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

The project, called Who Eats Whom, was launched in 2019 on iNaturalist, one of the world’s largest citizen science platforms, which holds hundreds of millions of geolocated, time-stamped photographs of wildlife. The idea is elegantly simple. Volunteers who happen to photograph an animal in the act of feeding, whether a glaucous-winged gull tearing into a starfish, a giraffe browsing a duiker-berry tree, or an American crocodile snatching an invasive lionfish, can submit their images to the project. Each feeding event generates two linked observations, one for the consumer and one for the consumed, and the iNaturalist community collectively proposes and refines the taxonomic identifications for both. Metadata tags then bind the pair together as a documented trophic interaction, complete with photographic evidence.

The scale of participation has grown far beyond what the founders initially promoted. Since its launch, Who Eats Whom has amassed nearly 14,000 observations of feeding interactions contributed by close to 2,000 observers spread across more than 100 countries. Remarkably, most of these contributions have come from volunteers who discovered the project on their own while browsing the iNaturalist database, rather than through targeted recruitment campaigns. This organic growth suggests that the photographic bycatch of ordinary biodiversity observation, images of feeding that people capture incidentally while documenting species, represents a vast and largely untapped reservoir of ecological information covering a geographic and taxonomic breadth that no targeted field study could ever achieve.

In 2025, the team began building a web application to transform this raw trove of observations into a usable scientific resource, releasing it in early 2026. The platform draws on the iNaturalist Application Programming Interface to collate data in real time, meaning the database grows dynamically as new observations pour in. The site restricts itself to so-called research-grade observations, those in which the iNaturalist community has reached high agreement on the identity of both species involved. The developers are careful to note that research-grade status confirms the taxonomy but does not independently verify that feeding actually occurred in every image, a distinction that matters for rigorous use. Even so, the filtering yields a network that, as of January 2026, connects 1,863 species through 1,607 unique feeding interactions, a figure the team expects to climb toward hundreds of thousands as iNaturalist itself continues its rapid expansion.

The technical architecture of the platform is designed around accessibility and scalability. Users type in a species of interest and can ask either what it eats or what eats it, then explore the results through five complementary visualization modes. A grid view displays the underlying photographs, putting faces, so to speak, on every interaction. A graph view and a network view render the data as directed networks in which nodes represent species and arrowheads point toward the consumer, with line widths encoding how frequently an interaction has been documented. A map view plots interactions geographically, while a global view renders the entire database as one sprawling interactive food web. Every search can also be exported as a CSV file, lowering the barrier for researchers who want to fold the data into their own analyses.

For ecologists, the applications are numerous. The database can supply basic natural history information about the dietary needs or natural enemies of poorly studied species, a gap that remains enormous given that the feeding ecology of most organisms on Earth has never been formally documented. It can surface rare or previously unrecorded interactions, flag highly connected prey species that may serve as keystone food sources, and expose taxa or regions where data are so sparse that targeted fieldwork is warranted. Conservation biologists could use it to model the potential trophic ripple effects of an invasive species or to search for candidate biological control agents. The temporal and spatial metadata attached to each observation open further avenues, from tracking how diets shift across seasons and life stages, a field known as feeding phenology and ontogeny, to cataloging which interactions currently exist so that they can be protected before they vanish.

The researchers are candid about the limitations inherent in unstructured citizen science data. Because observations are not collected under controlled sampling protocols, the database inevitably mirrors the biases of the people behind the cameras. Large, conspicuous, charismatic, and terrestrial organisms are overrepresented, while small, cryptic, aquatic, and nocturnal interactions remain comparatively rare. Who Eats Whom is therefore best understood as a living natural history archive rather than a representative sample of the planet’s trophic relationships. Even in well-studied and relatively simple ecosystems, food webs are extraordinarily complex, and constructing complete ones has proven stubbornly difficult. Yet incomplete food webs still carry genuine scientific value, particularly when analyzed with quantitative network methods, and centralized repositories like this one create the capacity to merge photographic evidence with complementary techniques such as DNA metabarcoding, producing more robust and multi-layered pictures of ecological networks than any single method could deliver alone.

