2004 335 +19.9428005218506 -34.8163986206055 Loligo reynaudii 2000 321 +19.4178009033203 -34.6828002929688 Loligo reynaudii 1998 22 +18.8278007507324 -34.3866996765137 Loligo reynaudii 1998 22 +17.6040000915527 -30.9162998199463 Loligo reynaudii 1988 8 +18.6616992950439 -34.2508010864258 Loligo reynaudii 2002 9 +19.9428005218506 -34.8163986206055 Loligo reynaudii 1998 103 +19.9428005218506 -34.8163986206055 Loligo reynaudii 1997 200 +18.497200012207 -34.3568992614746 Loligo reynaudii 1989 127 +19.9428005218506 -34.8163986206055 Loligo reynaudii 2005 202 +19.9428005218506 -34.8163986206055 Loligo reynaudii 1999 97 +19.4447002410889 -34.6478004455566 Loligo reynaudii 1989 4 +18.4731006622314 -34.2153015136719 Loligo reynaudii 1986 58 +18.497200012207 -34.3568992614746 Loligo reynaudii 1998 38 +18.86669921875 -34.1582984924316 Loligo reynaudii 1986 22 +18.497200012207 -34.3568992614746 Loligo reynaudii 1986 433 +18.448600769043 -34.127799987793 Loligo reynaudii 1987 167 +19.9428005218506 -34.8163986206055 Loligo reynaudii 1993 9 +19.3549995422363 -34.6164016723633 Loligo reynaudii 1986 133 +18.6616992950439 -34.2508010864258 Loligo reynaudii 1989 103 +19.3549995422363 -34.6164016723633 Loligo reynaudii 1989 21 +19.9428005218506 -34.8163986206055 Loligo reynaudii 1991 5 +18.497200012207 -34.3568992614746 Loligo reynaudii 1993 105 +18.497200012207 -34.3568992614746 Loligo reynaudii 1997 17 +18.6616992950439 -34.2508010864258 Loligo reynaudii 2001 43 +18.497200012207 -34.3568992614746 Loligo reynaudii 1991 32 +18.497200012207 -34.3568992614746 Loligo reynaudii 1987 141 +18.448600769043 -34.127799987793 Loligo reynaudii 1990 87 +19.3397006988525 -34.584400177002 Loligo reynaudii 1995 35 +18.5256004333496 -34.0666999816895 Loligo reynaudii 1988 42 +18.6616992950439 -34.2508010864258 Loligo reynaudii 1990 33 +18.4731006622314 -34.2153015136719 Loligo reynaudii 1990 46 +19.1238994598389 -34.415599822998 Loligo reynaudii 1990 11 +18.4353008270264 -34.136100769043 Loligo reynaudii 1999 56 +18.8306007385254 -34.2168998718262 Loligo reynaudii 1986 18 +18.448600769043 -34.127799987793 Loligo reynaudii 1989 130 +18.4353008270264 -34.136100769043 Loligo reynaudii 1988 210 +18.497200012207 -34.3568992614746 Loligo reynaudii 2002 26 +18.6616992950439 -34.2508010864258 Loligo reynaudii 1998 11 +18.6616992950439 -34.2508010864258 Loligo reynaudii 1993 24 +18.4610996246338 -34.3182983398438 Loligo reynaudii 1994 26 +18.4353008270264 -34.136100769043 Loligo reynaudii 1994 35 +18.4297008514404 -34.1913986206055 Loligo reynaudii 1994 6 +17.9489002227783 -33.0161018371582 Loligo reynaudii 2002 4 +18.448600769043 -34.127799987793 Loligo reynaudii 1998 13 +18.8332996368408 -34.11669921875 Loligo reynaudii 1988 4 +19.1238994598389 -34.415599822998 Loligo reynaudii 1989 12 +18.4731006622314 -34.2153015136719 Loligo reynaudii 1991 13 +18.6616992950439 -34.2508010864258 Loligo reynaudii 2000 7 +18.4297008514404 -34.1913986206055 Loligo reynaudii 1999 13 +18.4353008270264 -34.136100769043 Loligo reynaudii 2001 34 +18.497200012207 -34.3568992614746 Loligo reynaudii 2004 12 +18.4671993255615 -34.2933006286621 Loligo reynaudii 1986 7 +18.4353008270264 -34.136100769043 Loligo reynaudii 1991 46 +19.2999992370605 -34.63330078125 Loligo reynaudii 1988 3 +19.9428005218506 -34.8163986206055 Loligo reynaudii 1995 15 +18.3267002105713 -32.314998626709 Loligo reynaudii 2000 12 +18.86669921875 -34.1582984924316 Loligo reynaudii 2004 6 +19.4178009033203 -34.6828002929688 Loligo reynaudii 1997 23 +18.4353008270264 -34.136100769043 Loligo reynaudii 1992 46 +18.5832996368408 -34.1380996704102 Loligo reynaudii 1987 15 +18.3691997528076 -34.2089004516602 Loligo reynaudii 1988 1 +18.497200012207 -34.3568992614746 Loligo reynaudii 1994 56 +18.6616992950439 -34.2508010864258 Loligo reynaudii 1994 4 +18.6616992950439 -34.2508010864258 Loligo reynaudii 1986 116 +18.497200012207 -34.3568992614746 Loligo reynaudii 1999 48 +18.4610996246338 -34.3182983398438 Loligo reynaudii 1995 9 +11.6983003616333 -23.4316997528076 Cibicidoides robertsonianus 1992 32 +7.23999977111816 -24.6900005340576 Cibicidoides robertsonianus 1990 19 +18.4610996246338 -34.3182983398438 Loligo reynaudii 1990 45 +19.9428005218506 -34.8163986206055 Loligo reynaudii 2002 122 +18.4297008514404 -34.1913986206055 Loligo reynaudii 1987 92 +18.4731006622314 -34.2153015136719 Loligo reynaudii 