Urbanization drives the evolution of parallel clines in plant populations
Bibliographic record
Abstract
Urban ecosystems are an increasingly dominant feature of terrestrial landscapes. While evidence that species can adapt to urban environments is accumulating, the mechanisms through which urbanization imposes natural selection on populations are poorly understood. The identification of adaptive phenotypic changes (i.e. clines) along urbanization gradients would facilitate our understanding of the selective factors driving adaptation in cities. Here, we test for phenotypic clines in urban ecosystems by sampling the frequency of a Mendelian-inherited trait-cyanogenesis-in white clover (Trifolium repens L.) populations along urbanization gradients in four cities. Cyanogenesis protects plants from herbivores, but reduces tolerance to freezing temperatures. We found that the frequency of cyanogenic plants within populations decreased towards the urban centre in three of four cities. A field experiment indicated that spatial variation in herbivory is unlikely to explain these clines. Rather, colder minimum winter ground temperatures in urban areas compared with non-urban areas, caused by reduced snow cover in cities, may select against cyanogenesis. In the city with no cline, high snow cover might protect plants from freezing damage in the city centre. Our study suggests that populations are adapting to urbanization gradients, but regional climatic patterns may ultimately determine whether adaptation occurs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".