Cougar (Puma concolor) demography and foraging ecology in the southern interior of British Columbia, Canada
Bibliographic record
Abstract
Large carnivores face complex challenges in human-modified landscapes, where mortality risk, resource availability, and habitat structure interact to shape population dynamics and predation patterns. In this dissertation, I examined how environmental and anthropogenic factors influence cougar (Puma concolor) demography, survival, and foraging behaviour in the southern interior of British Columbia, Canada. Using GPS-collared individuals, kill site investigations, remote cameras, and spatial modeling, I integrated demographic, dietary, and spatial ecology approaches to understand how cougars navigate trade-offs between ecological opportunity and risk. In Chapter 2, I analyzed population demography and cause-specific mortality of 56 GPS-collared adults and 59 ear-tagged kittens. Human activities were the main source of adult mortality, while predation dominated kitten deaths. Survival varied by sex, season, and landscape features, with higher risk near roads and agricultural areas. Kittens were most vulnerable in early denning stages, particularly when maternal care was reduced or dens were distant from recent cutblocks and roads. In Chapter 3, I examined prey selection and dietary overlap among 38 cougars using 875 kill sites and prey availability data from 146 remote cameras. Males killed larger prey than females, and prey use varied seasonally. Diet similarity was highest at intermediate spatial overlap, indicating that territorial neighbors balance competition and prey availability in their dietary selection. In Chapter 4, I assessed habitat drivers of predation across 886 kill sites. Kill sites were concentrated in core home ranges and influenced by terrain and disturbance features. Deer kills were associated with rugged terrain in snow months and with cutblocks and burns 10–20 years post-disturbance in snow-free months, enhancing both prey catchability and abundance. Multi-species clustering was limited, but these habitats increased hunting success for multiple ungulate species. These chapters illustrate how mortality risk, prey dynamics, and landscape features interact to shape cougar ecology in human-dominated ecosystems. This work advances understanding of carnivore–prey dynamics and provides guidance for mitigating human–wildlife conflict and conserving wide-ranging predators in heterogeneous landscapes.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".