Forecasting the response of a recovered pinniped population to sustainable harvest strategies that reduce their impact as predators
Bibliographic record
Abstract
Abstract The recovery of marine mammal populations has led to increased predation on commercially valuable prey species, creating conflicts with fisheries and calls for predator control. Grey seals are important predators of Atlantic Cod and Winter Skate in the southern Gulf of St. Lawrence (sGSL), and both species are likely to be extirpated unless grey seal presence in that ecosystem is strongly reduced. We aimed to identify harvest strategies that reduced grey seal presence in the sGSL to levels that favour fish recovery while maintaining grey seal conservation goals. We fit an integrated population model to grey seal abundance, reproductive and mark-recapture data, and projected future presence in the sGSL while varying the magnitude and age-composition of the annual commercial quota. We found that both removal and conservation targets could be met with annual quotas of 6000 seals if 50% of hunted seals were young of the year (YOY), though small amounts of overhunting reduced seal abundance below limit reference levels. Harvest strategies that targeted higher proportions of YOY were less likely to trigger conservation concerns, though these strategies required much larger quotas to achieve removal targets.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".