A spatial genomic approach identifies time lags and historic barriers to gene flow in a rapidly fragmenting Appalachian landscape
Maigret, Thomas; Cox, John; Weisrock, David (2020), A spatial genomic approach identifies time lags and historic barriers to gene flow in a rapidly fragmenting Appalachian landscape, Dryad, Dataset, https://doi.org/10.5061/dryad.z08kprr8v
The resolution offered by genomic data sets coupled with recently developed spatially informed analyses are allowing researchers to quantify population structure at increasingly fine temporal and spatial scales. However, both empirical research and conservation measures have been limited by questions regarding the impacts of data set size, data quality thresholds, and the time scale at which barriers to gene flow become detectable. Here, we used restriction site associated DNA sequencing to generate a 2,140 SNP data set for the copperhead snake (Agkistrodon contortrix) and address the population genomic impacts of recent and widespread landscape modification across an approximately 1000 km2 region of eastern Kentucky, USA. Nonspatial population-based assignment and clustering methods supported little to no population structure. However, using individual-based spatial autocorrelation approaches we found evidence for genetic structuring which closely follows the path of a historically important highway which experienced high traffic volumes from ca. 1920 to 1970 before losing most traffic to a newly constructed alternate route. We found no similar spatial genomic signatures associated with more recently constructed highways or surface mining activity, though a time lag effect may be responsible for the lack of any emergent spatial genetic patterns. Subsampling of our SNP data set suggested that similar results could be obtained with as few as 250 SNPs, and a range of thresholds for missing data exhibited limited impacts on the spatial patterns we detected. While we were not able to estimate relative effects of land uses or precise time lags, our findings highlight the importance of temporal factors in landscape genetics approaches, and suggest the potential advantages of genomic data sets and fine-scale, spatially informed approaches for quantifying subtle genetic patterns in temporally complex landscapes.