Grizzly bear responses to restrictions of recreation in Yellowstone National Park
Bibliographic record
Abstract
Abstract Avoiding humans will be more difficult and energetically costly for animals as outdoor recreation increases and people venture farther into wildland areas that provide high‐quality habitat for wildlife. Restricting human access can be an attractive management tool to mitigate effects of human recreation activities on wildlife; however, the efficacy of such measures is rarely assessed. In 1982, Yellowstone National Park identified areas important to grizzly bears ( Ursus arctos ) to help protect critical grizzly bear habitat and reduce the likelihood of human injuries by bears. Referred to as bear management areas (BMAs), human access is restricted in these areas for 2–8 months each year, with timing and type of restrictions varying by area. We examined 2 datasets to evaluate grizzly bear selection of BMAs and differences of bear density in BMAs and non‐BMAs. First, we used 17 years of recent global positioning system telemetry data for grizzly bears to assess their selection of BMAs during periods when human access was allowed, and when access was restricted. We used step‐selection functions to test the hypothesis that bears spend time in places that allow them to avoid people and select quality food sources. There was support that grizzly bears differentially select for BMAs regardless of whether human access was restricted at the time, compared with areas outside BMAs, and that selection changed with sex and season. Only males during the summer and hyperphagic seasons changed their selection of BMAs based on whether access restrictions were in place, and overall, male bears preferred unrestricted BMAs (BMAs without restrictions in place). Females preferentially selected BMAs regardless of whether the area had access restrictions in place only during the mating season. Individuals varied widely in their preference for BMAs and access restrictions. Bears likely choose to spend time in BMAs based on available food resources rather than restrictions to human access. Supporting this interpretation, our analyses indicated that a greater proportion of BMA in an area was associated with higher densities of grizzly bear. Thus, restrictions to human access likely help reduce the potential for human–bear interactions, accomplishing one of the original objectives for establishing the BMAs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".