• Author: Bothwell, H., United States

    Environmental Genetics and Genomics Laboratory, Department of Biological Sciences, Northern Arizona University, United States

  • Author: Bisbing, S., United States

    Graduate Degree Program in Ecology, Colorado State University, United States

  • Author: Therkildsen, Nina Overgaard

    Section for Population Ecology and Genetics, National Institute of Aquatic Resources, Technical University of Denmark, Sektion for Populationsgenetik Vejlsøvej 39, 8600, Silkeborg, Denmark

  • Author: Crawford, L., Canada

    Department of Biology, Western University, Canada

  • Author: Holderegger, R., Switzerland

    WSL Swiss Federal Research Institute, Switzerland

  • Author: Alvarez, N., Switzerland

    Department of Ecology and Evolution, Biophore Building University of Lausanne, Switzerland

  • Author: Manel, S., France

    Laboratoire Population Environnement Développement, Université Aix-Marseille, France

View graph of relations

It is generally accepted that most plant populations are locally adapted. Yet, understanding how environmental forces give rise to adaptive genetic variation is a challenge in conservation genetics and crucial to the preservation of species under rapidly changing climatic conditions. Environmental variation, phylogeographic history, and population demographic processes all contribute to spatially structured genetic variation, however few current models attempt to separate these confounding effects. To illustrate the benefits of using a spatially-explicit model for identifying potentially adaptive loci, we compared outlier locus detection methods with a recently-developed landscape genetic approach. We analyzed 157 loci from samples of the alpine herb Gentiana nivalis collected across the European Alps. Principle coordinates of neighbor matrices (PCNM), eigenvectors that quantify multi-scale spatial variation present in a data set, were incorporated into a landscape genetic approach relating AFLP frequencies with 23 environmental variables. Four major findings emerged. 1) Fifteen loci were significantly correlated with at least one predictor variable (R adj 2 > 0.5). 2) Models including PCNM variables identified eight more potentially adaptive loci than models run without spatial variables. 3) When compared to outlier detection methods, the landscape genetic approach detected four of the same loci plus 11 additional loci. 4) Temperature, precipitation, and solar radiation were the three major environmental factors driving potentially adaptive genetic variation in G. nivalis. Techniques presented in this paper offer an efficient method for identifying potentially adaptive genetic variation and associated environmental forces of selection, providing an important step forward for the conservation of non-model species under global change
Original languageEnglish
JournalConservation Genetics
Publication date2013
Volume14
Journal number2
Pages467-481
ISSN1566-0621
DOIs
StatePublished
CitationsWeb of Science® Times Cited: 1
Download as:
Download as PDF
Select render style:
APAAuthorCBEHarvardMLAStandardVancouverShortLong
PDF
Download as HTML
Select render style:
APAAuthorCBEHarvardMLAStandardVancouverShortLong
HTML
Download as Word
Select render style:
APAAuthorCBEHarvardMLAStandardVancouverShortLong
Word

ID: 12310416