Harvesting as a potential selective pressure on behavioural traits
Bibliographic record
Abstract
Summary Human activities are a major evolutionary force affecting wild populations. Selective pressure from harvest has mainly been documented for life‐history and morphological traits. The probability for an individual to be harvested, however, may also depend on its behaviour. We report empirical studies that examined whether harvesting can exert selective pressures on behavioural traits. We show that harvest‐induced selection on behavioural traits is not specific to a particular harvest method and can occur throughout the animal kingdom. Synthesis and applications . Managers need to recognize that artificial selection caused by harvesting is possible. More empirical studies integrating physiological, behavioural, and life‐history traits should be carried out to test specific predictions of the potential for harvest‐induced selection on heritable traits using models developed in fisheries. To limit selective pressure on behaviour imposed by harvesting, managers could reduce harvest quotas or vary harvest regulations over time and/or space to reduce the strength of selection on a particular phenotype.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".