Drivers of Arctic female grizzly movement-related habitat use from den emergence to green-up
Bibliographic record
Abstract
Arctic female grizzly bears (Ursus arctos) with cubs face a challenging period following den emergence to find quality resources when risk of infanticide is high and food is scarce. We used global positioning system (GPS) telemetry data grouped by reproductive state (solitary, females with cubs <1 yr old, females with cubs ≥1 yr old) to examine trade-offs between access to security cover and access to food during the hypophagic period from 5 June to 4 August (2003–2006), in the Mackenzie Delta region, Northwest Territories, in the Canadian Arctic. We used step-selection functions to model habitat selection with parameters for land cover, land cover diversity, and topography measured along the step and at the end. We fit the models for each individual and used 2-stage model-averaging to determine the top model for describing landscape factors influencing movement. We found that while female movement rates (i.e., step length = m/4-hr location interval) differed with reproductive state, movement-related habitat selection was similar. Females with cubs <1 year old moved the least (x̄ step length = 700 m, 95% CI [655–745]) compared with females with cubs ≥1 year old (x̄ = 1,484 m [1,415–1,553], P < 0.001), and females without cubs (x̄ = 1,652 m [1,581–1,723], P < 0.001), who moved the most. Across all reproductive states, our results suggest that females prioritize security over food availability along movement paths. Our results contribute to a better understanding of the behavioral decisions made by female grizzly bears and provide insights into trade-offs between security and food resources, when resource availability is uncertain.
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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 | 0.000 | 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) | 0.001 | 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".