The low performance of forest versus rural coyotes in northeastern North America: Inequality between presence and availability of prey
Bibliographic record
Abstract
Coyotes, which originate from central and southwestern North America, recently extended their range into forests of the Northeast. Forest coyotes occur in lower densities, have lower body reserves, and consume more fruits during summer than their counterparts occupying adjacent rural landscapes. We hypothesised that the forest landscape offered less animal prey to coyotes during summer than did the rural landscape. Coyote densities were higher in the rural landscape (2.7 animals 10 km-2) than in the forest landscape (0.5 animals 10 km-2) during the summer of 1997. During the summers of 1996 and 1997, coyotes in both landscapes fed mainly on wildberries (< 45% of dry matter intake), small mammals (< 10%), and snowshoe hare (< 10%). The biomass of the most abundant animal prey, snowshoe hares, was greater in the forest landscape (1.24 and 1.53 kg ha-1 in 1996 and 1997, respectively) than in the rural landscape (0.46 and 0.40 kg ha-1 in corresponding years). The biomass of the other major animal prey (small mammals), was comparable in both landscapes but irrupted during the second summer (0.09 and 0.50 kg ha-1 in 1996 and 1997, respectively). The biomass of fruits remained relatively constant in the rural landscape during the summers of 1996 and 1997 (ª 6 kg ha-1), but it tripled in the forest landscape during the second year (1.69 kg ha-1 in 1996 versus 5.30 kg ha-1 in 1997). Contrary to our prediction, the availability of animal prey in the forest landscape exceeded that in the rural landscape. Our results illustrate that the presence of prey does not correspond to its availability to predators. Coyotes appear poorly adapted for hunting in dense forest vegetation during summer and compensate for shortage of animal prey by consuming more berries.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".