Population- and individual-level prey selection by a solitary predator as determined with two estimates of prey availability
Bibliographic record
Abstract
Prey selection is exhibited by predator populations that kill a prey species disproportionate to its availability, or alternatively, individual predators that select prey disproportionate to the mean selection exhibited by their populations. Prey selection is a simple calculation when one can determine prey availability; however, measuring prey availability is challenging. We compared population- and individual-level prey selection as determined with two measures of prey availability for five ungulate species killed by pumas (Puma concolor (L., 1771)) in the Southern Yellowstone Ecosystem, USA: (1) annual prey counts and (2) total prey killed by marked pumas. We also tested whether individual pumas in the population exhibited a narrower dietary niche breadth compared with their population as a whole. The two methods yielded different estimates of prey availability and highlighted the need to consciously match prey availability estimates with appropriate ecological questions. Prey counts may have overestimated elk (Cervus canadensis (Erxleben, 1777)) abundance and underestimated deer abundance, whereas predation data may have better captured the influence of prey size on puma-specific prey vulnerability and availability. Prey counts were the more appropriate metric for analyzing population-level prey selection or differences in interspecific foraging, whereas total prey killed was the more appropriate metric for intraspecific comparisons.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".