Landscape resistance and habitat combine to provide an optimal model of genetic structure and connectivity at the range margin of a small mammal

Abstract

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.

Effects of parasite and historic driven selection on the diversity and structure of a MHC-II gene in a small mammal species (Peromyscus leucopus) undergoing range expansion

“…With the DRB gene, we still detected two distinct clusters, but surprisingly populations from the East Coast appeared closer to populations from the northern shore of the St Lawrence river and the population from the Great Lakes region, than to populations from the southern shore of the St Lawrence river. Further sub-structure was detected among the populations south of the St Lawrence river, with an effect of the Richelieu river, as previously shown using neutral microsatellite markers (Rogic et al 2013;Marrotte et al 2014) and based on the morphological variation in skull shape (Ledevin and Millien 2013).…”

“…A combination of five mitochondrial and nuclear sequences revealed two distinct lineages of P. leucopus in Southern Quebec separated by the Saint-Lawrence River, that were assigned to clades identified in the North-Eastern and Central USA (Fiset et al 2015;Rowe et al 2006). At a smaller geographic scale, major landscape barriers such as rivers and roads are limiting gene flow and modulating the pattern of ongoing range expansion in P. leucopus (Rogic et al 2013;Marrotte et al 2014;Leo et al submitted). The monitoring of P. leucopus dispersal is of prime interest because this species is often considered as being the main reservoir for Lyme disease in eastern-North America (Ostfeld 2011).…”

The relationship between least-cost and resistance distance

“…Finally, aggregation is quite common for these types of analyses (e.g., [6,39]). Data manipulation eventually affects the relationship between the methods (Fig 6).…”

“…Our reasoning was that aggregation in any form should lead to a reduction in the number of pathways between focal points and subsequently both methods converge with increasing aggregation. Aggregation is very commonly used, since the algorithms using least-cost distance are processor and memory intensive, leading many researchers to spatially aggregate their data at coarse resolutions [6], [39], [40]. …”

Isolation by resistance analysis reveals major barrier effect imposed by the Tsinling Mountains on the Chinese wood frog

“…Lee‐Yaw et al , ; Marrotte, Gonzalez & Millien, ; Braaker et al , ; Olah et al , ), although sensitivity analyses are common across several methodologies (e.g. Lee‐Yaw et al , ; Marrotte et al , ). Thus, we posit that sensitivity analyses can further improve models previously assumed to be optimized in term of biological relevance.…”