Demographic response of a high-Arctic polar bear (Ursus maritimus) subpopulation to changes in sea ice and subsistence harvest
Bibliographic record
Abstract
Climate change is a long-term threat to polar bears. However, sea-ice loss is hypothesized to provide transient benefits in high latitudes, where thick multiyear ice historically limited biological productivity and seal abundance. We used joint live-recapture and dead-recovery mark-recapture models to analyze data for one of the most northerly polar bear subpopulations, Kane Basin. The data consisted of 277 initial live captures and genetic identifications (1992-1997 = 150, 2012-2014 = 127), 89 recaptures or re-identifications (1992-1997 = 53, 2012-2014 = 36), and 24 harvest returns of research-marked bears during 1992-2014. We estimated mean annual abundance of 357 bears (95% CI: 221-493) for 2013-2014. This suggests a likely increase relative to our estimate of 224 (95% CI: 145-303) bears in the mid-1990s and relative to a previously published estimate of 164 (95% CI: 94-234) bears in the mid-1990s that used some of the same data. This is also supported by an apparent increase in the density of bears in eastern Kane Basin during 2012-2014. Estimates of total survival for females ≥3 yr old (mean ± SE: 0.95 ± 0.04) and their dependent offspring were similar to previous estimates from the 1990s, and estimates of unharvested survival for females ≥3 yr (0.96 ± 0.04) appear sufficient for positive population growth. Estimates of total survival were lower for males ≥3 yr (0.87 ± 0.06). We documented a reduction in mortality associated with subsistence harvest, likely attributable to implementation of a harvest quota by Greenland in 2006. Our findings, together with evidence for increased range sizes, improved body condition for all sex and age classes, and stable reproductive metrics, show that this small high-Arctic polar bear subpopulation remains productive and healthy. These benefits are likely temporary given predictions for continued climate change.
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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".