1994 11 +19.4178009033203 -34.6828002929688 Loligo reynaudii 1999 3 +18.4353008270264 -34.136100769043 Loligo reynaudii 1997 38 +18.4610996246338 -34.3182983398438 Loligo reynaudii 1998 19 +18.4652996063232 -34.2167015075684 Loligo reynaudii 1990 1 +18.8332996368408 -34.11669921875 Loligo reynaudii 2001 21 +18.4353008270264 -34.136100769043 Loligo reynaudii 1990 61 +18.448600769043 -34.127799987793 Loligo reynaudii 1988 130 +18.4610996246338 -34.3182983398438 Loligo reynaudii 2000 7 +18.497200012207 -34.3568992614746 Loligo reynaudii 1985 6 +18.4610996246338 -34.3182983398438 Loligo reynaudii 1987 49 +18.4297008514404 -34.1913986206055 Loligo reynaudii 1991 21 +18.5832996368408 -34.1380996704102 Loligo reynaudii 1986 9 +18.6616992950439 -34.2508010864258 Loligo reynaudii 1992 12 +17.8332996368408 -32.8166999816895 Loligo reynaudii 1988 13 +19.9428005218506 -34.8163986206055 Loligo reynaudii 1996 75 +18.497200012207 -34.3568992614746 Loligo reynaudii 1988 97 +19.4178009033203 -34.6828002929688 Loligo reynaudii 1994 29 +18.4731006622314 -34.2153015136719 Loligo reynaudii 2003 2 +18.8332996368408 -34.11669921875 Loligo reynaudii 1989 1 +18.0666999816895 -34.75 Neoraja stehmanni 1988 1 +18.2000007629395 -34.9166984558105 Neoraja stehmanni 1988 1 +18.1000003814697 -34.7832984924316 Neoraja stehmanni 1988 1 +18.2000007629395 -34.9333000183105 Neoraja stehmanni 1988 1 +17.4167003631592 -33.63330078125 Neoraja stehmanni 1988 1 +18.4353008270264 -34.136100769043 Loligo reynaudii 1998 22 +18.4675006866455 -34.2980995178223 Loligo reynaudii 1986 15 +18.4731006622314 -34.2153015136719 Loligo reynaudii 1987 15 +18.6921997070312 -34.0724983215332 Loligo reynaudii 1988 1 +-59.617000579834 -53.1500015258789 Truncatoflabellum truncum 1962 1 +18.4731006622314 -34.2153015136719 Loligo reynaudii 1992 13 +-38.4379196166992 -16.4665508270264 Evermannella melanoderma 1999 1 +5.19479990005493 -26.529899597168 Rouleina attrita 2010 1 +6.47550010681152 -24.9570007324219 Rouleina attrita 2008 1 +6.01300001144409 -25.6823997497559 Rouleina attrita 2009 1 +5.85510015487671 -26.1534996032715 Rouleina attrita 2010 1 +5.79150009155273 -25.3381004333496 Rouleina attrita 2010 1 +6.33760023117065 -24.6756000518799 Rouleina attrita 2010 1 +11.7170000076294 -4.76700019836426 Vanstraelenia chirophthalma 1964 3 +18.2999992370605 -33.9329986572266 Clinus latipennis 6 +14.4169998168945 -30.0170001983643 Coryphaenoides striaturus 1998 2 +11.7170000076294 -4.76700019836426 Galeocerdo cuvier 2 +9.75 -3.33299994468689 Zenion hololepis 2 +-34.9617004394531 -8.36030006408691 Ostrea equestris 1 +-46.4153900146484 -33.1801109313965 Chaetoceros diversus 1995 1 +-52.1730003356934 -32.2060012817383 Rhizosolenia delicatula 1997 1 +-52.1730003356934 -32.2060012817383 Rhizosolenia delicatula 1998 3 +-52.1730003356934 -32.2060012817383 Rhizosolenia delicatula 2000 3 +-52.1020011901855 -32.1479988098145 Rhizosolenia delicatula 1994 4 +-52.1059989929199 -32.0250015258789 Rhizosolenia delicatula 1994 2 +-52.1020011901855 -32.1479988098145 Rhizosolenia delicatula 1995 3 +-52.1730003356934 -32.2060012817383 Rhizosolenia delicatula 1995 7 +-52.1059989929199 -32.0250015258789 Rhizosolenia delicatula 1995 1 +-52.1730003356934 -32.2060012817383 Rhizosolenia delicatula 1996 4 +-52.1020011901855 -32.1479988098145 Rhizosolenia delicatula 1996 2 +-52.1730003356934 -32.2060012817383 Rhizosolenia delicatula 2001 3 +-52.1059989929199 -32.0250015258789 Rhizosolenia delicatula 2001 1 +-52.1730003356934 -32.2060012817383 Rhizosolenia delicatula 1999 4 +-52.1059989929199 -32.0250015258789 Rhizosolenia delicatula 1999 1 +-52.1020011901855 -32.1479988098145 Rhizosolenia delicatula 2004 2 +-52.1059989929199 -32.0250015258789 Rhizosolenia delicatula 2004 2 +-52.1730003356934 -32.2060012817383 Rhizosolenia delicatula 2004 3 +-52.1730003356934 -32.2060012817383 Rhizosolenia delicatula 2005 4 +-52.1059989929199 -32.0250015258789 Rhizosolenia delicatula 2005 1 +-52.1020011901855 -32.1479988098145 Rhizosolenia delicatula 2006 3 +-52.1059989929199 -32.0250015258789 Rhizosolenia delicatula 2006 3 +-52.1730003356934 -32.2060012817383 Rhizosolenia delicatula 2006 4 +-52.1730003356934 -32.2060012817383 Rhizosolenia delicatula 2007 2 +-52.1730003356934 -32.2060012817383 Rhizosolenia delicatula 2002 3 +-52.1020011901855 -32.1479988098145 Rhizosolenia delicatula 2003 3 +-52.1020011901855 -32.1479988098145 Rhizosolenia delicatula 2011 1 +-52.1059989929199 -32.0250015258789 Rhizosolenia delicatula 2011 1 +-52.1730003356934 -32.2060012817383 Rhizosolenia delicatula 2013 3 +-52.1730003356934 -32.2060012817383 Rhizosolenia delicatula 2008 1 +-52.1020011901855 -32.1479988098145 Rhizosolenia delicatula 2010 2 +-52.1730003356934 -32.2060012817383 Rhizosolenia delicatula 2010 3 +12.0666999816895 -19.9666996002197 Todaropsis eblanae 1980 1 +12.75 -23.0333003997803 Melanocetus johnsonii 1984 1 +-5.25 -11.6000003814697 Grammicolepis brachiusculus 1998 2 +13.9167003631592 -22.4666996002197 Todaropsis eblanae 1979 1 +15.4333000183105 -29.0832996368408 Todaropsis eblanae 1986 1 +18.6333332061768 -34.1666679382324 Isodictya conulosa 1 +-41.0200004577637 -22.0400009155273 Sycon brasiliense 2 +-36.5133285522461 -54.0933303833008 Stibarobdella macrothela 1975 1 +-41.9833335876465 -22.966667175293 Orthopristis ruber 1985 1 +-50.3499984741211 -30.7666664123535 Orthopristis ruber 1 +18.4916667938232 -34.3283348083496 Etrumeus micropus 1958 1 +17.6666660308838 -33.0833320617676 Etrumeus micropus 1958 1 +-40.2666664123535 -21.6666660308838 Echiophis intertinctus 1974 1 +-44.283332824707 -23.1680564880371 Microphis lineatus 2004 1 +-44.283332824707 -23.1666660308838 Microphis lineatus 2005 5 +17.2333335876465 -33.0833320617676 Etrumeus micropus 1958 1 +-42.1666679382324 -22.9500007629395 Orthopristis ruber 1984 2 +-39.0999984741211 -53.7900009155273 Psychroteuthis glacialis 2009 1 +-66.3249969482422 -53.6749992370605 Philobrya atlantica 1964 1 +-35.7866706848145 -55.1216697692871 Stibarobdella macrothela 1975 1 +-64.3399963378906 -54.9199981689453 Seba typica 1971 1 +18.4750003814697 -34.3597221374512 Ctenoscina brevicaudata 1926 2 +-36.2999992370605 -54.2249984741211 Cetoconcha spinosula 1926 2 +-61 -52.4166984558105 Cetoconcha spinosula 1928 1 +-31.75 -9.10000038146973 Litiopa melanostoma 1902 1 +-60.4333000183105 -52.1833000183105 Margarella expansa 1902 1 +-57.9333000183105 -51.5833015441895 Margarella expansa 1902 1 +-58.1500015258789 -51.5499992370605 Margarella expansa 1902 6 +-58 -51.5499992370605 Margarella expansa 1902 1 +-58.11669921875 -51.5332984924316 Margarella expansa 1902 1 +-57.9166984558105 -51.5666999816895 Margarella expansa 1902 1 +-57.8499984741211 -51.6833000183105 Margarella expansa 1903 1 +-56.3730010986328 -35.4360008239746 Rapana venosa 1999 1 +-45.2999992370605 -23.8333320617676 Macrostomum phocurum 1 +-26.25 -56.3499984741211 Aptenodytes patagonicus 2 +-37 -54 Aptenodytes patagonicus 1978 4 +-14.3762168884277 -7.88759994506836 Ircinia richardsoni 2012 2 +-35.216667175293 -9 Cacospongia levis 1873 2 +-64.033332824707 -40.533332824707 Paravortex panopea 1 +-67.5123977661133 -53.9774017333984 Nitzschia panduriformis 1 +-35.1033325195312 -8.75694465637207 Lanicola hutchingsae 2014 1 +6.03333330154419 -45.5666656494141 Haploniscus spatulifrons 1 +6.03333330154419 -45.5666656494141 Haploniscus tuberculatus 1 +-33.8199996948242 -3.86999988555908 Actinostella correae 9 +9 -5 Alvinocaris muricola 2001 5 +9 -19 Abyssoninoe scopa 1971 1 +13.2166996002197 -20.3166999816895 Codium duthieae 1992 1 +18.4566993713379 -33.9099998474121 Codium duthieae 1869 2 +18.375 -33.9500007629395 Codium duthieae 2 +18.4505996704102 -34.1291999816895 Codium duthieae 1985 1 +18.8332996368408 -34.38330078125 Codium duthieae 1988 1 +9 -5 Alvinocaris muricola 2004 3 +8 -5 Chauliopleona amftae 2001 3 +8 -5 Chauliopleona amftae 2003 3 +11 -7 Chauliopleona amftae 2001 1 +11 -7 Rouleina attrita 2000 2 +11 -7 Chauliopleona amftae 2003 7 +11 -7 Chauliopleona amftae 2000 2 +9 -7 Chauliopleona amftae 2001 3 +-38.4830017089844 -12.9670000076294 Microphis lineatus 2 +-38.6351890563965 -3.68628001213074 Pterocladiella caerulescens 2010 4 +-39.2658882141113 -3.21766996383667 Pterocladiella caerulescens 2010 15 +-39.2251892089844 -3.24281001091003 Pterocladiella caerulescens 2010 26 +-39.2489395141602 -3.22324991226196 Pterocladiella caerulescens 2010 17 +-38.6088104248047 -3.68921995162964 Pterocladiella caerulescens 2010 2 +-68.75 -50 Eteone sculpta 1 +-55.5999984741211 -52.9300003051758 Iolanthe longiramosa 1 +-67 -52.7999992370605 Spongomorpha pacifica 1 +18.7750377655029 -54.2206077575684 Aptenodytes patagonicus 2013 1 +16.3923568725586 -53.6585731506348 Aptenodytes patagonicus 2013 1 +16.2886409759521 -53.6253318786621 