How machine learning could help save threatened species from extinction - The Verge

There are tens of thousands of species that scientists haven’t been able to study enough to determine whether they’re at risk of extinction. Using machine learning, researchers found that many of those "data deficient" species are in trouble.

There are thousands of species on Earth that we still don’t know much about — but we now know that they are already teetering on the edge of extinction. A new study used machine learning to figure out just how threatened these lesser-known species are, and the results were grim.

Some species of animals and plants are labeled “data deficient” because conservationists haven’t been able to gather enough information about them to understand how they live or how many of them are left. It turns out that those “data deficient” species are unfortunately even more threatened than other species that are more well known (to scientists, at least). The data from this study came from the International Union for Conservation of Nature (IUCN), which maintains a global “Red List” that ranks species based on how threatened they are.

More than half of the data deficient species included in this study, 56 percent, likely face the risk of extinction. In comparison, just 28 percent of better understood species on the Red List are at risk of extinction.

“Things could be worse than we actually realize now,” says Jan Borgelt, an ecologist at the Norwegian University of Science and Technology and the lead author of the study published today in the journal Communications Biology. “More species are likely to be threatened than we previously thought.”

Much of Borgelt’s work focuses on understanding how human activity — like hydroelectricity generation or plastic pollution — affects ecosystems and biodiversity. The Red List is an invaluable resource for those efforts. But more than 20,000 species are classified as data deficient. And that blind spot can potentially make research that relies on the Red List less accurate.

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