Grey Seal Abundance in Canadian Waters and Harvest Advice
Bibliographic record
Abstract
Here we introduce a new integrated population model (IPM) to provide harvest advice for the Gulf of St. Lawrence (Gulf), Coastal Nova Scotia (CNS) and Sable Island grey seal herds, and compare model outputs with estimates from a deterministic model used in previous assessments. The IPM was fit to the pup production estimates for the Scotian Shelf (CNS and Sable Island combined) and the Gulf. As with the previous assessment model, the new model was fit to both pup production and pregnancy rates, and includes an index for ice-related pup mortality in the Gulf. The new model includes both density-dependent and density-independent pup mortality, and fits to sighting histories of individually marked seals at the breeding colony on Sable Island to estimate sex- and age-specific survival and recruitment to the breeding colony. The model estimated that total pup production increased slightly from 92,300 (95% CI = 86,700–100,100) in 2016 to 99,300 (90,900–107,700) in 2021, while total abundance increased slightly from 339,400 (317,900–361,500) in 2016 to 366,400 (317,800–409,400) in 2021. The rate of growth of the population has continued to slow, declining from approximately 4% during the last assessment period, to 1.5% per year between 2016 and 2021. The updated population estimate from the previously accepted deterministic population model was 363,600 (298,700–450,000) for 2021, which is very similar to the estimate of abundance generated using the IPM. Although the population continues to grow, the current estimate is below that presented during the 2016 assessment. The difference is due to changes in the structure of the new population model and higher estimates of juvenile mortality produced by the model fit to the 2021 pup production estimates. Additional information on juvenile survival and how it responds to changes in abundance (density-dependent) and environmental (density independent) variation is needed as it represents a significant gap to our understanding of the dynamics of this population and of large marine mammals in general. Total allowable removals depend on age structure of the harvest and whether the harvests are conducted in winter at the breeding colonies, or at other times of the year when animals from all herds are mixed. Using an integrated model incorporates many of the inputs in a unified framework that allows for uncertainty to be propagated throughout the analyses.
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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) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".