Effects of predation risk on elk (<i>Cervus</i> <i>elaphus</i>) landscape use in a wolf (<i>Canis</i> <i>lupus</i>) dominated system
Bibliographic record
Abstract
Food acquisition and predation avoidance are key drivers of herbivore behaviour. We investigated the interaction of top-down (predator) and bottom-up (food, fire, thermal) effects by measuring the relationship between wolf (Canis lupus L., 1758) predation risk perceived by elk (Cervus elaphus L., 1758) and elk landscape use. We conducted fecal pellet and wolf scat surveys in three valleys with three wolf population levels (Saint Mary: low; Waterton: moderate; North Fork: high). In the North Fork, 90% of quaking aspen (Populus tremuloides Michx.) stands burned recently; the other valleys had no fire. We created predictive models of elk pellet density that incorporated bottom-up and top-down variables. All valleys had a high elk pellet density (≥10 per 100 m2). Wolf scat density was similar where there was no fire, but one order of magnitude greater in burned areas. Elk pellet density was lower in the North Fork, a predation-related response. In all valleys, site-specific elk density declined as impediments to detecting or escaping wolves increased, and elk avoided aspen, except for North Fork unburned areas. Models that best predicted elk density contained bottom-up and top-down effects. At local scales, high predation risk negatively influence elk occurrence, suggesting that even with minimal wolf exposure elk avoid risky sites.
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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 | 0.001 | 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".