Grey wolves (Canis lupus) shift selection of anthropogenic landscape features following predator control in the Nearctic boreal forest
Bibliographic record
Abstract
Conserving endangered species sometimes involves killing their predators. In the case of Nearctic wolves ( Canis lupus ), rarely are lethal control measures examined for ancillary effects on predator behaviour or community responses in a before-after design. We examined wolf relative abundance and spatial distribution in a northwestern boreal forest landscape for three years before and after the onset of wolf culling intended to conserve threatened woodland caribou ( Rangifer tarandus caribou ). We hypothesized that wolf occurrence would increase with density of anthropogenic features created by landscape development before the cull, but that wolves would avoid anthropogenic features after the cull due to associated mortality risk. We used generalized linear models in an information-theoretic framework to weigh evidence for our hypotheses. Post-control, independent wolf detections decreased to 24 % of pre-cull numbers, but wolves maintained 75 % of their distribution. Pre-control, wolves were positively associated with linear features, presumably for hunting efficiency, but post-cull wolves were negatively associated with these features. Thus, wolf control caused not only a numerical reduction of wolf numbers, but also a functional change in wolf behaviour that could further reduce predation pressure on caribou. However, post cull wolf occurrence was more strongly associated with anthropogenic block features which provide forage for alternate prey, potentially subsidizing their fast recovery. Conservation actions involving predator mortality alter landscape-scale distributions and behaviors of surviving predators, with potential indirect effects for the mammal community.
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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 | 0.000 | 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 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".