Trophic cascades linking wolves (<i>Canis lupus</i>), coyotes (<i>Canis latrans</i>), and small mammals
Bibliographic record
Abstract
When large carnivores are extirpated from ecosystems that evolved with apex predators, these systems can change at the herbivore and plant trophic levels. Such changes across trophic levels are called cascading effects and they are very important to conservation. Studies on the effects of reintroduced wolves in Yellowstone National Park have examined the interaction pathway of wolves ( Canis lupus L., 1758) to ungulates to plants. This study examines the interaction effects of wolves to coyotes to rodents (reversing mesopredator release in the absence of wolves). Coyotes ( Canis latrans Say, 1823) generally avoided areas near a wolf den. However, when in the proximity of a den, they used woody habitats (pine or sage) compared with herbaceous habitats (grass or forb or sedge)– when they were away from the wolf den. Our data suggested a significant increase in rodent numbers, particularly voles (genus Microtus Schrank, 1798), during the 3-year study on plots that were within 3 km of the wolf den, but we did not detect a significant change in rodent numbers over time for more distant plots. Predation by coyotes may have depressed numbers of small mammals in areas away from the wolf den. These factors indicate a top–down effect by wolves on coyotes and subsequently on the rodents of the area. Restoration of wolves could be a powerful tool for regulating predation at lower trophic levels.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".