From d316365b719f0447557eb6bb8aa09be54aa3cc33 Mon Sep 17 00:00:00 2001 From: Jason Everett Date: Tue, 10 Mar 2026 10:49:58 +1100 Subject: [PATCH] Update website and add Hex --- DESCRIPTION | 2 +- README.Rmd | 3 +- README.md | 4 +- _pkgdown.yml | 3 +- data-raw/Hex/zoomss.png | Bin 0 -> 84417 bytes data-raw/Hex/zoomss_hexsticker_ocean.png | Bin 0 -> 555026 bytes docs/404.html | 2 +- docs/404.md | 3 + docs/LICENSE-text.html | 2 +- docs/LICENSE-text.md | 4 + docs/LICENSE.html | 2 +- docs/LICENSE.md | 22 ++ docs/articles/index.html | 2 +- docs/articles/index.md | 5 + docs/articles/zoomss.html | 12 +- docs/articles/zoomss.md | 121 +++++++++++ .../figure-html/unnamed-chunk-8-1.png | Bin 209012 -> 209506 bytes docs/authors.html | 6 +- docs/authors.md | 31 +++ docs/deps/bootstrap-5.3.1/bootstrap.min.css | 4 +- docs/deps/bootstrap-5.3.1/font.css | 54 +++++ .../07d40e985ad7c747025dabb9f22142c4.woff2 | Bin 0 -> 16388 bytes .../fonts/1Ptug8zYS_SKggPNyC0ITw.woff2 | Bin 0 -> 48264 bytes 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mode 100644 docs/reference/reduceSize.md create mode 100644 docs/reference/reduceSpecies.md create mode 100644 docs/reference/untibble.md create mode 100644 docs/reference/validateGroups.md create mode 100644 docs/reference/zoomss_model.md create mode 100644 docs/reference/zoomss_mvf.md create mode 100644 docs/reference/zoomss_params.md create mode 100644 docs/reference/zoomss_run.md create mode 100644 docs/reference/zoomss_setup.md create mode 100644 man/figures/zoomss.png diff --git a/DESCRIPTION b/DESCRIPTION index 0ad0b2e..77a4138 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -45,7 +45,7 @@ URL: https://github.com/MathMarEcol/zoomss BugReports: https://github.com/MathMarEcol/zoomss/issues Encoding: UTF-8 Roxygen: list(markdown = TRUE) -RoxygenNote: 7.3.2 +RoxygenNote: 7.3.3 Suggests: knitr, rmarkdown, diff --git a/README.Rmd b/README.Rmd index 7b930de..01c183a 100644 --- a/README.Rmd +++ b/README.Rmd @@ -13,9 +13,8 @@ knitr::opts_chunk$set( ) ``` -# Zooplankton Model of Size Spectrum (ZooMSS) +# Zooplankton Model of Size Spectrum (ZooMSS) zoomss website - [![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental) [![Windows](https://github.com/MathMarEcol/zoomss/actions/workflows/Windows.yaml/badge.svg)](https://github.com/MathMarEcol/zoomss/actions/workflows/Windows.yaml) diff --git a/README.md b/README.md index ea4e321..869e5f6 100644 --- a/README.md +++ b/README.md @@ -1,9 +1,7 @@ -# Zooplankton Model of Size Spectrum (ZooMSS) - - +# Zooplankton Model of Size Spectrum (ZooMSS) zoomss website diff --git a/_pkgdown.yml b/_pkgdown.yml index 136fb39..ced5eb5 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -2,9 +2,10 @@ url: ~ template: bootstrap: 5 bslib: + primary: "#002F61" toc: true toc-expand: 3 - bootswatch: cerulean + bootswatch: flatly reference: - title: Data desc: Data related functions and datasets diff --git a/data-raw/Hex/zoomss.png 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-

Site built with pkgdown 2.1.3.

+

Site built with pkgdown 2.2.0.

diff --git a/docs/404.md b/docs/404.md new file mode 100644 index 0000000..5107f89 --- /dev/null +++ b/docs/404.md @@ -0,0 +1,3 @@ +Content not found. Please use links in the navbar. + +# Page not found (404) diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html index 9648e09..331759e 100644 --- a/docs/LICENSE-text.html +++ b/docs/LICENSE-text.html @@ -44,7 +44,7 @@

License

-

Site built with pkgdown 2.1.3.

+

Site built with pkgdown 2.2.0.

diff --git a/docs/LICENSE-text.md b/docs/LICENSE-text.md new file mode 100644 index 0000000..8b5daef --- /dev/null +++ b/docs/LICENSE-text.md @@ -0,0 +1,4 @@ +# License + + YEAR: 2025 + COPYRIGHT HOLDER: zoomss authors diff --git a/docs/LICENSE.html b/docs/LICENSE.html index cc23d28..67a7d98 100644 --- a/docs/LICENSE.html +++ b/docs/LICENSE.html @@ -48,7 +48,7 @@

MIT License

-

Site built with pkgdown 2.1.3.

+

Site built with pkgdown 2.2.0.

diff --git a/docs/LICENSE.md b/docs/LICENSE.md new file mode 100644 index 0000000..56439fb --- /dev/null +++ b/docs/LICENSE.md @@ -0,0 +1,22 @@ +# MIT License + +Copyright (c) 2025 zoomss authors + +Permission is hereby granted, free of charge, to any person obtaining a +copy of this software and associated documentation files (the +“Software”), to deal in the Software without restriction, including +without limitation the rights to use, copy, modify, merge, publish, +distribute, sublicense, and/or sell copies of the Software, and to +permit persons to whom the Software is furnished to do so, subject to +the following conditions: + +The above copyright notice and this permission notice shall be included +in all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS +OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. +IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY +CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, +TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE +SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. diff --git a/docs/articles/index.html b/docs/articles/index.html index c97f642..7c17c9f 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -46,7 +46,7 @@

All vignettes

-

Site built with pkgdown 2.1.3.

+

Site built with pkgdown 2.2.0.

diff --git a/docs/articles/index.md b/docs/articles/index.md new file mode 100644 index 0000000..ea30c44 --- /dev/null +++ b/docs/articles/index.md @@ -0,0 +1,5 @@ +# Articles + +### All vignettes + +- [Getting started with ZooMSS](zoomss.md): diff --git a/docs/articles/zoomss.html b/docs/articles/zoomss.html index ecf7ba5..ba7a60e 100644 --- a/docs/articles/zoomss.html +++ b/docs/articles/zoomss.html @@ -101,7 +101,7 @@

Running the default Model -

+

Now we run ZooMSS and save every isave timestep to reduce storage requirements.

@@ -127,7 +127,7 @@ 
Time Series Analysisp3 <- 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 
@@ -135,7 +135,7 @@ 
Time Series Analysisp6 <- 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 @@ -145,11 +145,11 @@

Static Plots for a given mode 
 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
-

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Default functional groups for the ZooMSS model
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+ +
getGroups()
Load default or custom functional groups for ZooMSS model
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+ +
calculatePhytoParam()
Calculate phytoplankton abundance spectrum from chlorophyll data
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+ +
createEnviroData()
Generate synthetic environmental data for ZooMSS testing
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+ +
createInputParams()
Create input parameters data frame for ZooMSS model runs
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validateGroups() @@ -83,8 +87,7 @@

Model Runs

Functions for running the model

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@@ -100,8 +103,7 @@

Plotting

Functions for plotting.

- -
+
@@ -112,19 +114,22 @@

Plotting
Plot environmental forcing data
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+ +
plotPPMR()
Visualize predator-prey mass ratio patterns in ZooMSS results
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+ +
plotSizeSpectra()
Visualize abundance size spectra across functional groups
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plotTimeSeries() @@ -135,8 +140,7 @@

Data wrangling

Helper functions to convert units and data format

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+
@@ -147,49 +151,57 @@

Data wrangling
Calculate mean of final portion of ZooMSS time series
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+ +
extractPPMR()
Calculate predator-prey mass ratio data for visualization
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+ +
extractSizeRange()
Extract specific size class range from model variable
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+ +
extractTrophicLevels()
Compute trophic levels for functional groups using diet composition
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+ +
getBiomass()
Convert ZooMSS abundance matrices to biomass by multiplying by body weights
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+ +
getGroups()
Load default or custom functional groups for ZooMSS model
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+ +
reduceAll()
Aggregate abundances across all groups and size classes
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reduceSize()
Aggregate ZooMSS abundances across all size classes
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reduceSpecies() @@ -205,7 +217,7 @@

Data wrangling
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Site built with pkgdown 2.1.3.

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Site built with pkgdown 2.2.0.

diff --git a/docs/reference/index.md b/docs/reference/index.md new file mode 100644 index 0000000..f540a7a --- /dev/null +++ b/docs/reference/index.md @@ -0,0 +1,61 @@ +# Package index + +## Data + +Data related functions and datasets + +- [`GroupInputs`](GroupInputs.md) : Default functional groups for the + ZooMSS model +- [`getGroups()`](getGroups.md) : Load default or custom functional + groups for ZooMSS model +- [`calculatePhytoParam()`](calculatePhytoParam.md) : Calculate + phytoplankton abundance spectrum from chlorophyll data +- [`createEnviroData()`](createEnviroData.md) : Generate synthetic + environmental data for ZooMSS testing +- [`createInputParams()`](createInputParams.md) : Create input + parameters data frame for ZooMSS model runs +- [`validateGroups()`](validateGroups.md) : Validate ZooMSS functional + groups data structure and values + +## Model Runs + +Functions for running the model + +- [`zoomss_model()`](zoomss_model.md) : Main ZooMSS model function for + complete simulations + +## Plotting + +Functions for plotting. + +- [`plotEnvironment()`](plotEnvironment.md) : Plot environmental forcing + data +- [`plotPPMR()`](plotPPMR.md) : Visualize predator-prey mass ratio + patterns in ZooMSS results +- [`plotSizeSpectra()`](plotSizeSpectra.md) : Visualize abundance size + spectra across functional groups +- [`plotTimeSeries()`](plotTimeSeries.md) : Unified function to + visualize time series changes for different metrics + +## Data wrangling + +Helper functions to convert units and data format + +- [`averageTimeSeries()`](averageTimeSeries.md) : Calculate mean of + final portion of ZooMSS time series +- [`extractPPMR()`](extractPPMR.md) : Calculate predator-prey mass ratio + data for visualization +- [`extractSizeRange()`](extractSizeRange.md) : Extract specific size + class range from model variable +- [`extractTrophicLevels()`](extractTrophicLevels.md) : Compute trophic + levels for functional groups using diet composition +- [`getBiomass()`](getBiomass.md) : Convert ZooMSS abundance matrices to + biomass by multiplying by body weights +- [`getGroups()`](getGroups.md) : Load default or custom functional + groups for ZooMSS model +- [`reduceAll()`](reduceAll.md) : Aggregate abundances across all groups + and size classes +- [`reduceSize()`](reduceSize.md) : Aggregate ZooMSS abundances across + all size classes +- [`reduceSpecies()`](reduceSpecies.md) : Aggregate ZooMSS abundances + across all species diff --git a/docs/reference/loadDefaultGroups.html b/docs/reference/loadDefaultGroups.html index f1bf273..ebb4bf4 100644 --- a/docs/reference/loadDefaultGroups.html +++ b/docs/reference/loadDefaultGroups.html @@ -65,7 +65,7 @@

Details
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Site built with pkgdown 2.1.3.

+

Site built with pkgdown 2.2.0.

diff --git a/docs/reference/loadDefaultGroups.md b/docs/reference/loadDefaultGroups.md new file mode 100644 index 0000000..7cd7376 --- /dev/null +++ b/docs/reference/loadDefaultGroups.md @@ -0,0 +1,21 @@ +# Internal function to load default ZooMSS groups + +Loads the default functional groups from the package data or CSV file. +This is an internal function used by getGroups(). + +## Usage + +``` r +loadDefaultGroups() +``` + +## Value + +Data frame with default functional groups + +## Details + +Load Default Functional Groups Data + +This function handles the actual loading of default groups data, whether +from package data (if available) or from the CSV file in data-raw. diff --git a/docs/reference/pipe.html b/docs/reference/pipe.html index b242e26..cb3c2ea 100644 --- a/docs/reference/pipe.html +++ b/docs/reference/pipe.html @@ -68,7 +68,7 @@

Value

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Site built with pkgdown 2.1.3.

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Site built with pkgdown 2.2.0.

diff --git a/docs/reference/pipe.md b/docs/reference/pipe.md new file mode 100644 index 0000000..41fbd48 --- /dev/null +++ b/docs/reference/pipe.md @@ -0,0 +1,23 @@ +# Pipe operator + +See `magrittr::%>%` for details. + +## Usage + +``` r +lhs %>% rhs +``` + +## Arguments + +- lhs: + + A value or the magrittr placeholder. + +- rhs: + + A function call using the magrittr semantics. + +## Value + +The result of calling `rhs(lhs)`. diff --git a/docs/reference/plotEnvironment.html b/docs/reference/plotEnvironment.html index ef7b901..90e8233 100644 --- a/docs/reference/plotEnvironment.html +++ b/docs/reference/plotEnvironment.html @@ -89,7 +89,7 @@

Examples -

Site built with pkgdown 2.1.3.

+

Site built with pkgdown 2.2.0.

diff --git a/docs/reference/plotEnvironment.md b/docs/reference/plotEnvironment.md new file mode 100644 index 0000000..be54cf8 --- /dev/null +++ b/docs/reference/plotEnvironment.md @@ -0,0 +1,47 @@ +# Plot environmental forcing data + +Creates plots of sea surface temperature and chlorophyll time series for +visualizing environmental forcing data used in ZooMSS model runs. + +## Usage + +``` r +plotEnvironment(env_data) +``` + +## Arguments + +- env_data: + + Environmental data frame with time, sst, chl columns + +## Value + +ggplot object (if patchwork available) or list of two ggplot objects + +## Details + +Plot Environmental Time Series + +This function creates two separate plots with different y-axes scales: + +- SST plot (red line) with temperature in deg C + +- Chlorophyll plot (green line) with concentration in mg/m^3 + +The plots can be combined using the patchwork package if available, +otherwise separate plots are returned as a list. This helps users +visualize the environmental forcing that drives ZooMSS model dynamics. + +## Examples + +``` r +# Create sample data and plot +env_data <- data.frame( + time = 1:100, + dt = 0.01, + sst = 15 + 3*sin(2*pi*(1:100)/50), + chl = 0.5 + 0.2*cos(2*pi*(1:100)/50) +) +plots <- plotEnvironment(env_data) +``` diff --git a/docs/reference/plotPPMR.html b/docs/reference/plotPPMR.html index 1f5b2aa..451b7b6 100644 --- a/docs/reference/plotPPMR.html +++ b/docs/reference/plotPPMR.html @@ -93,7 +93,7 @@

Examples -

Site built with pkgdown 2.1.3.

+

Site built with pkgdown 2.2.0.

diff --git a/docs/reference/plotPPMR.md b/docs/reference/plotPPMR.md new file mode 100644 index 0000000..49bd305 --- /dev/null +++ b/docs/reference/plotPPMR.md @@ -0,0 +1,55 @@ +# Visualize predator-prey mass ratio patterns in ZooMSS results + +Creates a plot showing the distribution of predator-prey mass ratios +(PPMR) across functional groups, providing insights into the trophic +structure of the ecosystem. + +## Usage + +``` r +plotPPMR(mdl, idx) +``` + +## Arguments + +- mdl: + + ZooMSS results object containing model outputs and parameters + +- idx: + + The time index to plot + +## Value + +ggplot object showing PPMR distribution with species-specific overlays + +## Details + +Plot Predator-Prey Mass Ratio (PPMR) + +This function calculates and visualizes PPMR patterns by: + +- Computing theoretical PPMR values for each functional group and size + class + +- Weighting by biomass to show realized community patterns + +- Creating a density plot of PPMR distribution across the community + +- Overlaying species-specific PPMR values as points + +PPMR is a key ecological metric that describes the size relationship +between predators and their prey, providing insight into food web +structure and energy transfer efficiency in marine ecosystems. + +## Examples + +``` r +if (FALSE) { # \dontrun{ +# After running ZooMSS model +results <- zoomss_model(input_params, Groups) +ppmr_plot <- plotPPMR(results) +print(ppmr_plot) +} # } +``` diff --git a/docs/reference/plotSizeSpectra.html b/docs/reference/plotSizeSpectra.html index e157bd3..f84b60a 100644 --- a/docs/reference/plotSizeSpectra.html +++ b/docs/reference/plotSizeSpectra.html @@ -98,7 +98,7 @@

Examples -

Site built with pkgdown 2.1.3.

+

Site built with pkgdown 2.2.0.

diff --git a/docs/reference/plotSizeSpectra.md b/docs/reference/plotSizeSpectra.md new file mode 100644 index 0000000..e55ea56 --- /dev/null +++ b/docs/reference/plotSizeSpectra.md @@ -0,0 +1,60 @@ +# Visualize abundance size spectra across functional groups + +Creates a log-log plot of abundance versus body size for all functional +groups, showing the classic size spectrum pattern in marine ecosystems. + +## Usage + +``` r +plotSizeSpectra(mdl, by = "abundance", n_years = 10) +``` + +## Arguments + +- mdl: + + ZooMSS results object containing model outputs and parameters + +- by: + + Character string specifying the metric to plot. Options: "abundance", + "biomass", "mortality", "growth" (default: "abundance") + +- n_years: + + The number of years (from the end) over which to average the size + spectra + +## Value + +ggplot object showing log abundance vs log body weight by species + +## Details + +Plot Size Spectra for ZooMSS Results + +This function visualizes the abundance size spectrum by: + +- Converting abundance data to long format with body weights + +- Filtering out zero abundances to focus on active size classes + +- Creating log-log plots colored by functional group + +- Using species-specific colors defined in the Groups parameter table + +Size spectra are fundamental patterns in marine ecology, typically +showing declining abundance with increasing body size. This +visualization helps assess model realism and identify dominant size +classes within each functional group. + +## Examples + +``` r +if (FALSE) { # \dontrun{ +# After running ZooMSS model +results <- zoomss_model(input_params, Groups) +size_plot <- plotSizeSpectra(results) +print(size_plot) +} # } +``` diff --git a/docs/reference/plotTimeSeries.html b/docs/reference/plotTimeSeries.html index 8be686b..0da8ea2 100644 --- a/docs/reference/plotTimeSeries.html +++ b/docs/reference/plotTimeSeries.html @@ -129,7 +129,7 @@

Examples -

Site built with pkgdown 2.1.3.

