Environmental variability controls recruitment but with different drivers among spawning components in Gulf of St. Lawrence herring stocks
Bibliographic record
Abstract
Abstract The factors affecting herring recruitment are still poorly understood, complicating the prediction of stock dynamics and the choice of operational management strategies. We investigated effects of intrinsic (SSB) and extrinsic factors (physical and biological environments, including competition and predation) on recruitment of the spring and fall spawning components of each of the two herring stocks occurring in the Gulf of the St. Lawrence between 1971 and 2014. Effects of potential explanatory factors on recruit (age 2) abundance were tested using Generalized Additive Models. Model fit was significantly improved by incorporating both physical and biological environmental variability, but effects of herring SSB and predation were not significant. Indices of zooplankton abundance and phenology explained more variance in recruitment than physical indices. Our results emphasize the dominance of bottom‐up processes over SSB in the regulation of herring recruitment. Environmental variability did not seem to act uniformly on the recruitment of either stock or their respective spawning components. A long‐term trend of decreasing recruitment in spring spawners was associated with a long‐term decline in abundance of cold water copepods. In fall spawners, optimal recruitment was dependent on warmer environmental conditions combined with an adequate supply (species composition and phenology) of zooplankton. These results provide the first empirical evidence that spring and fall spawning herring are adapted to contrasting environmental conditions and shed light on the potential mechanisms linking herring recruitment to key zooplankton community characteristics and phenology. Management strategies can be improved by incorporating this new knowledge on environmental drivers of herring recruitment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".