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Landscape Genomics for Wildlife Research

  • Brenna R. Forester
  • Erin L. Landguth
  • Brian K. Hand
  • Niko Balkenhol
Chapter
Part of the Population Genomics book series

Abstract

Landscape genomics investigates how spatial and environmental factors influence geographic patterns of genome-wide genetic variation. Adaptive landscape genomics focuses on how these spatial and environmental processes structure the amount and distribution of selection-driven genetic variation among populations, which ultimately determines how phenotypic variation is arrayed across landscapes. This adaptive landscape genomics approach can be used to identify the causal factors underlying local adaptation and has great potential to guide decision-making in applied wildlife research, especially in light of anthropogenic climate and land use change. Conservation and management applications include delineating conservation units, designing conservation monitoring programs, and predicting changes in the spatial distribution and potential loss of adaptive genomic variation under environmental change. However, there remains great untapped potential for the application of adaptive landscape genomics to wildlife research, including moving beyond correlative genotype-environment association tests. In this chapter, we explore and discuss the potential of adaptive landscape genomics for improving wildlife research, including case studies that illustrate its application in wildlife management and conservation. We also present a comprehensive workflow for adaptive landscape genomics studies in wildlife, including sampling design, genomic and environmental data production, and data analysis. We conclude with avenues and perspectives for future work and ongoing challenges in adaptive landscape genomics.

Keywords

Adaptive capacity Adaptive genetic variation Conservation genomics Genome-wide association studies Genotype-environment associations Natural selection 

Notes

Acknowledgments

Thanks to Kim Andrews and Paul Hohenlohe for helpful comments that improved the chapter. This work was supported in part by funds provided by National Science Foundation grants EF-1442597 and DEB-1340852 to ELL and BRF, NASA grant NNX14AC91G to ELL, and National Science Foundation grant DEB-1639014 and NASA grant NNX14AB84G to BKH.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Brenna R. Forester
    • 1
  • Erin L. Landguth
    • 2
  • Brian K. Hand
    • 3
  • Niko Balkenhol
    • 4
  1. 1.Department of BiologyColorado State UniversityFort CollinsUSA
  2. 2.School of Public and Community Health SciencesUniversity of MontanaMissoulaUSA
  3. 3.Flathead Lake Biological Station, University of MontanaPolsonUSA
  4. 4.Faculty of Forest Sciences, Wildlife SciencesUniversity of GoettingenGöttingenGermany

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