Aptenodytes patagonicus 2013 1 +16.1895198822021 -53.5884971618652 Aptenodytes patagonicus 2013 1 +16.0999794006348 -53.5478630065918 Aptenodytes patagonicus 2013 1 +16.0250930786133 -53.5031471252441 Aptenodytes patagonicus 2013 1 +15.9775285720825 -53.458568572998 Aptenodytes patagonicus 2013 1 +13.216046333313 -52.1150131225586 Aptenodytes patagonicus 2013 1 +13.2143049240112 -52.0774688720703 Aptenodytes patagonicus 2013 1 +13.208197593689 -52.0404930114746 Aptenodytes patagonicus 2013 1 +18.9993991851807 -50.5602531433105 Aptenodytes patagonicus 2013 1 +18.9402980804443 -50.6149635314941 Aptenodytes patagonicus 2013 1 +18.2665424346924 -51.8088989257812 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1 +17.5547389984131 -54.9109954833984 Aptenodytes patagonicus 2013 1 +17.6727542877197 -54.8738975524902 Aptenodytes patagonicus 2013 1 +17.7618827819824 -54.8430786132812 Aptenodytes patagonicus 2013 1 +17.8127574920654 -54.8215980529785 Aptenodytes patagonicus 2013 1 +17.8790435791016 -54.8006401062012 Aptenodytes patagonicus 2013 1 +17.982494354248 -54.777042388916 Aptenodytes patagonicus 2013 1 +18.0952301025391 -54.749584197998 Aptenodytes patagonicus 2013 1 +18.2153911590576 -54.7192115783691 Aptenodytes patagonicus 2013 1 +18.341194152832 -54.6868667602539 Aptenodytes patagonicus 2013 1 +5.32595729827881 -48.6690292358398 Aptenodytes patagonicus 2008 1 +5.37995767593384 -48.6463050842285 Aptenodytes patagonicus 2008 1 +5.43711233139038 -48.6284866333008 Aptenodytes patagonicus 2008 1 +5.50031661987305 -48.6179084777832 Aptenodytes patagonicus 2008 1 +5.57327938079834 -48.617244720459 Aptenodytes patagonicus 2008 1 +5.71203231811523 -48.6419830322266 Aptenodytes patagonicus 2008 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1 +17.6651287078857 -50.6687126159668 Aptenodytes patagonicus 2008 1 +17.4241790771484 -50.5128860473633 Aptenodytes patagonicus 2008 1 +17.3711414337158 -50.4726715087891 Aptenodytes patagonicus 2008 1 +17.2811412811279 -50.3931503295898 Aptenodytes patagonicus 2008 1 +17.2465267181396 -50.3545913696289 Aptenodytes patagonicus 2008 1 +17.2205352783203 -50.3173751831055 Aptenodytes patagonicus 2008 1 +17.2045612335205 -50.2819404602051 Aptenodytes patagonicus 2008 1 +17.2001247406006 -50.2487754821777 Aptenodytes patagonicus 2008 1 +17.2088775634766 -50.2184028625488 Aptenodytes patagonicus 2008 1 +17.2326316833496 -50.1913986206055 Aptenodytes patagonicus 2008 1 +17.273380279541 -50.1684036254883 Aptenodytes patagonicus 2008 1 +17.2875900268555 -50.1505279541016 Aptenodytes patagonicus 2008 1 +17.2957935333252 -50.1292304992676 Aptenodytes patagonicus 2008 1 +17.2987594604492 -50.1048774719238 Aptenodytes patagonicus 2008 1 +17.2971858978271 -50.077808380127 Aptenodytes patagonicus 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+19.982084274292 -47.3191757202148 Aptenodytes patagonicus 2008 1 +19.9981594085693 -47.3244781494141 Aptenodytes patagonicus 2008 1 +3.30570006370544 -22.3329658508301 Pseudomystides rarica 1 +-65.3616638183594 -49.1983337402344 Amastigia nuda 1 +-64.5899963378906 -48.8616676330566 Amastigia nuda 1 +-64.0833358764648 -49.9500007629395 Amastigia nuda 1 +-63.7649993896484 -46.8166656494141 Amastigia nuda 1 +-37.9033012390137 -54.2350006103516 Ammothea minor 1 +-27.4099998474121 -56.396598815918 Ammothea minor 1 +-37.63330078125 -53.8499984741211 Ammothea minor 1 +-35.8115997314453 -54.2915992736816 Austrodecus kelpi 1 +-37.396598815918 -54.8432998657227 Ammothea minor 1 +5.36700010299683 -30.0669994354248 Histioteuthis reversa 1971 2 +-6 -3 Ornithoteuthis antillarum 1964 1 +-19 -2.45000004768372 Histioteuthis reversa 1971 1 +-9.03299999237061 -13.1169996261597 Histioteuthis reversa 1971 1 +-49.7330017089844 -34.8499984741211 Ctenocalanus vanus 1967 5 +-18.9669990539551 -2.48300004005432 Histioteuthis reversa 1971 1 +18.0699996948242 -33.1500015258789 Haematopus moquini 2001 2 +18.5200004577637 -34.0800018310547 Haematopus moquini 2002 7 +18.4799995422363 -33.8499984741211 Haematopus moquini 2001 1 +18.7700004577637 -34.0800018310547 Haematopus moquini 2005 2 +18.9699993133545 -33.6800003051758 Larus cirrocephalus 1999 12 +18.5200004577637 -34.0800018310547 Haematopus moquini 2003 10 +18.3500003814697 -32.2200012207031 Haematopus moquini 1999 1 +19.0599994659424 -34.3400001525879 Haematopus moquini 2003 3 +18.0699996948242 -33.1500015258789 