Coyotes take advantage of ungulate carrion subsidies as wolves recolonize Washington
Bibliographic record
Abstract
Apex predators exert suppressive effects on mesocarnivores; however, they also provide important carrion subsidies. Optimal foraging theory predicts that individuals respond to resource competition by using high-value resources, while competition theory predicts that individuals respond by partitioning resources. This study investigated how the return of wolves ( Canis lupus Linneas, 1758) to Washington state impacted the diet of a subordinate carnivore—the coyote ( Canis latrans Say, 1823). We collected coyote scats from two areas of northern Washington with differing wolf densities and used traditional analysis of undigested remains to infer diet. We tested for differences in the volumes of prey categories, the proportion of ungulate prey that was scavenged, and diet diversity between seasons, study sites, and inside and outside of wolf pack territories. Coyote scats contained more adult ungulate remains inside of wolf pack territories (27%) compared to outside (14%), while seeds and berries were more commonly consumed outside of wolf pack territories (23%) than inside of wolf pack territories (4%). These findings suggest that coyotes are taking advantage of wolf kills to increase ungulate carrion consumption, as predicted by optimal foraging theory, which may substantially affect plant and wildlife communities as wolves continue to recover and coyote diets shift in response.
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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 | 0.000 | 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".