Evaluating the influence of anthropogenic landscape change on wolf distribution: implications for woodland caribou
Bibliographic record
Abstract
Abstract Across much of the range of woodland caribou ( Rangifer tarandus caribou ), predator–prey dynamics have changed as a result of large‐scale industrial development. Land clearing and associated early‐successional forests have resulted in a greater density and distribution of moose ( Alces alces ), deer ( Odocoileus spp.), and their associated predators. This process of apparent competition has resulted in increased predation on woodland caribou. We employed a combination of field and statistical methods to better understand the distribution and interactions of wolves ( Canis lupus ) and caribou across a region with high levels of industrial development. We used count models to investigate the frequency of wolf occurrence relative to landcover types, disturbance features, and caribou habitat. As predicted, the co‐occurrence between caribou and wolves was rare. Similarly, the remains of caribou were identified at a small proportion of the sites where wolves killed large prey. Caribou occurred at low densities across the study area, and thus, wolves likely pursued other more abundant deer species. Encounters between wolves and caribou habitat was most likely to occur in the low‐elevation boreal forest and areas closer to and with higher densities of forestry cutblocks. Our results highlight the importance of understanding the spatial dynamics of multi‐species interactions when developing recovery strategies for threatened and endangered species.
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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) | 0.001 | 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".