Using Big Data and AI to Model Hidden Patterns in Nature
Big data and artificial intelligence (AI) are now being utilized to uncover hidden patterns in nature. This revolutionary approach goes beyond just studying individual bird species; it focuses on entire ecological communities spanning continents. The Cornell Lab of Ornithology’s eBird program, supported by over 900,000 birders, is at the forefront of this biodiversity science project. By combining bird sightings with advanced AI technology, researchers are gaining unprecedented insights into bird biodiversity and the underlying processes that drive it.
A Breakthrough Collaboration
The Cornell Lab of Ornithology partnered with the Cornell Institute for Computational Sustainability to develop and apply this ground-breaking computational tool. Their work has recently been published in the journal Ecology. According to lead author Courtney Davis, this tool provides comprehensive information on species distribution, occurrence, relationships, and environmental conditions. The data obtained will aid in identifying and prioritizing landscapes with high conservation value, a crucial task amidst ongoing biodiversity loss.
Expanding the Model’s Capabilities
The model developed for joint bird species distribution modeling is adaptable to various tasks given sufficient data. The team is not only predicting species presence and absence but also working on models to estimate bird abundance. Furthermore, they aim to incorporate bird calls into the model alongside visual observations, thereby enhancing its accuracy.
Researcher Daniel Fink emphasizes the significance of interdisciplinary collaborations like this for the future of biodiversity conservation. Ecologists alone cannot tackle the immense challenge at hand. The expertise of computer scientists and computational sustainability professionals is necessary to develop targeted plans for landscape-scale conservation, restoration, and management worldwide.
This groundbreaking work received funding from the National Science Foundation, The Leon Levy Foundation, The Wolf Creek Foundation, the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship (part of the Schmidt Future program), the Air Force Office of Scientific Research, and the U.S. Department of Agriculture’s National Institute of Food and Agriculture.