The eco-evolutionary impacts of domestication and agricultural practices on wild species
Bibliographic record
Abstract
Agriculture is a dominant evolutionary force that drives the evolution of both domesticated and wild species. However, the various mechanisms of agriculture-induced evolution and their socio-ecological consequences are not often synthetically discussed. Here, we explore how agricultural practices and evolutionary changes in domesticated species cause evolution in wild species. We do so by examining three processes by which agriculture drives evolution. First, differences in the traits of domesticated species, compared with their wild ancestors, alter the selective environment and create opportunities for wild species to specialize. Second, selection caused by agricultural practices, including both those meant to maximize productivity and those meant to control pest species, can lead to pest adaptation. Third, agriculture can cause non-selective changes in patterns of gene flow in wild species. We review evidence for these processes and then discuss their ecological and sociological impacts. We finish by identifying important knowledge gaps and future directions related to the eco-evolutionary impacts of agriculture including their extent, how to prevent the detrimental evolution of wild species, and finally, how to use evolution to minimize the ecological impacts of agriculture.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 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".