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Site built with pkgdown 2.2.0.

diff --git a/docs/reference/plotTimeSeries.md b/docs/reference/plotTimeSeries.md new file mode 100644 index 0000000..0585190 --- /dev/null +++ b/docs/reference/plotTimeSeries.md @@ -0,0 +1,104 @@ +# 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 + +``` r +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 + +``` r +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")) +} # } +``` diff --git a/docs/reference/reduceAll.html b/docs/reference/reduceAll.html index faa5a7e..14d82b0 100644 --- a/docs/reference/reduceAll.html +++ b/docs/reference/reduceAll.html @@ -73,7 +73,7 @@

Details
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Site built with pkgdown 2.1.3.

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Site built with pkgdown 2.2.0.

diff --git a/docs/reference/reduceAll.md b/docs/reference/reduceAll.md new file mode 100644 index 0000000..a50d1be --- /dev/null +++ b/docs/reference/reduceAll.md @@ -0,0 +1,32 @@ +# Aggregate abundances across all groups and size classes + +Calculates total abundance across all functional groups and size classes +using the specified method. + +## Usage + +``` r +reduceAll(x, method = "sum") +``` + +## Arguments + +- x: + + 3D array outptut from ZooMSS model + +- method: + + Character string specifying aggregation method: "sum" (default) or + "mean". + +## Value + +Vector of total abundance values (one per spatial cell) + +## Details + +Aggregate abundances across all groups and size classes + +This function provides the most aggregated view of ZooMSS output by +applying the method across both functional groups and size classes. diff --git a/docs/reference/reduceSize.html b/docs/reference/reduceSize.html index 791e03c..aa6d405 100644 --- a/docs/reference/reduceSize.html +++ b/docs/reference/reduceSize.html @@ -88,7 +88,7 @@

Examples -

Site built with pkgdown 2.1.3.

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Site built with pkgdown 2.2.0.

diff --git a/docs/reference/reduceSize.md b/docs/reference/reduceSize.md new file mode 100644 index 0000000..df981de --- /dev/null +++ b/docs/reference/reduceSize.md @@ -0,0 +1,43 @@ +# Aggregate ZooMSS abundances across all size classes + +Sums abundance values across all size classes for each functional group, +providing total abundance per group. + +## Usage + +``` r +reduceSize(x, method = "sum") +``` + +## Arguments + +- x: + + 3D array outptut from ZooMSS model + +- method: + + Character string specifying aggregation method: "sum" (default) or + "mean". + +## Value + +List of vectors with total abundance per functional group + +## Details + +Sum ZooMSS Output Across Size Bins + +This function collapses the size dimension of ZooMSS output by summing +across all size classes. Useful for analyzing total abundance patterns +without size structure detail. + +## Examples + +``` r +if (FALSE) { # \dontrun{ +# After running ZooMSS model +results <- zoomss_model(input_params, Groups) +total_abundances <- reduceSize(results$abundances) +} # } +``` diff --git a/docs/reference/reduceSpecies.html b/docs/reference/reduceSpecies.html index 8847b24..e9e77f1 100644 --- a/docs/reference/reduceSpecies.html +++ b/docs/reference/reduceSpecies.html @@ -73,7 +73,7 @@

Details
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Site built with pkgdown 2.1.3.

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Site built with pkgdown 2.2.0.

diff --git a/docs/reference/reduceSpecies.md b/docs/reference/reduceSpecies.md new file mode 100644 index 0000000..707e39a --- /dev/null +++ b/docs/reference/reduceSpecies.md @@ -0,0 +1,32 @@ +# Aggregate ZooMSS abundances across all species + +Aggregates abundance values across all species bins for each functional +group and size class using the specified method. + +## Usage + +``` r +reduceSpecies(x, method = "sum") +``` + +## Arguments + +- x: + + 3D array outptut from ZooMSS model + +- method: + + Character string specifying aggregation method: "sum" (default) or + "mean". + +## Value + +Array with species dimension reduced using the specified method + +## Details + +Aggregate ZooMSS abundances across all species + +This function collapses the species dimension by applying the specified +method (sum or mean) across all species bins. diff --git a/docs/reference/untibble.md b/docs/reference/untibble.md new file mode 100644 index 0000000..310d808 --- /dev/null +++ b/docs/reference/untibble.md @@ -0,0 +1,28 @@ +# Convert tibble to data frame for efficiency + +Removes tibble attributes and converts to a plain data frame for +improved speed and memory efficiency in computational workflows. + +## Usage + +``` r +untibble(tibble) +``` + +## Arguments + +- tibble: + + A tibble or data frame object to convert + +## Value + +Plain data frame without tibble attributes + +## Details + +Remove Tibble Attributes + +This utility function strips tibble-specific attributes that can slow +down operations in tight computational loops. Used internally by ZooMSS +for performance optimization when working with large datasets. diff --git a/docs/reference/validateGroups.html b/docs/reference/validateGroups.html index 3ed71e2..33ba161 100644 --- a/docs/reference/validateGroups.html +++ b/docs/reference/validateGroups.html @@ -90,7 +90,7 @@

Examples -

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Site built with pkgdown 2.2.0.

diff --git a/docs/reference/validateGroups.md b/docs/reference/validateGroups.md new file mode 100644 index 0000000..6d82af8 --- /dev/null +++ b/docs/reference/validateGroups.md @@ -0,0 +1,50 @@ +# Validate ZooMSS functional groups data structure and values + +Performs comprehensive validation of functional groups data to ensure it +meets ZooMSS model requirements. + +## Usage + +``` r +validateGroups(groups) +``` + +## Arguments + +- groups: + + Data frame containing functional groups data + +## Value + +TRUE if validation passes (invisibly), otherwise throws an error + +## Details + +Validate Functional Groups Data + +This function validates: + +- Required column names are present + +- Data types are correct + +- Parameter values are within reasonable ranges + +- No missing values in critical columns + +- Size ranges are logical (W0 \< Wmax) + +## Examples + +``` r +if (FALSE) { # \dontrun{ +Groups <- getGroups() +validateGroups(Groups) # Should pass + +# This would fail validation: +bad_groups <- Groups +bad_groups$W0 <- NULL +validateGroups(bad_groups) # Error: missing required column +} # } +``` diff --git a/docs/reference/zoomss_model.html b/docs/reference/zoomss_model.html index d134c79..c5b8de6 100644 --- a/docs/reference/zoomss_model.html +++ b/docs/reference/zoomss_model.html @@ -119,7 +119,7 @@

