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Novel statistical methods for integrating genetic and stable isotope data to infer individual-level migratory connectivity

Authors

  • Colin W. Rundel,

    1. Department of Statistical Sciences, Duke University, Durham, NC, USA
    2. Department of Statistics, University of California, Los Angeles, Los Angeles, CA, USA
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  • Michael B. Wunder,

    1. Department of Integrative Biology, University of Colorado, Denver, Denver, CO, USA
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  • Allison H. Alvarado,

    1. Center for Tropical Research Institute of the Environment and Sustainability, University of California, Los Angeles, Los Angeles, CA, USA
    2. Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, USA
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  • Kristen C. Ruegg,

    1. Department of Integrative Biology, University of Colorado, Denver, Denver, CO, USA
    2. Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, Santa Cruz, CA, USA
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  • Ryan Harrigan,

    1. Center for Tropical Research Institute of the Environment and Sustainability, University of California, Los Angeles, Los Angeles, CA, USA
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  • Andrew Schuh,

    1. Cooperative Institute for Research in the Atmosphere (CIRA), Fort Collins, CO, USA
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  • Jeffrey F. Kelly,

    1. Oklahoma Biological Survey and Department of Biology, Ecology and Evolutionary Biology Program, University of Oklahoma, Norman, OK, USA
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  • Rodney B. Siegel,

    1. The Institute for Bird Populations, CA, USA
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  • David F. DeSante,

    1. The Institute for Bird Populations, CA, USA
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  • Thomas B. Smith,

    Corresponding author
    1. Center for Tropical Research Institute of the Environment and Sustainability, University of California, Los Angeles, Los Angeles, CA, USA
    2. Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, USA
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  • John Novembre

    Corresponding author
    1. Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, CA, USA
    2. Department of Human Genetics, Chicago, IL, USA
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Abstract

Methods for determining patterns of migratory connectivity in animal ecology have historically been limited due to logistical challenges. Recent progress in studying migratory bird connectivity has been made using genetic and stable-isotope markers to assign migratory individuals to their breeding grounds. Here, we present a novel Bayesian approach to jointly leverage genetic and isotopic markers and we test its utility on two migratory passerine bird species. Our approach represents a principled model-based combination of genetic and isotope data from samples collected on the breeding grounds and is able to achieve levels of assignment accuracy that exceed those of either method alone. When applied at large scale the method can reveal specific migratory connectivity patterns. In Wilson's warblers (Wilsonia pusilla), we detect a subgroup of birds wintering in Baja that uniquely migrate preferentially from the coastal Pacific Northwest. Our approach is implemented in a way that is easily extended to accommodate additional sources of information (e.g. bi-allelic markers, species distribution models, etc.) or adapted to other species or assignment problems.

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