Code and Data for "Effects of urbanization on selection, local adaptation, and eco-evolutionary feedbacks"
Bibliographic record
Abstract
R script and data files for reproducing the analyses presented in the mansucript "Effects of urbanization on selection, local adaptation, and eco-evolutionary feedbacks" by Ella Martin & Marc T.J. Johnson (2025). Analyses were conducted in R v. 4.4.2. Here, we conducted a reciprocal transplant experiment using white clover (Trifolium repens) from urban and rural populations in 5 urban common gardens and 5 rural common gardens. Half of the plants in gardens produced the antiherbivore chemical defense hydrogen cyanide (HCN), and the other half lacked the defence, since this trait is known to exhibit genetic clines along urbanization gradients. We measured multiple fitness traits and ecological interactions with herbivores, pollinators, and mutualistic root bacteria. We detected divergent selection on HCN between urban and rural environments, where HCN improved fitness in rural environments and reduced fitness in urban environments. Furthermore, there was genetic divergence between urban and rural white clover populations that drove a tradeoff in life history strategies, whereby urban plants invested more in vegetative growth whereas rural plants produced more flowers and seeds. Finally, we demonstrate eco-evolutionary feedbacks, with increased herbivory at rural sites, and increased pollinator visitation to acyanogenic plants at urban sites.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.382 | 0.146 |
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".