AI Turns Birdwatchers’ Notes Into a Goldmine of Ecological Data

Every day, millions of people around the world step outside, spot a bird at a feeder, a butterfly on a flower, or a goose grazing in a mixed flock, and record what they see on free online platforms such as eBird and iNaturalist. What most of these nature lovers never realize is that they are generating some of the rarest and most valuable data in ecology: direct observations of how different species interact with one another. Now, a new study led by the University of Michigan shows that artificial intelligence can unlock this hidden trove, sifting through oceans of casual comments to build rigorous scientific datasets that would otherwise take armies of field biologists decades to assemble.

The research, published in the Proceedings of the National Academy of Sciences and supported in part by funding from the U.S. National Science Foundation and the U.S. Department of Agriculture, demonstrates a proof of concept with real ecological payoff. By deploying large language models, the conversational AI systems behind tools like Claude and ChatGPT, the team identified comments containing pertinent ecological information, extracted the relevant details, and categorized them into structured datasets. In the process, they uncovered evidence of non-native plant species quietly creeping into ecosystems along America’s West Coast, signals that might have been missed entirely by conventional monitoring.

The core challenge the researchers set out to solve is one of the most stubborn in ecology: measuring species interactions. Modern technology has made it relatively easy to track individuals and groups of a single species in the wild, through camera traps, acoustic sensors, and citizen-science checklists. Interactions between different species are a far harder target. A butterfly pollinating a flower, a hawk eating a songbird, or two species of geese feeding side by side all require someone to be in the right place at the right moment, watching.

“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,” said Hengxing Zou, the study’s lead author, who performed the work as a postdoctoral scholar in the University of Michigan Institute for Global Change Biology. Yet these interactions matter enormously, he noted, because they are tied to ecosystem function, ecosystem stability, and many of the things ecologists care about most, especially as the global environment changes.

The solution, the team realized, was already out there in abundance. Instead of a handful of trained ecologists observing a single location nonstop, they could tap observations already made by a community of hundreds of millions of nature enthusiasts. On platforms like eBird and iNaturalist, users upload observations along with crucial metadata, such as when and where the sighting occurred. Often, they also add free-text comments, describing a butterfly landing on a flower, a bird eating another bird, a flock of mixed species feeding cooperatively, or two birds squabbling over a spot at a feeder. These comments are exactly the kind of direct interaction records ecologists crave, but they are buried in millions of entries written in casual, unstructured human language.

That is where natural language processing comes in. The researchers suspected that AI tools designed to understand human language could do more than quickly flag which observations had comments; they could also determine which comments actually documented interactions, even when the observers never used formal ecological vocabulary. Working with undergraduate researchers from ecology and computer science, the team crafted prompts that led both Claude and ChatGPT to extract and classify species interactions with good accuracy and precision, Zou said.

To illustrate just how transformative the time savings can be, the team ran a direct comparison. First, ChatGPT was asked to isolate comments from eBird data that contained species interactions. Then Olivia Stein, an undergraduate research assistant, took a random sampling of nearly 500 of those comments and manually classified which type of interaction each one contained. The team then gave Claude the identical task. What took Stein eight hours of careful human work, the AI completed in six minutes. The result was not a replacement for human expertise but a dramatic extension of it, multiplying the reach of a trained eye by orders of magnitude.

The study included two case studies that showcase the versatility of the approach. The first focused on plant-pollinator interactions involving the West Coast lady butterfly, which pollinates plants along the west coast of the United States and Mexico, using observations recorded on iNaturalist. Here, the key interaction was often as visually straightforward as the butterfly landing on a flower. The second case study examined the wider array of interactions among Michigan birds found in the comments of eBird observations, ranging from predation and being preyed upon to cooperative behaviors like flocking and competitive behaviors like fighting over a perch at a bird feeder.

“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 University of Michigan School for Environment and Sustainability. “AI can help turn scattered observations into ecological datasets that reveal how biological communities are responding to global change.” Brian Weeks, also a senior author and associate professor at the school, said he was surprised and delighted by the wide range of natural history observations that members of the public noted. Beyond their value as scientific data, he added, a highlight of the project was glimpsing the joy that observing birds clearly brings to people.

Zou is candid about the controversies swirling around artificial intelligence in research, but he frames this project as an example of AI being used to extend human effort rather than replace it. “I personally think it’s kind of a rustic way of using AI in this booming age where people are using it to solve Millennium Prize math problems,” he said. “It’d be almost impossible for humans to go through the millions of comments that are out there. We’re using the large language models to go through and generate data sets, instead of analyzing them or replacing any of the creative work that scientists are doing.” In other words, the machines handle the tedious mining; the scientists keep the interpretive and creative work.

There is also a pleasing historical resonance in the method. While large language models are brand-new technology, the observations they harvest reflect the founding spirit of ecology itself, a tradition of curious people going out into nature, watching closely, and recording whatever they find interesting. Zou believes far more information could be recovered from these kinds of comments beyond species interactions, potentially opening new lines of inquiry into behavior, phenology, and community dynamics. The research was a broad collaboration, with contributions from the University of California Santa Cruz, the University of California Davis, the Georgia Institute of Technology, the American Bird Conservancy, Notre Dame University, the Santa Fe Institute, and Michigan State University. Additional funding came from the David and Lucile Packard Foundation, the Alfred P. Sloan Foundation, the Institute for Global Change Biology, the University of Michigan Undergraduate Research Opportunity Program, and Michigan State University’s Ecology, Evolution, and Behavior Program, with computational resources provided by Advanced Research Computing at the University of Michigan. For the growing community of birdwatchers, butterfly chasers, and backyard naturalists, the message is clear: that casual note you leave alongside your photo may soon become a data point in the science of a changing planet.

Subject of Research: Using large language models to extract species interaction data from citizen-science observation comments

Article Title: How nature lovers can aid ecology with an assist from AI

Article References: How nature lovers can aid ecology with an assist from AI. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: artificial intelligence, large language models, ecology, species interactions, citizen science, eBird, iNaturalist, biodiversity, ecosystem monitoring, natural language processing, University of Michigan, PNAS