In the northwest, however, landscape genetic models did not perform better than the null isolation-by-distance model. Individual-based least cost path modeling showed limited influence of landscape variables on gene flow, with weak effects of variation in elevation in the northeast. This human disturbance also may have contributed to weak genetic structuring detected between the northeastern and northwestern part of the study area. We also found possible signs of recent bottlenecks, perhaps resulting from forest clearing for dairy farming in the central part of the study area. Consistent with previous work, we found low overall genetic diversity, likely owing to a combination of founder effects and extreme weather events thousands of years ago that likely caused large-scale population declines. Strong genetic structure was found between devils in the northern part of the study area and those found south of Macquarie Harbor, with weaker structure found between the northeastern and northwestern portion of our study area. Positive spatial autocorrelation declined at about 40 km, in contrast to 80 km in eastern populations, highlighting the need for range-wide genetic studies. In contrast to previous genetic studies on Tasmanian devils showing evidence for two genetic populations island-wide, our genetic based assignment tests and spatial principal components analyses suggest at least two, and possibly three, populations in a study area that is approximately 15% of the size of the overall species’ geographic range. Here, we present a landscape genetic analysis in the last remaining uninfected parts of the Tasmanian devil’s geographic range to: describe population genetic structure, characterize genetic diversity, and test the influence of landscape variables on Tasmanian devil gene flow to assess the potential for disease spread. The Tasmanian devil is threatened with extinction by a nearly 100% fatal transmissible cancer, which has spread across 95% of the species’ geographic range in 20 years. Landscape genetics can be used as a tool to predict disease emergence and spread. Emerging infectious diseases are increasingly recognized in species’ declines and extinctions.

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