Human influences on evolution, and the ecological and societal consequences
Bibliographic record
Abstract
Humans have dramatic, diverse and far-reaching influences on the evolution of other organisms. Numerous examples of this human-induced contemporary evolution have been reported in a number of 'contexts', including hunting, harvesting, fishing, agriculture, medicine, climate change, pollution, eutrophication, urbanization, habitat fragmentation, biological invasions and emerging/disappearing diseases. Although numerous papers, journal special issues and books have addressed each of these contexts individually, the time has come to consider them together and thereby seek important similarities and differences. The goal of this special issue, and this introductory paper, is to promote and expand this nascent integration. We first develop predictions as to which human contexts might cause the strongest and most consistent directional selection, the greatest changes in evolutionary potential, the greatest genetic (as opposed to plastic) changes and the greatest effects on evolutionary diversification We then develop predictions as to the contexts where human-induced evolutionary changes might have the strongest effects on the population dynamics of the focal evolving species, the structure of their communities, the functions of their ecosystems and the benefits and costs for human societies. These qualitative predictions are intended as a rallying point for broader and more detailed future discussions of how human influences shape evolution, and how that evolution then influences species traits, biodiversity, ecosystems and humans.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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 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".