Direct and indirect effects of cougar predation on bighorn sheep fitness
Bibliographic record
Abstract
Predation has direct effects on prey population dynamics through mortality, and it can induce indirect effects through fear. The indirect effects of predation have been documented experimentally, but few studies have quantified them in nature so that their role in prey population dynamics remains controversial. Given the expanding or reintroduced populations of large predators in many areas, the quantification of indirect effects of predation is crucial. We sought to evaluate the direct and indirect fitness effects of intense cougar (Puma concolor) predation using 48 years of data on marked bighorn sheep (Ovis canadensis) on Ram Mountain, Alberta, Canada. We compared years of intense cougar predation with years with no or occasional cougar predation. We first quantified the effects of predation on neonatal, weaning, and overwinter lamb survival, three metrics potentially affected by direct and indirect effects. We then investigated the possible indirect effects of intense cougar predation on lamb production, female summer mass gain, and lamb mass at weaning. We found strong effects of cougar predation on lamb survival, lamb production, and seasonal mass gain of lambs and adult females. In years with high predation, neonatal, weaning, and overwinter lamb survival declined by 18.4%, 19.7% and 20.8%, respectively. Indirect effects included a 14.2% decline in lamb production. Female summer mass gain decreased by 15.6% and lamb mass at weaning declined by 8.0% in years of intense cougar predation. Our findings bring key insights on the impacts of predation on prey fitness by reporting moderate to large effects on recruitment and illustrate the importance of indirect effects of predation on population dynamics.
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 | 0.001 |
| 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".