Influence of ecotourism on grizzly bear activity depends on salmon abundance in the Atnarko River corridor, Nuxalk Territory
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it