RT Journal Article
SR Electronic
T1 BirdFlow: Learning Seasonal Bird Movements from Citizen Science Data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.04.12.488057
DO 10.1101/2022.04.12.488057
A1 Miguel Fuentes
A1 Benjamin M. Van Doren
A1 Daniel Fink
A1 Daniel Sheldon
YR 2022
UL http://biorxiv.org/content/early/2022/04/13/2022.04.12.488057.abstract
AB Large-scale monitoring of seasonal animal movement is integral to science, conservation, and outreach. However, gathering representative movement data across entire species ranges is frequently intractable. Citizen science databases collect millions of animal observations throughout the year, but it is challenging to infer individual movement behavior solely from observational data. We present BirdFlow, a probabilistic modeling framework that draws on citizen science data from the eBird database to model the population flows of migratory birds. We apply the model to 11 species of North American birds, using GPS and satellite tracking data to tune and evaluate model performance. We show that BirdFlow models can accurately infer individual seasonal movement behavior directly from eBird relative abundance estimates. Supplementing the model with a sample of tracking data from wild birds improves performance. Researchers can extract a number of behavioral inferences from model results, including migration routes, timing, connectivty, and forecasts. The BirdFlow framework has the potential to advance migration ecology research, boost insights gained from direct tracking studies, and serve a number of applied functions in conservation, disease surveillance, aviation, and public outreach.Competing Interest StatementThe authors have declared no competing interest.