Coyote prey selection and community stability during a decline in food supply
Bibliographic record
Abstract
The foraging behavior of predators can have a large influence on community dynamics and has been shown to increase stability in some cases and decrease stability in others. I studied the foraging behavior of coyotes ( Canis latrans ) in the Alaska Range during the peak and decline of a snowshoe hare ( Lepus americanus ) population cycle (1999–2002). Coyote diet was compared with prey availability to test for changes in prey selection and to examine the effect of coyote predation on the vertebrate prey community. Coyotes responded to the hare decline by increasing selection for hares and porcupines, whereas selection for voles, ground squirrels and Dall sheep did not change. Although the study area was characterized by considerable habitat heterogeneity, coyotes utilized the area as a fine‐grained environment. Coyote foraging behavior was driven primarily by changes in snowshoe hare abundance, and their sensitivity to change in alternative prey density was low. Predation by coyotes may therefore decrease the stability of alternative prey populations rather than dampening fluctuations. In order for predation to enhance the stability of prey populations, I hypothesize that prey profitability must be determined primarily by abundance.
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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 | 0.001 | 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".