Increased herbivory but not cyanogenesis is associated with urbanization in a tropical wildflower
Bibliographic record
Abstract
Abstract Urbanization is associated with numerous changes to the biotic and abiotic environments, many of which lead to a loss of biodiversity. Some studies have documented increased herbivory rates in cities, which has been hypothesized to lead to the evolution of novel defence traits in plants. Yet evidence supporting this hypothesis is contradictory and entirely absent from South American cities. To address this research gap, we evaluated herbivory rates in the native urban wildflower, Turnera subulata (Turneraceae), along an urbanization gradient in Joao Pessoa, Brazil. We predicted that higher rates of herbivory in cities would lead to the expression of cyanogenesis, a chemical defence found in a closely related Turnera species. We estimated herbivory rates and screened for cyanogenesis in 32 populations along the urbanization gradient, quantified by the Human Footprint Index and the amount of impervious surface surrounding each site. We found herbivory rates increased in T. subulata populations with increasing urbanization, but we did not find evidence of cyanogenesis in any of the populations. Our results suggest that although herbivores respond positively to urbanization, the fitness effects of leaf herbivory may be insufficient to select for the evolution of cyanogenesis in some plants. Our results provide valuable insight into the effects of urbanization on plant‐herbivore interactions in the tropics.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".