Differential risk effects of wolves on wild versus domestic prey have consequences for conservation
Bibliographic record
Abstract
Predators play integral roles in shaping ecosystems through cascading effects to prey and vegetation. Such effects occur when prey species alter their behavior to avoid predators, a phenomenon called the risk effects of predators. Risk effects of wild predators such as wolves are well documented for wild prey, but not for free ranging domestic animals such as cattle despite their importance for ecosystem function and conservation. We compared risk effects of satellite‐collared wolves (n = 16) on habitat selection by global‐positioning‐system‐collared elk (n = 10) and cattle (n = 31). We calculated resource selection functions (RSFs) in periods before, during and after wolf visits in elk home ranges or cattle pastures. The habitat variables tested included: distance to roads and trails, terrain ruggedness, food‐quality and distance to forest. When wolves were present, elk stayed closer to forest cover and selected less for high‐quality‐food habitat. Thus, the risk effects of wolf presence on elk produced a change in the tradeoff between food and cover selection. Cattle responded by avoiding high‐quality‐food habitat and selecting areas closer to roads and trails (where people likely provided security), but these effects manifested only after wolves had left. Artificial selection in cattle may have attenuated natural anti‐predator behaviors. The effects of predators on ecosystems are likely different when mediated through risk effects on domestic compared to wild animals. Furthermore, predator control in response to livestock predation, an important conservation issue, may produce broad ecosystem effects triggered by decrease of an important predator species. Conservation planners should consider these effects where domestic herbivores are dominant species in the ecosystem.
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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 | 0.001 | 0.002 |
| 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 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".