RT Journal Article
SR Electronic
T1 Identifying Migrant Origins Using Genetics, Isotopes, and Habitat Suitability
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 085456
DO 10.1101/085456
A1 Kristen C. Ruegg
A1 Eric C. Anderson
A1 Ryan J. Harrigan
A1 Kristina L. Paxton
A1 Jeff Kelly
A1 Frank Moore
A1 Thomas B. Smith
YR 2016
UL http://biorxiv.org/content/early/2016/11/03/085456.abstract
AB Identifying migratory connections across the annual cycle is important for studies of migrant ecology, evolution, and conservation. While recent studies have demonstrated the utility of high-resolution SNP-based genetic markers for identifying population-specific migratory patterns, the accuracy of this approach relative to other intrinsic tagging techniques has not yet been assessed.Here, using a straightforward application of Bayes' Rule, we develop a method for combining inferences from high-resolution genetic markers, stable isotopes, and habitat suitability models, to spatially infer the breeding origin of migrants captured anywhere along their migratory pathway. Using leave-one-out cross validation, we compare the accuracy of this combined approach with the accuracy attained using each source of data independently.Our results indicate that when each method is considered in isolation, the accuracy of genetic assignments far exceeded that of assignments based on stable isotopes or habitat suitability models. However, our joint assignment method consistently resulted in small, but informative increases in accuracy and did help to correct misassignments based on genetic data alone. We demonstrate the utility of the combined method by identifying previously undetectable patterns in the timing of migration in a North American migratory songbird, the Wilson's warbler.Overall, our results support the idea that while genetic data provides the most accurate method for tracking animals using intrinsic markers when each method is considered independently, there is value in combining all three methods. The resulting methods are provided as part of a new computationally-efficient R-package, GIAIH, allowing broad application of our statistical framework to other migratory animal systems.