Simulating the spread of selection-driven genotypes using landscape resistance models for desert bighorn sheep

Abstract

Landscape genetic studies based on neutral genetic markers have contributed to our understanding of the influence of landscape composition and configuration on gene flow and genetic variation. However, the potential for species to adapt to changing landscapes will depend on how natural selection influences adaptive genetic variation. We demonstrate how landscape resistance models can be combined with genetic simulations incorporating natural selection to explore how the spread of adaptive variation is affected by landscape characteristics, using desert bighorn sheep (Ovis canadensis nelsoni) in three differing regions of the southwestern United States as an example. We conducted genetic sampling and least-cost path modeling to optimize landscape resistance models independently for each region, and then simulated the spread of an adaptive allele favored by selection across each region. Optimized landscape resistance models differed between regions with respect to landscape variables included and their relationships to resistance, but the slope of terrain and the presence of water barriers and major roads had the greatest impacts on gene flow. Genetic simulations showed that differences among landscapes strongly influenced spread of adaptive genetic variation, with faster spread (1) in landscapes with more continuously distributed habitat and (2) when a pre-existing allele (i.e., standing genetic variation) rather than a novel allele (i.e., mutation) served as the source of adaptive genetic variation. The combination of landscape resistance models and genetic simulations has broad conservation applications and can facilitate comparisons of adaptive potential within and between landscapes.

Fig 1. Genetic sampling locations.

Black dots show where unique genotypes were sampled in each of three study regions: (A) southern Mojave, n = 378; (B) Grand Canyon, n = 252; (C) Death Valley, n = 225. Red and blue lines show major barriers to dispersal (highways and waterways, respectively). Green polygons show occupied bighorn habitat within which individuals were randomly located for CDPOP simulations of gene flow. Note that many black dots (individual locations) overlap at the scale of the map.

Fig 2. Simulated change in adaptive allele frequency through time under the novel allele scenario.

Results are shown for multiple selection strengths and dispersal thresholds in the DEVA, GRCA, and MOJA regions. Simulations were initiated with a small cluster of adaptive alleles at the center of each region. Rows and columns correspond to selection strengths and dispersal thresholds, respectively. Solid and dashed lines represent means and 95 percent confidence limits, respectively, from 50 MC replicates per region.

Fig 3. Simulated change in adaptive allele frequency through time under the pre-existing allele scenario.

Results are shown for multiple selection strengths and dispersal thresholds in the DEVA, GRCA, and MOJA regions. Simulations were initiated with the adaptive allele randomly distributed throughout each region at 5 percent frequency. Rows and columns correspond to selection strengths and dispersal thresholds, respectively. Solid and dashed lines represent means and 95 percent confidence limits, respectively, from 50 MC replicates per region.

Fig 4. Simulated spatial spread of adaptive allele in different regions over 100 years.

Colored dots represent individual locations, and black polygons represent national park boundaries. Color gradient reflects proportion of MC replicates in which adaptive allele is present (≥1 copy) in each individual location at year 100 for each region and each scenario, assuming strong selection and medium dispersal threshold.