Data and analyses of "Has the global extinction risk been underestimated?"

This dataset contains the data and analyses of our paper (link to come) which updates global estimates of the number of threatened invertebrate species. The main R code, main_analysis.R, will usually be what you're after as a reader. It contains the code to analyse the data and draw the figures used in the paper. Apart from the R code, there is a folder of data (used by the R code), and a folder for the figures (created by the R code). The dataset contains: data Folder with the data: 20260122_redlist_raw.csv The (almost) raw data downloaded from the IUCN Red List. Some minor changes have been made after downloading, including adding a column with the year of species description. expected_values.csv Fitted values for the proportion of species that are threatened, as predicted by our model(s). Separate values for each family, for a sequence of years 1758-2024, for solely tropical vs also non-tropical species, and for different threat categories (threatened/not threatened, Least Concern, Near Threatened, Vulnerable, Endangered, Critically Endangered). redlist.csv The Red List data after processing by 'process_raw_redlist_data.R'. This is the main data used in the analyses. col_raw.zip The raw data on described invertebrate species and their synonym names, downloaded from the Catalogue of Life (version 2026-04-15, <https://doi.org/10.48580/dgxgy>). col.zip The Catalogue of Life data on described invertebrate species after processing by 'process_raw_col_data.R'. This and 'synonyms.zip' is the data used to find out, for each species, how manyth a described species it is. fit.rda Results from our fitting of models, as an R object. Read from here by our script to save time, since running the fitting from scratch can take at least several minutes. fits.rda Results from our fitting of various candidate models (in 'compare_models.R'), as an R object. Read from here by our script to save time, since running the fitting from scratch can take quite a while. synonyms.zip The Catalogue of Life data on synonym names of described invertebrate species after processing by 'process_raw_col_data.R'. This and 'col.zip' is the data used to find out, for each species, how manyth a described species it is. figures Folder with the figures: data_overview.pdf Overviews of how the proportion of threatened species varies with the year of species description and year of assessment for the Red List. These were used during the processing of the IUCN Red List data to get a feel for the data, and led to species assessed before 2007 being dropped from the data. figure1.png Figure 1 of the article. Proportion threatened vs year of description. figure2.png Figure 2 of the article. Threat categories (LC to CR) vs year of description. figure3.png Figure 3 of the article. Proportion threatened vs year of description, for solely tropical and also non-tropical species separately. figure4.png Figure 4 of the article. Proportion threatened vs number of species described for insects, and various estimates of the proportion threatened for all (described and undescribed) insects. R Folder with the R code used by the main scripts: analyses.R The sections of code used by the main analysis 'main_analysis.R'. The different sections are here to make the main script easier to read; the main script calls these one at a time. figures.R The code for drawing the figures. Called by the main script. helper_functions.R Miscellaneous helper functions used by the rest of the R code. compare_models.R The code used to compare different statistical models, and choose which to use in the main analysis. main_analysis.R The main script. Run this to rerun the analyses. For readability, many of the longer sections are in 'analyses.R' and are called from here. (i.e. open 'analyses.R' if you want more details on what some piece of the script is doing.) process_raw_col_data.R The code used to process the raw Catalogue of Life data, and save it as 'col.zip' and 'synonyms.zip'. process_raw_redlist_data.R The code used to process the raw IUCN Red List data, and save it as the main dataset,'redlist.csv'. ReadMe.md ReadMe file with the same information as here.