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+Input Data +
+ZooMSS requires two sets of input data:
+-
+
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:
+
+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
+
+ 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.
+
+
+library(patchwork)
+p1 <- plotTimeSeries(mdl, by = "abundance", transform = "log10") # Plot abundance time series
+p2 <- plotTimeSeries(mdl, by = "growth") # Plot growth rate time series
+p3 <- plotTimeSeries(mdl, by = "mortality") # Plot predation mortality time series
+
+wrap_plots(p1, p2, p3, nrow = 3, guides = "collect")
We can also plot total biomass through time.
+
+p4 <- plotTimeSeries(mdl, by = "biomass", transform = "log10") + theme(legend.position = "none") # Plot biomass
+p5 <- plotTimeSeries(mdl, by = "biomass", type = "stack", transform = "log10") # Plot stacked biomass
+p6 <- plotTimeSeries(mdl, by = "biomass", type = "fill") # Plot proportional stacked biomass
+
+wrap_plots(p4, p5, p6, nrow = 3, guides = "collect")
+
+
+
+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 abundance from 500 total saved time steps.
Plot predator-prey mass ratios for the idx timestep
+plotPPMR(mdl, idx = 500) # Plot final timestep