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: </archive/master.zip>

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