README.md

In Rekyt/ssdms_saturation_richness: Richness Predictions from Stacked SDMs between Threshold and Probabilities

Is prediction of species richness from Stacked Species Distribution Models biased by habitat saturation?

This repository contains the data and code for our paper:

Grenié, M., Violle, C., & Munoz, F. (2020). Is prediction of species richness from stacked species distribution models biased by habitat saturation?. Ecological Indicators, 111, 105970. https://doi.org/10.1016/j.ecolind.2019.105970

How to cite

Please cite this compendium as:

Grenié M., Violle C, Munoz F., (2022). Compendium of R code and data for Is prediction of species richness from Stacked Species Distribution Models biased by habitat saturation?. Accessed 25 mars 2022. Online at https://doi.org/10.5281/zenodo.3552836

🔧 How to download or install

You can download the compendium as a zip from from this URL:

Or you can install this compendium as an R package, `cssdms.saturation.richness, from GitHub with:

# install.packages("devtools")
remotes::install_github("Rekyt/ssdms_saturation_richness")

💻 How to run the analyses

This compendium uses drake to make analyses reproducible. To redo the analyses and rebuild the manuscript run the following lines (from the comsat folder):

# install.packages("devtools")
pkgload::load_all()  # Load all functions included in the package
make(saturation_workflow())  # Run Analyses

Beware that some code make time a long time to run, and it may be useful to run analyses in parallel.

You can run the analyses by clicking on the Binder badge:

Dependencies

As noted in the DESCRPTION files this project depends on:

• virtualspecies, to simulate species;
• drake, to execute a reproducible workflow;
• the tidyverse (dplyr, ggplot2, purrr, and tidyr) for data wrangling;
• ggpubr to customize plot

Rekyt/ssdms_saturation_richness documentation built on April 6, 2022, 1:23 a.m.