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<p>  </p> <p>Blanding’s Turtle populations face direct threats to their survival. To help protect populations, habitats that can best support Blanding’s Turtle populations need to be identified across their range. Blanding’s Turtles have been a difficult to detect species and may be present at a site even if not detected during targeted surveys. Additionally, Blanding’s Turtles may be present at a site but may have little to no recruitment so additional measures of site suitability beyond species presence are needed to determine more suitable or higher quality habitats. In my research, I attempt to determine suitability of sites for Blanding’s Turtles across Michigan and Ohio using data collected from rapid assessment protocols fit into single season occupancy models with wetland and upland landcover types as co-variates of occupancy. To further determine the suitability of sites based on these data, I created single season occupancy models for juvenile Blanding’s Turtles and used N-mixture abundance modelling to determine relative abundance of Blanding’s Turtles at a site using the same landcovers as covariates of occupancy and abundance. Both modelling frameworks also allowed me to include detection covariates that could increase Blanding’s Turtle detection in future surveys. </p> <p>Detection was largely influenced by Julian date with the highest probability of detection occurring from mid-May through late June. Length of trapping surveys was also found to influence Blanding’s Turtle detection with a substantial decrease in daily trap capture rates by the fourth trap night of a survey. Michigan occupancy and abundance models found that the most suitable sites in Michigan would have high percentages of high-quality upland forest and woody wetland landcovers, with the percentage of open water supporting the occupancy of turtles but having no discernable effect on abundance. Total upland forest also significantly increased the probability of juvenile occupancy in Michigan. In Michigan, I also observed that survey method can greatly influence the estimates of occupancy and abundance, and I determined that visual surveys cannot accurately determine these estimates. The heavily disturbed nature of Ohio’s landscape took away from the predictive power of landcovers used in my research for Blanding’s Turtle occupancy and abundance. The vast difference between occupied habitats in Michigan and Ohio also takes away from the predictive power of the regional level model and relative abundance of Blanding’s Turtle populations cannot be accurately determined at this scale using the spatial covariates I included. However, total undisturbed forest and total wetland proved to be positive covariates of Blanding’s Turtle abundance and occupancy for adult and juvenile turtles across both states, but the habitats used in each state vary greatly so future conservation decisions should be made on the state level as largest spatial scale. Using my models for Michigan suitable sites can be determined within the state and compare relative abundance between sites to determine healthier populations. For future analysis in Ohio, different, smaller scales spatial covariates should be used to explain differences in occupancy and abundance between sites.</p>

<p dir="ltr">The Blanding’s Turtle (<i>Emydoidea blandingii</i>) is a species of conservation needs that ranges across the U.S Midwest and Northeast, and Ontario/Nova Scotia, Canada. The species has experienced several range expansions and contractions due to glacial dynamics and industrial landscape changes, which have led to population isolation and bottlenecks. Understanding genetic variation and population structure across the species’ geographic range is essential for conservation efforts to maintain and restore populations. While several regional studies have evaluated genetic variation in <i>E. blandingii</i>, there has been little population sampling across Michigan and limited attempts to directly compare genetic variation across extensively sampled populations within both its main range and disjunct segments in the Northeast U.S. In this study, I utilized 12 microsatellite loci to directly compare the genetic diversity of <i>E. blandingii</i> across 153 localities in a portion of the Great Lakes and the Northeast of the range. Additionally, 13 microsatellite loci were used to assess genetic diversity across 92 localities in Indiana, Ohio, and Michigan, including further sampling within Michigan. My findings confirmed higher genetic diversity within the Great Lakes compared to the Northeast and revealed greater genetic differentiation in the Northeast than in the Great Lakes. Population structure in both regions was influenced by distance (IBD) and watersheds, with a more pronounced effect in the Northeast. Using four different genetic clustering approaches (PCA, sPCA, STRUCTURE, and TESS3r), I identified three range-wide clusters, three within the Northeast, and three within the Great Lakes. Within the Great Lakes, estimates of effective population size (<i>N</i>e) were high at both the population and watershed level, although influenced by sample size. The long lifespans of <i>E. blandingii</i> likely contribute to high levels of genetic diversity, while post-glacial gene flow across the landscape has resulted in low to moderate levels of differentiation within the regions. This study highlights poorly understood population structure and differences in genetic diversity between regions. Although Great Lakes populations are less isolated and more genetically diverse than those in the Northeast, this does not suggest that they are secure. Both regions face potential genetic loss over the next century, requiring further management implications to mitigate any further decline.</p>