Wolves, elk, and bison: reestablishing the "landscape of fear" in Yellowstone National Park, U.S.A.
Bibliographic record
Abstract
The elk or wapiti (Cervus elaphus) and bison (Bison bison) of Yellowstone National Park have lived in an environment free of wolves (Canis lupus) for the last 50 years. In the winter of 1994-1995, wolves were reintroduced into parts of Yellowstone National Park. Foraging theory predicts that elk and bison would respond to this threat by increasing their vigilance levels. We tested this prediction by comparing vigilance levels of elk and bison in areas with wolves with those of elk still in "wolf-free" zones of the Park. Male elk and bison showed no response to the reintroduction of wolves, maintaining the lowest levels of vigilance throughout the study ([Formula: see text]12 and 7% of the time was spent vigilant, respectively). Female elk and bison showed significantly higher vigilance levels in areas with wolves than in areas without wolves. The highest vigilance level (47.5 ± 4.1%; mean ± SE) was seen by the second year for female elk with calves in the areas with wolves and was maintained during the subsequent 3 years of the study. As wolves expanded into non-wolf areas, female elk with and without calves in these areas gradually increased their vigilance levels from initially 20.1 ± 3.5 and 11.5 ± 0.9% to 43.0 ± 5.9 and 30.5 ± 2.8% by the fifth year of the study, respectively. We discuss the possible reasons for the differences seen among the social groups. We suggest that these behavioural responses to the presence of wolves may have more far-reaching consequences for elk and bison ecology than the actual killing of individuals by wolves.
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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 | 0.000 |
| 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 | 0.000 |
| 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".