Examples -

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diff --git a/docs/reference/zoomss_model.md b/docs/reference/zoomss_model.md new file mode 100644 index 0000000..5d49068 --- /dev/null +++ b/docs/reference/zoomss_model.md @@ -0,0 +1,94 @@ +# Main ZooMSS model function for complete simulations + +This is the main wrapper function that orchestrates a complete ZooMSS +model simulation from parameter setup through model execution to output +processing. + +## Usage + +``` r +zoomss_model(input_params, Groups = NULL, isave = 1) +``` + +## Arguments + +- input_params: + + Data frame containing model parameters and environmental time series. + Must include columns: time (time vector in years), sst (sea surface + temperature), and chl (chlorophyll). Can optionally include cellID for + spatial data. The time step (dt) and maximum time (tmax) are + automatically calculated from the time vector. Can be created using + createInputParams(). + +- Groups: + + Data frame defining functional groups with their biological + parameters. Must include columns defining species characteristics, + size ranges, and feeding parameters. If NULL, uses default ZooMSS + functional groups. Can be obtained/customized using getGroups(). + +- isave: + + Save frequency in time steps (default: 10) + +## Value + +Complete ZooMSS model results object containing: + +- param: Model parameters and environmental forcing data + +- time: Time values corresponding to saved results (accounting for + isave) + +- abundance: Abundance time series (time x groups x size classes) + +- growth: Growth rate time series + +- mortality: Mortality rate time series + +- diet: Diet composition time series + +- Additional model structure and kernel data + +## Details + +Run Complete ZooMSS Model Simulation + +This function coordinates the entire ZooMSS modeling workflow: + +1. Validates that environmental time series data is provided + +2. Sets up model parameters using the Groups data and input parameters + +3. Initializes the model structure and feeding kernels + +4. Runs the model forward in time with dynamic environmental forcing + +5. Processes outputs by averaging the final 50% of the simulation + +6. Returns organized results including abundances, diets, growth, and + mortality + +This is the primary entry point for running ZooMSS simulations with +environmental forcing. + +## Examples + +``` r +if (FALSE) { # \dontrun{ +# Basic usage with default groups +env_data <- createEnviroData(10, 0.01) +input_params <- createInputParams(env_data$time, env_data$sst, env_data$chl) +results <- zoomss_model(input_params, isave = 50) + +# Using custom groups +Groups <- getGroups() # Get default groups +Groups$W0[1] <- -12.5 # Modify a parameter +results <- zoomss_model(input_params, Groups, isave = 100) + +# Loading groups from file +custom_groups <- getGroups(source = "file", file = "my_groups.csv") +results <- zoomss_model(input_params, custom_groups) +} # } +``` diff --git a/docs/reference/zoomss_mvf.md b/docs/reference/zoomss_mvf.md new file mode 100644 index 0000000..7d30457 --- /dev/null +++ b/docs/reference/zoomss_mvf.md @@ -0,0 +1,125 @@ +# Solve McKendrick-von Foerster equation for size-structured populations + +Solves the McKendrick-von Foerster (MvF) partial differential equation +for size-structured population dynamics using a finite difference +approach in base R. + +## Usage + +``` r +zoomss_mvf( + ngrps, + curr_min_size, + curr_max_size, + A_iter, + C_iter, + Nb_iter, + S_iter, + A, + B, + C, + Nb, + S +) +``` + +## Arguments + +- ngrps: + + Number of functional groups in the model + +- curr_min_size: + + Vector of minimum size class indices for each group + +- curr_max_size: + + Vector of maximum size class indices for each group + +- A_iter: + + Matrix of advection coefficients for current iteration + +- C_iter: + + Matrix of diagonal coefficients for current iteration + +- Nb_iter: + + Matrix to store updated abundances for current iteration + +- S_iter: + + Matrix of source terms for current iteration + +- A: + + Matrix of advection coefficients + +- B: + + Matrix of diffusion coefficients + +- C: + + Matrix of diagonal coefficients + +- Nb: + + Matrix of abundances to be updated + +- S: + + Matrix of source terms + +## Value + +Updated abundance matrix (Nb) with new size-class distributions + +## Details + +McKendrick-von Foerster Equation Solver (Base R Implementation) + +This function implements the numerical solution to the McKendrick-von +Foerster equation, which describes how populations change across size +classes over time. The equation is solved using an upwind finite +difference scheme that handles: + +- Growth through size classes (advection term) + +- Diffusion between adjacent size classes + +- Source and sink terms from feeding and mortality + +The function processes each functional group separately and applies +boundary conditions appropriate for size-structured models. The last +size class is set to zero abundance to represent maximum size limits. + +This is a core computational component of ZooMSS that updates population +abundances at each time step based on growth, mortality, and +reproduction processes. + +## Examples + +``` r +if (FALSE) { # \dontrun{ +# This function is typically called internally by zoomss_run +# Example shows the structure of parameters needed: +ngrps <- 9 +ngrid <- 100 +curr_min_size <- c(1, 10, 20, 30, 40, 50, 60, 70, 80) +curr_max_size <- c(30, 40, 50, 60, 70, 80, 90, 95, 100) + +# Initialize coefficient matrices +A <- matrix(0, nrow = ngrps, ncol = ngrid) +B <- matrix(0, nrow = ngrps, ncol = ngrid) +C <- matrix(1, nrow = ngrps, ncol = ngrid) +S <- matrix(0, nrow = ngrps, ncol = ngrid) +Nb <- matrix(0.1, nrow = ngrps, ncol = ngrid) + +# Run MvF solver +updated_abundances <- zoomss_mvf(ngrps, curr_min_size, curr_max_size, + A, C, Nb, S, A, B, C, Nb, S) +} # } +``` diff --git a/docs/reference/zoomss_params.md b/docs/reference/zoomss_params.md new file mode 100644 index 0000000..df618c5 --- /dev/null +++ b/docs/reference/zoomss_params.md @@ -0,0 +1,96 @@ +# Initialize and validate ZooMSS model parameters + +Sets up the complete parameter list for ZooMSS model runs, including +functional group parameters, model dimensions, and environmental forcing +data. + +## Usage + +``` r +zoomss_params(Groups, input_params, isave) +``` + +## Arguments + +- Groups: + + Data frame containing functional group definitions with columns: + Species, Type, W0 (log min size), Wmax (log max size), and various + biological parameters + +- input_params: + + Data frame with model parameters including: time (time vector in + years), sst (sea surface temperature), and chl (chlorophyll). The time + vector can start at any value and the model automatically calculates + dt (time step) and tmax (maximum time). + +- isave: + + Save frequency in time steps (default: 50) + +## Value + +List containing comprehensive model parameters: + +- Groups: Functional group definitions + +- ngrps: Number of functional groups + +- ngrid: Number of size classes + +- w: Size class weights (g) + +- tmax, dt, isave: Temporal parameters + +- zoo_grps, fish_grps: Indices for different organism types + +- phyto_int, phyto_slope: Time series of phytoplankton parameters + +- temp_eff_zoo, temp_eff_fish: Time series of temperature effects + +- Additional biological and physical parameters + +## Details + +Set Up ZooMSS Model Parameters + +This function creates a comprehensive parameter object that contains: + +**Static Parameters (fixed across time steps):** + +- Model dimensions (number of groups, size classes, time steps) + +- Biological parameters (growth efficiency, mortality rates) + +- Size class definitions and ranges for each functional group + +- Phytoplankton size spectrum parameters + +**Dynamic Parameters (calculated from environmental data):** + +- Phytoplankton abundance time series based on chlorophyll + +- Temperature effects on metabolism for zooplankton and fish + +- Environmental forcing validation and interpolation + +The function validates that environmental time series data covers the +full simulation period and pre-calculates time-varying parameters to +optimize model performance during the main simulation loop. + +## Examples + +``` r +if (FALSE) { # \dontrun{ +# Load functional groups +data(Groups) + +# Create environmental time series +env_data <- createEnviroData(10, 0.01) +input_params <- createInputParams(env_data$time, env_data$sst, env_data$chl) + +# Generate parameter list +params <- zoomss_params(Groups, input_params, isave = 50) +} # } +``` diff --git a/docs/reference/zoomss_run.md b/docs/reference/zoomss_run.md new file mode 100644 index 0000000..967c706 --- /dev/null +++ b/docs/reference/zoomss_run.md @@ -0,0 +1,98 @@ +# Execute the main ZooMSS simulation loop with dynamic environmental forcing + +Runs the ZooMSS model forward in time, updating environmental conditions +and population dynamics at each time step using the McKendrick-von +Foerster framework. + +## Usage + +``` r +zoomss_run(model) +``` + +## Arguments + +- model: + + Model object created by zoomss_setup containing: + + - param: Complete parameter list with environmental time series + + - Feeding kernels and biological rate parameters + + - Initial conditions and model structure + +## Value + +List containing complete model output: + +- param: Model parameters used in simulation + +- N: Abundance time series (time x groups x size classes) + +- gg: Growth rate time series + +- diet: Diet composition time series + +- Z: Mortality rate time series + +- time: Time values corresponding to saved results (accounting for + isave) + +- w: Size class weights (g) + +- Additional time series data and model results + +## Details + +Run ZooMSS Model Forward in Time + +This is the core simulation engine of ZooMSS that: + +**Environmental Dynamics:** + +- Updates phytoplankton abundance spectrum based on chlorophyll time + series + +- Applies temperature effects on zooplankton and fish metabolism + +- Recalculates feeding kernels with current environmental conditions + +**Population Dynamics:** + +- Solves McKendrick-von Foerster equation for size-structured growth + +- Updates feeding interactions between all size classes and groups + +- Calculates mortality from predation, senescence, and fishing + +- Handles recruitment and boundary conditions for each functional group + +**Time Integration:** + +- Processes model through all time steps with adaptive environmental + forcing + +- Saves output at specified intervals for memory efficiency + +- Maintains mass balance and numerical stability throughout simulation + +Unlike static models, this version dynamically updates phytoplankton +spectra and temperature effects at each time step based on provided +environmental data. + +## Examples + +``` r +if (FALSE) { # \dontrun{ +# Set up model parameters and structure +params <- zoomss_params(Groups, input_params) +model <- zoomss_setup(params) + +# Run the simulation +results <- zoomss_run(model) + +# Access final abundances +final_abundances <- results$N[dim(results$N)[1],,] +} # } +``` diff --git a/docs/reference/zoomss_setup.md b/docs/reference/zoomss_setup.md new file mode 100644 index 0000000..873d20b --- /dev/null +++ b/docs/reference/zoomss_setup.md @@ -0,0 +1,105 @@ +# Initialize ZooMSS model components and calculate feeding interactions + +Sets up the ZooMSS model structure by calculating feeding kernels, +mortality rates, and other model components that remain static during +the simulation. + +## Usage + +``` r +zoomss_setup(param) +``` + +## Arguments + +- param: + + Complete parameter list created by zoomss_params containing: + + - Groups: Functional group definitions and biological parameters + + - Model dimensions (ngrps, ngrid, time parameters) + + - Environmental forcing time series + + - Physical and biological constants + +## Value + +Model object containing: + +- param: Input parameters (passed through) + +- dynam_xxx: Dynamic feeding kernel arrays for group interactions (where + xxx = growthkernel, diffkernel, dietkernel, mortkernel) + +- phyto_xxx: Phytoplankton feeding kernel arrays (where xxx = + growthkernel, diffkernel, dietkernel) + +- nPP: Initial phytoplankton abundance spectrum + +- M_sb_base: Baseline senescence mortality rates + +- fish_mort: Fishing mortality rates + +- assim_eff: Assimilation efficiency matrix + +- temp_eff: Temperature effect matrix (initialized) + +- N: Initial abundance arrays + +- time: Time array for storing time values (initialized as NA) + +- Additional model structure components + +## Details + +Setup ZooMSS Model Structure and Feeding Kernels + +This function initializes the core ZooMSS model structure by +calculating: + +**Static Components (calculated once):** + +- Feeding preference kernels based on predator-prey size ratios + +- Search volumes and encounter rates between size classes + +- Baseline mortality rates (senescence, fishing) + +- Initial abundance distributions for all functional groups + +**Dynamic Component Structures (updated during run):** + +- Phytoplankton feeding kernels (structure calculated here, values + updated with environment) + +- Growth and diffusion kernels for zooplankton and fish interactions + +- Diet and mortality tracking arrays + +**Model Architecture:** + +- Size-structured populations across logarithmic size classes + +- Multiple functional groups with different feeding behaviors + +- Environmental coupling through phytoplankton and temperature + +The function separates static calculations (done once for efficiency) +from dynamic calculations (updated each time step in zoomss_run). + +## Examples + +``` r +if (FALSE) { # \dontrun{ +# Create parameters for model setup +params <- zoomss_params(Groups, input_params) + +# Initialize model structure +model <- zoomss_setup(params) + +# Model is now ready for time integration with zoomss_run +results <- zoomss_run(model) +} # } +``` diff --git a/docs/search.json b/docs/search.json index ffd8f3d..38374c1 100644 --- a/docs/search.json +++ b/docs/search.json @@ -1 +1 @@ -[{"path":"/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2025 zoomss authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"/articles/zoomss.html","id":"input-data","dir":"Articles","previous_headings":"","what":"Input Data","title":"Getting started with ZooMSS","text":"ZooMSS requires two sets input data: Groups - Contains taxa-specific parameter values model group, including size ranges functional group properties. Environmental data - Time-series dataframe time series environmental conditions time, sst, chl columns.","code":""},{"path":"/articles/zoomss.html","id":"running-the-default-model","dir":"Articles","previous_headings":"","what":"Running the default Model","title":"Getting started with ZooMSS","text":"Get default published Groups dataframe using: Now create environmental data time-series using helper function. time-series uses constant sea surface temperature (sst) chlorophyll (chl) 0.1 yr-1 timestep (dt). can look environment data check everything ok : Now run ZooMSS save every isave timestep reduce storage requirements.","code":"Groups <- getGroups() #> Using default ZooMSS functional groups. Use getGroups() to customize. 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^3 plotEnvironment(env_data) mdl <- zoomss_model(input_params = env_data, Groups = Groups, isave = 2) #> Functional groups validation passed #> Calculating phytoplankton parameters from environmental time series"},{"path":"/articles/zoomss.html","id":"plotting","dir":"Articles","previous_headings":"","what":"Plotting","title":"Getting started with ZooMSS","text":"model includes several built-plotting functions analysis visualization.","code":""},{"path":"/articles/zoomss.html","id":"time-series-analysis","dir":"Articles","previous_headings":"Plotting","what":"Time Series Analysis","title":"Getting started with ZooMSS","text":"plots display total abundance mean growth/mortality across size classes time. can also plot total biomass time.","code":"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\") 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\")"},{"path":"/articles/zoomss.html","id":"static-plots-for-a-given-model-time-point","dir":"Articles","previous_headings":"Plotting","what":"Static Plots for a given model time point","title":"Getting started with ZooMSS","text":"Plot mean species-resolved size spectra final n_years. Plot predator-prey mass ratios idx timestep","code":"plotSizeSpectra(mdl, n_years = 10) #> Averaging final 10 years (50 saved time steps with isave = 2) of abundance from 500 total saved time steps. plotPPMR(mdl, idx = 500) # Plot final timestep"},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Jason D. Everett. Author, maintainer. Ryan F. Heneghan. Author. Kieran Murphy. Author. Anthony J. Richardson. Author.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Everett J, Heneghan R, Murphy K, Richardson (2025). zoomss: Zooplankton Model Size Spectra. R package version 0.1.0, https://github.com/MathMarEcol/zoomss.","code":"@Manual{, title = {zoomss: Zooplankton Model of Size Spectra}, author = {Jason D. Everett and Ryan F. Heneghan and Kieran Murphy and Anthony J. Richardson}, year = {2025}, note = {R package version 0.1.0}, url = {https://github.com/MathMarEcol/zoomss}, }"},{"path":[]},{"path":"/index.html","id":"overview-of-zoomss","dir":"","previous_headings":"","what":"Overview of ZooMSS","title":"Zooplankton Model of Size Spectra","text":"Zooplankton Model Size Spectra (ZooMSS) functional size-spectrum model marine ecosystem (following Heneghan et al. 2016) resolve phytoplankton, nine zooplankton functional groups (heterotrophic flagellates ciliates, omnivorous carnivorous copepods, larvaceans, euphausiids, salps, chaetognaths jellyfish) three size-based fish groups. Zooplankton functional groups resolved using body-size ranges, size-based feeding characteristics carbon content, zooplankton community emerges model across global environmental gradients, depending functional traits different groups. developed Zooplankton Model Size Spectra ZooMSS based prototype Heneghan et al. (2016). ZooMSS uses functional size-spectrum framework (Blanchard et al., 2017) resolve body size ranges, size-based feeding characteristics carbon content nine zooplankton groups three fish groups. model supports time-varying environmental conditions enabling studies seasonal cycles, climate change scenarios, ecosystem responses environmental variability. ZooMSS represents marine ecosystem three communities: phytoplankton, zooplankton fish. zooplankton community consists nine abundant zooplankton groups, fish community made small, medium large group. Dynamics phytoplankton explicitly resolved model, rather mean size structure phytoplankton community estimated directly satellite chlorophyll observations (Brewin et al., 2010; Barnes et al., 2011; Hirata et al., 2011). Abundances zooplankton fish communities driven size-dependent processes growth mortality, temporal dynamics functional group governed separate second-order McKendrick-von Foerster equations.","code":""},{"path":"/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Zooplankton Model of Size Spectra","text":"can install development version zoomss GitHub :","code":"# install.packages(\"pak\") pak::pak(\"MathMarEcol/zoomss\")"},{"path":"/index.html","id":"publications","dir":"","previous_headings":"","what":"Publications","title":"Zooplankton Model of Size Spectra","text":"Heneghan, R.F., Everett, J.D., Blanchard, J.L., Richardson, .J., 2016. Zooplankton Fish: Improving Zooplankton Realism Size-Spectrum Models Mediates Energy Transfer Food Webs. Front. Mar. Sci. 3, 1–15. https://doi.org/10.3389/fmars.2016.00201 Heneghan, R.F., Everett, J.D., Sykes, P., Batten, S.D., Edwards, M., Takahashi, K., Suthers, .M., Blanchard, J.L., Richardson, .J., review, global size-spectrum model marine ecosystem resolves zooplankton composition. Ecological Modelling","code":""},{"path":"/index.html","id":"getting-help","dir":"","previous_headings":"","what":"Getting Help","title":"Zooplankton Model of Size Spectra","text":"encounter problems running model, raise issue GitHub: https://github.com/MathMarEcol/ZoopSizeSpectraModel/issues find errors want improve model, ’d love make changes submit pull request us review approve.","code":""},{"path":"/reference/GroupInputs.html","id":null,"dir":"Reference","previous_headings":"","what":"Default functional groups for the ZooMSS model — GroupInputs","title":"Default functional groups for the ZooMSS model — GroupInputs","text":"dataset containing biological parameters different functional groups used ZooMSS size-structured marine ecosystem model. represent various taxa flagellates large fish, defined feeding behavior, size ranges, physiological parameters.","code":""},{"path":"/reference/GroupInputs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Default functional groups for the ZooMSS model — GroupInputs","text":"","code":"GroupInputs"},{"path":"/reference/GroupInputs.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Default functional groups for the ZooMSS model — GroupInputs","text":"data frame 12 rows (functional groups) 19 columns: Species Character. Name functional group/taxa Type Character. Broad category (Zooplankton Fish) FeedType Character. Feeding strategy (Heterotroph, FilterFeeder, Omnivore, Carnivore) Prop Numeric. Initial proportion total biomass W0 Numeric. Log10 minimum body weight (g) group Wmax Numeric. Log10 maximum body weight (g) group Wmat Numeric. Log10 maturation body weight (g) SearchCoef Numeric. Search coefficient predation interactions SearchExp Numeric. Search exponent predation scaling PPMRscale Numeric. Predator-prey mass ratio scaling parameter PPMR Numeric. Predator-prey mass ratio (fish groups) FeedWidth Numeric. Feeding kernel width parameter GrossGEscale Numeric. Gross growth efficiency scaling Carbon Numeric. Carbon content proportion Repro Numeric. Reproduction parameter Fmort Numeric. Fishing mortality rate Fmort_W0 Numeric. Log10 minimum weight fishing mortality Fmort_Wmax Numeric. Log10 maximum weight fishing mortality PlotColour Character. Color code plotting functional group","code":""},{"path":"/reference/GroupInputs.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Default functional groups for the ZooMSS model — GroupInputs","text":"Marine ecological literature ZooMSS model development","code":""},{"path":"/reference/GroupInputs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Default functional groups for the ZooMSS model — GroupInputs","text":"ZooMSS Functional Groups Data GroupInputs dataset defines 12 functional groups spanning small microzooplankton (flagellates, ciliates) various mesozooplankton groups (copepods, euphausiids, chaetognaths) gelatinous zooplankton (salps, jellyfish) three fish size classes (small, medium, large). group characterized : Size ranges: W0 Wmax define body size spectrum Feeding behavior: Different strategies resource acquisition Interaction parameters: Search rates predator-prey relationships Physiological rates: Growth efficiency carbon content parameters based marine ecological literature represent typical values temperate marine ecosystems.","code":""},{"path":"/reference/GroupInputs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Default functional groups for the ZooMSS model — GroupInputs","text":"","code":"data(GroupInputs) head(GroupInputs) #> # A tibble: 6 × 19 #> Species Type FeedType Prop W0 Wmax Wmat SearchCoef SearchExp PPMRscale #> #> 1 Flagell… Zoop… Heterot… 0.1 -12 -6.8 -8.8 640 0.8 1.5 #> 2 Ciliates Zoop… Heterot… 0.1 -9.3 -6.3 -8.3 640 0.8 0.04 #> 3 Larvace… Zoop… FilterF… 0.1 -6.4 -3.2 -5.2 640 0.8 -3 #> 4 OmniCop… Zoop… Omnivore 0.04 -7.5 -3.5 -5.5 640 0.8 -0.5 #> 5 CarnCop… Zoop… Carnivo… 0.06 -7.5 -2.5 -4.5 640 0.8 1.5 #> 6 Euphaus… Zoop… Omnivore 0.1 -4.2 0.2 -1.8 640 0.8 -2 #> # ℹ 9 more variables: PPMR , FeedWidth , GrossGEscale , #> # Carbon , Repro , Fmort , Fmort_W0 , Fmort_Wmax , #> # PlotColour # View size ranges across groups plot(GroupInputs$W0, GroupInputs$Wmax, col = GroupInputs$PlotColour, xlab = \"Log10 Min Weight\", ylab = \"Log10 Max Weight\") text(GroupInputs$W0, GroupInputs$Wmax, GroupInputs$Species, pos = 3, cex = 0.7)"},{"path":"/reference/averageTimeSeries.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate mean of final portion of ZooMSS time series — averageTimeSeries","title":"Calculate mean of final portion of ZooMSS time series — averageTimeSeries","text":"Calculates mean final n years time series obtain equilibrium values model spin-period.","code":""},{"path":"/reference/averageTimeSeries.