Recent technological advancements in next generation sequencing techniques, enabling the use of thousands of genetic markers across an individual's genome, and continued improvements to the spatial resolution and information content of remote sensing data, present a unique opportunity to investigate the finest geographic scale at which genetic structuring within and between populations becomes detectable. However, in order to exploit the integration of both high resolution genetic and geospatial data, an understanding of the uncertainties associated with both data sets, and the accuracy requirements of landscape genetic analyses of geospatial models, is required. In this thesis, I begin by highlighting some sources of uncertainty and bias in landscape models derived from high resolution LiDAR data which have the potential to impact downstream analyses of the correlation between patterns of genetic structuring and landscape heterogeneity. I then investigate the patterns of genetic structuring between breeding aggregations of the spring peeper, Pseudacris crucifer.

I discovered that while different methodologies to derive land cover from airborne LiDAR data may result in similar overall accuracy, the configuration of landscape heterogeneity within the landscape, and class-specific recall and precision differed between models. A significant finding is that some classification methodologies did not accurately represent the contiguity of a road, which is often considered a putative barrier for amphibians. While ddRADseq could not resolve signatures of fine-scale genetic differentiation between breeding aggregations of Pseudacris crucifer within distances of <10 km, some differentiation between sampling locations separated by 60 km was detected. This grants some insight into the scale of genetic structuring of Pseudacris crucifer, and provides some representation of hylids in the population genetic literature. Ultimately this thesis highlights the importance for communication and collaboration between biologists and geospatial scientists to ensure the optimal modeling of heterogeneity with landscapes to address a wider array of applications in ecology and landscape genetics, as well as an accurate representation of uncertainty in geospatial models in ecological and landscape genetic analyses.