Urban spatial heterogeneity shapes the evolution of an antiherbivore defense trait and its genes in white clover
Bibliographic record
Abstract
Urbanization is a global threat to biodiversity due to its large impact on environmental changes. Recently, urban environmental change has been shown to impact the evolution of many species. However, much remains unknown about how urban environments influence evolutionary processes and outcomes due to the non‐linearity and discontinuity of environmental variables along urban–rural gradients. Here, we focused on the evolution of hydrogen cyanide (HCN) production and its components (presence/absence of cyanogenic glycosides and the hydrolytic enzyme linamarase) in the herbaceous plant white clover Trifolium repens , which thrive in both urban and rural areas. To comprehensively elucidate how plants evolve and adapt to heterogenous urban environments, we collected 3299 white clover plants from 122 populations throughout Sapporo, Japan. We examined the spatial variation in environmental factors, such as herbivory, sky openness, impervious surface cover, snow depth, and temperature, and how variation in these factors was related to the production of HCN, cyanogenic glycosides, and linamarase. Environmental factors showed complex spatial variation due to the heterogeneity of the urban landscape. Among these factors, herbivory, sky openness, and impervious surface cover were highly related to the frequency of plants producing HCN in populations. We also found that impervious surface cover was related to the frequency of plants producing cyanogenic glycosides, while herbivory pressure was not. As a result, the cyanogenic glycoside frequency showed a clearer trend along urban–rural gradient rather than HCN frequency, and thus, the predicted spatial distributions of HCN and cyanogenic glycosides were inconsistent. These results suggest that urban landscape heterogeneity and trait multifunctionality determines mosaic‐like spatial distribution of evolutionary traits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".