Human influences on the strength of phenotypic selection
Bibliographic record
Abstract
Human activities are driving rapid phenotypic change in many species, with harvesting considered to be a particularly potent evolutionary force. We hypothesized that faster evolutionary change in human-disturbed populations could be caused by a strengthening of phenotypic selection, for example, if human disturbances trigger maladaptation and/or increase the opportunity for selection. We tested this hypothesis by synthesizing 1,366 phenotypic selection coefficients from 37 species exposed to various anthropogenic disturbances, including harvest. We used a paired design that only included studies measuring selection on the same traits in both human-disturbed and control (not obviously human-disturbed "natural") populations. Surprisingly, this meta-analysis did not reveal stronger selection in human-disturbed environments; in fact, we even found some evidence that human disturbances might slightly reduce selection strength. The only clear exceptions were two fisheries showing very strong harvest selection. On closer inspection, we discovered that many disturbances weakened selection by increasing absolute fitness and by decreasing the opportunity for selection-thus explaining what initially seemed a counterintuitive result. We discuss how human disturbances can sometimes weaken rather than strengthen selection, and why measuring the total effect of disturbances on selection is exceedingly difficult. Despite these challenges, documenting human influences on selection can reveal disturbances with particularly strong effects (e.g., fishing), and thus better inform the management of populations exposed to these disturbances.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".