Spatial genetic structure of Rocky Mountain bighorn sheep (<i>Ovis canadensis canadensis</i>) at the northern limit of their native range
Bibliographic record
Abstract
The Canadian Rocky Mountains are one of the few places on Earth where the spatial genetic structure of wide-ranging species has been relatively unaffected by anthropogenic disturbance. We characterized the spatial genetic structure of Rocky Mountain bighorn sheep (Ovis canadensis canadensis Shaw, 1804) in the northern portion of their range. Using microsatellites from 1495 individuals and mitochondrial DNA sequences from 188 individuals, we examined both broad- and fine-scale spatial genetic structure, assessed sex-biased gene flow within the northern portion of the species range, and identified geographic patterns of genetic diversity. We found that broad-scale spatial genetic structure was consistent with barriers to movement created by major river valleys. The fine-scale spatial genetic structure was characterized by a strong isolation-by-distance pattern, and analysis of neighborhood size using spatial autocorrelation indicated gene flow frequently occurred over distances of up to 100 km. However, analysis of sex-specific spatial autocorrelation and analysis of mitochondrial haplotype distributions failed to detect any evidence of sex-biased gene flow. Finally, our analyses reveal decreasing genetic diversity with increasing latitude, consistent with patterns of post-glacial recolonization of the Rocky Mountains.
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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 | 0.000 | 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 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".