Seasonal variation in the feeding ecology of pumas (<i>Puma concolor</i>) in northern California
Bibliographic record
Abstract
Direct effects of predators depend upon factors that can vary across seasons, including variations in the abundance and vulnerability of migrating prey. Past studies show conflicting results of whether puma (Puma concolor (L., 1771)) feeding ecology varies among seasons. We employed GPS collars to study puma feeding ecology in a single-prey system with migratory black-tailed deer (Odocoileus hemionus columbianus (Richardson, 1829)). We hypothesized that puma feeding ecology would vary based on changes in prey abundance and spatial distribution, as well as competition with scavengers and decomposers. Our results supported these hypotheses. Kill rates in number of ungulates/week were significantly higher in summer and autumn than in winter, likely owing to the increased availability and density of black-tailed deer fawns. The handling times of black-tailed deer ≥1 year old were significantly higher in winter than in spring, summer, or autumn. We speculated that reduced handling time in summer may have been influenced by black bear (Ursus americanus Pallas, 1780) kleptoparasitism and the decomposition of kills. Pumas killed black-tailed deer at higher elevations in summer than in winter, spring, or autumn, and the elevations correlated significantly with seasonal elevations used by black-tailed deer, suggesting that pumas exhibited seasonal foraging behaviours and tracked prey availability in a system with migrating prey.
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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 | 0.001 | 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".