Drivers of polar bear behavior, and the possible effects of prey availability on foraging strategy
Bibliographic record
Abstract
1 Abstract Change in behavior is one of the earliest measurable responses to variation in habitat suitability, making the study of factors that promote behaviors particularly important in areas undergoing environmental change. We applied hidden Markov models to movement data of 14 polar bears, Ursus maritimus , from Western Hudson Bay, Canada between 2011 and 2021 during the foraging season (January–June) when bears inhabit the sea ice. The model incorporated bear movement and orientation relative to wind to classify three behaviors (stationary/drifting, area-restricted search, and olfactory search), and investigated 11 factors to identify conditions that may promote these behaviors. In contrast to other polar bear populations, we found high levels of evening activity, with active behaviors peaking around 20:00. We identified an increase in activity as the ice-covered season progressed. This apparent shift in foraging strategy from still-hunting to active search corresponds to a shift in prey availability (i.e., increase in haul-out behavior during winter to the spring pupping and molting seasons). Last, we described spatial patterns of distribution with respect to season and ice concentration that may be indicative of variation in habitat quality and segregation by bear age that may reflect competitive exclusion. Our observations were generally consistent with predictions of the marginal value theorem, and differences between our findings compared to other populations could be explained by variation in regional or temporal variation in resource abundance or distribution. Our findings and novel methodology can help identify periods, locations, and environmental conditions representing critical habitat.
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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 | 0.002 |
| 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 | 0.001 | 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".