Variation in cougar (<i>Puma concolor</i>) predation habits during wolf (<i>Canis lupus</i>) recovery in the southern Greater Yellowstone Ecosystem
Bibliographic record
Abstract
We examined predation habits of cougars (Puma concolor (L., 1771)) following the recent recovery of gray wolves (Canis lupus L., 1758) in the southern Greater Yellowstone Ecosystem. With the extirpation of wolves in the early 20th century, cougars likely expanded their niche space to include space vacated by wolves, and increased use of habitat better suited to the foraging of a coursing predator, like wolves. We predicted that as wolves recolonized their former range, competitive exclusion would compel cougars to cede portions of niche space occupied in the absence of wolves. To examine this hypothesis, we radio-tracked cougars and examined their predation sites from winter 2000–2001 through summer 2009. Variation in foraging by cougars was associated with increasing wolf presence. As wolf numbers increased and the mean distance between wolf pack activity centers and cougar predation sites decreased, cougars made kills at higher elevations on more north-facing slopes during summer and in more rugged areas during winter. In addition, cougars preyed on a higher proportion of mule deer (Odocoileus hemionus (Rafinesque, 1817)), consistent with predictions of exploitative competition with wolves. Observed changes in predation characteristics reflect differences in predation strategy between cougars and wolves, given that wolves are coursing predators and cougars are ambush predators. These possible predation effects should be considered when developing management strategies in systems where the recolonization of wolves may occur.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".