The nature of fisheries‐ and farming‐induced evolution
Bibliographic record
Abstract
Humans have a penchant for unintentionally selecting against that which they desire most. In fishes, unprecedented reductions in abundance have been associated with unprecedented changes in harvesting and aquaculture technologies. Fishing, the predominant cause of fish-population collapses, is increasingly believed to generate evolutionary changes to characters of import to individual fitness, population persistence and levels of sustainable yield. Human-induced genetic change to wild populations can also result from interactions with their domesticated counterparts. Our examination of fisheries- and farming-induced evolution includes factors that may influence the magnitude, rate and reversibility of genetic responses, the potential for shifts in reaction norms and reduced plasticity, loss of genetic variability, outbreeding depression and their demographic consequences to wild fishes. We also suggest management initiatives to mitigate the effects of fisheries- and farming-induced evolution. Ultimately, the question of whether fishing or fish farming can cause evolutionary change is moot. The key issue is whether such change is likely to have negative conservation- or socio-economic consequences. Although the study of human-induced evolution on fishes should continue to include estimates of the magnitude and rate of selection, there is a critical need for research that addresses short- and long-term demographic consequences to population persistence, plasticity, recovery and productivity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".