Genetic Patterns as a Function of Landscape Process: Applications of Neutral Genetic Markers for Predictive Modeling in Landscape Ecology

Murphy, Melanie A.

doi:10.1007/978-1-4419-7390-0_9

Cited by 3 publications

(1 citation statement)

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“…Cushman et al 2006) resistance surfaces. To date however, there are few examples where model optimization methods have used genetic data to empirically select habitat variables and develop parameter estimates which can then be used to build a resistance surface (but see Dyer et al 2010;Murphy & Evans 2011). Thus, the limitations inherent in using nongenetic field data or expert opinion to derive genetic resistance surfaces remain apparent in many studies (Spear et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Using a genetic network to parameterize a landscape resistance surface for fishers, Martes pennanti

2011

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Knowledge of dispersal-related gene flow is important for addressing many basic and applied questions in ecology and evolution. We used landscape genetics to understand the recovery of a recently expanded population of fishers (Martes pennanti) in Ontario, Canada. An important focus of landscape genetics is modelling the effects of landscape features on gene flow. Most often resistance surfaces in landscape genetic studies are built a priori based upon nongenetic field data or expert opinion. The resistance surface that best fits genetic data is then selected and interpreted. Given inherent biases in using expert opinion or movement data to model gene flow, we sought an alternative approach. We used estimates of conditional genetic distance derived from a network of genetic connectivity to parameterize landscape resistance and build a final resistance surface based upon information-theoretic model selection and multi-model averaging. We sampled 657 fishers from 31 landscapes, genotyped them at 16 microsatellite loci, and modelled the effects of snow depth, road density, river density, and coniferous forest on gene flow. Our final model suggested that road density, river density, and snow depth impeded gene flow during the fisher population expansion demonstrating that both human impacts and seasonal habitat variation affect gene flow for fishers. Our approach to building landscape genetic resistance surfaces mitigates many of the problems and caveats associated with using either nongenetic field data or expert opinion to derive resistance surfaces.

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