Cozy den or winter walk: the effects of climate and supplementary feeding on brown bear winter behavior
Bibliographic record
Abstract
Abstract Hibernation is a key adaptation for coping with unfavorable climatic conditions and low food availability in areas with severe winter conditions. While understanding the physiology and phenology of this adaptation has received considerable attention, comparatively little information is available on how hibernation will be affected by changing climate conditions. We used GPS telemetry data from 20 free‐ranging brown bears monitored over 31 winters between 2007 and 2022, to identify behavioral strategies of bears during winter. We applied behavioral change point analysis to quantify brown bears’ hibernation phenology in a population close to the bear's southern latitudinal range limit in Europe where supplementary food is available to bears year‐round. We observed winter behavior patterns that varied across age and reproductive classes but also within individuals between winters. Among 31 winter events, we registered six cases in which bears exhibited a single hibernation/stationary period and 19 events where hibernation was split into up to five stationary periods. Moreover, six winter events did not show behaviors consistent with hibernation and individuals remained partly or completely active throughout winter. The movement of these active bears decreased with increasing snow depth. In addition, these winter‐active bears showed higher fidelity to supplementary feeding sites during the winter period compared to the rest of the year. Our data suggest that an abundance of human‐provided food resources during winter may facilitate the emergence of different wintering strategies in brown bears. Furthermore, supplemental feeding sites in combination with predicted mild winters and prolonged natural food availability suggest that the use of hibernation as an energy‐saving strategy to overcome severe environmental conditions may decrease in the future.
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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 | 0.000 | 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".