Variation in reproductive potential and influence on Icelandic herring recruitment
Bibliographic record
Abstract
Abstract Explaining recruitment variation in fish is essential for successful fishery management and is consequently under constant review, with an increasing focus on how maternal factors, relative to environmental influences, operate at the level of individual female spawners and extend from the spawning stock through to recruitment. We estimate total egg production (E) in Icelandic summer‐spawning herring (Clupea harengus) from 1963 through 1999 by using sequential population analyses (SPA) and their estimates of stock biomass and recruitment, various size and maturity metrics, and individual fecundity estimates that rely on total length and the condition of the spawners. Generalized linear models indicate that maternal effects are of significance in explaining SPA‐based recruitment‐at‐age‐3 (R). The best model explained 64% of the variation in R and incorporates E constrained to the repeat spawners (40%), the NAO winter index (18%) and ocean temperature (6%). The latter two represent the winter and spring periods subsequent to year‐class formation. Recruit spawner contributions to E were of no significance in explaining variation in R despite the fact that they could contribute as much as 55% of E when their contribution to E was consistently underestimated by a factor of ∼ 2, based only on their contribution to spawning stock biomass. We conclude that the spawning potential of the repeat spawners should replace total spawning stock biomass for determining recruitment potential in stock assessment. In addition to the incorporation of oceanographic factors, this would provided a more cautious and risk‐adverse approach.
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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".