Multispecies mammal monitoring in Cathedral Provincial Park
Bibliographic record
Abstract
Globally, protected areas face a challenge of meeting the dual mandate of protecting biodiversity while providing recreational enjoyment for humans. Balancing these mandates is confounded by limited information on species status and insufficient recreation monitoring. Effective protected area (PA) management is critical for wildlife in this time of pervasive human impact known as the Anthropocene. Where non-consumptive human recreation is considered low impact, with growth in outdoor recreation it is important to know if recreation is impacting wildlife and how. Using camera traps, I assessed the potential for recreational impacts on mammal habitat use in space and time, in Cathedral Provincial Park, British Columbia, Canada. I also estimated population density for an at-risk mountain goat (Oreamnos americanus) population using two methods: spatial capture-recapture (SCR) and spatial mark-resight (SMR). I assessed recreational impacts on habitat use at the weekly scale, while also evaluating daily activity patterns for eight mammal species. I hypothesized that coexistence with recreation would require spatial and/or temporal niche segregation, with large carnivores being most sensitive and exhibiting spatial avoidance as suggested by the predator shield hypothesis. I predicted that mesocarnivores and ungulates would exploit this “shield” spatially, while exhibiting temporal avoidance of humans. I found spatial co-occurrence between ungulates and recreation, suggesting that these species may be using people as a shield from predators or for nutritional subsidies, but did not see the predicted negative relationship between predators and humans, except for coyotes (Canis latrans). Temporally, all species other than cougars (Puma concolor) had activity patterns significantly different from that of recreationists, suggesting stronger displacement in the temporal niche, while wolves (Canis lupus) and mountain goats showed significantly different use of on and off-trail habitat in time. Estimates of mountain goat density varied between methods, from a minimum 6.32 (95% CI; 2.98-13.40) to a maximum of 11.54 (6.97-19.13) goats per 100 km2. I found SMR estimates to have higher precision than SCR estimates across all three years. With this study I show that camera trap surveys can be used to assess interactions between wildlife and recreation, while also providing basis for monitoring population trends in sensitive species.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".