Bioeconomic consequences of fishing-induced evolution: a model predicts limited impact on net present value
Bibliographic record
Abstract
Fishing reduces stock size and shifts demographics, and selective mortality may also lead to evolutionary changes. Previous studies suggest that traits may change evolutionarily because of fishing on decadal time scales. Here we examine the potential bioeconomic impacts of fishing-induced evolutionary change. We used a life-history model with stock dynamics based on evolving maturation age, which has consequences for size-at-age, coupled with a fishing module that describes costs and economic yield. Size-dependent natural mortality and trawl-like fishing mortality are drivers of selection, and in the analysis we varied fishing mortality and size-selectivity of the fishing gear to determine trait evolution as well as economic yield. Comparison of two scenarios — allowing for evolution and assuming no evolution — shows that under current size selectivity, the fishing regimes generating maximum economic yield are not different when evolution is accounted for. However, ignoring evolution overestimates long-term yield under optimal fishing regimes and underestimates resilience to overfishing. Whether fishing-induced evolution matters for management strategies depends on size selectivity, stock state, how it acts on specific traits, and its sensitivity to the assumed discount rate, calling for a cautious use of net present value as sole criterion for management of evolving resources.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".