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BirdFlow: Learning Seasonal Bird Movements from eBird Data

View ORCID ProfileMiguel Fuentes, View ORCID ProfileBenjamin M. Van Doren, Daniel Fink, Daniel Sheldon
doi: https://doi.org/10.1101/2022.04.12.488057
Miguel Fuentes
1Manning College of Information and Computer Sciences, University of Massachusetts Amherst, 140 Governors Drive, Amherst, MA 01003, USA
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  • For correspondence: mmfuentes@cs.umass.edu
Benjamin M. Van Doren
2Cornell Lab of Ornithology, Cornell University, Ithaca, NY 14850, USA
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Daniel Fink
2Cornell Lab of Ornithology, Cornell University, Ithaca, NY 14850, USA
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Daniel Sheldon
1Manning College of Information and Computer Sciences, University of Massachusetts Amherst, 140 Governors Drive, Amherst, MA 01003, USA
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Abstract

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, connectivity, 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 Statement

The authors have declared no competing interest.

Footnotes

  • The methods section has been restructured and partly rewritten to provide a clearer explanation of the methodology. The methodological outline figure has also been updated to contain more details. The discussion section has also been reworked to focus topics of biological interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted November 08, 2022.
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BirdFlow: Learning Seasonal Bird Movements from eBird Data
Miguel Fuentes, Benjamin M. Van Doren, Daniel Fink, Daniel Sheldon
bioRxiv 2022.04.12.488057; doi: https://doi.org/10.1101/2022.04.12.488057
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BirdFlow: Learning Seasonal Bird Movements from eBird Data
Miguel Fuentes, Benjamin M. Van Doren, Daniel Fink, Daniel Sheldon
bioRxiv 2022.04.12.488057; doi: https://doi.org/10.1101/2022.04.12.488057

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