Haematopus moquini 2002 2 +18.5200004577637 -34.0800018310547 Larus cirrocephalus 1996 6 +19.1100006103516 -34.3600006103516 Haematopus moquini 2005 7 +18.1100006103516 -33.189998626709 Haematopus moquini 2005 2 +19.3500003814697 -34.4199981689453 Haematopus moquini 2004 5 +18.5200004577637 -34.0800018310547 Haematopus moquini 2001 11 +19.0599994659424 -34.3400001525879 Haematopus moquini 2005 2 +19.1000003814697 -34.3499984741211 Haematopus moquini 2004 13 +18.4200000762939 -32.3300018310547 Larus cirrocephalus 2004 1 +18.2000007629395 -31.7000007629395 Haematopus moquini 1996 1 +18.9699993133545 -33.6800003051758 Larus cirrocephalus 1996 11 +19.1299991607666 -34.3300018310547 Larus cirrocephalus 2006 1 +18.5200004577637 -34.0800018310547 Haematopus moquini 1993 11 +18.5200004577637 -34.0800018310547 Haematopus moquini 1997 9 +18.1499996185303 -32.7799987792969 Larus cirrocephalus 2006 1 +18.5200004577637 -34.0800018310547 Larus cirrocephalus 2005 2 +19.1100006103516 -34.3600006103516 Larus cirrocephalus 2005 3 +18.5200004577637 -34.0800018310547 Haematopus moquini 1994 12 +19.3500003814697 -34.4199981689453 Haematopus moquini 2005 5 +18.5200004577637 -34.0800018310547 Larus cirrocephalus 1997 7 +18.5200004577637 -34.0800018310547 Larus cirrocephalus 2000 6 +18.5200004577637 -34.0800018310547 Haematopus moquini 1989 12 +19.2199993133545 -34.0299987792969 Larus cirrocephalus 2004 3 +18.5 -33.8600006103516 Haematopus moquini 1993 2 +18.5200004577637 -34.0800018310547 Haematopus moquini 1999 8 +18.5200004577637 -34.0800018310547 Larus cirrocephalus 1995 7 +18.5200004577637 -34.0900001525879 Haematopus moquini 2005 4 +-44.8170013427734 -54.1669998168945 Nephtys ferruginea 1963 1 +-67.0670013427734 -53.0999984741211 Turritella algida 1970 2 +18.4799995422363 -33.8800010681152 Haematopus moquini 2006 2 +18.5200004577637 -34.0800018310547 Haematopus moquini 1985 10 +18.9699993133545 -33.6800003051758 Larus cirrocephalus 2002 12 +18.5 -33.8600006103516 Larus cirrocephalus 1994 1 +18.9699993133545 -33.6800003051758 Larus cirrocephalus 2004 11 +18.2199993133545 -32.8199996948242 Larus cirrocephalus 2005 2 +18.7700004577637 -34.0800018310547 Larus cirrocephalus 2003 2 +18.9699993133545 -33.6800003051758 Larus cirrocephalus 1995 10 +18.4699993133545 -34.1199989318848 Larus cirrocephalus 2003 2 +-64.4000015258789 -42.9000015258789 Tripylaster philippii 1978 1 +-64.8669967651367 -53.6333312988281 Natatolana oculata 1964 1 +18.9699993133545 -33.6800003051758 Larus cirrocephalus 2005 12 +19.0599994659424 -34.3400001525879 Haematopus moquini 2002 1 +18.0699996948242 -33.1500015258789 Haematopus moquini 2004 2 +19.3500003814697 -34.4199981689453 Haematopus moquini 2002 2 +18.5200004577637 -34.0800018310547 Haematopus moquini 1984 12 +18.5 -33.8600006103516 Haematopus moquini 2001 3 +19.2199993133545 -34.0299987792969 Larus cirrocephalus 2001 2 +18.2199993133545 -32.8199996948242 Larus cirrocephalus 1994 1 +18.5200004577637 -34.0800018310547 Haematopus moquini 1987 12 +18.5200004577637 -34.0800018310547 Haematopus moquini 1986 10 +18.9699993133545 -33.6800003051758 Larus cirrocephalus 1998 9 +18.4699993133545 -33.8699989318848 Haematopus moquini 2005 1 +18.5100002288818 -34.0900001525879 Larus cirrocephalus 2006 2 +18.5200004577637 -34.0800018310547 Larus cirrocephalus 1986 3 +18.1299991607666 -33.2700004577637 Haematopus moquini 2006 1 +18.2999992370605 -32.5999984741211 Haematopus moquini 2000 1 +18.5200004577637 -34.0800018310547 Larus cirrocephalus 2001 5 +18.2199993133545 -32.8199996948242 Larus cirrocephalus 1996 1 +18.0699996948242 -33.1500015258789 Haematopus moquini 1993 2 +18.1800003051758 -32.7999992370605 Larus cirrocephalus 2003 2 +19.0599994659424 -34.3400001525879 Larus cirrocephalus 2006 3 +18.4699993133545 -33.8300018310547 Larus cirrocephalus 2003 1 +18.2999992370605 -32.5999984741211 Haematopus moquini 1994 2 +18.4799995422363 -33.8499984741211 Haematopus moquini 2005 2 +19.1000003814697 -34.3499984741211 Haematopus moquini 1998 2 +18.2000007629395 -32.8300018310547 Larus cirrocephalus 2004 1 +18.5200004577637 -34.0800018310547 Larus cirrocephalus 2006 2 +18.5200004577637 -34.0800018310547 Larus cirrocephalus 1998 4 +18.5200004577637 -34.0800018310547 Haematopus moquini 1983 6 +18.5200004577637 -34.0800018310547 Haematopus moquini 1995 11 +18.9699993133545 -33.6800003051758 Larus cirrocephalus 