Comparison of the Conservation Genetics of Blanding’s Turtles (Emydoidea blandingii) in the Eastern Great Lakes & Northeast Regions
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".