Fishing, predation, and temperature drive herring decline in a large marine ecosystem
Bibliographic record
Abstract
Since 1960, landings of Atlantic herring have been the greatest of any marine species in Canada, surpassing Atlantic cod and accounting for 24% of the total seafood harvested in Atlantic Canada. The Scotian Shelf-Bay of Fundy herring fisheries (NAFO Division 4VWX) is among Canada's oldest and drives this productivity, accounting for up to 75% of the total herring catch in some years. The stocks' productivity and overall health have declined since 1965. Despite management measures to promote recovery implemented since 2003, biomass remains low and is declining. The factors that drive the productivity of 4VWX herring are primarily unresolved, likely impeding the effectiveness of management actions on this stock. We evaluated potential drivers of herring variability by analyzing 52 time-series that describe the temporal and spatial evolution of the 4VWX herring population and the physical, ecological, and anthropogenic factors that could affect them using structural equation models. Variation in herring biomass was best accounted for by the exploitation rate's negative effect and the geographic distribution of fishing and recruitment. Thermal phenology and temperature adversely and egg predation positively impacted the early life stage mortality rate and, ultimately, adult biomass. These findings are broadly relevant to fisheries management, but particularly for 4VWX herring, where the current management approach does not consider their early life stage dynamics or assess them within the ecosystem or climate change contexts.
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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".