Population structure and dispersal of wolves in the Canadian Rocky Mountains
Bibliographic record
Abstract
In the Canadian Rocky Mountains, the gray wolf (<it>Canis lupus</it>) has experienced range contractions and expansions, which can greatly affect pack stability as well as population structure. In addition, this area has a highly heterogeneous landscape that may form barriers to dispersal. To understand factors affecting pack structure and large-scale gene flow across the Rocky Mountains, we examined wolf genetic structure using 1,981 noninvasive and invasively collected samples. We sampled over 44 packs in Alberta and British Columbia and, from these, identified 540 individuals based on 12 microsatellites. Relatedness of individuals within packs was greater than between packs, and female relatedness was greater than males suggesting strong pack structure and female philopatry. Relatedness within packs was greater near major roads suggesting decreased dispersal from natal packs with proximity to roads. Across the study area, 2 significantly differentiated genetic clusters were identified, corresponding to a north/south split. Landcover distance was a significant correlate for 2 of 4 genetic distance measures, where packs in the north were in areas of dense coniferous forest, while packs in the south were primarily in open coniferous forest. These landcover differences suggest natal associations or could relate to prey distribution. Fine-scale investigation of pack dynamics across this continuous distribution, together with large-scale estimators of population structure, highlights different drivers of gene flow at the pack and population level.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".