Cougar spatiotemporal response to human activities in a multi‐use forest landscape on southern Vancouver Island
Bibliographic record
Abstract
Recreational activities have been shown to induce fear responses in carnivores, causing both spatial and temporal displacement that can have cascading effects on the wider ecological community. Cougars Puma concolor are one species that are particularly sensitive to human disturbances as they have been found to be impacted by various forms of recreational activities. This research aimed to quantify the spatial and temporal responses of cougars to human activities on southern Vancouver Island in British Columbia, Canada. Fifty camera traps were deployed in a systematic probabilistic sampling design and data from the summer season of 2022 was used for analyses. We modeled cougar weekly occurrence frequency in relation to human (hikers and bikers) occurrence frequency, vehicle occurrence frequency, and landscape features, using generalized linear mixed models. Temporal responses of cougars were investigated using activity pattern analysis to compare activity patterns within a recreation area and a restricted public access reserve. Cougars avoid occurring at sites where recreationalists frequently visited, but not sites frequented by vehicles on unpaved roads. We observed minimal influence of habitat features or prey availability on cougar occurrence frequency, as measured by information loss (AIC c scores). Activity pattern analysis did not demonstrate significant differences in cougar daily activity patterns between the recreation area and no‐entry reserve, suggesting a lack of temporal response. These results suggest recreational activities on southern Vancouver Island are impacting cougar spatial – but not temporal – behavioral dynamics. Insights on how cougars respond to recreational activities can help inform landscape management and policies by providing information that can be used to mitigate negative impacts and thus ensure that recreational activities are remaining compatible with conservation efforts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".