Machine learning reveals reptiles across the world are at greater risk than previously thought

What is it about?

The Red List of Threatened Species, published by the International Union for Conservation of Nature, is a crucial tool for conservation decision-making. The Red List classifies species into one of five categories, which tell us how likely a species is to go extinct in the near future. Despite substantial effort, numerous species have yet to be formally assessed by the Red List, due to the lack of human resources or sufficient data. Automated methods have shown promise in filling these gaps, allowing the instantaneous classification of species, using readily available data. In this work we present a machine learning–based automated extinction risk assessment method that can be used on less known species and use it to offer provisional assessments for all reptiles. We use the method presented here to assess 4,369 reptile species that were not assigned a proper extinction risk category by the Red List. Our method shows high levels of accuracy, and reveals that previously unassessed species are more likely to be threatened than assessed species. We also identified specific groups of reptiles and regions in the world that are likely to be highly threatened.

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Photo by ray rui on Unsplash

Photo by ray rui on Unsplash

Why is it important?

This work adds to mounting evidence that less known species are more likely to be threatened than better know species, and deserve increased conservation attention. The method we present here can be easily implemented to help bridge the assessment gap for other less known groups of organisms, providing provisional extinction risk categories that can be used to inform conservation planning. We also highlight which groups of reptiles and regions of the world that are more threatened than previously thought, which might help conservationists to adjust their strategies to help preserve this important biodiversity.

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