bioRxiv | 2019
Floral signals evolve in a predictable way under artificial and pollinator selection in Brassica rapa using a G-matrix
Abstract
Background Angiosperms employ an astonishing variety of visual and olfactory floral signals that are generally thought to evolve under natural selection. Mathematical tools for predicting multiple traits have been developed for decades and have advanced our understanding of evolution in various biological systems. Nevertheless, very few studies have yet attempted to predict the evolutionary trajectories of floral traits, particularly when considering a comprehensive set of genetically correlated floral traits. Results We used data from an artificial and a pollinator (bumblebee, hoverfly) selection experiment with fast cycling Brassica rapa plants to predict evolutionary changes of 12 floral volatiles and 4 morphological floral traits in response to selection. Using the observed selection gradients and the genetic variance-covariance matrix (G-matrix) of the traits, we showed that the responses of most floral traits including volatiles were predicted well in artificial- and bumblebee-selection experiment. Genetic covariance had a mixed of constrained and facilitated effects on evolutionary responses. We further revealed that G-matrix also evolved in the selection processes. Nevertheless, the ancestral G-matrix can still be used for predicting micro-evolutionary scenarios. Conclusions Overall, our integrative study shows that floral signals, and especially volatiles, evolve under selection in a mostly predictable way, at least during short term evolution. Evolutionary constraints stemming from genetic covariance affected traits evolutionary trajectories and thus it is important to include genetic covariance for predicting the evolutionary changes of a comprehensive suite of traits. Other processes such as resource limitation and selfing also needs to be considered for a better understanding of floral trait evolution.