Integrating tracking and resight data enables unbiased inferences about migratory connectivity and winter range survival from archival tags

Ornithological Applications
By: , and 

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Abstract

Archival geolocators have transformed the study of small, migratory organisms but analysis of data from these devices requires bias correction because tags are only recovered from individuals that survive and are re-captured at their tagging location. We show that integrating geolocator recovery data and mark–resight data enables unbiased estimates of both migratory connectivity between breeding and nonbreeding populations and region-specific survival probabilities for wintering locations. Using simulations, we first demonstrate that an integrated Bayesian model returns unbiased estimates of transition probabilities between seasonal ranges. We also used simulations to determine how different sampling designs influence the estimability of transition probabilities. We then parameterized the model with tracking data and mark–resight data from declining Painted Bunting (Passerina ciris) populations breeding in the eastern United States, hypothesized to be threatened by the illegal pet trade in parts of their Caribbean, nonbreeding range. Consistent with this hypothesis, we found that male buntings wintering in Cuba were 20% less likely to return to the breeding grounds than birds wintering elsewhere in their range. Improving inferences from archival tags through proper data collection and further development of integrated models will advance our understanding of the full annual cycle ecology of migratory species.

Publication type Article
Publication Subtype Journal Article
Title Integrating tracking and resight data enables unbiased inferences about migratory connectivity and winter range survival from archival tags
Series title Ornithological Applications
DOI 10.1093/ornithapp/duab010
Volume 123
Issue 2
Year Published 2021
Language English
Publisher Oxford Academic
Contributing office(s) Patuxent Wildlife Research Center, Eastern Ecological Science Center
Description duab010
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