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Genetic Mapping of Thermotolerance Differences Between Species of Saccharomyces Yeast via Genome-Wide Reciprocal Hemizygosity Analysis
Published on: August 12, 2019
Testing concordance and conflict in spatial replication of landscape genetics inferences
Van Wishingrad1, Robert C Thomson1
1School of Life Sciences, University of Hawai'i at Mānoa, Honolulu, Hawai'i, USA.
None:
The degree to which landscape genetics findings can be extrapolated to different areas of a species range is poorly understood. Here, we used a broadly distributed ectothermic lizard (Sceloporus occidentalis, Western Fence lizard) as a model species to evaluate the full role of topography, climate, vegetation, and roads on dispersal and genetic differentiation. We conducted landscape genetics analyses with a total of 119 individuals in five areas within the Sierra Nevada mountain range. Genetic distances calculated from thousands of ddRAD markers were used to optimize landscape resistance surfaces and infer the effects of landscape and topographic features on genetic connectivity. Across study areas, we found a great deal of consistency in the primary environmental gradients impacting genetic connectivity, along with some site-specific differences, and a range in the proportion of genetic variance explained by environmental factors across study sites. High-elevation colder areas were consistently found to be barriers to gene flow, as were areas of high ruggedness and slope. High temperature seasonality and high precipitation during the winter wet season also presented a substantial barrier to gene flow in a majority of study areas. The effect of other landscape variables on genetic differentiation was more idiosyncratic and depended on specific attributes at each site. Across study areas, canyon valleys were always implicated as facilitators to dispersal and key features linking populations and maintaining genetic connectivity, though the relative importance varied in different areas. We emphasize that spatial data layers are complex and multidimensional, and careful consideration of spatial data correlation structure and robust analytic frameworks will be critical to our continued understanding of spatial genetics processes.
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