Computer scientists at the University of Massachusetts Amherst, in collaboration with biologists at the Cornell Lab of Ornithology, recently announced a new, predictive model that is capable of accurately forecasting where a migratory bird will go next — one of the most difficult tasks in biology. The model is called BirdFlow, and it should be available to scientists within the year and will eventually make its way to the general public.
Many past and current studies have tagged and tracked individual birds but it’s difficult to physically tag birds in large enough numbers — not to mention the expense of such an undertaking — to form a complete enough picture to predict bird movements.
Birders around the world contribute more than 200 million annual bird sightings through eBird, a project managed by the Cornell Lab of Ornithology and international partners. These data facilitate state-of-the-art species distribution modeling through the Lab’s eBird Status & Trends project.
BirdFlow draws on eBird’s Status & Trends database and its estimates of relative bird abundance and then runs that information through a probabilistic machine-learning model. This model is tuned with real-time GPS and satellite tracking data so that it can “learn” to predict where individual birds will move next as they migrate.
The researchers tested BirdFlow on 11 species of North American birds — including the American Woodcock, Wood Thrush, and Swainson’s Hawk — and found that not only did BirdFlow outperform other models for tracking bird migration, it can accurately predict migration flows without the real-time GPS and satellite tracking data, which makes BirdFlow a valuable tool for tracking species that may literally fly under the radar.
The current plan is to release a BirdFlow software package for ecologists to use later this year, with future development aimed at visualization products geared toward the general public.
Source: Birdwatching February 2023
