Integrating spatial and behavioral data provides comprehensive assessment of grizzly bear-ecotourism coexistence in Nuxalk Territory
Bibliographic record
Abstract
Behavioral ecologists frequently focus on a single modality of wildlife response to disturbance, which can limit inference because different antipredator responses reflect various aspects of predation risk management. We investigated intrapopulation variation in tolerance to human-associated risk within a grizzly bear-ecotourism system in Nuxalk Territory, considering behavior results in tandem with spatial data from genetically tagged individuals. Whereas our behavioral analysis revealed no effects of ecotourism on alertness, our central measure of tolerance, we observed variation in space-use among individuals (n = 80). Only 12, primarily females (n = 10), showed preference for the area of highest ecotourism activity; others showed little to no use of the area, despite its close proximity and high resource abundance. These patterns suggest that behavioral data may have been biased towards individuals tolerant enough to coexist with ecotourism. Examining behavior of ostensibly tolerant ecotourism individuals alone would have overlooked intrapopulation variation in space-use at broader spatial scales. A comprehensive assessment that simultaneously draws upon both spatial and behavioral dimensions may therefore provide richer insight into coexistence than either lens alone. More broadly, coexistence dynamics in this and other systems might exclude some individuals within populations that are not tolerant enough to participate.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".