2006 9 +18.4799995422363 -33.8499984741211 Haematopus moquini 2003 2 +18.5 -33.8800010681152 Haematopus moquini 1995 7 +18.1499996185303 -32.7799987792969 Haematopus moquini 1999 2 +18.5200004577637 -34.0800018310547 Haematopus moquini 1992 10 +18.5200004577637 -34.0800018310547 Haematopus moquini 1991 12 +19.0900001525879 -34.3600006103516 Larus cirrocephalus 2006 1 +19.0599994659424 -34.3400001525879 Haematopus moquini 2004 3 +18.2999992370605 -33.4799995422363 Haematopus moquini 2000 2 +18.25 -32.8699989318848 Larus cirrocephalus 1996 1 +18.9699993133545 -33.6800003051758 Larus cirrocephalus 2003 11 +19.0599994659424 -34.3400001525879 Haematopus moquini 1996 2 +19.2199993133545 -34.0299987792969 Larus cirrocephalus 2000 2 +18.2000007629395 -32.7999992370605 Larus cirrocephalus 1995 2 +18.1800003051758 -32.7999992370605 Larus cirrocephalus 1999 1 +18.5200004577637 -34.0800018310547 Haematopus moquini 1998 11 +19.0599994659424 -34.3400001525879 Haematopus moquini 1999 1 +18.0699996948242 -33.1500015258789 Haematopus moquini 2003 2 +19.3500003814697 -34.4199981689453 Larus cirrocephalus 2001 1 +18.5200004577637 -34.0800018310547 Haematopus moquini 1996 11 +19.0499992370605 -33.3699989318848 Larus cirrocephalus 2005 1 +18.5200004577637 -34.0800018310547 Haematopus moquini 1990 10 +18.2999992370605 -33.4799995422363 Haematopus moquini 2005 2 +18.5200004577637 -34.0800018310547 Haematopus moquini 2004 8 +18.5 -33.8600006103516 Haematopus moquini 1994 2 +18.5200004577637 -34.0800018310547 Larus cirrocephalus 1991 3 +18.4200000762939 -32.3300018310547 Larus cirrocephalus 1998 2 +18.2000007629395 -31.7000007629395 Haematopus moquini 2002 1 +18.5200004577637 -34.0800018310547 Larus cirrocephalus 1994 4 +18.9699993133545 -33.6800003051758 Larus cirrocephalus 1994 7 +18.7700004577637 -34.0800018310547 Haematopus moquini 2004 2 +18.5300006866455 -34.0900001525879 Haematopus moquini 2005 6 +19.2199993133545 -34.0299987792969 Larus cirrocephalus 2003 2 +18.1800003051758 -32.7799987792969 Larus cirrocephalus 1994 2 +18.9699993133545 -33.6800003051758 Larus cirrocephalus 2001 9 +18.4200000762939 -32.3300018310547 Larus cirrocephalus 1993 2 +18.5200004577637 -34.0900001525879 Haematopus moquini 2006 4 +18.5 -33.8800010681152 Haematopus moquini 1997 5 +18.5100002288818 -34.0900001525879 Larus cirrocephalus 2005 1 +18.5 -33.8600006103516 Larus cirrocephalus 1993 2 +18.2999992370605 -33.4799995422363 Haematopus moquini 2001 2 +18.5 -33.8800010681152 Haematopus moquini 1988 1 +18.4200000762939 -32.3300018310547 Larus cirrocephalus 1992 2 +18.4799995422363 -33.8499984741211 Haematopus moquini 2004 1 +18.9699993133545 -33.6800003051758 Larus cirrocephalus 2000 11 +18.5200004577637 -34.0800018310547 Haematopus moquini 2005 6 +18.1299991607666 -33.2700004577637 Haematopus moquini 2005 2 +19.1000003814697 -34.3499984741211 Haematopus moquini 1997 1 +19.1000003814697 -34.3499984741211 Larus cirrocephalus 2001 2 +18.4699993133545 -34.0999984741211 Larus cirrocephalus 1994 1 +18.4699993133545 -33.8499984741211 Haematopus moquini 2006 2 +18.1499996185303 -32.7799987792969 Haematopus moquini 1998 1 +18.2999992370605 -33.4799995422363 Haematopus moquini 2003 2 +19.1000003814697 -34.3499984741211 Haematopus moquini 2000 2 +18.5200004577637 -34.0800018310547 Haematopus moquini 2000 10 +18.9699993133545 -33.6500015258789 Larus cirrocephalus 2003 1 +18.4799995422363 -33.9300003051758 Larus cirrocephalus 2005 1 +19.0599994659424 -34.3400001525879 Larus cirrocephalus 2000 1 +19.2199993133545 -34.0299987792969 Larus cirrocephalus 1998 2 +18.5200004577637 -34.0800018310547 Larus cirrocephalus 1985 1 +18.0499992370605 -33.1800003051758 Haematopus moquini 2000 1 +19.1000003814697 -34.3499984741211 Larus cirrocephalus 2004 5 +18.5200004577637 -34.0800018310547 Larus cirrocephalus 1983 3 +19.2199993133545 -34.0299987792969 Larus cirrocephalus 2002 2 +18.2000007629395 -32.7999992370605 Larus cirrocephalus 2003 2 +18.7700004577637 -34.0800018310547 Larus cirrocephalus 2006 1 +18.2999992370605 -32.5999984741211 Haematopus moquini 1998 1 +18.1800003051758 -32.7799987792969 Larus cirrocephalus 2004 1 +18.7700004577637 -34.0800018310547 Larus cirrocephalus 2004 1 +19.2199993133545 -34.0299987792969 Larus cirrocephalus 1999 2 +18.1800003051758 -32.7999992370605 Larus cirrocephalus 2005 3 +18.4699993133545 -33.8499984741211 Haematopus moquini 2003 