An integrated population model for the Belcher Islands - Eastern Hudson Bay (BEL-EHB) Beluga Whale Stock
Bibliographic record
Abstract
The Belcher Islands Eastern Hudson Bay (BEL-EHB) beluga stock is one of eight stocks found in Canada. It is one of the least abundant, with Eastern Hudson Bay beluga being currently designated as Threatened by COSEWIC. The BEL-EHB stock is harvested for subsistence by Nunavik communities and the Nunavut community of Sanikiluaq. Population models are used to estimate the population abundance and trend of the BEL-EHB stock, and to provide scientific advice on harvest levels compatible with identified management objectives. The BEL-EHB stock was last assessed using a stochastic Bayesian surplus-production model (SPM) incorporating information on harvest numbers, genetic-based composition of the harvest and aerial survey estimates. Several additional sources of information have now become available and can be used to produce a more comprehensive assessment of the stock. Here, we developed a new Integrated Population Model (IPM) for BEL-EHB beluga that integrates female reproductive status, as well as harvest sex and age composition as additional sources of information into the model structure. In addition, annual stochasticity was incorporated in the IPM as a component of mortality in addition to harvesting. We present the structure of the newly developed IPM and then compare the modeled population trends and abundance estimates to those from the SPM used in previous assessments. The IPM and SPM predicted similar overall population abundances and trends. However, uncertainties in model-based predictions were reduced with the IPM, and the most recent estimates of abundance diverged between modeling approaches. The IPM predicted that the BEL-EHB stock abundance was 3,600 in 2023, with a rate of decline between 2014-2023 corresponding to 2.24%. The 2023 estimate of BEL-EHB abundance using the SPM was 2,600 and the rate of decline between 2014-2023 was 3.82%. Both models yielded similar predictions of BEL-EHB beluga landings and carrying capacity. The IPM provided new information about the BEL-EHB stock that could not be obtained with the SPM. For instance, the IPM yielded insights into age-specific survival and pregnancy rates, as well as temporal trends in those demographic rates. Further, the IPM estimated that mortality resulting from harvest (including struck and lost) accounts for 19% to 43% of total annual mortality, with an increasing trend in recent years.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".