This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Millions of people worldwide, likely without realizing it, are capturing rare and valuable data that can help researchers assess trends in ecosystem health. Such data, however, are hidden in an ocean of comments on free online platforms like eBird and iNaturalist used by birdwatchers and other nature enthusiasts.
A new study led by the University of Michigan has shown that artificial intelligence can help researchers identify comments with pertinent data, then extract and categorize the information to build robust data sets. In this proof-of-concept study, the team found evidence of nonnative plant species creeping into ecosystems along America's West Coast that might otherwise be missed. "The potential here is really big," said Hengxing Zou, the lead author of the study who performed the work as a postdoctoral scholar in the U-M Institute for Global Change Biology.
"The approach is really versatile and we have a lot of data out there basically waiting to be mined." The work is published in the Proceedings of the National Academy of Sciences. In its study, the team was specifically interested in isolating comments that captured interactions between different species. While advances in technology have given researchers access to richer data on individuals and groups of a single species in the wild, interactions between different species are a different beast.
"In many cases, species interactions require direct observation and that's really hard to come by simply because you can't keep a lot of people in the field monitoring the same patch of an ecosystem for 24 hours a day, seven days a week," Zou said. "But it's very important data to have. It's connected to ecosystem function, ecosystem stability and a lot of the things we care about, especially in a changing global environment." The team has now shown there is a new way to access data on interactions.
Instead of a team of trained ecologists observing a location nonstop, they can use observations that have already been made by a community of hundreds of millions of nature lovers. "This approach gives us something ecologists have long needed: a way to examine species interactions across broad spatial and temporal scales," said Kai Zhu, a senior author and associate professor in the U-M School for Environment and Sustainability, or SEAS. "AI can help turn scattered observations into ecological data sets that reveal how biological communities are responding to global change." To prove the viability of its approach, the team used two case studies of species interactions.
One focused on plant-pollinator interactions between the West Coast lady butterfly and the plants it pollinates along the West Coast of the United States and Mexico, using observations recorded on iNaturalist. The other looked at various interactions between Michigan birds found in the comments of observations recorded on eBird. On these platforms, observations are uploaded with crucial metadata, such as when and where an observation was made.
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