Impacts of recolonizing gray wolves (<i>Canis lupus</i>) on survival and mortality in two sympatric ungulates
Bibliographic record
Abstract
There is growing recognition that humans may mediate the strength and nature of the ecological effects of large predators. We took advantage of ongoing gray wolf (Canis lupus Linnaeus, 1758) recolonization in Washington, USA, to contrast adult survival rates and sources of mortality for mule deer (Odocoileus hemionus (Rafinesque, 1817)) and white-tailed deer (Odocoileus virginianus (Zimmermann, 1780)) in areas with and without wolf packs in a managed landscape dominated by multiple human uses. We tested the hypothesis that the addition of wolves to the existing predator guild would augment predator-induced mortality rates for both ungulates. Source of mortality data from adult mule deer and white-tailed deer, respectively, revealed that wolf-related mortality was low compared with that inflicted by other predators or humans. Predator-caused mortality was largely confined to winter. There was little effect of wolf presence on adult deer mortality rates, and there was no difference in mortality between the two deer species relative to wolf-free or wolf-occupied sites. Although this study occurred early in wolf recovery in Washington, our results differ from those demonstrated for gray wolves in protected areas. Thus, we encourage further investigation of effects of direct predation by recolonizing large carnivores on prey in human-dominated landscapes.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".