Integrating data types to estimate spatial patterns of avian migration across the Western Hemisphere

Timothy D. Meehan, Sarah P. Saunders, William V. DeLuca, Nicole L. Michel, Joanna Grand, Jill L. Deppe, Miguel F. Jimenez, Erika J. Knight, Nathaniel E. Seavy, Melanie A. Smith, Lotem Taylor, Chad Witko, Michael E. Akresh, David R. Barber, Erin M. Bayne, James C. Beasley, Jerrold L. Belant, Richard O. Bierregaard, Keith L. Bildstein, Than J. Boves, John N. Brzorad, Steven P. Campbell, Antonio Celis‐Murillo, Hilary A. Cooke, Robert Domenech, Laurie Goodrich, Elizabeth A. Gow, Aaron Haines, Michael T. Hallworth, Jason M. Hill, Amanda E. Holland, Scott Jennings, Roland Kays, D. Tommy King, Stuart A. Mackenzie, Peter P. Marra, Rebecca A. McCabe, Kent P. McFarland, Michael J. McGrady, Ron Melcer, D. Ryan Norris, Russell E. Norvell, Olin E. Rhodes, Christopher C. Rimmer, Amy L. Scarpignato, Adam Shreading, Jesse L. Watson, Chad B. Wilsey

For many avian species, spatial migration patterns remain largely undescribed, especially across hemispheric extents. Recent advancements in tracking technologies and high-resolution species distribution models (i.e., eBird Status and Trends products) provide new insights into migratory bird movements and offer a promising opportunity for integrating independent data sources to describe avian migration. Here, we present a three-stage modeling framework for estimating spatial patterns of avian migration. First, we integrate tracking and band re-encounter data to quantify migratory connectivity, defined as the relative proportions of individuals migrating between breeding and nonbreeding regions. Next, we use estimated connectivity proportions along with eBird occurrence probabilities to produce probabilistic least-cost path (LCP) indices. In a final step, we use generalized additive mixed models (GAMMs) both to evaluate the ability of LCP indices to accurately predict (i.e., as a covariate) observed locations derived from tracking and band re-encounter datasets versus pseudo-absence locations during migratory periods, and to create a fully integrated (i.e., eBird occurrence, LCP, and tracking/band re-encounter data) spatial prediction index for mapping species-specific seasonal migrations. To illustrate this approach, we apply this framework to describe seasonal migrations of 12 bird species across the Western Hemisphere during pre- and post-breeding migratory periods (i.e., spring and fall, respectively). We found that including LCP indices with eBird occurrence in GAMMs generally improved the ability to accurately predict observed migratory locations, when compared to models with eBird occurrence alone. Using three performance metrics, the eBird + LCP model demonstrated equivalent or superior fit relative to the eBird-only model for 22 of 24 species-season GAMMs. In particular, the integrated index filled in spatial gaps for species with over-water movements and those that migrated over land where there were few eBird sightings, and thus, low predictive ability of eBird occurrence probabilities (e.g., Amazonian rainforest in South America). This methodology of combining individual-based seasonal movement data with temporally dynamic species distribution models provides a comprehensive approach for integrating multiple data types to describe broad-scale spatial patterns of animal movement. Further development and customization of this approach will continue to advance knowledge about the full annual cycle and conservation of migratory birds.