Assessing differential prey selection patterns between two sympatric large carnivores
Bibliographic record
Abstract
Several conceptual models describing patterns of prey selection by predators have been proposed, but such models rarely have been tested empirically, particularly with terrestrial carnivores. We examined patterns of prey selection by sympatric wolves ( Canis lupus ) and cougars ( Puma concolor ) to determine i) if both predators selected disadvantaged prey disproportionately from the prey population, and ii) if the specific nature and intensity of prey selection differed according to disparity in hunting behavior between predator species. We documented prey characteristics and kill site attributes of predator kills during winters 1999–2001 in Idaho, and located 120 wolf‐killed and 98 cougar‐killed ungulates on our study site. Elk ( Cervus elephus ) were the primary prey for both predators, followed by mule deer ( Odocoileus hemionus ). Both predators preyed disproportionately on elk calves and old individuals; among mule deer, wolves appeared to select for fawns, whereas cougars killed primarily adults. Nutritional status of prey, as determined by percent femur marrow fat, was consistently poorer in wolf‐killed prey. We found that wolf kills occurred in habitat that was more reflective of the entire study area than cougar kills, suggesting that the coursing hunting behavior of wolves likely operated on a larger spatial scale than did the ambush hunting strategy of cougars. We concluded that the disparity in prey selection and hunting habitat between predators probably was a function of predator‐specific hunting behavior and capture success, where the longer prey chases and lower capture success of wolf packs mandated a stronger selection for disadvantaged prey. For cougars, prey selection seemed to be limited primarily by prey size, which could be a function of the solitary hunting behavior of this species and the risks associated with capturing prime‐aged 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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".