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RESEARCH ARTICLE Why sampling scheme matters: the effect of sampling scheme

By Michael K. Schwartz, Æ Kevin, S. Mckelvey, M. K. Schwartz and K. S. Mckelvey

Abstract

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 The U.S. Government’s right to retain a non-exclusive, royalty-free license in and to any copyright is acknowledged

Year: 2011
OAI identifier: oai:CiteSeerX.psu:10.1.1.192.3029
Provided by: CiteSeerX
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