Using publicly available data to conduct rapid assessments of extinction risk

The IUCN Red List plays a key role in setting global conservation priorities and is populated via rigorous, time‐intensive assessments. Here, we test rapid preliminary assessments of plant extinction risk using one Red List metric: Extent of Occurrence (EOO). We developed REBA (Rapid EOO‐Based Assessment) to harvest and clean data from the Global Biodiversity Information Facility, calculate each species' EOO and assign EOO‐based Red List categories. We validated REBA classifications against 1671 North American plant species already on the Red List and found ~87% overlap between REBA’s classifications and the IUCN’s. However, REBA’s false‐negative rate for species outside the Least Concern category was substantial (~68%). To elucidate factors that might drive such a high rate of under‐classification, we used hierarchical Bayesian models to show that certain plant types (e.g., Geophytes) and threats (e.g., Invasive and Other Problematic Species, Genes, and Diseases) increased the probability of under‐classification. While REBA requires further refinement, it has yielded valuable insight into how preliminary assessment methodologies may become more effective. We developed REBA (Rapid Extent of Occurrence‐Based Assessment) to produce preliminary extinction risk assessments based on occurrence records from the Global Biodiversity Information Facility. We validated REBA's classifications against 1,671 North American plant species already on the Red List, and found ~87% overlap between REBA’s classifications and the IUCN’s—but also a high false‐negative rate for species outside the Least Concern category (~68%). REBA requires further refinement, but has yielded valuable insight into how preliminary assessment methodologies may become more effective.