SREL Reprint #3685

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

Timothy D. Meehan1, Sarah P. Saunders1, William V. DeLuca1, Nicole L. Michel1, Joanna Grand1, Jill L. Deppe2, Miguel F. Jimenez1, Erika J. Knight1, Nathaniel E. Seavy1, Melanie A. Smith1, Lotem Taylor1, Chad Witko1, Michael E. Akresh3, David R. Barber4, Erin M. Bayne5, James C. Beasley6,7, Jerrold L. Belant8, Richard O. Bierregaard9, Keith L. Bildstein4, Than J. Boves10, John N. Brzorad11,12, Steven P. Campbell13, Antonio Celis-Murillo14, Hilary A. Cooke15, Robert Domenech16, Laurie Goodrich4, Elizabeth A. Gow17,18, Aaron Haines19, Michael T. Hallworth20,21, Jason M. Hill21, Amanda E. Holland6,7, Scott Jennings22, Roland Kays23,24, D. Tommy King25, Stuart A. Mackenzie17, Peter P. Marra26, Rebecca A. McCabe4, Kent P. McFarland21, Michael J. McGrady27, Ron Melcer Jr28,29, D. Ryan Norris18, Russell E. Norvell30, Olin E. Rhodes Jr6, Christopher C. Rimmer20, Amy L. Scarpignato31, Adam Shreading16, Jesse L. Watson5,32, and Chad B. Wilsey1

1National Audubon Society, New York, New York, USA
2National Audubon Society, Washington, District of Columbia, USA
3Department of Environmental Studies, Antioch University New England, Keene, New Hampshire, USA
4Acopian Center for Conservation Learning, Hawk Mountain Sanctuary Association,
Orwigsburg, Pennsylvania, USA
5Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada
6Savannah River Ecology Laboratory, Aiken, South Carolina, USA
7Warnell College of Forestry & Natural Resources, University of Georgia, Athens, Georgia, USA
8Global Wildlife Conservation Center, State University of New York College of Environmental Science
and Forestry, Syracuse, New York, USA
9Department of Ornithology, Academy of Natural Sciences of Drexel University,
Philadelphia, Pennsylvania, USA
10Department of Biological Sciences, Arkansas State University, Jonesboro, Arkansas, USA
111000 Herons, Charlotte, North Carolina, USA
12Lenoir-Rhyne University, Hickory, North Carolina, USA
13Albany Pine Bush Preserve Commission, Albany, New York, USA
14U.S. Geological Survey, Eastern Ecological Center, Patuxent Research Refuge, Laurel, Maryland, USA
15Wildlife Conservation Society Canada, Whitehorse, Yukon Territories, Canada
16Raptor View Research Institute, Missoula, Montana, USA
17Birds Canada, Port Rowan, Ontario, Canada
18Department of Integrative Biology, University of Guelph, Guelph, Ontario, Canada
19Biology Department, Millersville University, Millersville, Pennsylvania, USA
20Cary Institute of Ecosystem Studies, Millbrook, New York, USA
21Vermont Center for Ecostudies, Norwich, Vermont, USA
22Cypress Grove Research Center, Audubon Canyon Ranch, Marshall, California, USA
23North Carolina Museum of Natural Sciences, Raleigh, North Carolina, USA
24Department of Forestry and Environmental Resources, North Carolina State University,
Raleigh, North Carolina, USA
25U.S. Department of Agriculture, Wildlife Services, National Wildlife Research Center,
Mississippi Field Station, Mississippi State University, Mississippi State, Mississippi, USA
26Department of Biology and McCourt School of Public Policy, Georgetown University,
Washington, District of Columbia, USA
27International Avian Research, Krems, Austria
28California State Parks, Sacramento, California, USA
29Geography Graduate Group, University of California, Davis, Davis, California, USA
30Utah Division of Wildlife Resources, Salt Lake City, Utah, USA
31Migratory Bird Center, Smithsonian Conservation Biology Institute, National Zoological Park,
Washington, District of Columbia, USA
32HawkWatch International, Salt Lake City, Utah, USA

Abstract: 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 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 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.

Keywords: annual cycle, band re-encounter, data integration, eBird, least-cost path, migratory connectivity, tracking

SREL Reprint #3685

Meehan, T. D., S. P. Saunders, W. V. DeLuca, N. L. Michel, J. Grand, J. L. Deppe, M. F. Jimenez, E. J. Knight, N. E. Seavy, M. A. Smith, L. Taylor, C. Witko, M. E. Akresh, D. R. Barber, E. M. Bayne, J. C. Beasley, J. L. Belant, R. O. Bierregaard, K. L. Bildstein, T. J. Boves, J. N. Brzorad, S. P. Campbell, A. Celis-Murillo, H. A. Cooke, R. Domenech, L. Goodrich, E. A. Gow, A. Haines, M. T. Hallworth, J. M. Hill, A. E. Holland, S. Jennings, R. Kays, D. T. King, S. A. Mackenzie, P. P. Marra, R. A. McCabe, K. P. McFarland, M. J. McGrady, R. Melcer Jr., D. R. Norris, R. E. Norvell, O. E. Rhodes Jr., C. C. Rimmer, A. L. Scarpignato, A. Shreading, J. L. Watson, and C. B. Wilsey. 2022. Integrating data types to estimate spatial patterns of avian migration across the Western Hemisphere. Ecological Applications 32(7): e2679.

This information was provided by the University of Georgia's Savannah River Ecology Laboratory (srel.uga.edu).