University of Massachusetts Amhurst News Release
Computer system researchers from the University of Massachusetts Amherst have actually established a brand-new design efficient in anticipating the motions of migratory birds, with the goal of assisting preservation action.
A brand-new predictive design called BirdFlow, revealed in Techniques in Ecology and Development, can properly anticipating where a migratory bird will go next – among the most uphill struggles in biology.
Dr Dan Sheldon, the paper’s senior author together with lead author Miguel Fuentes discuss “Humans have been trying to figure out bird migration for a long time, but it’s incredibly difficult to get precise, real-time information on their current location, let alone where they plan to go.”
There have actually been numerous efforts to tag and track private birds which have actually produced important insights. Nevertheless, it is hard to physically tag a big sufficient variety of birds to form a precise forecast of bird motions. “It’s hard to understand how an entire species moves across the continent with tracking methods, as they only tell you the route of birds caught in specific locations rather than every location,” Sheldon says.
Recent years have seen an explosion in the number of ‘citizen scientists’ monitoring and reporting sightings of migratory birds. Bird watchers around the world contribute over 200 million annual bird sightings through ‘eBird’, one of the largest biodiversity related projects in existence. Managed by the Cornell Lab of Ornithology (CLoO), the Lab’s eBird Status and Trends project shows the location of birds every week across their entire range. However it does not track individuals.
BirdFlow draws on eBird’s estimates of relative bird abundance and runs the informaton through a probabilistic machine-learning model. The model is tuned with real-time GPS and satellite tracking data, thus enabling it to ‘learn’ to predict the next motions of individual birds.
“Birds are experiencing rapid environmental changes, and many species are declining”
The researchers checked BirdFlow on 11 species of North American birds- including the American Woodcock, Wood Thrush and Swainson’s Hawk.
Not only did BirdFlow outperform other models for tracking bird migration, it can also accurately predict migration patterns without the GPS/satellite tracking information, a valuable tool for tracking low flying species.
Dr Benjamin Van Doren, CLoO postdoctoral fellow and co-author of the study explains “Birds today are experiencing rapid environmental change, and many species are declining. Using BirdFlow, we can unite different data sources and paint a more complete picture of bird movements. With exciting applications for guiding conservation action.”
With an $827,00 grant from the National Science Foundation, Dr Sheldon and his colleagues plan to release a software package for ecologist use. Future developments will be aimed at visualization products for the public.
The full article can be read for free for a limited time, here:
, , , & (2023). BirdFlow: Learning seasonal bird motions from eBird information. Techniques in Ecology and Development, 00, 1– 16.
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