Drivers of Atlantic herring decline and evidence basis for fisheries closures and rebuilding plans
Bibliographic record
Abstract
Atlantic herring ( Clupea harengus) are economically and ecologically significant but have been in decline in Atlantic Canada due to an uncertain combination of environmental recruitment controls, predation, and fishing (commercial fishery and poorly documented bait removals). Fisheries and Oceans Canada partially closed Atlantic mackerel ( Scomber scombrus) and herring fisheries in March 2022 amid controversy and disagreement. Here, we develop a conceptual model for natural and anthropogenic controls on herring abundance centered on the southern Gulf of St. Lawrence (sGSL) and evaluate the likely importance of each. We provide the first estimates of the magnitude of bait fishery withdrawals in the sGSL. The decline in sGSL herring is likely driven by natural predation exacerbated by fishing. Bait fishery removals were 182 tonnes in 2021, suggesting that previous estimates for removals of spring-spawning herring were ∼30% too low. Fisheries closure is consistent with a precautionary approach given uncertainties and irreversibility of stock collapse. Ecosystemic models are needed but will be difficult to develop given the incomplete understanding of prey substitutability and net effect of interacting environmental processes.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".