Life-history traits and time needed to detect barriers in landscape genetics

Authors and Affiliations: 

V. Helfer1, S. Johnson2, E. L. Landguth3

1 Department of Organismic Biology, University of Salzburg, Hellbrunnerstrasse 34, 5020 Salzburg, Austria

2 USDA/APHIS/WS/National Wildlife Research Center, 4101 LaPorte Ave, Fort Collins, CO 80521

3 Division of Biological Sciences, University of Montana, 32 Campus Drive, Missoula, MT 59812, USA


Landscape genetics studies attempt to describe how spatial genetic patterns in natural populations respond to landscape change and structure. However, only a few studies have attempted to test the reliability of genetic analytical tools to detect the impact of recent landscape changes on population genetic structure. Computer simulation tools can provide a powerful framework for studying complex systems such as in landscape genetics (Epperson et al 2010). For example, recent simulation studies evaluated several analytical techniques in detecting barriers to gene flow and/or population boundaries (e. g. Blair et al. 2012; Landguth et al. 2010 Safner et al. 2011). These studies were dealing with non-overlapping generation systems and it has remained unclear how life-history traits might affect the impact of landscape changes on spatio-temporal genetic patterns. In this study, we investigated how natural life-history traits influence spatial population genetic structure and how fast the latter will get modified after the onset of new landscape barriers. Our simulation project has assessed a suit of partial to complete barrier landscapes and their influence on population genetic structure for different life-history strategies. We focused on the effect of species longevity (1, 5 and 20 years), mortality rate (100% = non-overlapping generation system versus 1% or 20% mortality rate = overlapping generation systems), dispersal strategies (male-biased, female-biased or unbiased dispersal rates; isolation-by-distance and panmixia), and reproductive mode (monogamy, polygyny). A factorial design allowed evaluating the effect of each parameter, in a spatially explicit individual-based simulation framework (CDPOP). The power and sensitivity of several analytical methods to detect barriers to gene flow were evaluated: 1) Mantel’s r using the individual-based genetic metric Dps, 2) population-based statistics (FST), 3) non-spatial (STRUCTURE) and spatial (GENELAND) Bayesian clustering approaches. We will discuss the utility and efficiency of the various analytical methods for detecting partial to complete barriers to gene flow, depending of specific life-history traits.


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Landguth EL, Cushman SA, Schwartz MK, et al. (2010) Quantifying the lag time to detect barriers in landscape genetics. Mol Ecol.

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