BirdFlow: Learning seasonal bird movements from eBird data

Abstract

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 behaviour solely from observational data. We present BirdFlow, a probabilistic modelling 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 behaviour 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 behavioural 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.

Keywords

• big data
• bird migration
• forecasting
• graphical models
• movement ecology
• species distributions