Economic repercussions of fisheries-induced evolution
Bibliographic record
Abstract
Fish stocks experiencing high fishing mortality show a tendency to mature earlier and at a smaller size, which may have a genetic component and therefore long-lasting economic and biological effects. To date, the economic effects of such ecoevolutionary dynamics have not been empirically investigated. Using 70 y of data, we develop a bioeconomic model for Northeast Arctic cod to compare the economic yield in a model in which life-history traits can vary only through phenotypic plasticity with a model in which, in addition, genetic changes can occur. We find that evolutionary changes toward faster growth and earlier maturation occur consistently even if a stock is optimally managed. However, if a stock is managed optimally, the evolutionary changes actually increase economic yield because faster growth and earlier maturation raise the stock's productivity. The optimal fishing mortality is almost identical for the evolutionary and nonevolutionary model and substantially lower than what it has been historically. Therefore, the costs of ignoring evolution under optimal management regimes are negligible. However, if fishing mortality is as high as it has been historically, evolutionary changes may result in economic losses, but only if the fishery is selecting for medium-sized individuals. Because evolution facilitates growth, the fish are younger and still immature when they are susceptible to getting caught, which outweighs the increase in productivity due to fish spawning at an earlier age.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".