Temporal dynamics of human-polar bear conflicts in Churchill, Manitoba
Bibliographic record
Abstract
Identifying factors that influence human-wildlife conflicts is essential to the management of these interactions. Polar bears (Ursus maritimus) come into conflict with humans and these conflicts may become more frequent as the bears spend more time on land due to climate warming induced sea ice loss. To reduce human-bear conflicts, polar bears near Churchill, Manitoba, Canada, are deterred from human areas or caught, held temporarily, and relocated by wildlife officials. We evaluated data for 2061 bear captures intended to reduce human-bear conflicts from 1970 to 2018 to understand temporal dynamics relative to population trends and sea ice indices. On average, 42 different conflict bears/year (SE = 3.6, range = 3 to 110) were handled. The number of conflict bears increased up to a 2001 breakpoint with no trend afterwards. The proportion of conflict bears relative to the population size increased until a breakpoint in 1998 with no trend afterwards. The mean age of conflict bears was 5.5 years (SE = 0.01, range = 1 to 31) and increased over time from 2.6 in 1970 to 6.7 in 2018. Pooling years, subadults were the most common group in conflict and comprised 55% of the bears handled. Age/sex class composition varied significantly before and after the 2001 breakpoint, with subadults comprising a lower proportion of conflict bears after the breakpoint. We found different temporal trends in the number of bears caught in each age/sex class, as well as the entire population, suggesting that multiple factors were involved. The number of conflict bears increased with the length of the ice-free period and there was a positive interaction between abundance and year on the number of conflict bears, indicating that when abundance was higher, the effect of year was higher. Observed changes may be associated with increasing effects of climate change on body condition, longer on-land periods, altered migration routes, altered summering habitat, and food-seeking behaviour. Definitive explanations for the patterns, however, are challenged by shifts in management activities.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".