Back

Road to ruin: Herbivory in a common tropical weed (Turnera subulata) along a rural-urban gradient

Moura, A.; Rivkin, L. R.

2020-09-28 ecology
10.1101/2020.09.26.314765 bioRxiv
Show 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.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.