Recovery From Reduction: The M’Clintock Channel Polar Bear Subpopulation, Nunavut, Canada
Bibliographic record
Abstract
To retain viable polar bear subpopulations, scientific monitoring studies are conducted to inform adaptive management frameworks. Here we report the results of the second structured population study for polar bears in the M’Clintock Channel (MC) subpopulation. Data included biopsy samples collected during a 2014 – 16 subpopulation-wide survey, live mark-recapture data collected during the first subpopulation study from 1998 to 2000, and harvest recovery data from 1998 to 2016. Results of a closed capture-recapture model, implemented in a Bayesian framework for animals over 2 yr., produced a mean abundance estimate of 716 (95% Credible Interval = 545 – 955) for 2014 – 16, indicating an increase from the 1998 – 2000 study estimate (284; our Bayesian-calculated estimate: 325 bears). However, closed model assumptions mean our estimate represents the superpopulation. Mean litter sizes did not differ between study periods, but mean number of yearlings per adult female declined from 0.39 ± 0.10 (SE) to 0.27 ± 0.06 between 1998 – 2000 and 2014 – 16. Apparent survival estimates from observed data were biased low (0.88 ± 0.02) due to unknown immigration and emigration. However, survival calculated using the change in abundance estimates between study periods equaled 0.93, representing a population growth rate of 2%. Body condition improved between study periods. Our findings indicate the MC subpopulation recovered from overharvesting between 1979 and 1999 and may be transiently benefitting from increased biological productivity associated with local sea ice changes. Our demographic analyses were constrained by low density, low harvest, small sample sizes, low recapture probability, and lack of movement information; hence, harvest management decisions should be applied with appropriate caution.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".