Comparing the population structure of the specialist Butler’s Gartersnake (<i>Thamnophis butleri</i>) and the generalist Eastern Gartersnake (<i>Thamnophis sirtalis sirtalis</i>) in Ontario (Canada) and Michigan (USA)
Bibliographic record
Abstract
Species differing in life-history attributes vary in their responses to features within a shared landscape. We evaluated genetic structure of sympatric gartersnake species in southwestern Ontario (Canada) and southeastern Michigan (USA), where habitat fragmentation is high due to agriculture and urbanization. We surveyed genetic structure of a habitat specialist, Butler’s Gartersnake (Thamnophis butleri (Cope, 1889)), and a habitat generalist, Eastern Gartersnake (Thamnophis sirtalis sirtalis (Linnaeus, 1758)), using DNA microsatellites. Bayesian clustering, discriminant analysis of principal components, and pairwise population comparisons revealed genetic differentiation among three major regional clusters of Butler’s Gartersnake with evidence of further division within one. Genetic clustering of Butler’s Gartersnake suggest that inhospitable habitat limits dispersal. Eastern Gartersnakes showed less structure, with assignment tests implying a single genetic cluster. We found positive significant Mantel’s r for both species in the smallest distance class (<15 km), but significant isolation by distance for Butler’s Gartersnake only. These findings together imply that connectivity for Eastern Gartersnakes is less impacted by habitat loss and fragmentation or that we were less able to detect their effects. Our study shows the value of multispecies comparisons in studies seeking to understand the underlying causes of genetic structure in natural populations.
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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 | 0.000 |
| 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) | 0.001 | 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".