BirdFlow: Learning Seasonal Bird Movements from Citizen Science Data

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 BO_SCPLOWIRDC_SCPLOWFO_SCPLOWLOWC_SCPLOW, 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 BO_SCPLOWIRDC_SCPLOWFO_SCPLOWLOWC_SCPLOW 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 BO_SCPLOWIRDC_SCPLOWFO_SCPLOWLOWC_SCPLOW 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.