Beyond the laboratory, the project has been deliberately shaped as a tool for education and public engagement. Food webs are a staple of secondary school science curricula, and questions about what animals eat resonate far beyond academia, whether a gardener wondering which plants best support native pollinators or a hiker curious about the predator lurking at the top of a local trail. The team is co-developing guidelines with the iNaturalist community for classifying the different types of feeding interactions, distinguishing parasitism from herbivory, nectar feeding from scavenging, and plans future updates including improved search queries, richer network visualizations, a dedicated R package for programmatic access, and a community vetting system for data quality. With an organized corps of volunteers reviewing submissions in real time and the sophisticated iNaturalist identification machinery working underneath, the database is effectively self-improving.

There is a philosophical dimension to the project as well. When John Muir wrote that trying to pick out anything by itself reveals it hitched to everything else in the universe, he was describing precisely the relational view of nature that Who Eats Whom seeks to operationalize. The wonder inspired by organisms such as Maculinea caterpillars, which mimic the pheromones and even the sounds of ant queens to be adopted into colonies and fed by worker ants, lies not in their existence as isolated species but in the astonishing specificity of their relationships. By shifting attention from species in isolation to the diversity of connections among them, the project offers a corrective to a reductionist habit in Western science and makes those connections visible and tangible to anyone with a web browser.

What began as a modest effort to harvest the accidental bycatch of wildlife photography has matured into the first global food web built from verifiable citizen science images, a complement to existing interaction databases such as Global Biotic Interactions and a citizen science project in its own right. Its creators anticipate that letting the public visualize and manipulate the tangled web of who consumes whom could deepen appreciation for the interconnected natural world and, perhaps, strengthen the resolve to conserve it. In the meantime, every hiker, gardener, and beachcomber with a smartphone holds the potential to add another thread to the map, one photograph of lunch in the wild at a time.

Subject of Research: A citizen science-derived global database of feeding interactions between species built from iNaturalist photographs

Article Title: Who Eats Whom? A global food web derived from citizen science

Article References: Allf, B. C., Mallavarapu, A., Kikuchi, D. W., Vasudeva, N., & Dunn, R. R. (2026). Who Eats Whom? A global food web derived from citizen science. PLOS Biology, 24(9), e3003988. https://doi.org/10.1371/journal.pbio.3003988

Image Credits: AI Generated

DOI: 10.1371/journal.pbio.3003988

Keywords: citizen science, food webs, iNaturalist, species interactions, trophic ecology, biodiversity, ecological networks, predation, natural history, conservation, data visualization, public engagement

The project taps into iNaturalist, one of the world’s largest biodiversity platforms, where members of the public upload photos, video and audio recordings of the organisms they encounter. While iNaturalist has long been used to map where species live, the new initiative extends its power to a different layer of ecological information: what those species are actually doing when they encounter one another. Users of the platform can now explicitly tag both predator and prey within a single photograph or video, capturing a feeding interaction at the moment it happens. The predator in question may be a lion taking down an antelope or a caterpillar munching on a garden leaf; the database treats both interactions as equally valuable data points in the planetary food web.

Bradley Allf, corresponding author of the paper describing the project and currently a postdoctoral researcher at Colorado State University, began the work while a graduate student at North Carolina State University. He explains that knowledge of what organisms eat remains fundamentally incomplete and fragmented across the academic literature. iNaturalist, he notes, has already proven its worth in documenting which organisms live in different locations, and this project extends that success to feeding relationships. The result is a publicly available website, who eats whom dot org, where anyone can explore recorded trophic interactions through an easily searchable interface. What began as an effort to consolidate scattered knowledge has quickly become one of the largest structured datasets of its kind.

The numbers are already striking. To date, the Who Eats Whom project has logged approximately seventeen thousand observations of trophic interactions involving roughly five thousand seven hundred species. These records have come from around two thousand observers based in more than one hundred countries, spanning ecosystems from tropical forests to suburban backyards. Each observation represents a moment when a person witnessed and documented one organism consuming another, creating a verifiable record that researchers can analyze, visualize and cross-reference with existing scientific literature. In aggregate, these individual moments begin to sketch the outline of a global food web, a planetary-scale network of feeding dependencies that has never been mapped at this level of public participation.