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate mean of final portion of ZooMSS time series — averageTimeSeries","text":"","code":"averageTimeSeries(mdl, var, n_years = 10)"},{"path":"/reference/averageTimeSeries.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate mean of final portion of ZooMSS time series — averageTimeSeries","text":"mdl ZooMSS model results object containing model parameters output arrays var Character string specifying variable extract average (e.g., \"N\", \"Growth\", \"Mort\") n_years Number years end time series average (default: 10)","code":""},{"path":"/reference/averageTimeSeries.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate mean of final portion of ZooMSS time series — averageTimeSeries","text":"2D array averaged values (groups x size_classes)","code":""},{"path":"/reference/averageTimeSeries.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate mean of final portion of ZooMSS time series — averageTimeSeries","text":"Calculate Average Output Model Time Series function removes initial transient period time series data calculates mean final n years, providing representative steady-state values. Essential obtaining equilibrium abundances, growth rates, model outputs model reached dynamic equilibrium.","code":""},{"path":"/reference/averageTimeSeries.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate mean of final portion of ZooMSS time series — averageTimeSeries","text":"","code":"if (FALSE) { # \\dontrun{ # Run ZooMSS model results <- zoomss_model(input_params, Groups) # Average final 3 years of abundance data avg_abundance <- averageTimeSeries(results, \"N\", n_years = 3) # Average final 10 years of growth data (default) avg_growth <- averageTimeSeries(results, \"gg\") } # }"},{"path":"/reference/calculatePhytoParam.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate phytoplankton abundance spectrum from chlorophyll data — calculatePhytoParam","title":"Calculate phytoplankton abundance spectrum from chlorophyll data — calculatePhytoParam","text":"Converts chlorophyll concentration data phytoplankton size spectrum parameters (slope, intercept, maximum size) using established oceanographic relationships.","code":""},{"path":"/reference/calculatePhytoParam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate phytoplankton abundance spectrum from chlorophyll data — calculatePhytoParam","text":"","code":"calculatePhytoParam(dat)"},{"path":"/reference/calculatePhytoParam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate phytoplankton abundance spectrum from chlorophyll data — calculatePhytoParam","text":"dat Data frame containing chlorophyll data (chl column mg/m^3) optionally phytoplankton biomass (phy column g/m^3)","code":""},{"path":"/reference/calculatePhytoParam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate phytoplankton abundance spectrum from chlorophyll data — calculatePhytoParam","text":"Data frame added columns: phyto_slope: Power law slope phytoplankton size spectrum phyto_int: Log10 intercept phytoplankton abundance phyto_max: Maximum phytoplankton size (log10 grams) pico_biom, nano_biom, micro_biom: Biomass size class","code":""},{"path":"/reference/calculatePhytoParam.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate phytoplankton abundance spectrum from chlorophyll data — calculatePhytoParam","text":"Calculate Phytoplankton Size Spectrum Parameters function implements Brewin et al. (2015) algorithm partition chlorophyll among picophytoplankton, nanophytoplankton, microphytoplankton size classes, calculates: Size spectrum slope intercept parameters Maximum phytoplankton size based micro proportion Biomass estimates size class parameters drive dynamic phytoplankton spectrum ZooMSS serves base food web. function can work either chlorophyll-data (using empirical relationships) direct phytoplankton biomass measurements.","code":""},{"path":"/reference/calculatePhytoParam.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Calculate phytoplankton abundance spectrum from chlorophyll data — calculatePhytoParam","text":"Brewin, R.J.W., et al. (2015). three-component model phytoplankton size class Atlantic Ocean. Ecological Modelling, 306, 90-101. Maranon, E., et al. (2014). Resource supply overrides temperature controlling factor marine phytoplankton growth. PLoS ONE, 9(6), e99312.","code":""},{"path":"/reference/createEnviroData.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate synthetic environmental data for ZooMSS testing — createEnviroData","title":"Generate synthetic environmental data for ZooMSS testing — createEnviroData","text":"Creates simple synthetic environmental time series optional seasonal variation testing ZooMSS model runs real environmental data available.","code":""},{"path":"/reference/createEnviroData.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate synthetic environmental data for ZooMSS testing — createEnviroData","text":"","code":"createEnviroData( n_years, dt, base_sst = 15, base_chl = 0.5, seasonal = TRUE, sst_amplitude = 3, chl_amplitude = 0.2 )"},{"path":"/reference/createEnviroData.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate synthetic environmental data for ZooMSS testing — createEnviroData","text":"n_years Number years generate dt Time step size years base_sst Base sea surface temperature deg C (default: 15) base_chl Base chlorophyll concentration mg/m^3 (default: 0.5) seasonal Logical, whether add seasonal variation (default: TRUE) sst_amplitude Amplitude SST seasonal variations deg C (default: 3) chl_amplitude Amplitude chlorophyll seasonal variations mg/m^3 (default: 0.2)","code":""},{"path":"/reference/createEnviroData.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate synthetic environmental data for ZooMSS testing — createEnviroData","text":"Data frame columns: time, sst, chl","code":""},{"path":"/reference/createEnviroData.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate synthetic environmental data for ZooMSS testing — createEnviroData","text":"Create Environmental Time Series function generates synthetic sea surface temperature chlorophyll time series can used testing ZooMSS model behavior. function can create either static environmental conditions seasonal cycles sinusoidal variation. particularly useful : Testing model sensitivity environmental forcing Creating idealized scenarios model exploration Generating data real environmental data unavailable seasonal option creates SST chlorophyll cycles phase, mimicking typical ocean patterns chlorophyll peaks SST lower.","code":""},{"path":"/reference/createEnviroData.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate synthetic environmental data for ZooMSS testing — createEnviroData","text":"","code":"# Create seasonal environmental data env_data <- createEnviroData( n_years = 10, dt = 0.01, seasonal = TRUE ) # Create static environmental conditions static_data <- createEnviroData( n_years = 5, dt = 0.01, seasonal = FALSE, base_sst = 20, base_chl = 1.0 )"},{"path":"/reference/createInputParams.html","id":null,"dir":"Reference","previous_headings":"","what":"Create input parameters data frame for ZooMSS model runs — createInputParams","title":"Create input parameters data frame for ZooMSS model runs — createInputParams","text":"Creates properly formatted input parameters data frame ZooMSS model simulations, combining temporal parameters environmental time series data.","code":""},{"path":"/reference/createInputParams.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create input parameters data frame for ZooMSS model runs — createInputParams","text":"","code":"createInputParams(time, sst, chl, cellID = NULL)"},{"path":"/reference/createInputParams.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create input parameters data frame for ZooMSS model runs — createInputParams","text":"time Numeric vector time values years (must increasing uniform, can start value) sst Numeric vector sea surface temperature values deg C chl Numeric vector chlorophyll concentration values mg/m^3 cellID Optional numeric vector cell identifiers spatial data (default: NULL)","code":""},{"path":"/reference/createInputParams.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create input parameters data frame for ZooMSS model runs — createInputParams","text":"Data frame columns: time, time_step, sst, chl, cellID (provided)","code":""},{"path":"/reference/createInputParams.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create input parameters data frame for ZooMSS model runs — createInputParams","text":"Create ZooMSS Input Parameters Object function combines environmental time series (SST chlorophyll) time data create input_params object required zoomss_model(). function performs validation checks using assertthat ensure: input vectors numeric equal length SST values within reasonable ocean range (-2 35 deg C) Chlorophyll values positive within typical range (0 50 mg/m^3) Time values increasing reasonable","code":""},{"path":"/reference/createInputParams.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create input parameters data frame for ZooMSS model runs — createInputParams","text":"","code":"if (FALSE) { # \\dontrun{ # Create simple environmental time series time_vec <- seq(0, 10, 0.01) # 10 years with 0.01 year time steps sst_vec <- 15 + 3*sin(2*pi*time_vec/1) # annual cycle chl_vec <- 0.5 + 0.2*cos(2*pi*time_vec/1) # annual cycle # Create input parameters object input_params <- createInputParams(time_vec, sst_vec, chl_vec) # Use with ZooMSS model results <- zoomss_model(input_params, Groups, isave = 50) } # }"},{"path":"/reference/extractPPMR.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate predator-prey mass ratio data for visualization — extractPPMR","title":"Calculate predator-prey mass ratio data for visualization — extractPPMR","text":"Calculates predator-prey mass ratio (PPMR) values biomass weightings creating PPMR distribution plots ZooMSS analysis.","code":""},{"path":"/reference/extractPPMR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate predator-prey mass ratio data for visualization — extractPPMR","text":"","code":"extractPPMR(mdl)"},{"path":"/reference/extractPPMR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate predator-prey mass ratio data for visualization — extractPPMR","text":"mdl ZooMSS results object containing abundance data (N) model parameters","code":""},{"path":"/reference/extractPPMR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate predator-prey mass ratio data for visualization — extractPPMR","text":"2D input: List containing PPMR density data species-specific values plotting 3D input: Array first dimension time, containing PPMR results timestep","code":""},{"path":"/reference/extractPPMR.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate predator-prey mass ratio data for visualization — extractPPMR","text":"Calculate PPMR Data Plotting function computes theoretical realized PPMR patterns : Calculating size-dependent PPMR values using Wirtz 2012 equations Weighting biomass show community-level patterns Computing species-specific PPMR values Handling special cases filter feeders (larvaceans, salps) Processing either time-averaged abundances (2D) full time series (3D) function dynamically determines size class ranges larvaceans salps based W0 Wmax values. 3D abundance arrays, calculates PPMR time step separately. helper function primarily used plotPPMR visualization. PPMR analysis provides insights food web structure predation patterns.","code":""},{"path":"/reference/extractSizeRange.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract specific size class range from model variable — extractSizeRange","title":"Extract specific size class range from model variable — extractSizeRange","text":"Subsets ZooMSS model output include specified size range, useful focusing analysis particular size ranges.","code":""},{"path":"/reference/extractSizeRange.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract specific size class range from model variable — extractSizeRange","text":"","code":"extractSizeRange(mdl, var, min_size, max_size)"},{"path":"/reference/extractSizeRange.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract specific size class range from model variable — extractSizeRange","text":"mdl ZooMSS model results object containing model parameters output arrays var Character string specifying variable extract (e.g., \"N\", \"Z\", \"Growth\") min_size Minimum size (log10 grams) extract max_size Maximum size (log10 grams) extract","code":""},{"path":"/reference/extractSizeRange.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract specific size class range from model variable — extractSizeRange","text":"Array dimensions original variable subsetted size range","code":""},{"path":"/reference/extractSizeRange.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract specific size class range from model variable — extractSizeRange","text":"Extract Size Range ZooMSS Output function extracts subset size classes specified ZooMSS model variable. Useful analyzing specific size ranges (e.g., microzooplankton, mesozooplankton) excluding boundary effects model analysis. function converts log10 size values size class indices automatically.","code":""},{"path":"/reference/extractSizeRange.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract specific size class range from model variable — extractSizeRange","text":"","code":"if (FALSE) { # \\dontrun{ # Run ZooMSS model results <- zoomss_model(input_params, Groups) # Extract mesozooplankton size range from abundance data meso_abundance <- extractSizeRange(results, \"N\", min_size = -8, max_size = -5) # Extract microzooplankton size range from growth data micro_growth <- extractSizeRange(results, \"Growth\", min_size = -10, max_size = -8) } # }"},{"path":"/reference/extractTrophicLevels.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute trophic levels for functional groups using diet composition — extractTrophicLevels","title":"Compute trophic levels for functional groups using diet composition — extractTrophicLevels","text":"Calculates trophic levels functional group based diet composition using iterative Gauss-Seidel algorithm.","code":""},{"path":"/reference/extractTrophicLevels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute trophic levels for functional groups using diet composition — extractTrophicLevels","text":"","code":"extractTrophicLevels(mdl)"},{"path":"/reference/extractTrophicLevels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute trophic levels for functional groups using diet composition — extractTrophicLevels","text":"mdl ZooMSS model results object containing 3D diet data (mdl$diet). Dimensions time, groups, prey_items columns 1:3 always phytoplankton size classes remaining columns zooplankton/fish groups.","code":""},{"path":"/reference/extractTrophicLevels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute trophic levels for functional groups using diet composition — extractTrophicLevels","text":"Matrix rows time steps columns functional groups","code":""},{"path":"/reference/extractTrophicLevels.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute trophic levels for functional groups using diet composition — extractTrophicLevels","text":"Calculate Trophic Levels Diet Matrix function computes trophic levels : Starting phytoplankton trophic level 1.0 Initializing groups trophic level 2.0 Iteratively updating trophic levels based weighted diet composition Continuing convergence (difference < 0.01) maximum iterations (100) Processing 3D diet arrays time series data Trophic level calculation follows: TL = 1 + sum(diet_fraction_i * TL_prey_i) function calculates trophic levels time step separately dynamically determines number groups diet matrix dimensions. provides quantitative measure group's position food web useful analyzing ecosystem structure energy transfer efficiency.","code":""},{"path":"/reference/extractTrophicLevels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute trophic levels for functional groups using diet composition — extractTrophicLevels","text":"","code":"if (FALSE) { # \\dontrun{ # After running ZooMSS model with 3D time series results <- zoomss_model(input_params, Groups) trophic_levels <- extractTrophicLevels(results) # Returns matrix (time x groups) # View trophic levels by group for final time step final_tl <- trophic_levels[nrow(trophic_levels), ] names(final_tl) <- results$param$Groups$Species print(final_tl) } # }"},{"path":"/reference/getBiomass.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert ZooMSS abundance matrices to biomass by multiplying by body weights — getBiomass","title":"Convert ZooMSS abundance matrices to biomass by multiplying by body weights — getBiomass","text":"Converts abundance data wet weight biomass multiplying abundances corresponding body weights size class. Optionally converts carbon biomass.","code":""},{"path":"/reference/getBiomass.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert ZooMSS abundance matrices to biomass by multiplying by body weights — getBiomass","text":"","code":"getBiomass(mdl, units = \"ww\")"},{"path":"/reference/getBiomass.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert ZooMSS abundance matrices to biomass by multiplying by body weights — getBiomass","text":"mdl ZooMSS model object containing abundance array (N) weight vector (param$w) units Character string specifying biomass units: \"ww\" wet weight (default) \"carbon\" carbon biomass","code":""},{"path":"/reference/getBiomass.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert ZooMSS abundance matrices to biomass by multiplying by body weights — getBiomass","text":"3D array biomass values dimensions N. Units depend units parameter: \"ww\": grams wet weight \"carbon\": grams carbon","code":""},{"path":"/reference/getBiomass.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert ZooMSS abundance matrices to biomass by multiplying by body weights — getBiomass","text":"Convert Abundance Biomass function transforms abundance matrices biomass applying weight vector across size classes. Essential analyses requiring biomass units rather abundance counts. Works 3D arrays (time, groups, size_classes). Can convert either wet weight carbon biomass units.","code":""},{"path":"/reference/getBiomass.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert ZooMSS abundance matrices to biomass by multiplying by body weights — getBiomass","text":"","code":"if (FALSE) { # \\dontrun{ # Run ZooMSS model results <- zoomss_model(input_params, Groups) # Convert abundances to wet weight biomass biomass_ww <- getBiomass(results, units = \"ww\") # Convert abundances to carbon biomass biomass_carbon <- getBiomass(results, units = \"carbon\") } # }"},{"path":"/reference/getGroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Load default or custom functional groups for ZooMSS model — getGroups","title":"Load default or custom functional groups for ZooMSS model — getGroups","text":"Provides access default ZooMSS functional groups loads custom groups file. function primary way obtain Groups data ZooMSS model runs.","code":""},{"path":"/reference/getGroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Load default or custom functional groups for ZooMSS model — getGroups","text":"","code":"getGroups(source = \"default\", file = NULL)"},{"path":"/reference/getGroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Load default or custom functional groups for ZooMSS model — getGroups","text":"source Character string specifying data source. Options: \"default\": Use built-ZooMSS functional groups (default) \"file\": Load groups CSV file \"template\": Export default groups file modification file Path CSV file source=\"file\" source=\"template\"","code":""},{"path":"/reference/getGroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Load default or custom functional groups for ZooMSS model — getGroups","text":"Data frame containing functional groups required columns: Species, Type, W0, Wmax, biological parameters","code":""},{"path":"/reference/getGroups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Load default or custom functional groups for ZooMSS model — getGroups","text":"Get Default ZooMSS Functional Groups function provides flexible access functional groups data: Default groups: Returns standard ZooMSS functional groups (12 groups) Custom file: Loads validates groups user-provided CSV file Template creation: Exports default groups file user modification default groups include: Flagellates, Ciliates, Larvaceans, OmniCopepods, CarnCopepods, Euphausiids, Chaetognaths, Salps, Jellyfish, three Fish groups (Small, Medium, Large). groups data validated ensure contains required columns reasonable parameter values successful model runs.","code":""},{"path":"/reference/getGroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Load default or custom functional groups for ZooMSS model — getGroups","text":"","code":"if (FALSE) { # \\dontrun{ # Use default groups Groups <- getGroups() # Create a template file for modification getGroups(source = \"template\", file = \"my_groups.csv\") # Load custom groups from file custom_groups <- getGroups(source = \"file\", file = \"my_groups.csv\") # Modify default groups programmatically Groups <- getGroups() Groups$W0[Groups$Species == \"Flagellates\"] <- -12.5 # Modify minimum size } # }"},{"path":"/reference/loadDefaultGroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Internal function to load default ZooMSS groups — loadDefaultGroups","title":"Internal function to load default ZooMSS groups — loadDefaultGroups","text":"Loads default functional groups package data CSV file. internal function used getGroups().","code":""},{"path":"/reference/loadDefaultGroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Internal function to load default ZooMSS groups — loadDefaultGroups","text":"","code":"loadDefaultGroups()"},{"path":"/reference/loadDefaultGroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Internal function to load default ZooMSS groups — loadDefaultGroups","text":"Data frame default functional groups","code":""},{"path":"/reference/loadDefaultGroups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Internal function to load default ZooMSS groups — loadDefaultGroups","text":"Load Default Functional Groups Data function handles actual loading default groups data, whether package data (available) CSV file data-raw.","code":""},{"path":"/reference/pipe.html","id":null,"dir":"Reference","previous_headings":"","what":"Pipe operator — %>%","title":"Pipe operator — %>%","text":"See magrittr::%>% details.","code":""},{"path":"/reference/pipe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pipe operator — %>%","text":"","code":"lhs %>% rhs"},{"path":"/reference/pipe.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pipe operator — %>%","text":"lhs value magrittr placeholder. rhs function call using magrittr semantics.","code":""},{"path":"/reference/pipe.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pipe operator — %>%","text":"result calling rhs(lhs).","code":""},{"path":"/reference/plotEnvironment.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot environmental forcing data — plotEnvironment","title":"Plot environmental forcing data — plotEnvironment","text":"Creates plots sea surface temperature chlorophyll time series visualizing environmental forcing data used ZooMSS model runs.","code":""},{"path":"/reference/plotEnvironment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot environmental forcing data — plotEnvironment","text":"","code":"plotEnvironment(env_data)"},{"path":"/reference/plotEnvironment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot environmental forcing data — plotEnvironment","text":"env_data Environmental data frame time, sst, chl columns","code":""},{"path":"/reference/plotEnvironment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot environmental forcing data — plotEnvironment","text":"ggplot object (patchwork available) list two ggplot objects","code":""},{"path":"/reference/plotEnvironment.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot environmental forcing data — plotEnvironment","text":"Plot Environmental Time Series function creates two separate plots different y-axes scales: SST plot (red line) temperature deg C Chlorophyll plot (green line) concentration mg/m^3 plots can combined using patchwork package available, otherwise separate plots returned list. helps users visualize environmental forcing drives ZooMSS model dynamics.","code":""},{"path":"/reference/plotEnvironment.