2 +18.4699993133545 -34.1199989318848 Larus cirrocephalus 2002 1 +18.2999992370605 -33.4799995422363 Haematopus moquini 1998 2 +18.5 -33.8600006103516 Larus cirrocephalus 1996 1 +18.1499996185303 -32.7799987792969 Haematopus moquini 1997 2 +18.1800003051758 -32.7999992370605 Larus cirrocephalus 2002 1 +18.2000007629395 -32.7999992370605 Larus cirrocephalus 1999 1 +18.5 -33.8800010681152 Haematopus moquini 1990 1 +18.3500003814697 -32.2200012207031 Haematopus moquini 2001 1 +18.5 -33.8600006103516 Haematopus moquini 1997 1 +18.0699996948242 -33.1500015258789 Haematopus moquini 1995 2 +18.5200004577637 -34.0800018310547 Larus cirrocephalus 2004 3 +18.1800003051758 -32.7799987792969 Larus cirrocephalus 2002 1 +18.5200004577637 -34.0800018310547 Larus cirrocephalus 2002 3 +19.2199993133545 -34.0299987792969 Larus cirrocephalus 1997 2 +18.1800003051758 -32.7799987792969 Larus cirrocephalus 2005 2 +18.5 -33.8800010681152 Larus cirrocephalus 1994 1 +19.1100006103516 -34.3600006103516 Haematopus moquini 2006 4 +19.1100006103516 -34.3600006103516 Haematopus moquini 2004 2 +18.3500003814697 -32.2200012207031 Haematopus moquini 2003 1 +18.0900001525879 -33.2000007629395 Haematopus moquini 2005 1 +19.0499992370605 -33.3699989318848 Larus cirrocephalus 1996 1 +18.4699993133545 -33.8699989318848 Haematopus moquini 2004 1 +18.4699993133545 -33.8499984741211 Haematopus moquini 2004 3 +18.3199996948242 -32.0800018310547 Haematopus moquini 2002 2 +18.2999992370605 -32.5999984741211 Larus cirrocephalus 1993 1 +19.2199993133545 -34.0299987792969 Larus cirrocephalus 2005 2 +18.25 -32.8699989318848 Larus cirrocephalus 2005 1 +18.1499996185303 -32.7799987792969 Haematopus moquini 1996 2 +18.9699993133545 -33.6800003051758 Larus cirrocephalus 1993 2 +-37.6766700744629 -54.0016708374023 Echiurus antarcticus 1975 1 +12 -14.9169998168945 Odontostomias micropogon 1961 8 +10.5500001907349 -14.5830001831055 Stomias colubrinus 1961 2 +10.1999998092651 -17.367000579834 Stomias boa 1961 2 +12.75 -10.1669998168945 Stomias colubrinus 1961 4 +11.5500001907349 -15.8830003738403 Odontostomias micropogon 1961 2 +10.25 -11.6169996261597 Stomias colubrinus 1961 2 +12.75 -10.1669998168945 Odontostomias micropogon 1961 6 +11.75 -7.44999980926514 Stomias colubrinus 1961 8 +7 -0.699999988079071 Stomias colubrinus 1960 4 +12 -14.9169998168945 Stomias colubrinus 1961 6 +19.033332824707 -34.3472213745117 Nienburgia serrata 1 +17.9833335876465 -33.1166648864746 Ulva lactuca 1813 1 +-66.3858337402344 -53.2702331542969 Trophon coulmanensis 2006 1 +-58.5 -30.6669998168945 Paralichthys orbignyanus 2 +-36.4166984558105 -54.2999992370605 Amauropsis anderssoni 1951 1 +-36.36669921875 -54.4000015258789 Amauropsis anderssoni 1902 1 +-57.6833000183105 -51.6666984558105 Amauropsis anderssoni 1902 1 +-36.4667015075684 -54.2832984924316 Amauropsis anderssoni 1902 1 +-36.2999992370605 -54.1833000183105 Amauropsis anderssoni 1902 1 +-57.5332984924316 -54.4166984558105 Turritella algida 1903 1 +-36.4667015075684 -54.2832984924316 Amauropsis anderssoni 1908 1 +-36.4667015075684 -54.2167015075684 Amauropsis anderssoni 1908 1 +-66.6699981689453 -55.1199989318848 Turritella algida 1994 1 +18.4655990600586 -33.9105987548828 Chelon richardsonii 2004 3 +-38.4669990539551 -54.2830009460449 Echiurus antarcticus 1986 1 +-35.7330017089844 -9.66699981689453 Pseudosquillisma oculata 1899 1 +-40.9070014953613 -53.6279983520508 Austrodecus kelpi 2006 3 +-26 -32 Solmissus faberi 1 +-33.7599983215332 -3.83833003044128 Neogonodactylus minutus 1967 1 +4.68833017349243 -18.3050003051758 Momedossa longipedis 1 +-33.7999992370605 -3.86666655540466 Clathrina zelinhae 2011 1 +-48.5447006225586 -27.9374008178711 Ciocalypta alba 1 +-34.8333320617676 -9.08333301544189 Characella aspera 1873 2 +-62.3333015441895 -43.6666984558105 Illex argentinus 1 +-62.2832984924316 -50.9166984558105 Illex argentinus 1 +-58.8333015441895 -52.8333015441895 Illex argentinus 1 +-60.5833015441895 -49.0499992370605 Illex argentinus 1 +-60.7832984924316 -47.3166999816895 Illex argentinus 1 +-59.9166984558105 -49.5332984924316 Illex argentinus 1 +-60.0499992370605 -49.25 Illex argentinus 1 +-60.4333000183105 -48.7832984924316 Illex argentinus 1 +-60.7999992370605 -47.3499984741211 Illex argentinus 1 +-61.1500015258789 -49.75 Illex argentinus 1 +-60.9333000183105 -47.9333000183105 Illex argentinus 1 +-58.0332984924316 -51.0833015441895 Illex argentinus 1 +-65.2332992553711 -48.5 Illex argentinus 1 +-62.0167007446289 -50.5499992370605 Illex argentinus 1 +-60.5833015441895 -46.5666007995605 Illex argentinus 1 +-45.4091987609863 -23.7591991424561 Derbesia tenuissima 1984 2 +-46.8264007568359 -24.2061004638672 Derbesia tenuissima 1985 6 +-45.4091987609863 -23.7591991424561 Derbesia tenuissima 1983 4 +-45.2811012268066 -23.5716991424561 Wilsonosiphonia howei 1953 4 +-46.2667007446289 -24.0074996948242 Derbesia tenuissima 1986 2 +-45.4091987609863 -23.7591991424561 Derbesia tenuissima 1982 2 +-45.2008018493652 -23.520299911499 Wilsonosiphonia howei 1957 6 +-45.1110992431641 -23.5186004638672 Wilsonosiphonia howei 1962 4 +-45.4536018371582 -23.8216991424561 Wilsonosiphonia howei 1951 2 +4.6933331489563 -18.3141670227051 Momedossa longipedis 1 +18.4336109161377 -34.1580543518066 Grania cryptica 2011 1 +5.61700010299683 -1.39999997615814 Pseudosquillisma oculata 1965 1 +9 -14 Asperarca nodulosa 1971 1 +12 -7 Coelorinchus polli 2000 1 +-52.1730003356934 -32.2060012817383 Dinophysis caudata 2016 1 +18.4407997131348 -34.187198638916 Ulva lactuca 1987 7 +14.539400100708 -22.8430995941162 Codium decorticatum 1992 2 +14.5249996185303 -22.6667003631592 Codium decorticatum 1957 2 +14.5249996185303 -22.6667003631592 Ulva lactuca 1990 1 +14.542200088501 -22.8166999816895 Codium decorticatum 1992 1 +18.6833000183105 -34.0833015441895 Ulva lactuca 1985 4 +18.4500007629395 -34.13330078125 Ulva lactuca 1 +18.4599990844727 -34.1194000244141 Ulva lactuca 1930 1 +17.9500007629395 -33.0167007446289 Ulva lactuca 2001 1 +18.8999996185303 -34.36669921875 Phloiocaulon squamulosum 1983 1 +18.3805999755859 -34.25 Ulva lactuca 1954 1 +14.960000038147 -26.3066997528076 Rhodymenia linearis 1992 1 +18.8332996368408 -34.38330078125 Ulva lactuca 1990 3 +18.4407997131348 -34.187198638916 Ulva lactuca 2001 1 +14.9167003631592 -25.8999996185303 Rhodymenia linearis 1990 1 +18.0333003997803 -33.13330078125 Ulva lactuca 1985 1 + + + + + diff --git a/tools/obisindicators/visualize.r b/tools/obisindicators/visualize.r new file mode 100644 index 000000000..18075e7c1 --- /dev/null +++ b/tools/obisindicators/visualize.r @@ -0,0 +1,56 @@ +#' Statically map indicators using ggplot +#' +#' @param grid spatial features, e.g. hexagons, to plot; requires a geometry +#' spatial column +#' @param column column name with indicator; default="shannon" +#' @param label label to show on legend +#' @param crs coordinate reference system; see `sf::st_crs()` +#' @param trans For continuous scales, the name of a transformation object or +#' the object itself. Built-in transformations include "asn", "atanh", +#' "boxcox", "date", "exp", "hms", "identity" (default), "log", "log10", "log1p", +#' "log2", "logit", "modulus", "probability", "probit", "pseudo_log", +#' "reciprocal", "reverse", "sqrt" and "time". See `ggplot2::continuous_scale` +#' +#' @return ggplot2 plot +#' @concept visualize +#' @export +#' @import rnaturalearth viridis ggplot2 +#' +#' @examples +gmap_indicator <- function( + grid, column = "shannon", label = "Shannon index", trans = "identity", + crs = "+proj=robin +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs") { + + world <- rnaturalearth::ne_countries(scale = "medium", returnclass = "sf") + bb <- sf::st_bbox( + sf::st_transform(grid, crs)) + + ggplot2::ggplot() + + ggplot2::geom_sf( + data = grid, ggplot2::aes_string( + fill = column, geometry = "geometry"), lwd = 0) + + viridis::scale_color_viridis( + option = "inferno", na.value = "white", + name = label, trans = trans) + + viridis::scale_fill_viridis( + option = "inferno", na.value = "white", + name = label, trans = trans) + + ggplot2::geom_sf( + data = world, fill = "#dddddd", color = NA) + + ggplot2::theme( + panel.grid.major.x = ggplot2::element_blank(), + panel.grid.major.y = ggplot2::element_blank(), + panel.grid.minor.x = ggplot2::element_blank(), + panel.grid.minor.y = ggplot2::element_blank(), + panel.background = ggplot2::element_blank(), + axis.text.x = ggplot2::element_blank(), + axis.text.y = ggplot2::element_blank(), + axis.ticks = ggplot2::element_blank(), + axis.title.x = ggplot2::element_blank(), + axis.title.y = ggplot2::element_blank()) + + ggplot2::xlab("") + ggplot2::ylab("") + + ggplot2::coord_sf( + crs = crs, + xlim = bb[c("xmin", "xmax")], + ylim = bb[c("ymin", "ymax")]) +}