Unified function to visualize time series changes for different metrics

Creates time series plots showing how abundance, biomass, mortality, or growth rates of functional groups change throughout the ZooMSS simulation period.

Usage

plotTimeSeries(
  mdl,
  by = "abundance",
  type = "line",
  transform = "identity",
  species = NULL
)

Arguments

mdl: ZooMSS results object containing model outputs with time series data
by: Character string specifying the metric to plot. Options: "abundance", "biomass", "mortality", "growth" (default: "abundance")
type: Character vector of plot type. Use line for the default line plot, stack or fill (as per geom_area) for stacked or proportional plots. (default: "line")
transform: Character vector of the required y-axis transformation. Options from scale_*_continuous (Default: "identity).
species: Character vector of species names to include. If NULL, all species included (default: NULL, applies to all metrics)

Value

ggplot object showing the requested time series by species

Details

Plot Time Series Data for ZooMSS Results

This function creates time series visualizations by:

Abundance: Summing abundances across size classes, log-transformed y-axis
Biomass: Calculating biomass (abundance × weight), with optional stacking and proportional scaling
Mortality: Averaging predation mortality rates across size classes
Growth: Averaging growth rates across size classes, log-transformed y-axis

All plots use species-specific colors and filter out zero values. Time series plots help identify:

Equilibration time for model runs
Seasonal or cyclical patterns in ecological metrics
Relative patterns between functional groups
Model stability and convergence behavior

Examples

if (FALSE) { # \dontrun{
# After running ZooMSS model
results <- zoomss_model(input_params, Groups)

# Plot different metrics
abundance_plot <- plotTimeSeries(results, by = "abundance", transform = "log10")
biomass_plot <- plotTimeSeries(results, by = "biomass", transform = "log10")
mortality_plot <- plotTimeSeries(results, by = "mortality")
growth_plot <- plotTimeSeries(results, by = "growth")

stacked_plot <- plotTimeSeries(results, by = "biomass", type = "stack")
prop_plot <- plotTimeSeries(results, by = "biomass", type = "fill")

# Focus on specific species (works for all metrics)
copepod_plot <- plotTimeSeries(results, by = "biomass",
                              species = c("OmniCopepods", "CarnCopepods"))
abundance_copepods <- plotTimeSeries(results, by = "abundance",
                                    species = c("OmniCopepods", "CarnCopepods"))
mortality_copepods <- plotTimeSeries(results, by = "mortality",
                                    species = c("OmniCopepods", "CarnCopepods"))
growth_copepods <- plotTimeSeries(results, by = "growth",
                                 species = c("OmniCopepods", "CarnCopepods"))
} # }