Environmental determinants of larval herring (<i>Clupea harengus</i>) abundance and distribution in the western Baltic Sea
Bibliographic record
Abstract
Abstract The analysis of the abundance and distribution of the early life‐history stages of the western Baltic spring‐spawning herring population, spanning 23 yr, revealed that the abundance of yolk‐sac larvae was strongly spatially structured and significantly more abundant during the optimal temperature window for viable hatch of herring larvae (9°C to 13°C). The percentage of time encompassing this optimal temperature window exhibited a major contraction between 1995 and 2000 that may have contributed to reducing the supply of yolk‐sac larvae and ultimately contributed to reducing recruitment observed after 2000. Temperature also significantly influenced the abundance of preflexion and flexion larvae, defining dome‐shaped responses with maximum abundances observed between 10.27°C and 13.43°C and between 13.68°C and 18.53°C, respectively. The abundances of postflexion stage larvae were greatest at temperatures exceeding 14°C with no apparent decline in abundance at temperatures of up to 20°C. Temperature also played a significant role in influencing the abundance of larvae through its interaction with the abundance of the preceding developmental stage and provided evidence of stage‐specific temperature‐related mortality. There was also evidence of reduced survival during the preflexion–flexion transition in cohorts produced early in the season relative to those cohorts produced later. Although phenologically plastic species such as herring may be able to alter the dates of spawning, thus partially mitigating the effects of increasing rates of spring warming, constriction of the temperature windows for successful hatching and subsequent ontogenetic development may not be so easily mitigated by changes in phenology.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 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".