RESEARCH ARTICLE Dispersal promotes high gene flow among Canada lynx populations across mainland North America
Bibliographic record
Abstract
Abstract The amount and extent of dispersal can have a large effect on the evolutionary trajectory, dynamics and structure of populations. Thus, understanding patterns of genetic structure provide information about the needs and approaches for population management and species con-servation. To date studies addressing the population structure of Canada lynx (Lynx canadensis) have been surprisingly equivocal, despite a large amount of research quantifying population cyclicity and synchrony and the species ’ species at-risk status in the contiguous United States and eastern provinces of Canada. Here we use 17 microsatellite loci to conduct a large-scale genetic struc-turing assessment for Canada lynx, including most of its geographic range from Alaska to Newfoundland. We found large differentiation between lynx populations on the island of Newfoundland and those on the mainland. Yet, contrary to previous studies we found little genetic differentiation (FST, Dest, RST) owing to the Rocky Mountains, but some evidence of a subtle gene flow restriction between Ontario and Manitoba as previously proposed to be the result of a climatic barrier. Bayesian clustering analysis, however, only suggested two genetic clusters, one consisting of lynx from Newfoundland, and the other consisting of lynx from the rest of the North American range. Because Canada lynx are harvested for fur across most of their range, our results are informative for effective management strategies (e.g., defining management units) aimed at ensuring long-term population connectivity and species persistence.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".