A spatial genomic approach identifies time lags and historic barriers to gene flow in a rapidly fragmenting Appalachian landscape

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.