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Meehan TD, Saunders SP, DeLuca WV, Michel NL, Grand J, Deppe JL, Jimenez MF, Knight EJ, Seavy NE, Smith MA, Taylor L, Witko C, Akresh ME, Barber DR, Bayne EM, Beasley JC, Belant JL, Bierregaard RO, Bildstein KL, Boves TJ, Brzorad JN, Campbell SP, Celis‐Murillo A, Cooke HA, Domenech R, Goodrich L, Gow EA, Haines A, Hallworth MT, Hill JM, Holland AE, Jennings S, Kays R, King DT, Mackenzie SA, Marra PP, McCabe RA, McFarland KP, McGrady MJ, Melcer R, Norris DR, Norvell RE, Rhodes OE, Rimmer CC, Scarpignato AL, Shreading A, Watson JL, Wilsey CB. Integrating data types to estimate spatial patterns of avian migration across the Western Hemisphere. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2022; 32:e2679. [PMID: 35588285 PMCID: PMC9787853 DOI: 10.1002/eap.2679] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 01/25/2022] [Accepted: 02/02/2022] [Indexed: 06/15/2023]
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 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.
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Belaire JA, Kreakie BJ, Keitt T, Minor E. Predicting and mapping potential Whooping Crane stopover habitat to guide site selection for wind energy projects. CONSERVATION BIOLOGY : THE JOURNAL OF THE SOCIETY FOR CONSERVATION BIOLOGY 2014; 28:541-550. [PMID: 24372936 DOI: 10.1111/cobi.12199] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2012] [Accepted: 07/17/2013] [Indexed: 06/03/2023]
Abstract
Migratory stopover habitats are often not part of planning for conservation or new development projects. We identified potential stopover habitats within an avian migratory flyway and demonstrated how this information can guide the site-selection process for new development. We used the random forests modeling approach to map the distribution of predicted stopover habitat for the Whooping Crane (Grus americana), an endangered species whose migratory flyway overlaps with an area where wind energy development is expected to become increasingly important. We then used this information to identify areas for potential wind power development in a U.S. state within the flyway (Nebraska) that minimize conflicts between Whooping Crane stopover habitat and the development of clean, renewable energy sources. Up to 54% of our study area was predicted to be unsuitable as Whooping Crane stopover habitat and could be considered relatively low risk for conflicts between Whooping Cranes and wind energy development. We suggest that this type of analysis be incorporated into the habitat conservation planning process in areas where incidental take permits are being considered for Whooping Cranes or other species of concern. Field surveys should always be conducted prior to construction to verify model predictions and understand baseline conditions.
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Affiliation(s)
- J Amy Belaire
- Department of Biological Sciences, University of Illinois at Chicago, 845 W. Taylor St (MC 066), Chicago, IL, 60607, U.S.A..
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