ORIGINAL ARTICLE: Implications of fisheries‐induced evolution for stock rebuilding and recovery
Bibliographic record
Abstract
Worldwide depletion of fish stocks has led fisheries managers to become increasingly concerned about rebuilding and recovery planning. To succeed, factors affecting recovery dynamics need to be understood, including the role of fisheries-induced evolution. Here we investigate a stock's response to fishing followed by a harvest moratorium by analyzing an individual-based evolutionary model parameterized for Atlantic cod Gadus morhua from its northern range, representative of long-lived, late-maturing species. The model allows evolution of life-history processes including maturation, reproduction, and growth. It also incorporates environmental variability, phenotypic plasticity, and density-dependent feedbacks. Fisheries-induced evolution affects recovery in several ways. The first decades of recovery were dominated by demographic and density-dependent processes. Biomass rebuilding was only lightly influenced by fisheries-induced evolution, whereas other stock characteristics such as maturation age, spawning stock biomass, and recruitment were substantially affected, recovering to new demographic equilibria below their preharvest levels. This is because genetic traits took thousands of years to evolve back to preharvest levels, indicating that natural selection driving recovery of these traits is weaker than fisheries-induced selection was. Our results strengthen the case for proactive management of fisheries-induced evolution, as the restoration of genetic traits altered by fishing is slow and may even be impractical.
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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 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".