No one can predict exactly where birds go, but this mathematical model gets close

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No one can predict exactly where birds go, but this mathematical model gets close
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When in doubt, go with the BirdFlow.

, on the other hand, are good for observing individual behaviors, but rely too heavily on cosmic timing. Besides, from year to year, birds may alter their routes. And like all wild animals, they’re inherently unpredictable.

To get a more accurate, live read on migratory birds, BirdFlow, the probability-estimating machine-learning model the team developed, uses information about weeklyfor training. It was fine-turned with up-to-date GPS and satellite tracking data in order to predict where certain birds are headed next in their journey.

“BirdFlow models can be trained on any species, even those not tracked by eBird, as long as relative abundance models are available,” the researchers wrote in the paper. The model was tested on 11 species of North American birds like the American woodcock, wood thrush and Swainson’s hawk, and it outperformed other migration prediction models. Plus, it can correctly predict a bird’s flight path even without real-time GPS or tracking data.

to further improve BirdFlow and prepare a software package for ecologists, which they expect to release later this year. The researchers are also working on a more visual interface based on these models to engage the general public with. “In addition to the ecological questions investigated in our case study, samples from BirdFlow models can be used to study other phenomena such as stopover behavior and responses to global change,” the authors wrote. “Finally, BirdFlow can raise public awareness about biodiversity and ecosystem health by providing a tool for outreach to engage scientists, bird-watchers, policymakers and the general public.

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