Mechanisms influencing the winter distribution of wolverine Gulo gulo luscus in the southern Columbia Mountains, Canada
Bibliographic record
Abstract
To better understand the mechanisms driving the distribution of a threatened carnivore, wolverine Gulo gulo, in the southern Columbia Mountains, we contrasted four hypotheses; climate, food, human disturbance and trapping harvest. We used non-invasive hair snagging methods to examine wolverine occupancy with respect to these factors, collectively and by sex, at a 5 km radius scale around sampling sites and at a larger 10 km radius to evaluate consistency over changes in scale. For all analyses our top models included food and human disturbance. Of the four food items examined; caribou Rangifer tarandus, mountain goat Oreamnos americanus, moose Alces alces and hoary marmot Marmota caligata, wolverine occurrence was most closely related to hoary marmot habitat. With regards to human disturbance, we documented a negative association with forestry road density and a positive association with protected areas. The importance of climate was low compared to the food and disturbance hypotheses. Somewhat surprisingly, recent harvest was positively associated with subsequent wolverine presence. Our top 10 km scale models were similar to the 5 km scale, but with a stronger positive link to caribou distribution. We show that marmot habitat is important to wolverine in winter and suggest that management actions for conservation prioritize factors related to female occurrence, as these were more clearly defined than male. We demonstrate that human disturbance is a major driver of wolverine distribution. Protected areas appear to be providing secure habitat, and reducing road density or mechanized use of roads in winter should be considered for species conservation. A positive spatial relationship with recent harvest, in the context of a heavily harvested population, suggests that harvest data alone may not be useful in detecting population declines.
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 | 0.001 | 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) | 0.001 | 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 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".