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Conservation Genetics

, 10:441 | Cite as

Why sampling scheme matters: the effect of sampling scheme on landscape genetic results

  • Michael K. Schwartz
  • Kevin S. McKelvey
Research Article

Abstract

There has been a recent trend in genetic studies of wild populations where researchers have changed their sampling schemes from sampling pre-defined populations to sampling individuals uniformly across landscapes. This reflects the fact that many species under study are continuously distributed rather than clumped into obvious “populations”. Once individual samples are collected, many landscape genetic studies use clustering algorithms and multilocus genetic data to group samples into subpopulations. After clusters are derived, landscape features that may be acting as barriers are examined and described. In theory, if populations were evenly sampled, this course of action should reliably identify population structure. However, genetic gradients and irregularly collected samples may impact the composition and location of clusters. We built genetic models where individual genotypes were either randomly distributed across a landscape or contained gradients created by neighbor mating for multiple generations. We investigated the influence of six different sampling protocols on population clustering using program STRUCTURE, the most commonly used model-based clustering method for multilocus genotype data. For models where individuals (and their alleles) were randomly distributed across a landscape, STRUCTURE correctly predicted that only one population was being sampled. However, when gradients created by neighbor mating existed, STRUCTURE detected multiple, but different numbers of clusters, depending on sampling protocols. We recommend testing for fine scale autocorrelation patterns prior to sample clustering, as the scale of the autocorrelation appears to influence the results. Further, we recommend that researchers pay attention to the impacts that sampling may have on subsequent population and landscape genetic results.

Keywords

Landscape genetics Microsatellite Population structure Sample design Sampling 

Notes

Acknowledgements

This research was supported by a Presidential Early Career Award for Science and Engineering to MKS. We thank Sam Cushman, Gordon Luikart, Fred Allendorf, and the Allendorf lab seminar group for thoughts on this work. In addition, we thank Jodi Copeland for help running simulations. We thank Jonathan Pritchard, Eric Anderson, and two anonymous reviewers for comments on earlier drafts of this manuscript.

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Copyright information

© US government employee 2008

Authors and Affiliations

  1. 1.USDA Forest ServiceRocky Mountain Research StationMissoulaUSA

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