Mark-Recapture and Stochastic Population Models for Polar Bears of the High Arctic
Bibliographic record
Abstract
We used mark-recapture data and population viability analysis (PVA) to estimate demographic parameters, abundance, and harvest risks for two adjacent populations of polar bears (Ursus maritimus) inhabiting Lancaster Sound and Norwegian Bay, Canada. Analyses were based on data from 1871 bears that were uniquely marked during the period 1972–97. Our best-fitting mark-recapture model specified sex and age effects on probabilities of survival and an effect of prior recapture (dependence) on capture probability. The most parsimonious solution in our analysis of survival was to assume the same rate for the Lancaster Sound and Norwegian Bay populations. Total (harvested) annual survival rates (mean ± 1 SE) for females included: 0.749 ± 0.105 (cubs), 0.879 ± 0.050 (ages 1–4), 0.936 ± 0.019 (ages 5– 20), and 0.758 ± 0.054 (ages 21+). Mean litter size was 1.69 ± 0.01 cubs for females of Lancaster Sound and 1.71 ± 0.08 cubs for females of Norwegian Bay. By age six, on average 0.31 ± 0.21 females of Lancaster Sound were producing litters (first age of reproduction was five years); however, females of Norwegian Bay did not reproduce until age seven or more. Total abundance (1995–97) averaged 2541 ± 391 bears in Lancaster Sound and 203 ± 44 bears in Norwegian Bay. The finite rate of increase (lambda) during the study period was estimated to be 1.001 ± 0.013 for bears of Lancaster Sound and 0.981 ± 0.027 for bears of Norwegian Bay. We incorporated demographic parameters into a harvest-explicit PVA to model short-term (15 yr) probabilities of overharvesting (i.e., 1997–2012). Our harvest simulations suggest that current levels of kill are approaching and perhaps exceeding the sustainable yield in both populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".