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

Journal Article · · Ecological Applications
DOI:https://doi.org/10.1002/eap.2679· OSTI ID:1958573
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  1. National Audubon Society, New York, NY (United States); Savannah River Ecology Laboratory University of Georgia
  2. National Audubon Society, New York, NY (United States)
  3. National Audubon Society, Washington, DC (United States)
  4. Antioch Univ., Keene, NH (United States)
  5. Hawk Mountain Sanctuary Association, Orwigsburg, PA (United States)
  6. Univ. of Alberta, Edmonton, AB (Canada)
  7. Savannah River Ecology Lab., Aiken, SC (United States); Univ. of Georgia, Athens, GA (United States)
  8. State Univ. of New York College of Environmental Science and Forestry, Syracuse, NY (United States)
  9. Drexel Univ., Philadelphia, PA (United States)
  10. Arkansas State Univ., Jonesboro, AR (United States)
  11. Charlotte, NC (United States); Lenoir-Rhyne Univ., Hickory, NC (United States)
  12. Albany Pine Bush Preserve Commission, NY (United States)
  13. U.S. Geological Survey, Laurel, MD (United States)
  14. Wildlife Conservation Society Canada, Yukon Territories (Canada)
  15. Raptor View Research Inst., Missoula, MT (United States)
  16. Birds Canada, Ontario (Canada); Univ. of Guelph, Ontario (Canada)
  17. Millersville Univ., PA (United States)
  18. Cary Inst. of Ecosystem Studies, Millbrook, NY (United States); Vermont Center for Ecostudies, Norwich, VT (United States)
  19. Vermont Center for Ecostudies, Norwich, VT (United States)
  20. Audubon Canyon Ranch, Marshall, CA (United States)
  21. North Carolina Museum of Natural Sciences, Raleigh, NC (United States); North Carolina State Univ., Raleigh, NC (United States)
  22. Mississippi State Univ., Mississippi State, MS (United States)
  23. Birds Canada, Ontario (Canada)
  24. Georgetown Univ., Washington, DC (United States)
  25. International Avian Research, Krems (Austria)
  26. California State Parks, Sacramento, CA (United States); Univ. of California, Davis, CA (United States)
  27. Univ. of Guelph, Ontario (Canada)
  28. Utah Division of Wildlife Resources, Salt Lake City, UT (United States)
  29. Savannah River Ecology Lab., Aiken, SC (United States)
  30. Cary Inst. of Ecosystem Studies, Millbrook, NY (United States)
  31. Smithsonian Conservation Biology Institute, Washington, DC (United States)
  32. Univ. of Alberta, Edmonton, AB (Canada); HawkWatch International, Salt Lake City, UT (United States)
+ Show Author Affiliations
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.
Research Organization:
Savannah River Ecology Laboratory (SREL), Aiken, SC (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
EM0005228
OSTI ID:
1958573
Journal Information:
Ecological Applications, Journal Name: Ecological Applications Journal Issue: 7 Vol. 32; ISSN 1051-0761
Publisher:
Ecological Society of AmericaCopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

59 BASIC BIOLOGICAL SCIENCES
annual cycle
band re-encounter
data integration
eBird
least-cost path
migratory connectivity
tracking