Data from: Landscape resistance and habitat combine to provide an optimal model of genetic structure and connectivity at the range margin of a small mammal
Marrotte, Robby R., McGill University
Gonzalez, Andrew, McGill University
Millien, Virginie, McGill University
Published Jun 27, 2014 on Dryad.
Cite this dataset
Marrotte, Robby R.; Gonzalez, Andrew; Millien, Virginie (2014). Data from: Landscape resistance and habitat combine to provide an optimal model of genetic structure and connectivity at the range margin of a small mammal [Dataset]. Dryad. https://doi.org/10.5061/dryad.5sk8g
We evaluated the effect of habitat and landscape characteristics on the population genetic structure of the white-footed mouse. We develop a new approach that uses numerical optimization to define a model that combines site differences and landscape resistance to explain the genetic differentiation between mouse populations inhabiting forest patches in southern Québec. We used ecological distance computed from resistance surfaces with Circuitscape to infer the effect of the landscape matrix on gene flow. We calculated site differences using a site index of habitat characteristics. A model that combined site differences and resistance distances explained a high proportion of the variance in genetic differentiation and outperformed models that used geographical distance alone. Urban and agriculture related land uses were, respectively, the most and the least resistant landscape features influencing gene flow. Our method detected the effect of rivers and highways as highly resistant linear barriers. The density of grass and shrubs on the ground best explained the variation in the site index of habitat characteristics. Our model indicates that movement of white-footed mouse in this region is constrained along routes of low resistance. Our approach can generate models that may improve predictions of future northward range expansion of this small mammal.
RRM_Optimization: datasets. CS_ini.ini: Circuitscape template option file; fst.txt: Genetic data from Rogic et al. (2013); landscape7.asc: Land use raster with 7 classes; patches_centroids_100.asc: Mice population raster. workdir (EMPTY). Example.R: Main script to run the optimization. function_library.R: My function library. It is called from the