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 in the Dec. 3, 2018 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.