Combined effects of exploitation and environmental change on life history: a comparative analysis on Atlantic herring
Bibliographic record
Abstract
Abstract The consequences of fisheries-induced evolution on stock productivity and yield depend, to a large extent, on the general prospects for growth and survival. Here, we compare the selection pressures imposed by two distinct patterns of exploitation—principally targeting spawning or non-spawning aggregations—on age at maturity among 15 Canadian stocks of Atlantic herring (Clupea harengus) that have exhibited a consistent pattern of length-at-age responses to common large-scale environmental drivers since the 1960s. In accordance with expectations for maturity-dependent harvesting, the establishment of a spawner-targeted fishery in the southern Gulf of St. Lawrence coincided with a shift towards delayed maturity in both resident stocks, whereas stocks elsewhere subject to fisheries that also exploited juveniles more commonly exhibited trends towards earlier maturity. Despite these differences, we find that environmentally driven changes in length at maturation, combined with total mortality, may overwhelmingly determine lifetime reproductive success and possibly fitness. By linking phenotypic changes experienced in the juvenile period to simple correlates of egg production in mature age classes, our study highlights the importance of managing fisheries in the context of ubiquitous but contrasting environmental constraints on life histories.
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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 | 0.001 |
| 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 | 0.000 | 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".