AI Case Study
University of Maryland researchers identify 10% of 150K plant species worldwide as being threatened by extinction
University of Maryland researchers develop methods for predicting risks of different levels of endangerment for 150,000 plant species worldwide. This prediction allows more efficient identification of potentially endangered plants, thus allowing for more efficient allocation of conservation efforts. The researchers find 10% of the assessed species are at high risk of being considered at least "near-threatened".
Public And Social Sector
Education And Academia
"The research team created and trained a machine learning algorithm to assess more than 150,000 species of plants from all corners of the world, making their project among the largest assessments of conservation risk to date. The algorithm is a predictive model that can be applied to any grouping of species at any scale, from the entire globe to a single city park. The researchers then applied the model to the many thousands of plant species that remain unlisted by IUCN."
R And D
Core Research And Development
"The International Union for Conservation of Nature's (IUCN) Red List of Threatened Species is a powerful tool for researchers and policymakers working to stem the tide of species loss across the globe. But adding even a single species to the list is no small task, demanding countless hours of expensive, rigorous and highly specialized research.
As a result of these limitations, a large number of known species have not yet been formally assessed by the IUCN and ranked in one of five categories, from least concern to critically endangered. This deficit is quite apparent in plants: Only about 5 percent of all currently known plant species appear on IUCN's Red List in any capacity."
"According to the results, more than 15,000 of the species—roughly 10 percent of the total assessed by the team—have a high probability of qualifying as near-threatened, at a minimum. The model also flagged a few surprising areas not typically known for their biodiversity, such as the southern coast of the Arabian Peninsula, as having a high number of at-risk species."
The researchers used "open-access data from the Global Biodiversity Information Facility (GBIF) and the TRY Plant Trait Database." The model was trained "using GBIF and TRY data from the relatively small group of plant species already on the IUCN Red List. The Red List sorts non-extinct species into one of five classification categories: least concern, near-threatened, vulnerable, endangered and critically endangered."