Influence of ecotourism on grizzly bear activity depends on salmon abundance in the Atnarko River corridor, Nuxalk Territory
Bibliographic record
Abstract
Abstract Ecotourism management can draw on theory and data related to non‐consumptive effects of risk on wildlife. The asset protection principle (APP) predicts that variable food supply and its associated risks will affect antipredator behavior; responses to predation risk should dominate when food reserves are high, while nutritional risk becomes more important when food reserves are limited. Additionally, the human shield hypothesis (HSH) describes how some individuals might seek human presence if it repels potential sources of risk. Using camera traps, we used generalized linear mixed effects and multinomial regression models to test components of the APP and HSH where ecotourism co‐occurs with grizzly bear ( Ursus arctos ) foraging during hyperphagia. When salmon abundance was high (+1 SD), bear activity (weekly detections) decreased by 13% with every 100 visitors/week. Under low salmon conditions, bear activity increased with visitor numbers, creating ‘high bear‐high visitor’ conditions. Consistent with HSH, detection data revealed an increased likelihood of detecting subordinate age‐sex classes compared with adult males when visitor numbers were high. Our findings suggest that when salmon are low, managers might consider limiting visitors to mitigate disturbance. More broadly, understanding how wildlife allocate anti‐predator behavior as a function of risk and food can inform conservation science and practice.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".