Polar Bear Harvest Patterns Across the Circumpolar Arctic
Bibliographic record
Abstract
Wildlife harvest remains a conservation concern for many species and assessing patterns of harvest can provide insights on sustainability and inform management. Polar bears ( Ursus maritimus ) are harvested over a large part of their range by local people. The species has a history of unsustainable harvest that was largely rectified by an international agreement that required science-based management. The objective of our study was to examine the temporal patterns in the number of polar bears harvested, harvest sex ratios, and harvest rates from 1970 to 2018. We analyzed data from 39,049 harvested polar bears (annual mean 797 bears) collected from 1970 to 2018. Harvest varied across populations and times that reflect varying management objectives, episodic events, and changes based on new population estimates. More males than females were harvested with an overall M:F sex ratio of 1.84. Harvest varied by jurisdiction with 68.0% of bears harvested in Canada, 18.0% in Greenland, 11.8% in the USA, and 2.2% in Norway. Harvest rate was often near the 4.5% target rate. Where data allowed harvest rate estimation, the target rate was exceeded in 11 of 13 populations with 1–5 populations per year above the target since 1978. Harvest rates at times were up to 15.9% of the estimated population size suggesting rare episodes of severe over-harvest. Harvest rate was unrelated to a proxy for ecosystem productivity (area of continental shelf within each population) but was correlated with prey diversity. In the last 5–10 years, monitored populations all had harvest rates near sustainable limits, suggesting improvements in management. Polar bear harvest management has reduced the threat it once posed to the species. However, infrequent estimates of abundance, new management objectives, and climate change have raised new concerns about the effects of harvest.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".