A novel mark-recapture-recovery survey using genetic sampling for polar bears Ursus maritimus in Baffin Bay
Bibliographic record
Abstract
Changes in sea-ice dynamics are affecting polar bears Ursus maritimus across their circumpolar range, which highlights the importance of periodic demographic assessments to inform management and conservation. We used genetic mark-recapture-recovery to derive estimates of abundance and survival for the Baffin Bay (BB) polar bear subpopulation—the first time this method has been used successfully for this species. Genetic data from tissue samples we collected via biopsy darting were combined with historical physical capture and harvest recovery data. The combined data set consisted of 1410 genetic samples (2011-2013), 914 physical captures (1993-1995, 1997), and 234 harvest returns of marked bears (1993-2013). The estimate of mean subpopulation abundance was 2826 (95% CI = 2284-3367) in 2012-2013. Estimates of annual survival (mean ± SE) were 0.90 ± 0.05 and 0.78 ± 0.06 for females and males age ≥2 yr, respectively. The proportion of total mortality of adult females and males that was attributed to legal harvest was 0.16 ± 0.05 and 0.26 ± 0.06, respectively. Remote sensing sea-ice data, telemetry data, and spatial distribution of onshore sampling indicated that polar bears were more likely to use offshore sea-ice habitat during the 1990s sampling period compared to the 2010s. Furthermore, in the 1990s, sampling of deep fjords and inland areas was limited, and no offshore sampling occurred in either time period, which precluded comparisons of abundance between the 1993-1997 and 2011-2013 study periods. Our findings demonstrate that genetic sampling can be a practical method for demographic assessment of polar bears over large spatial and temporal scales.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".