Aditi Mallavarapu, co-author of the paper and an assistant professor of computer science at NC State, approached the project from the perspective of human-computer interaction and tool design. Her interest, she says, lies in building usable interactive tools and learning environments, and the food web presented an irresistibly complex system to work with. The design goals were explicit: the site had to be usable, visually engaging and ethically responsible in the way it handled data contributed by citizen scientists. Mallavarapu emphasizes that the platform is intended to serve both researchers and the general public, helping each group grasp feeding relationships that are too numerous and too intricate to be understood through static tables or technical papers alone.

One of the most revealing early findings concerns what motivates participants. Mallavarapu observes that public interest extends well beyond charismatic predators such as lions and wolves. Contributors are documenting smaller, quieter trophic relationships, such as the insects that feed on persimmon leaves, and are using the site to explore interactions tied to their personal passions and local environments. This breadth of engagement matters scientifically. Ecological research has historically skewed toward large, photogenic species, leaving the vast majority of feeding interactions — particularly those involving invertebrates, plants and microbes — poorly documented. A platform driven by thousands of curious amateurs naturally fills some of those gaps, recording interactions that professional ecologists would rarely have the time or funding to chase.

From a technical standpoint, the database opens several promising research directions. Allf notes that the team can use Who Eats Whom to identify novel trophic relationships that have not yet appeared in the academic literature, then contribute those findings formally to scientific publications. The long-term ambition is for the platform to function as a parallel database that complements the peer-reviewed literature, compiling as much feeding information as possible into a single, accessible location. Because observations are time-stamped and geolocated through iNaturalist, the dataset also carries temporal and spatial dimensions that static literature reviews cannot match, potentially allowing scientists to track how feeding relationships shift across seasons, habitats and years.

Visualization is central to that ambition, and it is where Mallavarapu’s team is investing heavily. She describes a planned visual representation of this planetary-scale food web, designed to capture how trophic interactions relate to one another and how those relationships change over time. A simple version of the interface is already live, but the developers are building richer features that will let users explore feeding patterns and pose practical, personal questions — such as what to plant in a North Carolina garden to attract hummingbirds. That framing reveals the project’s dual identity: it is simultaneously a serious scientific database and an invitation for ordinary people to see themselves as participants in the ecological networks surrounding them.

Data quality remains a central challenge for any citizen science effort, and the team is developing tools specifically to validate submissions tagged on iNaturalist. Verification workflows will help ensure that predator and prey identifications are accurate and that interactions are recorded consistently, protecting the database’s scientific value as it scales. Beyond research, the project has an educational mission. The team has already created educational games built on the database, aimed at helping children understand the food web and the interdependencies it captures, with plans to expand that work if additional funding can be secured. The project was developed with substantial contributions from NC State students, including Maithili Bhoop, Adam Biscoe, Surabhi Nair and Lavanya Middha, alongside the thousands of iNaturalist users whose field observations make the entire enterprise possible.

The underlying research, titled Who Eats Whom? A global food web derived from citizen science, was published on September 22 in the open-access journal PLOS Biology. Senior author is Rob Dunn, a professor in NC State’s College of Agriculture and Life Sciences; additional authors include Nikhil Vasudeva, a former NC State undergraduate, and David Kikuchi, an assistant professor of integrative biology at Oregon State University. The researchers report no conflicts of interest. As the database grows, its creators hope it will transform not only how scientists study food webs but also how the public understands them — turning every hiker, gardener and backyard naturalist into a node in a worldwide effort to document the fundamental question of ecology: what eats what, and why it matters for the living planet.

Subject of Research: A citizen science platform that compiles global predator-prey interaction data from iNaturalist to map the planetary food web.

Article Title: How amateur naturalists are helping scientists track what everything eats

Article References: How amateur naturalists are helping scientists track what everything eats. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: citizen science, food web, iNaturalist, trophic interactions, ecology, biodiversity, Who Eats Whom, open-source database, PLOS Biology, public engagement, predator-prey relationships, data visualization