Data from: Global conservation prioritisation approach provides credible results at a regional scale

Overview

This repository contains code and data for Roswell and Espíndola "Global conservation prioritization approach provides credible results at a regional scale" (doi:10.1111/ddi.13969), a manuscript about predicting which unassessed regional taxa are likely to faceconservation threats... using occurrence data, covariates, and random forest classifiers. The development version is on GitHub https://github.com/mikeroswell/threatRF.git

General organization

code contains R scripts to download occurrence data and GIS layers, process them, and fit the Random Forests.

data contains all the downloaded and manufactured datasets (these are often large) for this project

data/fromR mainly contains tables generated by the scripts in code

data/GIS_downloads contains raster layers downloaded from various sources

Workflow within `code/`: 

Utilities

data cleanup

1. tidy_flora.R uses regex matching to turn .pdf into a flat file2. robust_gbif_namesearch.R wraps an `rgbif` function to try to get nice matches for taxon names without returning synonyms if a valid match exists.

model fitting, etc. 1. fix_mod.R handles novel factor levels when using various `predict` functions.2. RF_tuner.R specifies how to tune and fit the random forests3. RF_setup.R creates folds for model fitting, cleans up model formulae

Data download and analysis scripts (may call 1 or more utilities above)

download_gis.R documents the sources of many of the GIS layers used downstream. Created a long time ago and unstable. Do not run

download_occurrences_and_statuses.R documents the queries in GBIF and natureserve. Largely stable but not rerun; the dataset liable to change if rerun.

crunch_GIS.R Should be rel. stable, all GIS work done in R

fit_RF.R Fits random forests

graphing_model_outputs.R generates figures and tabular results

Data

The data input for analyses is saved as a .RDA file data/fromR/lfs/to_predict.RDA

This dataset is generated by cleaning and harmonizing occurrence data (GBIF.org (08 May 2024) GBIF Occurrence Download https://doi.org/10.15468/dl.9jrwwd.) with conservation status data from Nature Serve and geographic covariates from a variety of sources, with further details in scripts described above.