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-Input Data -
-ZooMSS requires two sets of input data:
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Groups - Contains all taxa-specific parameter -values for each model group, including size ranges and functional group -properties.
-Environmental data - A Time-series dataframe -with time series of environmental conditions with
time, -sst, andchlcolumns.
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-Running the default Model -
-Get the default published Groups dataframe using:
-Groups <- getGroups()
-#> Using default ZooMSS functional groups. Use getGroups() to customize.Now create an environmental data time-series using the helper
-function. This time-series uses a constant sea surface temperature
-(sst) and chlorophyll a (chl) with a
-0.1 yr-1 timestep (dt).
-env_data <- createInputParams(time = seq(0, 100, by = 0.1) ,
- sst = 15,
- chl = 0.15)
-#> ZooMSS input parameters created:
-#> - Time points: 1001 (time values provided)
-#> - Time steps: 1000 (intervals to simulate)
-#> - Time range: 0 to 100 years
-#> - dt = 0.1 years
-#> - SST range: 15 to 15 deg C
-#> - Chlorophyll range: 0.15 to 0.15 mg/m^3We can look at the environment data and check everything is ok -with:
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-plotEnvironment(env_data)
Now we run ZooMSS and save every isave timestep to
-reduce storage requirements.
-mdl <- zoomss_model(input_params = env_data, Groups = Groups, isave = 2)
-#> Functional groups validation passed
-#> Calculating phytoplankton parameters from environmental time series
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- Plotting -
-The model includes several built-in plotting functions for analysis -and visualization.
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-Time Series Analysis -
-These plots display total abundance and mean growth/mortality across -all size classes through time.
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-library(patchwork)
-p1 <- plotTimeSeries(mdl, by = "abundance") # Plot abundance time series
-p2 <- plotTimeSeries(mdl, by = "growth") # Plot growth rate time series
-p3 <- plotTimeSeries(mdl, by = "mortality") # Plot predation mortality time series
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-wrap_plots(p1, p2, p3, nrow = 3, guides = "collect")
We can also plot total biomass through time.
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-p4 <- plotTimeSeries(mdl, by = "biomass") + theme(legend.position = "none") # Plot biomass
-p5 <- plotTimeSeries(mdl, by = "biomass", stacked = TRUE) # Plot stacked biomass
-p6 <- plotTimeSeries(mdl, by = "biomass", stacked = TRUE, proportion = TRUE) # Plot proportional stacked biomass
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-wrap_plots(p4, p5, p6, nrow = 3, guides = "collect")
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-Static Plots for a given model time point -
-Plot mean species-resolved size spectra for the final
-n_years.
-plotSizeSpectra(mdl, n_years = 10)
-#> Averaging final 10 years (50 saved time steps with isave = 2) of N from 500 total saved time steps.
Plot predator-prey mass ratios for the idx timestep
-plotPPMR(mdl, idx = 500) # Plot final timestep
