Get IUCN red list data with redlist R package • redlist

What is IUCN red list ?

The IUCN Red List of Threatened Species is a comprehensive global inventory that assesses the conservation status of plant, animal, and fungi species. Managed by the International Union for Conservation of Nature (IUCN), it evaluates species based on factors like population size, rate of decline, and geographic range to classify them into categories such as Least Concern, Vulnerable, Endangered, and Critically Endangered. The Red List serves as a critical tool for conservation planning, policy-making, and raising awareness about the risk of extinction facing species worldwide. In this article, I demonstrate how to access Red List data using the redlist R package.

Install redlist R package

The package can be installed from github with pak package manager. The pak package in R offers a faster, more reliable, and efficient way to install packages compared to traditional methods. It handles dependencies automatically, supports parallel downloads. Alternately is {devtool} or remotes. I prefered to use pak.

# Install pak if not available
if (!requireNamespace("pak", quietly = TRUE)) {
  install.packages("pak")
}

# Then install redlist
pak::pkg_install("stangandaho/redlist")
# Load the package
library(redlist)

Set up an API key

If you’re using this package for the first time, you’ll likely need an IUCN Red List API key. You can check whether it’s set by running rl_check_api(). If this throws an error like ‘! No Redlist API key found… ’, you’ll need to set an API key before using any of the package functions. Just follow these two simple steps:
1. Click here to create an account if you don’t already have one. Once logged in, you can generate your API key.
2. Copy your API key and set it using the rl_set_api() function, like this rl_set_api("2GoWiThmYrEDlitApiThatWorkS4me").
You can then run rl_check_api() again to confirm that your API key is set successfully.

Get data

The IUCN provides extensive data through the Red List across various categories, including assessments, biogeographical realms, comprehensive groups, conservation actions, habitats, population trends, red list categories, taxonomic levels, and more. A complete overview, along with the corresponding access functions, can be found here. In this article, we will focus specifically on data related to threatened species that have been assessed in Benin.

benin_redlist <- rl_countries(code = "BJ")
head(benin_redlist)

country_description_en	country_code	assessments_year_published	assessments_latest	assessments_possibly_extinct	assessments_possibly_extinct_in_the_wild	assessments_sis_taxon_id	assessments_url	assessments_taxon_scientific_name	assessments_red_list_category_code	assessments_assessment_id	assessments_code	assessments_code_type	assessments_scopes_description_en	assessments_scopes_code
Benin	BJ	2013	FALSE	FALSE	FALSE	137286	https://www.iucnredlist.org/species/137286/522738	Caccobius ferrugineus	LC	522738	BJ	country	Global	1
Benin	BJ	2025	TRUE	FALSE	FALSE	137829	https://www.iucnredlist.org/species/137829/531737	Garreta laetus	LC	531737	BJ	country	Global	1
Benin	BJ	2014	TRUE	FALSE	FALSE	137859	https://www.iucnredlist.org/species/137859/532227	Pedaria estellae	LC	532227	BJ	country	Global	1
Benin	BJ	2013	TRUE	FALSE	FALSE	137957	https://www.iucnredlist.org/species/137957/533937	Trichonthophagus juvencus	LC	533937	BJ	country	Global	1
Benin	BJ	2014	TRUE	FALSE	FALSE	138000	https://www.iucnredlist.org/species/138000/534595	Pedaria criberrima	LC	534595	BJ	country	Global	1
Benin	BJ	2013	TRUE	FALSE	FALSE	138301	https://www.iucnredlist.org/species/138301/539328	Latodrepanus laticollis	LC	539328	BJ	country	Global	1

By default, the function retrieves the first page of results, which includes the first 100 records across various years. You can specify one or more pages, as well as specific publication years, to filter the data accordingly. If you want to retrieve all available data without restricting by page, you can set the page argument to NA or NULL.

# Get data from five first pages
benin_redlist <- rl_countries(code = "BJ", page = 1:5)
# Get data from two first pages specifically for 2023
benin_redlist <- rl_countries(code = "BJ", page = 1:5, year_published = 2023)
# Get all data on Benin
benin_redlist <- rl_countries(code = "BJ", page = NA)

As displayed, each output from redlist functions is returned as a tibble, allowing us to easily filter observations for analytical purposes. In the article on data visulisation, I’ll present a few examples of plots to illustrate how the data can be explored visually.

To get detailed information about each species or observation, you can use one of two functions by passing data from this output. For example, you can use the function rl_sis() and pass a species sis_id (e.g., 137286 for the first row). Alternatively, you can use the rl_assessment_id() function, which accepts a species assessment_id (e.g., 522738). The rl_assessment_id() function returns more detailed data than rl_sis().

If you need information for multiple species, you can simply loop through the assessments_assessment_id column and combine the data row by row.

all_species_details <- lapply(benin_redlist$assessments_assessment_id, function(x){
  rl_assessment_id(id)
}) %>% 
  dplyr::bind_rows()