Unravelling landscape variables with multiple approaches to overcome scarce species knowledge: a landscape genetic study of the slow worm
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Landscape genetics was developed to detect landscape elements shaping genetic population structure, including the effects of fragmentation. Multifarious environmental variables can influence gene flow in different ways and expert knowledge is frequently used to construct friction maps. However, the extent of the migration and the movement of single individuals are frequently unknown, especially for non-model species, and friction maps only based on expert knowledge can be misleading. In this study, we used three different methods: isolation by distance (IBD), least-cost modelling and a strip-based approach to disentangle the human implication in the fragmentation process in the slow worm (Anguis fragilis), as well as the specific landscape elements shaping the genetic structure in a highly anthropized 16 km2 area in Switzerland. Friction maps were constructed using expert opinion, but also based on the combination of all possible weightings for all landscape elements. The IBD indicated a significant effect of geographic distance on genetic differentiation. Further approaches demonstrated that highways and railways were the most important elements impeding the gene flow in this area. Surprisingly, we also found that agricultural areas and dense forests seemed to be used as dispersal corridors. These results confirmed that the slow worm has relatively unspecific habitat requirements. Finally, we showed that our models based on expert knowledge performed poorly compared to cautious analysis of each variable. This study demonstrated that landscape genetic analyses should take expert knowledge with caution and exhaustive analyses of each landscape element without a priori knowledge and different methods can be recommended.
KeywordsPopulation genetics Microsatellite markers 454 Sequencing Anguis fragilis Landscape genetics Least-cost path
Samples were taken with the permission of the Conservation de la Faune du Canton de Vaud (Switzerland). Field work was made possible by the numerous owners allowing the installation of plates on their lot. We acknowledge Matthieu Raemy and Hans-Peter Rusterholz for their help in the laboratory. In addition, discussions with Jean-Claude Monney greatly improved the manuscript.
- Goudet J (1995) FSTAT (Version 1.2): a computer program to calculate F-statistics. J Hered 86:485–486Google Scholar
- Lehmann A, Maggini R, Lehmann C (2000) Downscaling land use using nearest neighbours and expert systems. unpublishedGoogle Scholar
- Monney JC, Meyer A (2005) Liste rouge des espèces menacées en Suisse: reptiles. http://www.bafu.admin.ch/publikationen/publikation/00918/index.html?lang=de
- R Development Core Team (2011) R: a language and environment for statistical computing. R Development Core Team, ViennaGoogle Scholar
- Ray N, Excoffier L (2003) MANTELN 1.1. Software for the computation of mantel tests for multiple matrices. Genetics and Biometry, Laboratory University of Geneva, SwitzerlandGoogle Scholar
- Sala OE, Chapin FS, Armesto JJ, Berlow E, Bloomfield J, Dirzo R, Huber-Sanwald E, Huenneke LF, Jackson RB, Kinzig A, Leemans R, Lodge DM, Mooney HA, Oesterheld M, Poff NL, Sykes MT, Walker BH, Walker M, Wall DH (2000) Biodiversity—global biodiversity scenarios for the year 2100. Science 287:1770–1774PubMedCrossRefGoogle Scholar
- Völkl W, Alfermann D (2007) Die blindschleiche: die vergessene echse. Laurenti, BielefeldGoogle Scholar