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot environmental forcing data — plotEnvironment","text":"","code":"# Create sample data and plot env_data <- data.frame( time = 1:100, dt = 0.01, sst = 15 + 3*sin(2*pi*(1:100)/50), chl = 0.5 + 0.2*cos(2*pi*(1:100)/50) ) plots <- plotEnvironment(env_data)"},{"path":"/reference/plotPPMR.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize predator-prey mass ratio patterns in ZooMSS results — plotPPMR","title":"Visualize predator-prey mass ratio patterns in ZooMSS results — plotPPMR","text":"Creates plot showing distribution predator-prey mass ratios (PPMR) across functional groups, providing insights trophic structure ecosystem.","code":""},{"path":"/reference/plotPPMR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize predator-prey mass ratio patterns in ZooMSS results — plotPPMR","text":"","code":"plotPPMR(mdl, idx)"},{"path":"/reference/plotPPMR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize predator-prey mass ratio patterns in ZooMSS results — plotPPMR","text":"mdl ZooMSS results object containing model outputs parameters idx time index plot","code":""},{"path":"/reference/plotPPMR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Visualize predator-prey mass ratio patterns in ZooMSS results — plotPPMR","text":"ggplot object showing PPMR distribution species-specific overlays","code":""},{"path":"/reference/plotPPMR.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Visualize predator-prey mass ratio patterns in ZooMSS results — plotPPMR","text":"Plot Predator-Prey Mass Ratio (PPMR) function calculates visualizes PPMR patterns : Computing theoretical PPMR values functional group size class Weighting biomass show realized community patterns Creating density plot PPMR distribution across community Overlaying species-specific PPMR values points PPMR key ecological metric describes size relationship predators prey, providing insight food web structure energy transfer efficiency marine ecosystems.","code":""},{"path":"/reference/plotPPMR.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualize predator-prey mass ratio patterns in ZooMSS results — plotPPMR","text":"","code":"if (FALSE) { # \\dontrun{ # After running ZooMSS model results <- zoomss_model(input_params, Groups) ppmr_plot <- plotPPMR(results) print(ppmr_plot) } # }"},{"path":"/reference/plotSizeSpectra.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize abundance size spectra across functional groups — plotSizeSpectra","title":"Visualize abundance size spectra across functional groups — plotSizeSpectra","text":"Creates log-log plot abundance versus body size functional groups, showing classic size spectrum pattern marine ecosystems.","code":""},{"path":"/reference/plotSizeSpectra.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize abundance size spectra across functional groups — plotSizeSpectra","text":"","code":"plotSizeSpectra(mdl, by = \"abundance\", n_years = 10)"},{"path":"/reference/plotSizeSpectra.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize abundance size spectra across functional groups — plotSizeSpectra","text":"mdl ZooMSS results object containing model outputs parameters Character string specifying metric plot. Options: \"abundance\", \"biomass\", \"mortality\", \"growth\" (default: \"abundance\") n_years number years (end) average size spectra","code":""},{"path":"/reference/plotSizeSpectra.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Visualize abundance size spectra across functional groups — plotSizeSpectra","text":"ggplot object showing log abundance vs log body weight species","code":""},{"path":"/reference/plotSizeSpectra.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Visualize abundance size spectra across functional groups — plotSizeSpectra","text":"Plot Size Spectra ZooMSS Results function visualizes abundance size spectrum : Converting abundance data long format body weights Filtering zero abundances focus active size classes Creating log-log plots colored functional group Using species-specific colors defined Groups parameter table Size spectra fundamental patterns marine ecology, typically showing declining abundance increasing body size. visualization helps assess model realism identify dominant size classes within functional group.","code":""},{"path":"/reference/plotSizeSpectra.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualize abundance size spectra across functional groups — plotSizeSpectra","text":"","code":"if (FALSE) { # \\dontrun{ # After running ZooMSS model results <- zoomss_model(input_params, Groups) size_plot <- plotSizeSpectra(results) print(size_plot) } # }"},{"path":"/reference/plotTimeSeries.html","id":null,"dir":"Reference","previous_headings":"","what":"Unified function to visualize time series changes for different metrics — plotTimeSeries","title":"Unified function to visualize time series changes for different metrics — plotTimeSeries","text":"Creates time series plots showing abundance, biomass, mortality, growth rates functional groups change throughout ZooMSS simulation period.","code":""},{"path":"/reference/plotTimeSeries.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unified function to visualize time series changes for different metrics — plotTimeSeries","text":"","code":"plotTimeSeries( mdl, by = \"abundance\", type = \"line\", transform = \"identity\", species = NULL )"},{"path":"/reference/plotTimeSeries.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unified function to visualize time series changes for different metrics — plotTimeSeries","text":"mdl ZooMSS results object containing model outputs time series data Character string specifying metric plot. Options: \"abundance\", \"biomass\", \"mortality\", \"growth\" (default: \"abundance\") type Character vector plot type. Use line default line plot, stack fill (per geom_area) stacked proportional plots. (default: \"line\") transform Character vector required y-axis transformation. Options scale_*_continuous (Default: \"identity). species Character vector species names include. NULL, species included (default: NULL, applies metrics)","code":""},{"path":"/reference/plotTimeSeries.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unified function to visualize time series changes for different metrics — plotTimeSeries","text":"ggplot object showing requested time series species","code":""},{"path":"/reference/plotTimeSeries.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Unified function to visualize time series changes for different metrics — plotTimeSeries","text":"Plot Time Series Data ZooMSS Results function creates time series visualizations : Abundance: Summing abundances across size classes, log-transformed y-axis Biomass: Calculating biomass (abundance × weight), optional stacking proportional scaling Mortality: Averaging predation mortality rates across size classes Growth: Averaging growth rates across size classes, log-transformed y-axis plots use species-specific colors filter zero values. Time series plots help identify: Equilibration time model runs Seasonal cyclical patterns ecological metrics Relative patterns functional groups Model stability convergence behavior","code":""},{"path":"/reference/plotTimeSeries.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unified function to visualize time series changes for different metrics — plotTimeSeries","text":"","code":"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\")) } # }"},{"path":"/reference/reduceAll.html","id":null,"dir":"Reference","previous_headings":"","what":"Aggregate abundances across all groups and size classes — reduceAll","title":"Aggregate abundances across all groups and size classes — reduceAll","text":"Calculates total abundance across functional groups size classes using specified method.","code":""},{"path":"/reference/reduceAll.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Aggregate abundances across all groups and size classes — reduceAll","text":"","code":"reduceAll(x, method = \"sum\")"},{"path":"/reference/reduceAll.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Aggregate abundances across all groups and size classes — reduceAll","text":"x 3D array outptut ZooMSS model method Character string specifying aggregation method: \"sum\" (default) \"mean\".","code":""},{"path":"/reference/reduceAll.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Aggregate abundances across all groups and size classes — reduceAll","text":"Vector total abundance values (one per spatial cell)","code":""},{"path":"/reference/reduceAll.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Aggregate abundances across all groups and size classes — reduceAll","text":"Aggregate abundances across groups size classes function provides aggregated view ZooMSS output applying method across functional groups size classes.","code":""},{"path":"/reference/reduceSize.html","id":null,"dir":"Reference","previous_headings":"","what":"Aggregate ZooMSS abundances across all size classes — reduceSize","title":"Aggregate ZooMSS abundances across all size classes — reduceSize","text":"Sums abundance values across size classes functional group, providing total abundance per group.","code":""},{"path":"/reference/reduceSize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Aggregate ZooMSS abundances across all size classes — reduceSize","text":"","code":"reduceSize(x, method = \"sum\")"},{"path":"/reference/reduceSize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Aggregate ZooMSS abundances across all size classes — reduceSize","text":"x 3D array outptut ZooMSS model method Character string specifying aggregation method: \"sum\" (default) \"mean\".","code":""},{"path":"/reference/reduceSize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Aggregate ZooMSS abundances across all size classes — reduceSize","text":"List vectors total abundance per functional group","code":""},{"path":"/reference/reduceSize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Aggregate ZooMSS abundances across all size classes — reduceSize","text":"Sum ZooMSS Output Across Size Bins function collapses size dimension ZooMSS output summing across size classes. Useful analyzing total abundance patterns without size structure detail.","code":""},{"path":"/reference/reduceSize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Aggregate ZooMSS abundances across all size classes — reduceSize","text":"","code":"if (FALSE) { # \\dontrun{ # After running ZooMSS model results <- zoomss_model(input_params, Groups) total_abundances <- reduceSize(results$abundances) } # }"},{"path":"/reference/reduceSpecies.html","id":null,"dir":"Reference","previous_headings":"","what":"Aggregate ZooMSS abundances across all species — reduceSpecies","title":"Aggregate ZooMSS abundances across all species — reduceSpecies","text":"Aggregates abundance values across species bins functional group size class using specified method.","code":""},{"path":"/reference/reduceSpecies.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Aggregate ZooMSS abundances across all species — reduceSpecies","text":"","code":"reduceSpecies(x, method = \"sum\")"},{"path":"/reference/reduceSpecies.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Aggregate ZooMSS abundances across all species — reduceSpecies","text":"x 3D array outptut ZooMSS model method Character string specifying aggregation method: \"sum\" (default) \"mean\".","code":""},{"path":"/reference/reduceSpecies.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Aggregate ZooMSS abundances across all species — reduceSpecies","text":"Array species dimension reduced using specified method","code":""},{"path":"/reference/reduceSpecies.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Aggregate ZooMSS abundances across all species — reduceSpecies","text":"Aggregate ZooMSS abundances across species function collapses species dimension applying specified method (sum mean) across species bins.","code":""},{"path":"/reference/untibble.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert tibble to data frame for efficiency — untibble","title":"Convert tibble to data frame for efficiency — untibble","text":"Removes tibble attributes converts plain data frame improved speed memory efficiency computational workflows.","code":""},{"path":"/reference/untibble.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert tibble to data frame for efficiency — untibble","text":"","code":"untibble(tibble)"},{"path":"/reference/untibble.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert tibble to data frame for efficiency — untibble","text":"tibble tibble data frame object convert","code":""},{"path":"/reference/untibble.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert tibble to data frame for efficiency — untibble","text":"Plain data frame without tibble attributes","code":""},{"path":"/reference/untibble.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert tibble to data frame for efficiency — untibble","text":"Remove Tibble Attributes utility function strips tibble-specific attributes can slow operations tight computational loops. Used internally ZooMSS performance optimization working large datasets.","code":""},{"path":"/reference/validateGroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate ZooMSS functional groups data structure and values — validateGroups","title":"Validate ZooMSS functional groups data structure and values — validateGroups","text":"Performs comprehensive validation functional groups data ensure meets ZooMSS model requirements.","code":""},{"path":"/reference/validateGroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate ZooMSS functional groups data structure and values — validateGroups","text":"","code":"validateGroups(groups)"},{"path":"/reference/validateGroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate ZooMSS functional groups data structure and values — validateGroups","text":"groups Data frame containing functional groups data","code":""},{"path":"/reference/validateGroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Validate ZooMSS functional groups data structure and values — validateGroups","text":"TRUE validation passes (invisibly), otherwise throws error","code":""},{"path":"/reference/validateGroups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Validate ZooMSS functional groups data structure and values — validateGroups","text":"Validate Functional Groups Data function validates: Required column names present Data types correct Parameter values within reasonable ranges missing values critical columns Size ranges logical (W0 < Wmax)","code":""},{"path":"/reference/validateGroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Validate ZooMSS functional groups data structure and values — validateGroups","text":"","code":"if (FALSE) { # \\dontrun{ Groups <- getGroups() validateGroups(Groups) # Should pass # This would fail validation: bad_groups <- Groups bad_groups$W0 <- NULL validateGroups(bad_groups) # Error: missing required column } # }"},{"path":"/reference/zoomss_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Main ZooMSS model function for complete simulations — zoomss_model","title":"Main ZooMSS model function for complete simulations — zoomss_model","text":"main wrapper function orchestrates complete ZooMSS model simulation parameter setup model execution output processing.","code":""},{"path":"/reference/zoomss_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Main ZooMSS model function for complete simulations — zoomss_model","text":"","code":"zoomss_model(input_params, Groups = NULL, isave = 1)"},{"path":"/reference/zoomss_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Main ZooMSS model function for complete simulations — zoomss_model","text":"input_params Data frame containing model parameters environmental time series. Must include columns: time (time vector years), sst (sea surface temperature), chl (chlorophyll). Can optionally include cellID spatial data. time step (dt) maximum time (tmax) automatically calculated time vector. Can created using createInputParams(). Groups Data frame defining functional groups biological parameters. Must include columns defining species characteristics, size ranges, feeding parameters. NULL, uses default ZooMSS functional groups. Can obtained/customized using getGroups(). isave Save frequency time steps (default: 10)","code":""},{"path":"/reference/zoomss_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Main ZooMSS model function for complete simulations — zoomss_model","text":"Complete ZooMSS model results object containing: param: Model parameters environmental forcing data time: Time values corresponding saved results (accounting isave) abundance: Abundance time series (time x groups x size classes) growth: Growth rate time series mortality: Mortality rate time series diet: Diet composition time series Additional model structure kernel data","code":""},{"path":"/reference/zoomss_model.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Main ZooMSS model function for complete simulations — zoomss_model","text":"Run Complete ZooMSS Model Simulation function coordinates entire ZooMSS modeling workflow: Validates environmental time series data provided Sets model parameters using Groups data input parameters Initializes model structure feeding kernels Runs model forward time dynamic environmental forcing Processes outputs averaging final 50% simulation Returns organized results including abundances, diets, growth, mortality primary entry point running ZooMSS simulations environmental forcing.","code":""},{"path":"/reference/zoomss_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Main ZooMSS model function for complete simulations — zoomss_model","text":"","code":"if (FALSE) { # \\dontrun{ # Basic usage with default groups env_data <- createEnviroData(10, 0.01) input_params <- createInputParams(env_data$time, env_data$sst, env_data$chl) results <- zoomss_model(input_params, isave = 50) # Using custom groups Groups <- getGroups() # Get default groups Groups$W0[1] <- -12.5 # Modify a parameter results <- zoomss_model(input_params, Groups, isave = 100) # Loading groups from file custom_groups <- getGroups(source = \"file\", file = \"my_groups.csv\") results <- zoomss_model(input_params, custom_groups) } # }"},{"path":"/reference/zoomss_mvf.html","id":null,"dir":"Reference","previous_headings":"","what":"Solve McKendrick-von Foerster equation for size-structured populations — zoomss_mvf","title":"Solve McKendrick-von Foerster equation for size-structured populations — zoomss_mvf","text":"Solves McKendrick-von Foerster (MvF) partial differential equation size-structured population dynamics using finite difference approach base R.","code":""},{"path":"/reference/zoomss_mvf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Solve McKendrick-von Foerster equation for size-structured populations — zoomss_mvf","text":"","code":"zoomss_mvf( ngrps, curr_min_size, curr_max_size, A_iter, C_iter, Nb_iter, S_iter, A, B, C, Nb, S )"},{"path":"/reference/zoomss_mvf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Solve McKendrick-von Foerster equation for size-structured populations — zoomss_mvf","text":"ngrps Number functional groups model curr_min_size Vector minimum size class indices group curr_max_size Vector maximum size class indices group A_iter Matrix advection coefficients current iteration C_iter Matrix diagonal coefficients current iteration Nb_iter Matrix store updated abundances current iteration S_iter Matrix source terms current iteration Matrix advection coefficients B Matrix diffusion coefficients C Matrix diagonal coefficients Nb Matrix abundances updated S Matrix source terms","code":""},{"path":"/reference/zoomss_mvf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Solve McKendrick-von Foerster equation for size-structured populations — zoomss_mvf","text":"Updated abundance matrix (Nb) new size-class distributions","code":""},{"path":"/reference/zoomss_mvf.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Solve McKendrick-von Foerster equation for size-structured populations — zoomss_mvf","text":"McKendrick-von Foerster Equation Solver (Base R Implementation) function implements numerical solution McKendrick-von Foerster equation, describes populations change across size classes time. equation solved using upwind finite difference scheme handles: Growth size classes (advection term) Diffusion adjacent size classes Source sink terms feeding mortality function processes functional group separately applies boundary conditions appropriate size-structured models. last size class set zero abundance represent maximum size limits. core computational component ZooMSS updates population abundances time step based growth, mortality, reproduction processes.","code":""},{"path":"/reference/zoomss_mvf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Solve McKendrick-von Foerster equation for size-structured populations — zoomss_mvf","text":"","code":"if (FALSE) { # \\dontrun{ # This function is typically called internally by zoomss_run # Example shows the structure of parameters needed: ngrps <- 9 ngrid <- 100 curr_min_size <- c(1, 10, 20, 30, 40, 50, 60, 70, 80) curr_max_size <- c(30, 40, 50, 60, 70, 80, 90, 95, 100) # Initialize coefficient matrices A <- matrix(0, nrow = ngrps, ncol = ngrid) B <- matrix(0, nrow = ngrps, ncol = ngrid) C <- matrix(1, nrow = ngrps, ncol = ngrid) S <- matrix(0, nrow = ngrps, ncol = ngrid) Nb <- matrix(0.1, nrow = ngrps, ncol = ngrid) # Run MvF solver updated_abundances <- zoomss_mvf(ngrps, curr_min_size, curr_max_size, A, C, Nb, S, A, B, C, Nb, S) } # }"},{"path":"/reference/zoomss_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Initialize and validate ZooMSS model parameters — zoomss_params","title":"Initialize and validate ZooMSS model parameters — zoomss_params","text":"Sets complete parameter list ZooMSS model runs, including functional group parameters, model dimensions, environmental forcing data.","code":""},{"path":"/reference/zoomss_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initialize and validate ZooMSS model parameters — zoomss_params","text":"","code":"zoomss_params(Groups, input_params, isave)"},{"path":"/reference/zoomss_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initialize and validate ZooMSS model parameters — zoomss_params","text":"Groups Data frame containing functional group definitions columns: Species, Type, W0 (log min size), Wmax (log max size), various biological parameters input_params Data frame model parameters including: time (time vector years), sst (sea surface temperature), chl (chlorophyll). time vector can start value model automatically calculates dt (time step) tmax (maximum time). isave Save frequency time steps (default: 50)","code":""},{"path":"/reference/zoomss_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initialize and validate ZooMSS model parameters — zoomss_params","text":"List containing comprehensive model parameters: Groups: Functional group definitions ngrps: Number functional groups ngrid: Number size classes w: Size class weights (g) tmax, dt, isave: Temporal parameters zoo_grps, fish_grps: Indices different organism types phyto_int, phyto_slope: Time series phytoplankton parameters temp_eff_zoo, temp_eff_fish: Time series temperature effects Additional biological physical parameters","code":""},{"path":"/reference/zoomss_params.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Initialize and validate ZooMSS model parameters — zoomss_params","text":"Set ZooMSS Model Parameters function creates comprehensive parameter object contains: Static Parameters (fixed across time steps): Model dimensions (number groups, size classes, time steps) Biological parameters (growth efficiency, mortality rates) Size class definitions ranges functional group Phytoplankton size spectrum parameters Dynamic Parameters (calculated environmental data): Phytoplankton abundance time series based chlorophyll Temperature effects metabolism zooplankton fish Environmental forcing validation interpolation function validates environmental time series data covers full simulation period pre-calculates time-varying parameters optimize model performance main simulation loop.","code":""},{"path":"/reference/zoomss_params.