Road to ruin? Urbanization increases herbivory in a tropical wildflower but does not lead to the evolution of chemical defence traits
Bibliographic record
Abstract
Abstract Urbanization is associated with numerous changes to the biotic and abiotic environment, many of which degrade the environment and lead to a loss of biodiversity. Cities often have elevated pollution levels that harm wildlife; however, the increased concentration of some pollutants can fertilize urban plants, leading to corresponding positive effects on herbivore populations. Increases in herbivory rates may lead to natural selection for greater defence phenotypes in plants. However, evidence supporting increased herbivory leading to the evolution of plant defence in urban environments is contradictory, and entirely absent from tropical regions of the world. To address these research gaps, we evaluated herbivory on Turnera subulata , a common urban wildflower, along an urbanization gradient in Joao Pessoa, Brazil. We predicted that higher rates of herbivory in urban areas would lead these populations to evolve cyanogenesis, a chemical defence found in a closely related Turnera species. We assessed herbivory and screened for cyanogenesis in 32 populations along the urbanization gradient, quantified by the Human Footprint Index. Our results show that urbanization is significantly associated with increased herbivory rates in T. subulata populations. Despite elevated herbivory, we found no evidence for the evolution of cyanongenesis in any of the populations, suggesting that the fitness effects of leaf herbivory are not extreme enough to select for the evolution of plant defence in these populations. Habitat loss, predator release, and nutrient enrichment likely act together to increase the abundance of herbivorous arthropods, influencing the herbivory patterns observed in our study.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".