If you know the animals in your neighborhood but not the plants, you're not alone.

Scientists have documented nearly 400,000 plant species and expect to identify many more. But unlike well-known endangered animals, such as elephants, tigers, and parrots, we don't currently understand the conservation status of more than 90 percent of the world's plant species. Plant growth and communities drive the ecosystems, food chains, and agriculture on every continent, yet we don't know the conditions that cause them to thrive or disappear.

Understanding how threatened a specific plant species is requires broad information on where it lives and what it looks like. But finding plants in the wild to determine where they are and where they aren't requires time, money, and expertise.

A multi-institutional research team used the power of open-access databases and machine learning to predict the conservation status of more than 150,000 plants. In their study published last month in the Proceedings of the National Academy of Sciences, the team tested whether their machine learning algorithm could track patterns in plant locations, climatic patterns, habitat features, and morphologies — their form and structure — and use that information to identify species that were likely at risk of extinction.

Read the full article about how machine learning can help plant conservationists by Sue Palminteri at Pacific Standard.