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initialize and validate ZooMSS model parameters — zoomss_params","text":"","code":"if (FALSE) { # \\dontrun{ # Load functional groups data(Groups) # Create environmental time series env_data <- createEnviroData(10, 0.01) input_params <- createInputParams(env_data$time, env_data$sst, env_data$chl) # Generate parameter list params <- zoomss_params(Groups, input_params, isave = 50) } # }"},{"path":"/reference/zoomss_run.html","id":null,"dir":"Reference","previous_headings":"","what":"Execute the main ZooMSS simulation loop with dynamic environmental forcing — zoomss_run","title":"Execute the main ZooMSS simulation loop with dynamic environmental forcing — zoomss_run","text":"Runs ZooMSS model forward time, updating environmental conditions population dynamics time step using McKendrick-von Foerster framework.","code":""},{"path":"/reference/zoomss_run.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Execute the main ZooMSS simulation loop with dynamic environmental forcing — zoomss_run","text":"","code":"zoomss_run(model)"},{"path":"/reference/zoomss_run.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Execute the main ZooMSS simulation loop with dynamic environmental forcing — zoomss_run","text":"model Model object created zoomss_setup containing: param: Complete parameter list environmental time series Feeding kernels biological rate parameters Initial conditions model structure","code":""},{"path":"/reference/zoomss_run.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Execute the main ZooMSS simulation loop with dynamic environmental forcing — zoomss_run","text":"List containing complete model output: param: Model parameters used simulation N: Abundance time series (time x groups x size classes) gg: Growth rate time series diet: Diet composition time series Z: Mortality rate time series time: Time values corresponding saved results (accounting isave) w: Size class weights (g) Additional time series data model results","code":""},{"path":"/reference/zoomss_run.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Execute the main ZooMSS simulation loop with dynamic environmental forcing — zoomss_run","text":"Run ZooMSS Model Forward Time core simulation engine ZooMSS : Environmental Dynamics: Updates phytoplankton abundance spectrum based chlorophyll time series Applies temperature effects zooplankton fish metabolism Recalculates feeding kernels current environmental conditions Population Dynamics: Solves McKendrick-von Foerster equation size-structured growth Updates feeding interactions size classes groups Calculates mortality predation, senescence, fishing Handles recruitment boundary conditions functional group Time Integration: Processes model time steps adaptive environmental forcing Saves output specified intervals memory efficiency Maintains mass balance numerical stability throughout simulation Unlike static models, version dynamically updates phytoplankton spectra temperature effects time step based provided environmental data.","code":""},{"path":"/reference/zoomss_run.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Execute the main ZooMSS simulation loop with dynamic environmental forcing — zoomss_run","text":"","code":"if (FALSE) { # \\dontrun{ # Set up model parameters and structure params <- zoomss_params(Groups, input_params) model <- zoomss_setup(params) # Run the simulation results <- zoomss_run(model) # Access final abundances final_abundances <- results$N[dim(results$N)[1],,] } # }"},{"path":"/reference/zoomss_setup.html","id":null,"dir":"Reference","previous_headings":"","what":"Initialize ZooMSS model components and calculate feeding interactions — zoomss_setup","title":"Initialize ZooMSS model components and calculate feeding interactions — zoomss_setup","text":"Sets ZooMSS model structure calculating feeding kernels, mortality rates, model components remain static simulation.","code":""},{"path":"/reference/zoomss_setup.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initialize ZooMSS model components and calculate feeding interactions — zoomss_setup","text":"","code":"zoomss_setup(param)"},{"path":"/reference/zoomss_setup.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initialize ZooMSS model components and calculate feeding interactions — zoomss_setup","text":"param Complete parameter list created zoomss_params containing: Groups: Functional group definitions biological parameters Model dimensions (ngrps, ngrid, time parameters) Environmental forcing time series Physical biological constants","code":""},{"path":"/reference/zoomss_setup.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initialize ZooMSS model components and calculate feeding interactions — zoomss_setup","text":"Model object containing: param: Input parameters (passed ) dynam_xxx: Dynamic feeding kernel arrays group interactions (xxx = growthkernel, diffkernel, dietkernel, mortkernel) phyto_xxx: Phytoplankton feeding kernel arrays (xxx = growthkernel, diffkernel, dietkernel) nPP: Initial phytoplankton abundance spectrum M_sb_base: Baseline senescence mortality rates fish_mort: Fishing mortality rates assim_eff: Assimilation efficiency matrix temp_eff: Temperature effect matrix (initialized) N: Initial abundance arrays time: Time array storing time values (initialized NA) Additional model structure components","code":""},{"path":"/reference/zoomss_setup.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Initialize ZooMSS model components and calculate feeding interactions — zoomss_setup","text":"Setup ZooMSS Model Structure Feeding Kernels function initializes core ZooMSS model structure calculating: Static Components (calculated ): Feeding preference kernels based predator-prey size ratios Search volumes encounter rates size classes Baseline mortality rates (senescence, fishing) Initial abundance distributions functional groups Dynamic Component Structures (updated run): Phytoplankton feeding kernels (structure calculated , values updated environment) Growth diffusion kernels zooplankton fish interactions Diet mortality tracking arrays Model Architecture: Size-structured populations across logarithmic size classes Multiple functional groups different feeding behaviors Environmental coupling phytoplankton temperature function separates static calculations (done efficiency) dynamic calculations (updated time step zoomss_run).","code":""},{"path":"/reference/zoomss_setup.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initialize ZooMSS model components and calculate feeding interactions — zoomss_setup","text":"","code":"if (FALSE) { # \\dontrun{ # Create parameters for model setup params <- zoomss_params(Groups, input_params) # Initialize model structure model <- zoomss_setup(params) # Model is now ready for time integration with zoomss_run results <- zoomss_run(model) } # }"}] +[{"path":"/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2025 zoomss authors Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"/articles/zoomss.html","id":"input-data","dir":"Articles","previous_headings":"","what":"Input Data","title":"Getting started with ZooMSS","text":"ZooMSS requires two sets input data: Groups - Contains taxa-specific parameter values model group, including size ranges functional group properties. Environmental data - Time-series dataframe time series environmental conditions time, sst, chl columns.","code":""},{"path":"/articles/zoomss.html","id":"running-the-default-model","dir":"Articles","previous_headings":"","what":"Running the default Model","title":"Getting started with ZooMSS","text":"Get default published Groups dataframe using: Now create environmental data time-series using helper function. time-series uses constant sea surface temperature (sst) chlorophyll (chl) 0.1 yr-1 timestep (dt). can look environment data check everything ok : Now run ZooMSS save every isave timestep reduce storage requirements.","code":"Groups <- getGroups() #> Using default ZooMSS functional groups. Use getGroups() to customize. 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^3 plotEnvironment(env_data) mdl <- zoomss_model(input_params = env_data, Groups = Groups, isave = 2) #> Functional groups validation passed #> Calculating phytoplankton parameters from environmental time series"},{"path":"/articles/zoomss.html","id":"plotting","dir":"Articles","previous_headings":"","what":"Plotting","title":"Getting started with ZooMSS","text":"model includes several built-plotting functions analysis visualization.","code":""},{"path":"/articles/zoomss.html","id":"time-series-analysis","dir":"Articles","previous_headings":"Plotting","what":"Time Series Analysis","title":"Getting started with ZooMSS","text":"plots display total abundance mean growth/mortality across size classes time. can also plot total biomass time.","code":"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\") 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\")"},{"path":"/articles/zoomss.html","id":"static-plots-for-a-given-model-time-point","dir":"Articles","previous_headings":"Plotting","what":"Static Plots for a given model time point","title":"Getting started with ZooMSS","text":"Plot mean species-resolved size spectra final n_years. Plot predator-prey mass ratios idx timestep","code":"plotSizeSpectra(mdl, n_years = 10) #> Averaging final 10 years (50 saved time steps with isave = 2) of abundance from 500 total saved time steps. plotPPMR(mdl, idx = 500) # Plot final timestep"},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Jason D. Everett. Author, maintainer. Ryan F. Heneghan. Author. Kieran Murphy. Author. Anthony J. Richardson. Author.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Everett J, Heneghan R, Murphy K, Richardson (2026). zoomss: Zooplankton Model Size Spectra. R package version 0.1.0, https://github.com/MathMarEcol/zoomss.","code":"@Manual{, title = {zoomss: Zooplankton Model of Size Spectra}, author = {Jason D. Everett and Ryan F. Heneghan and Kieran Murphy and Anthony J. Richardson}, year = {2026}, note = {R package version 0.1.0}, url = {https://github.com/MathMarEcol/zoomss}, }"},{"path":[]},{"path":"/index.html","id":"overview-of-zoomss","dir":"","previous_headings":"","what":"Overview of ZooMSS","title":"Zooplankton Model of Size Spectra","text":"Zooplankton Model Size Spectra (ZooMSS) functional size-spectrum model marine ecosystem (following Heneghan et al. 2016) resolve phytoplankton, nine zooplankton functional groups (heterotrophic flagellates ciliates, omnivorous carnivorous copepods, larvaceans, euphausiids, salps, chaetognaths jellyfish) three size-based fish groups. Zooplankton functional groups resolved using body-size ranges, size-based feeding characteristics carbon content, zooplankton community emerges model across global environmental gradients, depending functional traits different groups. developed Zooplankton Model Size Spectra ZooMSS based prototype Heneghan et al. (2016). ZooMSS uses functional size-spectrum framework (Blanchard et al., 2017) resolve body size ranges, size-based feeding characteristics carbon content nine zooplankton groups three fish groups. model supports time-varying environmental conditions enabling studies seasonal cycles, climate change scenarios, ecosystem responses environmental variability. ZooMSS represents marine ecosystem three communities: phytoplankton, zooplankton fish. zooplankton community consists nine abundant zooplankton groups, fish community made small, medium large group. Dynamics phytoplankton explicitly resolved model, rather mean size structure phytoplankton community estimated directly satellite chlorophyll observations (Brewin et al., 2010; Barnes et al., 2011; Hirata et al., 2011). Abundances zooplankton fish communities driven size-dependent processes growth mortality, temporal dynamics functional group governed separate second-order McKendrick-von Foerster equations.","code":""},{"path":"/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Zooplankton Model of Size Spectra","text":"can install development version zoomss GitHub :","code":"# install.packages(\"pak\") pak::pak(\"MathMarEcol/zoomss\")"},{"path":"/index.html","id":"publications","dir":"","previous_headings":"","what":"Publications","title":"Zooplankton Model of Size Spectra","text":"Heneghan, R.F., Everett, J.D., Blanchard, J.L., Richardson, .J., 2016. Zooplankton Fish: Improving Zooplankton Realism Size-Spectrum Models Mediates Energy Transfer Food Webs. Front. Mar. Sci. 3, 1–15. https://doi.org/10.3389/fmars.2016.00201 Heneghan, R.F., Everett, J.D., Sykes, P., Batten, S.D., Edwards, M., Takahashi, K., Suthers, .M., Blanchard, J.L., Richardson, .J., review, global size-spectrum model marine ecosystem resolves zooplankton composition. Ecological Modelling","code":""},{"path":"/index.html","id":"getting-help","dir":"","previous_headings":"","what":"Getting Help","title":"Zooplankton Model of Size Spectra","text":"encounter problems running model, raise issue GitHub: https://github.com/MathMarEcol/ZoopSizeSpectraModel/issues find errors want improve model, ’d love make changes submit pull request us review approve.","code":""},{"path":"/reference/GroupInputs.html","id":null,"dir":"Reference","previous_headings":"","what":"Default functional groups for the ZooMSS model — GroupInputs","title":"Default functional groups for the ZooMSS model — GroupInputs","text":"dataset containing biological parameters different functional groups used ZooMSS size-structured marine ecosystem model. represent various taxa flagellates large fish, defined feeding behavior, size ranges, physiological parameters.","code":""},{"path":"/reference/GroupInputs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Default functional groups for the ZooMSS model — GroupInputs","text":"","code":"GroupInputs"},{"path":"/reference/GroupInputs.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Default functional groups for the ZooMSS model — GroupInputs","text":"data frame 12 rows (functional groups) 19 columns: Species Character. Name functional group/taxa Type Character. Broad category (Zooplankton Fish) FeedType Character. Feeding strategy (Heterotroph, FilterFeeder, Omnivore, Carnivore) Prop Numeric. Initial proportion total biomass W0 Numeric. Log10 minimum body weight (g) group Wmax Numeric. Log10 maximum body weight (g) group Wmat Numeric. Log10 maturation body weight (g) SearchCoef Numeric. Search coefficient predation interactions SearchExp Numeric. Search exponent predation scaling PPMRscale Numeric. Predator-prey mass ratio scaling parameter PPMR Numeric. Predator-prey mass ratio (fish groups) FeedWidth Numeric. Feeding kernel width parameter GrossGEscale Numeric. Gross growth efficiency scaling Carbon Numeric. Carbon content proportion Repro Numeric. Reproduction parameter Fmort Numeric. Fishing mortality rate Fmort_W0 Numeric. Log10 minimum weight fishing mortality Fmort_Wmax Numeric. Log10 maximum weight fishing mortality PlotColour Character. Color code plotting functional group","code":""},{"path":"/reference/GroupInputs.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Default functional groups for the ZooMSS model — GroupInputs","text":"Marine ecological literature ZooMSS model development","code":""},{"path":"/reference/GroupInputs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Default functional groups for the ZooMSS model — GroupInputs","text":"ZooMSS Functional Groups Data GroupInputs dataset defines 12 functional groups spanning small microzooplankton (flagellates, ciliates) various mesozooplankton groups (copepods, euphausiids, chaetognaths) gelatinous zooplankton (salps, jellyfish) three fish size classes (small, medium, large). group characterized : Size ranges: W0 Wmax define body size spectrum Feeding behavior: Different strategies resource acquisition Interaction parameters: Search rates predator-prey relationships Physiological rates: Growth efficiency carbon content parameters based marine ecological literature represent typical values temperate marine ecosystems.","code":""},{"path":"/reference/GroupInputs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Default functional groups for the ZooMSS model — GroupInputs","text":"","code":"data(GroupInputs) head(GroupInputs) #> # A tibble: 6 × 19 #> Species Type FeedType Prop W0 Wmax Wmat SearchCoef SearchExp PPMRscale #> #> 1 Flagell… Zoop… Heterot… 0.1 -12 -6.8 -8.8 640 0.8 1.5 #> 2 Ciliates Zoop… Heterot… 0.1 -9.3 -6.3 -8.3 640 0.8 0.04 #> 3 Larvace… Zoop… FilterF… 0.1 -6.4 -3.2 -5.2 640 0.8 -3 #> 4 OmniCop… Zoop… Omnivore 0.04 -7.5 -3.5 -5.5 640 0.8 -0.5 #> 5 CarnCop… Zoop… Carnivo… 0.06 -7.5 -2.5 -4.5 640 0.8 1.5 #> 6 Euphaus… Zoop… Omnivore 0.1 -4.2 0.2 -1.8 640 0.8 -2 #> # ℹ 9 more variables: PPMR , FeedWidth , GrossGEscale , #> # Carbon , Repro , Fmort , Fmort_W0 , Fmort_Wmax , #> # PlotColour # View size ranges across groups plot(GroupInputs$W0, GroupInputs$Wmax, col = GroupInputs$PlotColour, xlab = \"Log10 Min Weight\", ylab = \"Log10 Max Weight\") text(GroupInputs$W0, GroupInputs$Wmax, GroupInputs$Species, pos = 3, cex = 0.7)"},{"path":"/reference/averageTimeSeries.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate mean of final portion of ZooMSS time series — averageTimeSeries","title":"Calculate mean of final portion of ZooMSS time series — averageTimeSeries","text":"Calculates mean final n years time series obtain equilibrium values model spin-period.","code":""},{"path":"/reference/averageTimeSeries.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate mean of final portion of ZooMSS time series — averageTimeSeries","text":"","code":"averageTimeSeries(mdl, var, n_years = 10)"},{"path":"/reference/averageTimeSeries.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate mean of final portion of ZooMSS time series — averageTimeSeries","text":"mdl ZooMSS model results object containing model parameters output arrays var Character string specifying variable extract average (e.g., \"N\", \"Growth\", \"Mort\") n_years Number years end time series average (default: 10)","code":""},{"path":"/reference/averageTimeSeries.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate mean of final portion of ZooMSS time series — averageTimeSeries","text":"2D array averaged values (groups x size_classes)","code":""},{"path":"/reference/averageTimeSeries.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate mean of final portion of ZooMSS time series — averageTimeSeries","text":"Calculate Average Output Model Time Series function removes initial transient period time series data calculates mean final n years, providing representative steady-state values. Essential obtaining equilibrium abundances, growth rates, model outputs model reached dynamic equilibrium.","code":""},{"path":"/reference/averageTimeSeries.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate mean of final portion of ZooMSS time series — averageTimeSeries","text":"","code":"if (FALSE) { # \\dontrun{ # Run ZooMSS model results <- zoomss_model(input_params, Groups) # Average final 3 years of abundance data avg_abundance <- averageTimeSeries(results, \"N\", n_years = 3) # Average final 10 years of growth data (default) avg_growth <- averageTimeSeries(results, \"gg\") } # }"},{"path":"/reference/calculatePhytoParam.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate phytoplankton abundance spectrum from chlorophyll data — calculatePhytoParam","title":"Calculate phytoplankton abundance spectrum from chlorophyll data — calculatePhytoParam","text":"Converts chlorophyll concentration data phytoplankton size spectrum parameters (slope, intercept, maximum size) using established oceanographic relationships.","code":""},{"path":"/reference/calculatePhytoParam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate phytoplankton abundance spectrum from chlorophyll data — calculatePhytoParam","text":"","code":"calculatePhytoParam(dat)"},{"path":"/reference/calculatePhytoParam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate phytoplankton abundance spectrum from chlorophyll data — calculatePhytoParam","text":"dat Data frame containing chlorophyll data (chl column mg/m^3) optionally phytoplankton biomass (phy column g/m^3)","code":""},{"path":"/reference/calculatePhytoParam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate phytoplankton abundance spectrum from chlorophyll data — calculatePhytoParam","text":"Data frame added columns: phyto_slope: Power law slope phytoplankton size spectrum phyto_int: Log10 intercept phytoplankton abundance phyto_max: Maximum phytoplankton size (log10 grams) pico_biom, nano_biom, micro_biom: Biomass size class","code":""},{"path":"/reference/calculatePhytoParam.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate phytoplankton abundance spectrum from chlorophyll data — calculatePhytoParam","text":"Calculate Phytoplankton Size Spectrum Parameters function implements Brewin et al. (2015) algorithm partition chlorophyll among picophytoplankton, nanophytoplankton, microphytoplankton size classes, calculates: Size spectrum slope intercept parameters Maximum phytoplankton size based micro proportion Biomass estimates size class parameters drive dynamic phytoplankton spectrum ZooMSS serves base food web. function can work either chlorophyll-data (using empirical relationships) direct phytoplankton biomass measurements.","code":""},{"path":"/reference/calculatePhytoParam.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Calculate phytoplankton abundance spectrum from chlorophyll data — calculatePhytoParam","text":"Brewin, R.J.W., et al. (2015). three-component model phytoplankton size class Atlantic Ocean. Ecological Modelling, 306, 90-101. Maranon, E., et al. (2014). Resource supply overrides temperature controlling factor marine phytoplankton growth. PLoS ONE, 9(6), e99312.","code":""},{"path":"/reference/createEnviroData.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate synthetic environmental data for ZooMSS testing — createEnviroData","title":"Generate synthetic environmental data for ZooMSS testing — createEnviroData","text":"Creates simple synthetic environmental time series optional seasonal variation testing ZooMSS model runs real environmental data available.","code":""},{"path":"/reference/createEnviroData.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate synthetic environmental data for ZooMSS testing — createEnviroData","text":"","code":"createEnviroData( n_years, dt, base_sst = 15, base_chl = 0.5, seasonal = TRUE, sst_amplitude = 3, chl_amplitude = 0.2 )"},{"path":"/reference/createEnviroData.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate synthetic environmental data for ZooMSS testing — createEnviroData","text":"n_years Number years generate dt Time step size years base_sst Base sea surface temperature deg C (default: 15) base_chl Base chlorophyll concentration mg/m^3 (default: 0.5) seasonal Logical, whether add seasonal variation (default: TRUE) sst_amplitude Amplitude SST seasonal variations deg C (default: 3) chl_amplitude Amplitude chlorophyll seasonal variations mg/m^3 (default: 0.2)","code":""},{"path":"/reference/createEnviroData.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate synthetic environmental data for ZooMSS testing — createEnviroData","text":"Data frame columns: time, sst, chl","code":""},{"path":"/reference/createEnviroData.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate synthetic environmental data for ZooMSS testing — createEnviroData","text":"Create Environmental Time Series function generates synthetic sea surface temperature chlorophyll time series can used testing ZooMSS model behavior. function can create either static environmental conditions seasonal cycles sinusoidal variation. particularly useful : Testing model sensitivity environmental forcing Creating idealized scenarios model exploration Generating data real environmental data unavailable seasonal option creates SST chlorophyll cycles phase, mimicking typical ocean patterns chlorophyll peaks SST lower.","code":""},{"path":"/reference/createEnviroData.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate synthetic environmental data for ZooMSS testing — createEnviroData","text":"","code":"# Create seasonal environmental data env_data <- createEnviroData( n_years = 10, dt = 0.01, seasonal = TRUE ) # Create static environmental conditions static_data <- createEnviroData( n_years = 5, dt = 0.01, seasonal = FALSE, base_sst = 20, base_chl = 1.0 )"},{"path":"/reference/createInputParams.html","id":null,"dir":"Reference","previous_headings":"","what":"Create input parameters data frame for ZooMSS model runs — createInputParams","title":"Create input parameters data frame for ZooMSS model runs — createInputParams","text":"Creates properly formatted input parameters data frame ZooMSS model simulations, combining temporal parameters environmental time series data.","code":""},{"path":"/reference/createInputParams.