Wildlife ecotourism elicits spatial and temporal shifts in grizzly bear activity in Kitasoo Xai’xais Territory on the Central Coast of British Columbia
Bibliographic record
Abstract
Ecotourism offers a non-consumptive form of economic activity globally. Human activity, however, might negatively affect the ecology of areas and their biota, likely varying with type and intensity of ecotourism. Wildlife, for example, might perceive ecotourists as predators, and adjust behaviour accordingly (i.e., human avoidance). Alternatively, wildlife might actually seek human activity if it protects them from greater risks (Human Shield Hypothesis). The Anthropause, a period of decreased human activity due to COVID-19, provided unparalleled opportunity to examine wildlife behaviour when perceived risks from humans were removed. In partnership with the Kitasoo Xai’xais First Nation (KX), we assessed if and how ecotourism, in the form of bear-viewing, might influence spatial and temporal activity of grizzly bears. We deployed remote cameras in the Khutze watershed in 2020 in the absence of human use. To provide increased inference when tourism resumed in 2021, KX implemented alternating spatial closure experiments within the watershed. Additionally, in 2021 we implemented a tourist group size experiment in a second watershed, Green River. In Khutze, we found that a closure of 25 days was required for bear detection rate to return to the 2020 (non-ecotourism) level. We did not observe an influence of the alternating within-watershed north-south closures on activity. The data also revealed complex relationships among bear detections, ecotourism activity, and salmon availability, varying by age and sex of bears. Specifically, we found a human shield effect for females with young when salmon levels were moderate to high, but this effect diminished in times of low salmon. An activity pattern analysis in Khutze did not show an effect of ecotourism. In Green, where inference was likely constrained by our short-term experiment, we found a positive influence of the number of days since people were present on detection rate. We additionally found temporal avoidance of within 100m of the viewing site on days when people were present. These patterns from both watersheds show the complex ways in which wildlife can respond to even seemingly benign human presence. Inference from this research has application to wildlife, land, and ecotour management by the KX, who are reasserting authority in governance. More broadly, this study contributes to literature on the dynamic landscape of fear induced by spatiotemporal variation in human activity.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".