2017 ESA Annual Meeting (August 6 -- 11)

COS 138-7 - Simulating the spread of selection-driven genotypes using landscape resistance models for desert bighorn sheep

Thursday, August 10, 2017: 10:10 AM
C122, Oregon Convention Center
Tyler G. Creech1,2, Clinton Epps2, Erin L. Landguth3, John D. Wehausen4, Rachel Crowhurst2, Brandon Holton5 and Ryan Monello6, (1)Center for Large Landscape Conservation, Bozeman, MT, (2)Department of Fisheries and Wildlife, Oregon State University, Corvallis, OR, (3)Division of Biological Sciences, University of Montana, Missoula, MT, (4)White Mountain Research Station, University of California, Bishop, CA, (5)Grand Canyon National Park, National Park Service, Grand Canyon, AZ, (6)Pacific Islands Network, National Park Service, Hilo, HI
Background/Question/Methods

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. One potential approach for bridging this gap and exploring how the spread of adaptive variation is affected by landscape characteristics is to combine landscape resistance models with genetic simulations incorporating natural selection. We demonstrated this approach using desert bighorn sheep (Ovis canadensis nelsoni) in three regions of the southwestern United States with different habitat configuration and factors influencing landscape resistance. 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. We considered two scenarios under which adaptive genetic variation could arise and spread throughout a region to facilitate adaptation: (1) a novel allele favored by selection is introduced at one location (e.g., by mutation) and spreads outward; and (2) an allele already widely distributed at low frequency becomes selectively advantageous due to environmental change and subsequently increases in frequency.

Results/Conclusions

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. We observed faster spread in landscapes with more continuously distributed habitat, and 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. Simulations suggested that the spread of adaptive genetic variation is likely to occur slowly for desert bighorn sheep, even in places where connectivity has not been compromised and natural selection strongly favors an adaptive allele. The combination of landscape resistance models and genetic simulations has broad conservation applications, such as facilitating comparisons of adaptive potential between landscapes and identifying optimal locations to translocate individuals possessing favorable genotypes in order to maximize spread of adaptive alleles.