Is niche separation between wolves and cougars realized in the Rocky Mountains?
Bibliographic record
Abstract
Multiple carnivore species can have greater population limiting effects than single carnivores. Two coexisting carnivores can only be similar up to a certain extent. I investigate how two carnivores, wolves (Canis lupus) and cougars (Puma concolor), coexist through niche partitioning in the central east slopes of the Alberta Rocky Mountains. Wolf packs spatio-temporally avoided other wolf packs more than they did cougars, while cougars avoided conspecifics as much as wolves. Reinforcing spatial separation, temporally wolves had two crepuscular movement peaks while cougars had just one. Male cougar movements peaked in the late evening and was high over night, while female cougar movement increased throughout the day and peaked in the evening. Female cougars selected different habitat features from male cougars and from wolves during both the day and night, while male cougars had more habitat selection differences from wolves at night. I found some evidence that cougars were more influenced by landscape features than wolves. Differences in the predators’ habitat selection were primarily for prey density contingent upon habitat features, likely related to maximizing hunting efficiency. Both species killed primarily deer (Odocoileus virginianus, O. hemionus), though wolves and male cougars killed and selected more large-bodied ungulate prey, such as elk (Cervus elaphus), moose (Alces alces) and/or feral horses (Equus calabus) than female cougars, who strongly selected for deer. It is advantageous to consider both these species together when building management plans for both predator species as well as for their ungulate prey.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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 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".