Linking environmental variability and parental attributes to recruitment dynamics of the Southwest Nova Scotia/Bay of fundy Atlantic herring stock
Bibliographic record
Abstract
Abstract High recruitment variability in marine small pelagic species, such as Atlantic herring (Clupea harengus), poses major challenges for stock assessment and resource management. A 48-year time series (1970–2017) of recruitment was analyzed using Generalized Additive Models (GAMs) and Empirical Dynamic Modeling (EDM) to evaluate the relative importance of environmental drivers and parental attributes in impacting recruitment of Southwest Nova Scotia/Bay of Fundy (SWNS/BoF) herring. Both modeling approaches identified ocean stratification, Spawning Stock Biomass (SSB), parental average length, and body condition as key predictors of recruitment. Stratification was identified to be the dominant driver, likely enhancing recruitment through larval retention in gyre and frontal systems and improved adult condition via prey concentration. In contrast, temperature, salinity, and haddock abundance had little detectable influence on recruitment. Despite some data limitations, our results provide both statistical and mechanistic evidence suggesting that environmental variability and parental attributes jointly influence herring recruitment across life stages, highlighting the importance of SSB, size structure, and parental condition. Incorporating these covariates into stock assessment frameworks could improve assessment accuracy, strengthen ecosystem-based management, and support recovery of the SWNS/BoF herring stock.
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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 | 0.000 | 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".