February 2023 – A new computer model using machine learning to predict migratory bird movement could open the door to new insights on migration timing, stopover sites, bird response to climate change, light pollution and more, as it learns the patterns and variations in movement for individual species.
The model, called BirdFlow, spearheaded by the University of Massachusetts, Amherst, and the Cornell Lab of Ornithology, is explained in “BirdFlow: Learning Seasonal Bird Movements From eBird Data,” published Feb. 1 in the journal Methods in Ecology and Evolution.
“A particularly exciting aspect of this research is being able to take limited information from different sources and run it through BirdFlow,” said study co-author and Cornell Lab postdoctoral researcher Benjamin Van Doren. “We’ll be able to learn as much as we can about species movement through space and time.”
