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Whether and how migratory organisms exhibit inter-individual behavioral and/or physiological variation across movement strategies remains an open question. The Dark-eyed Junco (Junco hyemalis) is a migratory songbird known for its intra-species variation displayed in relation to morphology, song repertoires, and migration. Thus, studies focusing on juncos can reveal how migratory strategy may covary with other individual-scale factors and, therefore, identify the selective forces driving intra-species variations throughout its distribution. We used Dark-eyed Junco hydrogen stable isotope feather values (delta2H) and implemented a Bayesian framework to infer the breeding and molting origin of migratory juncos captured on their winter grounds in Oklahoma, United States (U.S.). We modeled the distribution of feather hydrogen stable isotope values as a function of five morphological variables including body mass and fat deposition measured in Oklahoma during the winter. We then investigated the trade-off between longer and more energetically costly migration strategies in relation to diet preferences through carbon (delta13C) and nitrogen (delta15N) stable isotope analysis from feather values. Dark-eyed Juncos wintering in south central U.S. likely originate from multiple breeding populations in northern U.S. and Canada. Body condition at the wintering ground (e.g., mass) had no effect on feather hydrogen stable isotope abundance. However, we found a positive correlation between nitrogen and hydrogen stable isotopes, suggesting that a trophic level shift towards insect consumption might occur in individuals migrating from southern latitudes. Increased insect-derived protein consumption might be explained by reduced fatty-acid reserves necessary to complete a shorter migratory journey.
avian migration, QL1-991, hydrogen, stable isotope, linear models, Zoology, SF1-1100, Animal culture, posterior probabilities
avian migration, QL1-991, hydrogen, stable isotope, linear models, Zoology, SF1-1100, Animal culture, posterior probabilities
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