From genes to populations: how fisheries‐induced evolution alters stock productivity
Bibliographic record
Abstract
By removing individuals with certain heritable characteristics such as large body size, harvesting may induce rapid evolutionary change in fish life history. There is controversy, however, as to the prevalence of fisheries-induced evolution (FIE) and to what extent it should be considered as part of sustainable resource management. Recent research has shown that FIE can be difficult to detect and its economic effects might not always be significant. Here, we show how population growth rate (r), a critical factor affecting sustainability and recovery, is affected by FIE through the analysis of a simulation model that demonstrates the link between individual-level genetic processes and stock dynamics. We examine how different levels of evolvability, fishing intensity, and density-dependence interact to influence r in three commercially harvested species: Atlantic cod (Gadus morhua), lake whitefish (Coregonus clupeaformis), and yellow perch (Perca flavescens). We demonstrate that at low harvest levels, evolution has minimal effect on r for all three species. However, at the harvest rates experienced by many fish stocks, evolution increases r and reduces the risk of collapse for cod and whitefish. During the initial stages of a harvest moratorium, a switch occurs, and r becomes reduced as a consequence of evolution. These results explain how evolution increases stock resilience, but also impedes recovery after periods of intense harvesting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".