Conservation Genetic Analysis of Blanding’s Turtles across Ohio, Indiana, and Michigan
Bibliographic record
Abstract
The Blanding’s Turtle (Emydoidea blandingii) is a species in need of conservation across much of its geographic range. A key aspect to conserving a species is understanding the genetic diversity and population structure across the landscape. Several researchers have focused on E. blandingii genetic diversity in the northeastern United States, Canada, and parts of the Midwestern United States; however, little investigation has been carried out on localities within the Great Lakes region of Indiana, Michigan, and Ohio. Understanding genetic trends within this region will assist with conservation planning by documenting levels of genetic variation within and among localities and developing hypotheses that have led to the observed patterns. We used 14 microsatellite loci to characterize the genetic diversity of E. blandingii in 16 localities in Indiana, Ohio, and southeast Michigan (with one northwestern locality). Overall, genetic diversity within localities tended to be high and little differentiation was observed among sample localities. No consistent evidence of bottlenecks was detected, and effective population size (Ne) estimates were generally high, but likely biased by sample size. A minimum of two clusters, and as many as seven clusters in a hierarchical analysis, were identified using three methods for grouping individuals (STRUCTURE, TESS3r, and sPCA). A correlation between geographic distance and genetic differentiation (isolation by distance) was observed. The long lifespan and historic gene flow of E. blandingii is likely responsible for the observed genetic diversity and lack of differentiation between localities. This should not suggest that populations are secure in the Great Lakes Region. Modeling aimed at estimating future genetic variation in populations under realistic demographic scenarios indicates that many localities in the region are likely to be vulnerable to genetic loss in the next 200 years.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".