There are many organizations monitoring endangered species such as elephants and tigers, but what about the millions of other species on the planet — ones that most people have never heard of or don’t think about? How do scientists assess the threat level of, say, the plicate rocksnail, Caribbean spiny lobster or Torrey pine tree?
A new approach co-developed at The Ohio State University uses data analytics and machine learning to predict the conservation status of more than 150,000 plants worldwide. Results suggest that more than 15,000 species likely qualify as near-threatened, vulnerable, endangered or critically endangered.
The approach will allow conservationists and researchers to identify the species most at risk, and also to pinpoint the geographic areas where those species are highly concentrated.
The study appears online today (Dec. 3, 2018) in the journal Proceedings of the National Academy of Sciences.
“Plants form the basic habitat that all species rely on, so it made sense to start with plants,” said Bryan Carstens, a professor of evolution, ecology and organismal biology at Ohio State.
“A lot of times in conservation, people focus on big, charismatic animals, but it’s actually habitat that matters. We can protect all the lions, tigers and elephants we want, but they have to have a place to live in.”
Currently, the International Union for the Conservation of Nature — which produces the world’s most comprehensive inventory of threatened species (the “Red List”) — more or less works on a species-by-species basis, requiring more resources and specialized work than is available to accurately assign a conservation-risk category to every species.
Of the nearly 100,000 species currently on the Red List, plants are among the least represented, with only 5 percent of all currently known species accounted for.
Read more at Ohio State University