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Abstract Seasonal migration has fascinated scientists and natural historians for centuries. While the genetic basis of migration has been widely studied across different taxa, there is little consensus regarding which genomic regions play a role in the ability to migrate and whether they are similar across species. Here, we examine the genetic basis of intraspecific variation within and between distinct migratory phenotypes in a songbird. We focus on the Common Yellowthroat (Geothlypis trichas) as a model system because the polyphyletic origin of eastern and western clades across North America provides a strong framework for understanding the extent to which there has been parallel or convergent evolution in the genes associated with migratory behavior. First, we investigate genome-wide population genetic structure in the Common Yellowthroat in 196 individuals collected from 22 locations across breeding range. Then, to identify candidate genes involved in seasonal migration, we identify signals of putative selection in replicate comparisons between resident and migratory phenotypes within and between eastern and western clades. Overall, we find wide-spread support for parallel evolution at the genic level, particularly in genes that mediate biological timekeeping. However, we find little evidence of parallelism at the individual SNP level, supporting the idea that there are multiple genetic pathways involved in the modulation of migration. 

Abstract Understanding migratory connectivity, or the linkage of populations between seasons, is critical for effective conservation and management of migratory wildlife. A growing number of tools are available for understanding where migratory individuals and populations occur throughout the annual cycle. Integration of the diverse measures of migratory movements can help elucidate migratory connectivity patterns with methodology that accounts for differences in sampling design, directionality, effort, precision and bias inherent to each data type.The R packageMigConnectivitywas developed to estimate population‐specific connectivity and the range‐wide strength of those connections. New functions allow users to integrate intrinsic markers, tracking and long‐distance reencounter data, collected from the same or different individuals, to estimate population‐specific transition probabilities (estTransition) and the range‐wide strength of those transition probabilities (estStrength). We used simulation and real‐world case studies to explore the challenges and limitations of data integration based on data from three migratory bird species, Painted Bunting (Passerina ciris), Yellow Warbler (Setophaga petechia) and Bald Eagle (Haliaeetus leucocephalus), two of which had bidirectional data.We found data integration is useful for quantifying migratory connectivity, as single data sources are less likely to be available across the species range. Furthermore, accurate strength estimates can be obtained from either breeding‐to‐nonbreeding or nonbreeding‐to‐breeding data. For bidirectional data, integration can lead to more accurate estimates when data are available from all regions in at least one season.The ability to conduct combined analyses that account for the unique limitations and biases of each data type is a promising possibility for overcoming the challenge of range‐wide coverage that has been hard to achieve using single data types. The best‐case scenario for data integration is to have data from all regions, especially if the question is range‐wide or data are bidirectional. Multiple data types on animal movements are becoming increasingly available and integration of these growing datasets will lead to a better understanding of the full annual cycle of migratory animals. 

Abstract Tracking climatic conditions throughout the year is often assumed to be an adaptive behaviour underlying seasonal migration patterns in animal populations. We investigate this hypothesis using genetic markers data to map migratory connectivity for 27 genetically distinct bird populations from 7 species. We found that the variation in seasonal climate tracking across our suite of populations at a continental scale is more likely a consequence, rather than a direct driver, of migratory connectivity, which is primarily shaped by energy efficiency—i.e., optimizing the balance between accessing available resources and movement costs. However, our results also suggest that regional‐scale seasonal precipitation tracking affects population migration destinations, thus revealing a potential scale dependency of ecological processes driving migration. Our results have implications for the conservation of these migratory species under climate change, as populations tracking climate seasonally are potentially at higher risk if they adapt to a narrow range of climatic conditions.