Data from: Behavioural responses of brown bears to roads and hunting disturbance
Bibliographic record
Abstract
Harvest regulations commonly attenuate the consequences of hunting on specific segments of a population. However, regulations may not protect individuals from non-lethal effects of hunting and their consequences remain poorly understood. In this study, we compared the movement rates of Scandinavian brown bears (Ursus arctos, n = 47) across spatiotemporal variations in risk in relation to the onset of bear hunting. We tested two alternative hypotheses based on whether behavioural responses to hunting involve hiding or escaping. If bears try to reduce risk exposure by avoiding being detected by hunters, we expect individuals from all demographic groups to reduce their movement rate during the hunting season. On the other hand, if bear avoid hunters by escaping, we expect them to increase their movement rate in order to leave high-risk areas faster. We found an increased movement rate in females accompanied by dependent offspring during the morning hours of the bear hunting season, a general decrease in movement rate in adult lone females, and no changes in males and subadult females. The increased movement rate that we observed in females with dependant offspring during the hunting season was likely an antipredator response because it only occurred in areas located closer to roads, whereas the decreased movement rate in lone females could be either part of a seasonal activity patterns or be associated with an increased selection for better concealment. Our study suggests that female brown bears accompanied by offspring likely move faster in high-risk areas to minimize risk exposure as well as the costly trade-offs (i.e., time spent foraging versus time spent hiding) typically associated with anti-predator tactics that involve changes in resource selection. Our study also highlights the importance of modeling fine-scale spatiotemporal variations in risk to adequately capture the complexity in behavioural responses caused by human activities in wildlife.
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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) | 0.006 | 0.002 |
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".