Modeling population dynamics of beluga whales in the Eastern High Arctic – Baffin Bay population
Bibliographic record
Abstract
Abstract Beluga whales (Delphinapterus leucas) have a long history of subsistence harvest that has continued into the present and a history of exploitation through commercial harvest. The Eastern High Arctic – Baffin Bay (EHA) population of beluga whales has the northernmost distribution of any beluga whale population in Canada. Beluga whales from the EHA population have a single complete abundance estimate from 1996 and 3 partial abundance estimates in 1981, 2010, and 2012 from aerial surveys; reliable estimates from surveys are lacking in recent years, limiting the ability to determine population dynamics. In 2020 satellite imagery was used to estimate abundance for beluga whales in the EHA population for estuaries in the whales' summering area. We built a stochastic stock production model to estimate population dynamics from the start of reliably compiled harvest history data in 1977 to 2022, using abundance estimates from 1981, 1996, 2010, 2012, and 2020. We also extended the model 10 years into the future under 7 potential annual harvest scenarios (within the range of annual reported harvests) to calculate the probability of decline. We estimated the starting population in 1977 as 29,615 whales (95% CI = 20,765–46,251), and the estimate from 2022 was 16,495 whales (95% CI = 6,731–33,504). Landed catch of 0, 100, 200, 300, 400, 500, and 600 beluga whales annually resulted in <1%, 3%, 23%, 50%, 70%, 83%, and 90% probabilities of decline, respectively. There was a notable decrease in abundance over the time series, likely caused by harvest pressure. Harvest in recent years has ranged from 155–553 catches per year, and our results suggest that 108 landed catches per year equates to a 5% risk of decline, the low risk goal for precautionary fisheries management, which aligns with population conservation goals. Our model is a first step in understanding the EHA beluga whale population dynamics using what data are available and suggests that current harvests in some years may result in a population decline and should be monitored.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".