Differentiation of tundra/taiga and boreal coniferous forest wolves: genetics, coat colour and association with migratory caribou
Bibliographic record
Abstract
The grey wolf has one of the largest historic distributions of any terrestrial mammal and can disperse over great distances across imposing topographic barriers. As a result, geographical distance and physical obstacles to dispersal may not be consequential factors in the evolutionary divergence of wolf populations. However, recent studies suggest ecological features can constrain gene flow. We tested whether wolf-prey associations in uninterrupted tundra and forested regions of Canada explained differences in migratory behaviour, genetics, and coat colour of wolves. Satellite-telemetry data demonstrated that tundra wolves (n = 19) migrate annually with caribou (n = 19) from denning areas in the tundra to wintering areas south of the treeline. In contrast, nearby boreal coniferous forest wolves are territorial and associated year round with resident prey. Spatially explicit analysis of 14 autosomal microsatellite loci (n = 404 individuals) found two genetic clusters corresponding to tundra vs. boreal coniferous forest wolves. A sex bias in gene flow was inferred based on higher levels of mtDNA divergence (F(ST) = 0.282, 0.028 and 0.033; P < 0.0001 for mitochondrial, nuclear autosomal and Y-chromosome markers, respectively). Phenotypic differentiation was substantial as 93% of wolves from tundra populations exhibited light colouration whereas only 38% of boreal coniferous forest wolves did (chi(2) = 64.52, P < 0.0001). The sharp boundary representing this discontinuity was the southern limit of the caribou migration. These findings show that substantial genetic and phenotypic differentiation in highly mobile mammals can be caused by prey-habitat specialization rather than distance or topographic barriers. The presence of a distinct wolf ecotype in the tundra of North America highlights the need to preserve migratory populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".