These two species of warbler are not likely to be found together in the same habitat.
Newswise — Ithaca, NY—For the first time, big data and artificial intelligence (AI) are being used to model hidden patterns in nature, not just for one bird species, but for entire ecological communities across continents. And the models follow each species’ full annual life cycle, from breeding to fall migration to nonbreeding grounds, and back north again during spring migration. It begins with the more than 900,000 birders who report their sightings to the Cornell Lab of Ornithology's eBird program, one of the world's largest biodiversity science projects. When combined with innovations in technology and artificial intelligence–the same innovations that power self-driving cars and real-time language translation–these sightings are revealing more than ever about patterns of bird biodiversity, and the processes that underlie them.
The development and application of this revolutionary computational tool is the result of a collaboration between the Cornell Lab of Ornithology and the Cornell Institute for Computational Sustainability. This work is now published in the journal Ecology.
"This method uniquely tells us which species occur where, when, with what other species, and under what environmental conditions," said lead author Courtney Davis, a researcher at the Cornell Lab. "With that type of information, we can identify and prioritize landscapes of high conservation value — vital information in this era of ongoing biodiversity loss."
The researchers used data on 500 North American bird species from more than 9 million eBird checklists. They combined it with data on 72 environmental variables for topography, land cover, and much more, to estimate species' distributions, which environments they tend to be found in, and interactions between species. Until now, scientists could only analyze a handful environmental variables and a few species at a time. Now there are no limits to how many species or variables can be analyzed, at continental scales, beyond the availability of enough data.
"This was a very challenging computational problem," said co-author Carla Gomes, professor of computer science and director of Cornell’s Institute for Computational Sustainability. "We could take on this challenge because of recent advancements in AI, especially in deep learning, and developments in Graphics Processing Units (GPUs). Originally developed for computer games that demand intricate graphics and swift data processing, GPUs have since become indispensable for AI applications, offering rapid processing of vast amounts of data."
"In our application, the model learns about how species interact with their environment and with other species, making the predictions about which and how many species occur where and when more accurate than previous approaches," explained Davis. "This information vastly improves our understanding of natural and human factors that can contribute to species declines.
The scientists highlighted the utility of the model's outputs for conservation by identifying areas of high importance for North American wood warblers, a group of migratory species known to be in decline. The authors were able to pinpoint areas of highest importance year-round and in each of the breeding, nonbreeding, and migratory seasons. The scientists are working now to make this method's outputs available to a broad array of users so they don't need computational expertise to reap the benefits—and its not just useful for ornithology.
"This model is very general and is suitable for various tasks, provided there's enough data," Gomes said. "This work on joint bird species distribution modeling is about predicting the presence and absence of species, but we are also developing models to estimate bird abundance—the number of individual birds per species. We’re also aiming to enhance the model by incorporating bird calls alongside visual observations."
Cross-disciplinary collaborations like this are necessary for the future of biodiversity conservation, according to Daniel Fink, researcher at the Cornell Lab and senior author of the study.
"The task at hand is too big for ecologists to do on their own–we need the expertise of our colleagues in computer science and computational sustainability to develop targeted plans for landscape-scale conservation, restoration, and management around the world."
This work was funded by the National Science Foundation, The Leon Levy Foundation, The Wolf Creek Foundation, the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship—a Schmidt Future program, the Air Force Office of Scientific Research, and the U.S. Department of Agriculture’s National Institute of Food and Agriculture.
Reference:
Courtney L. Davis, Yiwei Bai, Di Chen, Orin Robinson, Viviana Ruiz-Gutierrez, Carla P. Gomes, and Daniel Fink. Deep learning with citizen science data enables estimation of species diversity and composition at continental extents. Ecology. September 2023. DOI: https://doi.org/10.1002/ecy.4175
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