Understanding factors influencing Atlantic herring (<scp><i>Clupea harengus</i></scp>) recruitment: From egg deposition to juveniles
Bibliographic record
Abstract
Abstract Recruitment is a critical component of population dynamics and variability in recruitment underpins large fluctuations in population abundances of commercially valuable marine fishes. Marine pelagic fishes such as Atlantic herring (Clupea harengus) experience relatively high variability in recruitment that is driven by a wide range of biotic and abiotic factors. The relative importance and interaction of each factor for determining recruitment is poorly understood, and consequently, recruitment estimates are one of the largest uncertainties in fisheries management and predictions of future population sizes. Poor recruitment of Atlantic herring has been identified as a major issue and bottleneck for the species; therefore, factors influencing successful recruitment are of great interest to fisheries managers. Here we review studies that have examined the factors influencing survival at the egg stage, early larval stage, late larval stage, and juvenile stage to develop a more comprehensive understanding of the recruitment of Atlantic herring and provide recommendations to guide future research. We identified nine biotic and eight abiotic factors that have been found to substantially impact the recruitment of Atlantic herring, with temperature, prey availability, and predation being the most commonly investigated factors. We conclude it is not one factor that primarily determines recruitment, but rather a collection of many factors that vary temporally and spatially that drive the large variation observed in Atlantic herring recruitment year over year. A holistic approach is required to better understand recruitment and improve fisheries management decisions regarding Atlantic herring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".