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create input parameters data frame for ZooMSS model runs — createInputParams","text":"","code":"createInputParams(time, sst, chl, cellID = NULL)"},{"path":"/reference/createInputParams.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create input parameters data frame for ZooMSS model runs — createInputParams","text":"time Numeric vector time values years (must increasing uniform, can start value) sst Numeric vector sea surface temperature values deg C chl Numeric vector chlorophyll concentration values mg/m^3 cellID Optional numeric vector cell identifiers spatial data (default: NULL)","code":""},{"path":"/reference/createInputParams.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create input parameters data frame for ZooMSS model runs — createInputParams","text":"Data frame columns: time, time_step, sst, chl, cellID (provided)","code":""},{"path":"/reference/createInputParams.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create input parameters data frame for ZooMSS model runs — createInputParams","text":"Create ZooMSS Input Parameters Object function combines environmental time series (SST chlorophyll) time data create input_params object required zoomss_model(). function performs validation checks using assertthat ensure: input vectors numeric equal length SST values within reasonable ocean range (-2 35 deg C) Chlorophyll values positive within typical range (0 50 mg/m^3) Time values increasing reasonable","code":""},{"path":"/reference/createInputParams.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create input parameters data frame for ZooMSS model runs — createInputParams","text":"","code":"if (FALSE) { # \\dontrun{ # Create simple environmental time series time_vec <- seq(0, 10, 0.01) # 10 years with 0.01 year time steps sst_vec <- 15 + 3*sin(2*pi*time_vec/1) # annual cycle chl_vec <- 0.5 + 0.2*cos(2*pi*time_vec/1) # annual cycle # Create input parameters object input_params <- createInputParams(time_vec, sst_vec, chl_vec) # Use with ZooMSS model results <- zoomss_model(input_params, Groups, isave = 50) } # }"},{"path":"/reference/extractPPMR.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate predator-prey mass ratio data for visualization — extractPPMR","title":"Calculate predator-prey mass ratio data for visualization — extractPPMR","text":"Calculates predator-prey mass ratio (PPMR) values biomass weightings creating PPMR distribution plots ZooMSS analysis.","code":""},{"path":"/reference/extractPPMR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate predator-prey mass ratio data for visualization — extractPPMR","text":"","code":"extractPPMR(mdl)"},{"path":"/reference/extractPPMR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate predator-prey mass ratio data for visualization — extractPPMR","text":"mdl ZooMSS results object containing abundance data (N) model parameters","code":""},{"path":"/reference/extractPPMR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate predator-prey mass ratio data for visualization — extractPPMR","text":"2D input: List containing PPMR density data species-specific values plotting 3D input: Array first dimension time, containing PPMR results timestep","code":""},{"path":"/reference/extractPPMR.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate predator-prey mass ratio data for visualization — extractPPMR","text":"Calculate PPMR Data Plotting function computes theoretical realized PPMR patterns : Calculating size-dependent PPMR values using Wirtz 2012 equations Weighting biomass show community-level patterns Computing species-specific PPMR values Handling special cases filter feeders (larvaceans, salps) Processing either time-averaged abundances (2D) full time series (3D) function dynamically determines size class ranges larvaceans salps based W0 Wmax values. 3D abundance arrays, calculates PPMR time step separately. helper function primarily used plotPPMR visualization. PPMR analysis provides insights food web structure predation patterns.","code":""},{"path":"/reference/extractSizeRange.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract specific size class range from model variable — extractSizeRange","title":"Extract specific size class range from model variable — extractSizeRange","text":"Subsets ZooMSS model output include specified size range, useful focusing analysis particular size ranges.","code":""},{"path":"/reference/extractSizeRange.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract specific size class range from model variable — extractSizeRange","text":"","code":"extractSizeRange(mdl, var, min_size, max_size)"},{"path":"/reference/extractSizeRange.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract specific size class range from model variable — extractSizeRange","text":"mdl ZooMSS model results object containing model parameters output arrays var Character string specifying variable extract (e.g., \"N\", \"Z\", \"Growth\") min_size Minimum size (log10 grams) extract max_size Maximum size (log10 grams) extract","code":""},{"path":"/reference/extractSizeRange.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract specific size class range from model variable — extractSizeRange","text":"Array dimensions original variable subsetted size range","code":""},{"path":"/reference/extractSizeRange.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract specific size class range from model variable — extractSizeRange","text":"Extract Size Range ZooMSS Output function extracts subset size classes specified ZooMSS model variable. Useful analyzing specific size ranges (e.g., microzooplankton, mesozooplankton) excluding boundary effects model analysis. function converts log10 size values size class indices automatically.","code":""},{"path":"/reference/extractSizeRange.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract specific size class range from model variable — extractSizeRange","text":"","code":"if (FALSE) { # \\dontrun{ # Run ZooMSS model results <- zoomss_model(input_params, Groups) # Extract mesozooplankton size range from abundance data meso_abundance <- extractSizeRange(results, \"N\", min_size = -8, max_size = -5) # Extract microzooplankton size range from growth data micro_growth <- extractSizeRange(results, \"Growth\", min_size = -10, max_size = -8) } # }"},{"path":"/reference/extractTrophicLevels.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute trophic levels for functional groups using diet composition — extractTrophicLevels","title":"Compute trophic levels for functional groups using diet composition — extractTrophicLevels","text":"Calculates trophic levels functional group based diet composition using iterative Gauss-Seidel algorithm.","code":""},{"path":"/reference/extractTrophicLevels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute trophic levels for functional groups using diet composition — extractTrophicLevels","text":"","code":"extractTrophicLevels(mdl)"},{"path":"/reference/extractTrophicLevels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute trophic levels for functional groups using diet composition — extractTrophicLevels","text":"mdl ZooMSS model results object containing 3D diet data (mdl$diet). Dimensions time, groups, prey_items columns 1:3 always phytoplankton size classes remaining columns zooplankton/fish groups.","code":""},{"path":"/reference/extractTrophicLevels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute trophic levels for functional groups using diet composition — extractTrophicLevels","text":"Matrix rows time steps columns functional groups","code":""},{"path":"/reference/extractTrophicLevels.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute trophic levels for functional groups using diet composition — extractTrophicLevels","text":"Calculate Trophic Levels Diet Matrix function computes trophic levels : Starting phytoplankton trophic level 1.0 Initializing groups trophic level 2.0 Iteratively updating trophic levels based weighted diet composition Continuing convergence (difference < 0.01) maximum iterations (100) Processing 3D diet arrays time series data Trophic level calculation follows: TL = 1 + sum(diet_fraction_i * TL_prey_i) function calculates trophic levels time step separately dynamically determines number groups diet matrix dimensions. provides quantitative measure group's position food web useful analyzing ecosystem structure energy transfer efficiency.","code":""},{"path":"/reference/extractTrophicLevels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute trophic levels for functional groups using diet composition — extractTrophicLevels","text":"","code":"if (FALSE) { # \\dontrun{ # After running ZooMSS model with 3D time series results <- zoomss_model(input_params, Groups) trophic_levels <- extractTrophicLevels(results) # Returns matrix (time x groups) # View trophic levels by group for final time step final_tl <- trophic_levels[nrow(trophic_levels), ] names(final_tl) <- results$param$Groups$Species print(final_tl) } # }"},{"path":"/reference/getBiomass.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert ZooMSS abundance matrices to biomass by multiplying by body weights — getBiomass","title":"Convert ZooMSS abundance matrices to biomass by multiplying by body weights — getBiomass","text":"Converts abundance data wet weight biomass multiplying abundances corresponding body weights size class. Optionally converts carbon biomass.","code":""},{"path":"/reference/getBiomass.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert ZooMSS abundance matrices to biomass by multiplying by body weights — getBiomass","text":"","code":"getBiomass(mdl, units = \"ww\")"},{"path":"/reference/getBiomass.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert ZooMSS abundance matrices to biomass by multiplying by body weights — getBiomass","text":"mdl ZooMSS model object containing abundance array (N) weight vector (param$w) units Character string specifying biomass units: \"ww\" wet weight (default) \"carbon\" carbon biomass","code":""},{"path":"/reference/getBiomass.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert ZooMSS abundance matrices to biomass by multiplying by body weights — getBiomass","text":"3D array biomass values dimensions N. Units depend units parameter: \"ww\": grams wet weight \"carbon\": grams carbon","code":""},{"path":"/reference/getBiomass.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert ZooMSS abundance matrices to biomass by multiplying by body weights — getBiomass","text":"Convert Abundance Biomass function transforms abundance matrices biomass applying weight vector across size classes. Essential analyses requiring biomass units rather abundance counts. Works 3D arrays (time, groups, size_classes). Can convert either wet weight carbon biomass units.","code":""},{"path":"/reference/getBiomass.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert ZooMSS abundance matrices to biomass by multiplying by body weights — getBiomass","text":"","code":"if (FALSE) { # \\dontrun{ # Run ZooMSS model results <- zoomss_model(input_params, Groups) # Convert abundances to wet weight biomass biomass_ww <- getBiomass(results, units = \"ww\") # Convert abundances to carbon biomass biomass_carbon <- getBiomass(results, units = \"carbon\") } # }"},{"path":"/reference/getGroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Load default or custom functional groups for ZooMSS model — getGroups","title":"Load default or custom functional groups for ZooMSS model — getGroups","text":"Provides access default ZooMSS functional groups loads custom groups file. function primary way obtain Groups data ZooMSS model runs.","code":""},{"path":"/reference/getGroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Load default or custom functional groups for ZooMSS model — getGroups","text":"","code":"getGroups(source = \"default\", file = NULL)"},{"path":"/reference/getGroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Load default or custom functional groups for ZooMSS model — getGroups","text":"source Character string specifying data source. Options: \"default\": Use built-ZooMSS functional groups (default) \"file\": Load groups CSV file \"template\": Export default groups file modification file Path CSV file source=\"file\" source=\"template\"","code":""},{"path":"/reference/getGroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Load default or custom functional groups for ZooMSS model — getGroups","text":"Data frame containing functional groups required columns: Species, Type, W0, Wmax, biological parameters","code":""},{"path":"/reference/getGroups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Load default or custom functional groups for ZooMSS model — getGroups","text":"Get Default ZooMSS Functional Groups function provides flexible access functional groups data: Default groups: Returns standard ZooMSS functional groups (12 groups) Custom file: Loads validates groups user-provided CSV file Template creation: Exports default groups file user modification default groups include: Flagellates, Ciliates, Larvaceans, OmniCopepods, CarnCopepods, Euphausiids, Chaetognaths, Salps, Jellyfish, three Fish groups (Small, Medium, Large). groups data validated ensure contains required columns reasonable parameter values successful model runs.","code":""},{"path":"/reference/getGroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Load default or custom functional groups for ZooMSS model — getGroups","text":"","code":"if (FALSE) { # \\dontrun{ # Use default groups Groups <- getGroups() # Create a template file for modification getGroups(source = \"template\", file = \"my_groups.csv\") # Load custom groups from file custom_groups <- getGroups(source = \"file\", file = \"my_groups.csv\") # Modify default groups programmatically Groups <- getGroups() Groups$W0[Groups$Species == \"Flagellates\"] <- -12.5 # Modify minimum size } # }"},{"path":"/reference/loadDefaultGroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Internal function to load default ZooMSS groups — loadDefaultGroups","title":"Internal function to load default ZooMSS groups — loadDefaultGroups","text":"Loads default functional groups package data CSV file. internal function used getGroups().","code":""},{"path":"/reference/loadDefaultGroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Internal function to load default ZooMSS groups — loadDefaultGroups","text":"","code":"loadDefaultGroups()"},{"path":"/reference/loadDefaultGroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Internal function to load default ZooMSS groups — loadDefaultGroups","text":"Data frame default functional groups","code":""},{"path":"/reference/loadDefaultGroups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Internal function to load default ZooMSS groups — loadDefaultGroups","text":"Load Default Functional Groups Data function handles actual loading default groups data, whether package data (available) CSV file data-raw.","code":""},{"path":"/reference/pipe.html","id":null,"dir":"Reference","previous_headings":"","what":"Pipe operator — %>%","title":"Pipe operator — %>%","text":"See magrittr::%>% details.","code":""},{"path":"/reference/pipe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pipe operator — %>%","text":"","code":"lhs %>% rhs"},{"path":"/reference/pipe.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pipe operator — %>%","text":"lhs value magrittr placeholder. rhs function call using magrittr semantics.","code":""},{"path":"/reference/pipe.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Pipe operator — %>%","text":"result calling rhs(lhs).","code":""},{"path":"/reference/plotEnvironment.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot environmental forcing data — plotEnvironment","title":"Plot environmental forcing data — plotEnvironment","text":"Creates plots sea surface temperature chlorophyll time series visualizing environmental forcing data used ZooMSS model runs.","code":""},{"path":"/reference/plotEnvironment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot environmental forcing data — plotEnvironment","text":"","code":"plotEnvironment(env_data)"},{"path":"/reference/plotEnvironment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot environmental forcing data — plotEnvironment","text":"env_data Environmental data frame time, sst, chl columns","code":""},{"path":"/reference/plotEnvironment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot environmental forcing data — plotEnvironment","text":"ggplot object (patchwork available) list two ggplot objects","code":""},{"path":"/reference/plotEnvironment.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot environmental forcing data — plotEnvironment","text":"Plot Environmental Time Series function creates two separate plots different y-axes scales: SST plot (red line) temperature deg C Chlorophyll plot (green line) concentration mg/m^3 plots can combined using patchwork package available, otherwise separate plots returned list. helps users visualize environmental forcing drives ZooMSS model dynamics.","code":""},{"path":"/reference/plotEnvironment.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot environmental forcing data — plotEnvironment","text":"","code":"# Create sample data and plot env_data <- data.frame( time = 1:100, dt = 0.01, sst = 15 + 3*sin(2*pi*(1:100)/50), chl = 0.5 + 0.2*cos(2*pi*(1:100)/50) ) plots <- plotEnvironment(env_data)"},{"path":"/reference/plotPPMR.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize predator-prey mass ratio patterns in ZooMSS results — plotPPMR","title":"Visualize predator-prey mass ratio patterns in ZooMSS results — plotPPMR","text":"Creates plot showing distribution predator-prey mass ratios (PPMR) across functional groups, providing insights trophic structure ecosystem.","code":""},{"path":"/reference/plotPPMR.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize predator-prey mass ratio patterns in ZooMSS results — plotPPMR","text":"","code":"plotPPMR(mdl, idx)"},{"path":"/reference/plotPPMR.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize predator-prey mass ratio patterns in ZooMSS results — plotPPMR","text":"mdl ZooMSS results object containing model outputs parameters idx time index plot","code":""},{"path":"/reference/plotPPMR.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Visualize predator-prey mass ratio patterns in ZooMSS results — plotPPMR","text":"ggplot object showing PPMR distribution species-specific overlays","code":""},{"path":"/reference/plotPPMR.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Visualize predator-prey mass ratio patterns in ZooMSS results — plotPPMR","text":"Plot Predator-Prey Mass Ratio (PPMR) function calculates visualizes PPMR patterns : Computing theoretical PPMR values functional group size class Weighting biomass show realized community patterns Creating density plot PPMR distribution across community Overlaying species-specific PPMR values points PPMR key ecological metric describes size relationship predators prey, providing insight food web structure energy transfer efficiency marine ecosystems.","code":""},{"path":"/reference/plotPPMR.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualize predator-prey mass ratio patterns in ZooMSS results — plotPPMR","text":"","code":"if (FALSE) { # \\dontrun{ # After running ZooMSS model results <- zoomss_model(input_params, Groups) ppmr_plot <- plotPPMR(results) print(ppmr_plot) } # }"},{"path":"/reference/plotSizeSpectra.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize abundance size spectra across functional groups — plotSizeSpectra","title":"Visualize abundance size spectra across functional groups — plotSizeSpectra","text":"Creates log-log plot abundance versus body size functional groups, showing classic size spectrum pattern marine ecosystems.","code":""},{"path":"/reference/plotSizeSpectra.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize abundance size spectra across functional groups — plotSizeSpectra","text":"","code":"plotSizeSpectra(mdl, by = \"abundance\", n_years = 10)"},{"path":"/reference/plotSizeSpectra.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize abundance size spectra across functional groups — plotSizeSpectra","text":"mdl ZooMSS results object containing model outputs parameters Character string specifying metric plot. Options: \"abundance\", \"biomass\", \"mortality\", \"growth\" (default: \"abundance\") n_years number years (end) average size spectra","code":""},{"path":"/reference/plotSizeSpectra.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Visualize abundance size spectra across functional groups — plotSizeSpectra","text":"ggplot object showing log abundance vs log body weight species","code":""},{"path":"/reference/plotSizeSpectra.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Visualize abundance size spectra across functional groups — plotSizeSpectra","text":"Plot Size Spectra ZooMSS Results function visualizes abundance size spectrum : Converting abundance data long format body weights Filtering zero abundances focus active size classes Creating log-log plots colored functional group Using species-specific colors defined Groups parameter table Size spectra fundamental patterns marine ecology, typically showing declining abundance increasing body size. visualization helps assess model realism identify dominant size classes within functional group.","code":""},{"path":"/reference/plotSizeSpectra.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualize abundance size spectra across functional groups — plotSizeSpectra","text":"","code":"if (FALSE) { # \\dontrun{ # After running ZooMSS model results <- zoomss_model(input_params, Groups) size_plot <- plotSizeSpectra(results) print(size_plot) } # }"},{"path":"/reference/plotTimeSeries.html","id":null,"dir":"Reference","previous_headings":"","what":"Unified function to visualize time series changes for different metrics — plotTimeSeries","title":"Unified function to visualize time series changes for different metrics — plotTimeSeries","text":"Creates time series plots showing abundance, biomass, mortality, growth rates functional groups change throughout ZooMSS simulation period.","code":""},{"path":"/reference/plotTimeSeries.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unified function to visualize time series changes for different metrics — plotTimeSeries","text":"","code":"plotTimeSeries( mdl, by = \"abundance\", type = \"line\", transform = \"identity\", species = NULL )"},{"path":"/reference/plotTimeSeries.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unified function to visualize time series changes for different metrics — plotTimeSeries","text":"mdl ZooMSS results object containing model outputs time series data Character string specifying metric plot. Options: \"abundance\", \"biomass\", \"mortality\", \"growth\" (default: \"abundance\") type Character vector plot type. Use line default line plot, stack fill (per geom_area) stacked proportional plots. (default: \"line\") transform Character vector required y-axis transformation. Options scale_*_continuous (Default: \"identity). species Character vector species names include. NULL, species included (default: NULL, applies metrics)","code":""},{"path":"/reference/plotTimeSeries.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unified function to visualize time series changes for different metrics — plotTimeSeries","text":"ggplot object showing requested time series species","code":""},{"path":"/reference/plotTimeSeries.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Unified function to visualize time series changes for different metrics — plotTimeSeries","text":"Plot Time Series Data ZooMSS Results function creates time series visualizations : Abundance: Summing abundances across size classes, log-transformed y-axis Biomass: Calculating biomass (abundance × weight), optional stacking proportional scaling Mortality: Averaging predation mortality rates across size classes Growth: Averaging growth rates across size classes, log-transformed y-axis plots use species-specific colors filter zero values. Time series plots help identify: Equilibration time model runs Seasonal cyclical patterns ecological metrics Relative patterns functional groups Model stability convergence behavior","code":""},{"path":"/reference/plotTimeSeries.