The IUCN Red List of Threatened Species is one of the world's most important conservation resources–often referred to as a Barometer of Life. It provides a standardised, evidence-based assessment framework for grouping species into extinction risk categories (from Least Concern to Critically Endangered using quantitative criteria.
The Red List is thus a critical indicator of the health of the world’s biodiversity, and guides policy and conservation action worldwide. However, Red List coverage is constrained by funding and availability of trained assessors. This results in significant data gaps (for example, fewer than 2% of invertebrates have been assessed), and a long tail of outdated assessments (over 25% are at least 10 years old).
This project is leading research into how agentic AI could support the Red List workflow. Initial results show that AI coding agents can reliably pass the official Red List assessor training exam and, crucially, explain their answers with citations to official guidelines.
We're also leveraginge agentic coding to rapidly develop and maintain an interactive real-time evidence "evidence-base" dashboard for the Red List, integrating live citizen science observations with relevant scientific literature. Ultimately, the aim is to see whether AI-assisted assessments can improve both assessment speed and quality, so that we can identify and protect threatened species faster.