Canada lynx (<i>Lynx canadensis</i>) gene flow across a mountain transition zone in western North America
Bibliographic record
Abstract
Mountain ecotones have the potential to cause multiple patterns in divergence, from simple barrier effects to more fundamental ecological divergence. Most work in mountain ecotones in North America has focused on reinforcement between refugial populations, making prediction of how mountains impact species that are not restricted to separate glacial refugia remains difficult. This study focused on the Canada lynx (Lynx canadensis Kerr, 1792), a highly mobile felid considered to be a habitat and dietary specialist. Specifically, we used 14 microsatellite loci and landscape genetic tools to investigate if the Rocky Mountains and associated climatic transitions influence lynx genetic differentiation in western North America. Although lynx exhibited high gene flow across the region, analyses detected structuring of neutral genetic variation across our study area. Gene flow for lynx most strongly related to temperature and elevation compared with other landscape variables (terrain roughness, percent forest cover, and habitat suitability index) and geographic distance alone. Overall, genetic structure in lynx is most consistent with barrier effects created by the Rocky Mountains rather than ecological divergence. Furthermore, warmer temperatures had a measurable impact on gene flow, which suggests connectivity may further decrease in peripheral or fragmented populations as climate warms.
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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".