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unified function to visualize time series changes for different metrics — plotTimeSeries","text":"","code":"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\")) } # }"},{"path":"/reference/reduceAll.html","id":null,"dir":"Reference","previous_headings":"","what":"Aggregate abundances across all groups and size classes — reduceAll","title":"Aggregate abundances across all groups and size classes — reduceAll","text":"Calculates total abundance across functional groups size classes using specified method.","code":""},{"path":"/reference/reduceAll.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Aggregate abundances across all groups and size classes — reduceAll","text":"","code":"reduceAll(x, method = \"sum\")"},{"path":"/reference/reduceAll.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Aggregate abundances across all groups and size classes — reduceAll","text":"x 3D array outptut ZooMSS model method Character string specifying aggregation method: \"sum\" (default) \"mean\".","code":""},{"path":"/reference/reduceAll.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Aggregate abundances across all groups and size classes — reduceAll","text":"Vector total abundance values (one per spatial cell)","code":""},{"path":"/reference/reduceAll.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Aggregate abundances across all groups and size classes — reduceAll","text":"Aggregate abundances across groups size classes function provides aggregated view ZooMSS output applying method across functional groups size classes.","code":""},{"path":"/reference/reduceSize.html","id":null,"dir":"Reference","previous_headings":"","what":"Aggregate ZooMSS abundances across all size classes — reduceSize","title":"Aggregate ZooMSS abundances across all size classes — reduceSize","text":"Sums abundance values across size classes functional group, providing total abundance per group.","code":""},{"path":"/reference/reduceSize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Aggregate ZooMSS abundances across all size classes — reduceSize","text":"","code":"reduceSize(x, method = \"sum\")"},{"path":"/reference/reduceSize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Aggregate ZooMSS abundances across all size classes — reduceSize","text":"x 3D array outptut ZooMSS model method Character string specifying aggregation method: \"sum\" (default) \"mean\".","code":""},{"path":"/reference/reduceSize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Aggregate ZooMSS abundances across all size classes — reduceSize","text":"List vectors total abundance per functional group","code":""},{"path":"/reference/reduceSize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Aggregate ZooMSS abundances across all size classes — reduceSize","text":"Sum ZooMSS Output Across Size Bins function collapses size dimension ZooMSS output summing across size classes. Useful analyzing total abundance patterns without size structure detail.","code":""},{"path":"/reference/reduceSize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Aggregate ZooMSS abundances across all size classes — reduceSize","text":"","code":"if (FALSE) { # \\dontrun{ # After running ZooMSS model results <- zoomss_model(input_params, Groups) total_abundances <- reduceSize(results$abundances) } # }"},{"path":"/reference/reduceSpecies.html","id":null,"dir":"Reference","previous_headings":"","what":"Aggregate ZooMSS abundances across all species — reduceSpecies","title":"Aggregate ZooMSS abundances across all species — reduceSpecies","text":"Aggregates abundance values across species bins functional group size class using specified method.","code":""},{"path":"/reference/reduceSpecies.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Aggregate ZooMSS abundances across all species — reduceSpecies","text":"","code":"reduceSpecies(x, method = \"sum\")"},{"path":"/reference/reduceSpecies.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Aggregate ZooMSS abundances across all species — reduceSpecies","text":"x 3D array outptut ZooMSS model method Character string specifying aggregation method: \"sum\" (default) \"mean\".","code":""},{"path":"/reference/reduceSpecies.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Aggregate ZooMSS abundances across all species — reduceSpecies","text":"Array species dimension reduced using specified method","code":""},{"path":"/reference/reduceSpecies.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Aggregate ZooMSS abundances across all species — reduceSpecies","text":"Aggregate ZooMSS abundances across species function collapses species dimension applying specified method (sum mean) across species bins.","code":""},{"path":"/reference/untibble.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert tibble to data frame for efficiency — untibble","title":"Convert tibble to data frame for efficiency — untibble","text":"Removes tibble attributes converts plain data frame improved speed memory efficiency computational workflows.","code":""},{"path":"/reference/untibble.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert tibble to data frame for efficiency — untibble","text":"","code":"untibble(tibble)"},{"path":"/reference/untibble.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert tibble to data frame for efficiency — untibble","text":"tibble tibble data frame object convert","code":""},{"path":"/reference/untibble.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert tibble to data frame for efficiency — untibble","text":"Plain data frame without tibble attributes","code":""},{"path":"/reference/untibble.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert tibble to data frame for efficiency — untibble","text":"Remove Tibble Attributes utility function strips tibble-specific attributes can slow operations tight computational loops. Used internally ZooMSS performance optimization working large datasets.","code":""},{"path":"/reference/validateGroups.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate ZooMSS functional groups data structure and values — validateGroups","title":"Validate ZooMSS functional groups data structure and values — validateGroups","text":"Performs comprehensive validation functional groups data ensure meets ZooMSS model requirements.","code":""},{"path":"/reference/validateGroups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate ZooMSS functional groups data structure and values — validateGroups","text":"","code":"validateGroups(groups)"},{"path":"/reference/validateGroups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate ZooMSS functional groups data structure and values — validateGroups","text":"groups Data frame containing functional groups data","code":""},{"path":"/reference/validateGroups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Validate ZooMSS functional groups data structure and values — validateGroups","text":"TRUE validation passes (invisibly), otherwise throws error","code":""},{"path":"/reference/validateGroups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Validate ZooMSS functional groups data structure and values — validateGroups","text":"Validate Functional Groups Data function validates: Required column names present Data types correct Parameter values within reasonable ranges missing values critical columns Size ranges logical (W0 < Wmax)","code":""},{"path":"/reference/validateGroups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Validate ZooMSS functional groups data structure and values — validateGroups","text":"","code":"if (FALSE) { # \\dontrun{ Groups <- getGroups() validateGroups(Groups) # Should pass # This would fail validation: bad_groups <- Groups bad_groups$W0 <- NULL validateGroups(bad_groups) # Error: missing required column } # }"},{"path":"/reference/zoomss_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Main ZooMSS model function for complete simulations — zoomss_model","title":"Main ZooMSS model function for complete simulations — zoomss_model","text":"main wrapper function orchestrates complete ZooMSS model simulation parameter setup model execution output processing.","code":""},{"path":"/reference/zoomss_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Main ZooMSS model function for complete simulations — zoomss_model","text":"","code":"zoomss_model(input_params, Groups = NULL, isave = 1)"},{"path":"/reference/zoomss_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Main ZooMSS model function for complete simulations — zoomss_model","text":"input_params Data frame containing model parameters environmental time series. Must include columns: time (time vector years), sst (sea surface temperature), chl (chlorophyll). Can optionally include cellID spatial data. time step (dt) maximum time (tmax) automatically calculated time vector. Can created using createInputParams(). Groups Data frame defining functional groups biological parameters. Must include columns defining species characteristics, size ranges, feeding parameters. NULL, uses default ZooMSS functional groups. Can obtained/customized using getGroups(). isave Save frequency time steps (default: 10)","code":""},{"path":"/reference/zoomss_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Main ZooMSS model function for complete simulations — zoomss_model","text":"Complete ZooMSS model results object containing: param: Model parameters environmental forcing data time: Time values corresponding saved results (accounting isave) abundance: Abundance time series (time x groups x size classes) growth: Growth rate time series mortality: Mortality rate time series diet: Diet composition time series Additional model structure kernel data","code":""},{"path":"/reference/zoomss_model.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Main ZooMSS model function for complete simulations — zoomss_model","text":"Run Complete ZooMSS Model Simulation function coordinates entire ZooMSS modeling workflow: Validates environmental time series data provided Sets model parameters using Groups data input parameters Initializes model structure feeding kernels Runs model forward time dynamic environmental forcing Processes outputs averaging final 50% simulation Returns organized results including abundances, diets, growth, mortality primary entry point running ZooMSS simulations environmental forcing.","code":""},{"path":"/reference/zoomss_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Main ZooMSS model function for complete simulations — zoomss_model","text":"","code":"if (FALSE) { # \\dontrun{ # Basic usage with default groups env_data <- createEnviroData(10, 0.01) input_params <- createInputParams(env_data$time, env_data$sst, env_data$chl) results <- zoomss_model(input_params, isave = 50) # Using custom groups Groups <- getGroups() # Get default groups Groups$W0[1] <- -12.5 # Modify a parameter results <- zoomss_model(input_params, Groups, isave = 100) # Loading groups from file custom_groups <- getGroups(source = \"file\", file = \"my_groups.csv\") results <- zoomss_model(input_params, custom_groups) } # }"},{"path":"/reference/zoomss_mvf.html","id":null,"dir":"Reference","previous_headings":"","what":"Solve McKendrick-von Foerster equation for size-structured populations — zoomss_mvf","title":"Solve McKendrick-von Foerster equation for size-structured populations — zoomss_mvf","text":"Solves McKendrick-von Foerster (MvF) partial differential equation size-structured population dynamics using finite difference approach base R.","code":""},{"path":"/reference/zoomss_mvf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Solve McKendrick-von Foerster equation for size-structured populations — zoomss_mvf","text":"","code":"zoomss_mvf( ngrps, curr_min_size, curr_max_size, A_iter, C_iter, Nb_iter, S_iter, A, B, C, Nb, S )"},{"path":"/reference/zoomss_mvf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Solve McKendrick-von Foerster equation for size-structured populations — zoomss_mvf","text":"ngrps Number functional groups model curr_min_size Vector minimum size class indices group curr_max_size Vector maximum size class indices group A_iter Matrix advection coefficients current iteration C_iter Matrix diagonal coefficients current iteration Nb_iter Matrix store updated abundances current iteration S_iter Matrix source terms current iteration Matrix advection coefficients B Matrix diffusion coefficients C Matrix diagonal coefficients Nb Matrix abundances updated S Matrix source terms","code":""},{"path":"/reference/zoomss_mvf.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Solve McKendrick-von Foerster equation for size-structured populations — zoomss_mvf","text":"Updated abundance matrix (Nb) new size-class distributions","code":""},{"path":"/reference/zoomss_mvf.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Solve McKendrick-von Foerster equation for size-structured populations — zoomss_mvf","text":"McKendrick-von Foerster Equation Solver (Base R Implementation) function implements numerical solution McKendrick-von Foerster equation, describes populations change across size classes time. equation solved using upwind finite difference scheme handles: Growth size classes (advection term) Diffusion adjacent size classes Source sink terms feeding mortality function processes functional group separately applies boundary conditions appropriate size-structured models. last size class set zero abundance represent maximum size limits. core computational component ZooMSS updates population abundances time step based growth, mortality, reproduction processes.","code":""},{"path":"/reference/zoomss_mvf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Solve McKendrick-von Foerster equation for size-structured populations — zoomss_mvf","text":"","code":"if (FALSE) { # \\dontrun{ # This function is typically called internally by zoomss_run # Example shows the structure of parameters needed: ngrps <- 9 ngrid <- 100 curr_min_size <- c(1, 10, 20, 30, 40, 50, 60, 70, 80) curr_max_size <- c(30, 40, 50, 60, 70, 80, 90, 95, 100) # Initialize coefficient matrices A <- matrix(0, nrow = ngrps, ncol = ngrid) B <- matrix(0, nrow = ngrps, ncol = ngrid) C <- matrix(1, nrow = ngrps, ncol = ngrid) S <- matrix(0, nrow = ngrps, ncol = ngrid) Nb <- matrix(0.1, nrow = ngrps, ncol = ngrid) # Run MvF solver updated_abundances <- zoomss_mvf(ngrps, curr_min_size, curr_max_size, A, C, Nb, S, A, B, C, Nb, S) } # }"},{"path":"/reference/zoomss_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Initialize and validate ZooMSS model parameters — zoomss_params","title":"Initialize and validate ZooMSS model parameters — zoomss_params","text":"Sets complete parameter list ZooMSS model runs, including functional group parameters, model dimensions, environmental forcing data.","code":""},{"path":"/reference/zoomss_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initialize and validate ZooMSS model parameters — zoomss_params","text":"","code":"zoomss_params(Groups, input_params, isave)"},{"path":"/reference/zoomss_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initialize and validate ZooMSS model parameters — zoomss_params","text":"Groups Data frame containing functional group definitions columns: Species, Type, W0 (log min size), Wmax (log max size), various biological parameters input_params Data frame model parameters including: time (time vector years), sst (sea surface temperature), chl (chlorophyll). time vector can start value model automatically calculates dt (time step) tmax (maximum time). isave Save frequency time steps (default: 50)","code":""},{"path":"/reference/zoomss_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initialize and validate ZooMSS model parameters — zoomss_params","text":"List containing comprehensive model parameters: Groups: Functional group definitions ngrps: Number functional groups ngrid: Number size classes w: Size class weights (g) tmax, dt, isave: Temporal parameters zoo_grps, fish_grps: Indices different organism types phyto_int, phyto_slope: Time series phytoplankton parameters temp_eff_zoo, temp_eff_fish: Time series temperature effects Additional biological physical parameters","code":""},{"path":"/reference/zoomss_params.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Initialize and validate ZooMSS model parameters — zoomss_params","text":"Set ZooMSS Model Parameters function creates comprehensive parameter object contains: Static Parameters (fixed across time steps): Model dimensions (number groups, size classes, time steps) Biological parameters (growth efficiency, mortality rates) Size class definitions ranges functional group Phytoplankton size spectrum parameters Dynamic Parameters (calculated environmental data): Phytoplankton abundance time series based chlorophyll Temperature effects metabolism zooplankton fish Environmental forcing validation interpolation function validates environmental time series data covers full simulation period pre-calculates time-varying parameters optimize model performance main simulation loop.","code":""},{"path":"/reference/zoomss_params.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initialize and validate ZooMSS model parameters — zoomss_params","text":"","code":"if (FALSE) { # \\dontrun{ # Load functional groups data(Groups) # Create environmental time series env_data <- createEnviroData(10, 0.01) input_params <- createInputParams(env_data$time, env_data$sst, env_data$chl) # Generate parameter list params <- zoomss_params(Groups, input_params, isave = 50) } # }"},{"path":"/reference/zoomss_run.html","id":null,"dir":"Reference","previous_headings":"","what":"Execute the main ZooMSS simulation loop with dynamic environmental forcing — zoomss_run","title":"Execute the main ZooMSS simulation loop with dynamic environmental forcing — zoomss_run","text":"Runs ZooMSS model forward time, updating environmental conditions population dynamics time step using McKendrick-von Foerster framework.","code":""},{"path":"/reference/zoomss_run.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Execute the main ZooMSS simulation loop with dynamic environmental forcing — zoomss_run","text":"","code":"zoomss_run(model)"},{"path":"/reference/zoomss_run.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Execute the main ZooMSS simulation loop with dynamic environmental forcing — zoomss_run","text":"model Model object created zoomss_setup containing: param: Complete parameter list environmental time series Feeding kernels biological rate parameters Initial conditions model structure","code":""},{"path":"/reference/zoomss_run.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Execute the main ZooMSS simulation loop with dynamic environmental forcing — zoomss_run","text":"List containing complete model output: param: Model parameters used simulation N: Abundance time series (time x groups x size classes) gg: Growth rate time series diet: Diet composition time series Z: Mortality rate time series time: Time values corresponding saved results (accounting isave) w: Size class weights (g) Additional time series data model results","code":""},{"path":"/reference/zoomss_run.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Execute the main ZooMSS simulation loop with dynamic environmental forcing — zoomss_run","text":"Run ZooMSS Model Forward Time core simulation engine ZooMSS : Environmental Dynamics: Updates phytoplankton abundance spectrum based chlorophyll time series Applies temperature effects zooplankton fish metabolism Recalculates feeding kernels current environmental conditions Population Dynamics: Solves McKendrick-von Foerster equation size-structured growth Updates feeding interactions size classes groups Calculates mortality predation, senescence, fishing Handles recruitment boundary conditions functional group Time Integration: Processes model time steps adaptive environmental forcing Saves output specified intervals memory efficiency Maintains mass balance numerical stability throughout simulation Unlike static models, version dynamically updates phytoplankton spectra temperature effects time step based provided environmental data.","code":""},{"path":"/reference/zoomss_run.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Execute the main ZooMSS simulation loop with dynamic environmental forcing — zoomss_run","text":"","code":"if (FALSE) { # \\dontrun{ # Set up model parameters and structure params <- zoomss_params(Groups, input_params) model <- zoomss_setup(params) # Run the simulation results <- zoomss_run(model) # Access final abundances final_abundances <- results$N[dim(results$N)[1],,] } # }"},{"path":"/reference/zoomss_setup.html","id":null,"dir":"Reference","previous_headings":"","what":"Initialize ZooMSS model components and calculate feeding interactions — zoomss_setup","title":"Initialize ZooMSS model components and calculate feeding interactions — zoomss_setup","text":"Sets ZooMSS model structure calculating feeding kernels, mortality rates, model components remain static simulation.","code":""},{"path":"/reference/zoomss_setup.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Initialize ZooMSS model components and calculate feeding interactions — zoomss_setup","text":"","code":"zoomss_setup(param)"},{"path":"/reference/zoomss_setup.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Initialize ZooMSS model components and calculate feeding interactions — zoomss_setup","text":"param Complete parameter list created zoomss_params containing: Groups: Functional group definitions biological parameters Model dimensions (ngrps, ngrid, time parameters) Environmental forcing time series Physical biological constants","code":""},{"path":"/reference/zoomss_setup.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Initialize ZooMSS model components and calculate feeding interactions — zoomss_setup","text":"Model object containing: param: Input parameters (passed ) dynam_xxx: Dynamic feeding kernel arrays group interactions (xxx = growthkernel, diffkernel, dietkernel, mortkernel) phyto_xxx: Phytoplankton feeding kernel arrays (xxx = growthkernel, diffkernel, dietkernel) nPP: Initial phytoplankton abundance spectrum M_sb_base: Baseline senescence mortality rates fish_mort: Fishing mortality rates assim_eff: Assimilation efficiency matrix temp_eff: Temperature effect matrix (initialized) N: Initial abundance arrays time: Time array storing time values (initialized NA) Additional model structure components","code":""},{"path":"/reference/zoomss_setup.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Initialize ZooMSS model components and calculate feeding interactions — zoomss_setup","text":"Setup ZooMSS Model Structure Feeding Kernels function initializes core ZooMSS model structure calculating: Static Components (calculated ): Feeding preference kernels based predator-prey size ratios Search volumes encounter rates size classes Baseline mortality rates (senescence, fishing) Initial abundance distributions functional groups Dynamic Component Structures (updated run): Phytoplankton feeding kernels (structure calculated , values updated environment) Growth diffusion kernels zooplankton fish interactions Diet mortality tracking arrays Model Architecture: Size-structured populations across logarithmic size classes Multiple functional groups different feeding behaviors Environmental coupling phytoplankton temperature function separates static calculations (done efficiency) dynamic calculations (updated time step zoomss_run).","code":""},{"path":"/reference/zoomss_setup.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Initialize ZooMSS model components and calculate feeding interactions — zoomss_setup","text":"","code":"if (FALSE) { # \\dontrun{ # Create parameters for model setup params <- zoomss_params(Groups, input_params) # Initialize model structure model <- zoomss_setup(params) # Model is now ready for time integration with zoomss_run results <- zoomss_run(model) } # }"}] diff --git a/man/figures/zoomss.png b/man/figures/zoomss.png new file mode 100644 index 0000000000000000000000000000000000000000..e1088ee01d94eb622548cf5fa2932e6a4004388d GIT binary patch literal 84417 zcmZsC1ymf((&*xgy9Ku-xCVkQ?!n#NSzLk#4GzKGgS!WJ!Xm+Cad!=FkMG|5-~YXH z-t3vF>Yl3V>Yh3?Rn@iON(xfwDDO}J006p-w7AL}JO=<^@PUYL*lM{I;TwQ)QIQe_ zRE?7!yfuQ&wPY;5d;u`L;XnWa3=RP9ACk8p0OlP4;a@laAPYnCUt9&|<9}#i0e~=T z0Q`Swbl%`UhxFU|#{C}%mkaa15_4hyOMRXT_dhrc{y%K!zm|L70J5XBwhI7&iu=zA 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