U.S. Department of Agriculture: Animal and Plant Health Inspection Service



Timothy D. Meehan, National Audubon Society
Sarah P. Saunders, National Audubon SocietyFollow
William V. DeLuca, National Audubon Society
Nicole L. Michel, National Audubon Society
Joanna Grand, National Audubon Society
Jill L. Deppe, National Audubon Society
Miguel F. Jimenez, National Audubon Society
Erika J. Knight, National Audubon Society
Nathaniel E. Seavy, National Audubon Society
Melanie A. Smith, National Audubon Society
Lotem Taylor, National Audubon Society
Chad Witko, National Audubon Society
Michael E. Akresh, Antioch University New England
David R. Barber, Hawk Mountain Sanctuary
Erin M. Bayne, University of Alberta
James C. Beasley, Savannah River Ecology Laboratory
Jerrold L. Belant, SUNY College of Environmental Science and Forestry
Richard O. Bierregaard, Academy of Natural Sciences Philadelphia
Keith L. Bildstein, Hawk Mountain Sanctuary
Than J. Boves, Arkansas State University
John N. Brzorad, 1000 Herons
Steven P. Campbell, Albany Pine Bush Preserve Commission
Antonio Celis-Murillo, Patuxent Wildlife Research Center
Hilary A. Cooke, Wildlife Conservation Society Canada
Robert Domenech, Raptor View Research Institute
Laurie Goodrich, Hawk Mountain Sanctuary
Elizabeth A. Gow, Birds Canada
Aaron Haines, Millersville University
Michael T. Hallworth, Institute of Ecosystem Studies
Jason M. Hill, Vermont Center for Ecostudies
Amanda E. Holland, Savannah River Ecology Laboratory
Scott Jennings, Cypress Grove Research Center
Roland Kays, North Carolina State Museum of Natural Sciences
Tommy King, APHISFollow
Stuart A. Mackenzie, Birds Canada
Peter P. Marra, Georgetown University
Rebecca A. McCabe, Hawk Mountain Sanctuary
Kent P. McFarland, Vermont Center for Ecostudies
Michael J. McGrady, International Avian Research
Ron Melcer, California State Parks
D. Ryan Norris, Albany Pine Bush Preserve Commission, Albany, New York
Russell E. Norvell, Utah Division of Wildlife Resources
Olin E. Rhodes, Savannah River Ecology Laboratory, Aiken, South Carolina,
Christopher C. Rimmer, Cary Institute of Ecosystem Studies, Millbrook, New York,
Amy L. Scarpignato, National Zoological Park, Washington
Adam Shreading, Raptor View Research Institute, Missoula, Montana,
Jesse L. Watson, University of Alberta
Chad R. Wilsey, National Audubon Society

Date of this Version



Ecological Applications. 2022;32:e2679.



This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License


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 data sets 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 postbreeding 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 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 to 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.