Female and male grizzly bears differ in their responses to low‐intensity recreation in a protected area
Bibliographic record
Abstract
Abstract Strategies animals use to navigate human‐dominated landscapes frequently mimic anti‐predator responses employed by prey species. Understanding how large carnivores respond to outdoor recreation is important for conservation, particularly in protected areas with preservation mandates. Visitation to Yellowstone National Park doubled from 1980 to 2015, increasing the need to examine potential changes in behavior of grizzly bears ( Ursus arctos ) in relation to human recreation sites (trails, backcountry campsites). We developed integrated step‐selection functions to explore how recreation sites influenced the movement rate and selection by male and female grizzly bears. Further, we tested whether time of day (diurnal, crepuscular, nocturnal) and restrictions to human access (i.e., restricted, unrestricted) modified bear responses and then compared behaviors based on proximity to recreation sites. Male grizzly bears used trails to travel during crepuscular and nocturnal hours and exhibited more pronounced behavior in restricted areas compared with unrestricted areas, suggesting recreation in unrestricted areas influenced the behavior of male bears. In contrast, female bears varied their movement rate and selection of trails in restricted areas much more than in unrestricted areas, suggesting females may make security tradeoffs between male bears and people. Both sexes used trails, likely as energetically efficient travel corridors; however, our analyses did not indicate that bears spent time near backcountry campsites. The sex‐based differences in selection and movement patterns associated with trails and campsites suggest a single management approach for recreation may not equally benefit all bears. Recreation impacts on wildlife are complex to characterize and predict, but simultaneously modeling movement and selection provides a more comprehensive assessment of strategies animals use to navigate perceived risk.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".