Fishery‐induced demographic changes in the timing of spawning: consequences for reproductive success*
Bibliographic record
Abstract
Abstract Demography can have a significant effect on reproductive timing and the magnitude of such an effect can be comparable to environmentally induced variability. This effect arises because the individuals of many fish species spawn progressively earlier within a season and may produce more egg batches over a longer period as they get older, thus extending their lifetime spawning duration. Inter‐annual variation in spawning time is a critical factor in reproductive success because it affects the early environmental conditions experienced by progeny and the period they have to complete phases of development. By reducing the average lifetime spawning duration within a fish stock, fishing pressure could be increasing the variability in reproductive success and reducing long‐term stock reproductive potential. Empirical estimates of selection on birth date, from experiments and using otolith microstructure, demonstrate that there is considerable variation in selection on birth date both within a spawning season and between years. The few multi‐year studies that have linked egg production with the survival of progeny to the juvenile stage further highlight the uncertainty that adults face in timing their spawning to optimize offspring survival. The production of many small batches of eggs over a long period of time within a season and over a lifetime is therefore likely to decrease variance and increase mean progeny survival. Quantifying this effect of demography on variability in survival requires a focus on lifetime reproductive success rather than year specific relationships between recruitment and stock reproductive potential. Modelling approaches are suggested that can better quantify the likely impact of changing spawning times on year‐class strength and lifetime reproductive potential. The evidence presented strengthens the need to avoid fishing severely age truncated fish stocks.
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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 | 0.000 | 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) | 0.002 | 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".