Human influences on rates of phenotypic change in wild animal populations
Bibliographic record
Abstract
Human activities can expose populations to dramatic environmental perturbations, which may then precipitate adaptive phenotypic change. We ask whether or not phenotypic changes associated with human-disturbed (anthropogenic) contexts are greater than those associated with more 'natural' contexts. Our meta-analysis is based on more than 3000 rates of phenotypic change in 68 'systems', each representing a given species in a particular geographical area. We find that rates of phenotypic change are greater in anthropogenic contexts than in natural contexts. This difference may be influenced by phenotypic plasticity - because it was evident for studies of wild-caught individuals (which integrate both genetic and plastic effects) but not for common-garden or quantitative genetic studies (which minimize plastic effects). We also find that phenotypic changes in response to disturbance can be remarkably abrupt, perhaps again because of plasticity. In short, humans are an important agent driving phenotypic change in contemporary populations. Although these changes sometimes have a genetic basis, our analyses suggest a particularly important contribution from phenotypic plasticity.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".