Harvest-induced evolution: insights from aquatic and terrestrial systems
Bibliographic record
Abstract
Commercial and recreational harvests create selection pressures for fitness-related phenotypic traits that are partly under genetic control. Consequently, harvesting can drive evolution in targeted traits. However, the quantification of harvest-induced evolutionary life history and phenotypic changes is challenging, because both density-dependent feedback and environmental changes may also affect these changes through phenotypic plasticity. Here, we synthesize current knowledge and uncertainties on six key points: (i) whether or not harvest-induced evolution is happening, (ii) whether or not it is beneficial, (iii) how it shapes biological systems, (iv) how it could be avoided, (v) its importance relative to other drivers of phenotypic changes, and (vi) whether or not it should be explicitly accounted for in management. We do this by reviewing findings from aquatic systems exposed to fishing and terrestrial systems targeted by hunting. Evidence from aquatic systems emphasizes evolutionary effects on age and size at maturity, while in terrestrial systems changes are seen in weapon size and date of parturition. We suggest that while harvest-induced evolution is likely to occur and negatively affect populations, the rate of evolutionary changes and their ecological implications can be managed efficiently by simply reducing harvest intensity.This article is part of the themed issue 'Human influences on evolution, and the ecological and societal consequences'.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.002 | 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".