Evolutionary Genomics of Flowering Behaviour in the Perennial Model Species Arabis alpina
Flowering behavior is a crucial component of plant fitness that is tuned to local environments by cues such as day length and winter temperatures. A fitness trade-off between the time of flowering in response to environment and the size of the plant at reproduction is expected to result in stabilizing selection within populations. However, empirical observations frequently report directional selection favoring early flowering. Here, we will study this long-standing paradox by focusing on Arabis alpina populations under selection for the late flowering strategy and comparing these with other populations in which early-flowering occurs. We will use computational and population genomic approaches to reveal adaptive dynamics at known flowering genes, as well as to identify new players in this process. Performing these studies in the perennial A. alpina is expected to reveal factors that contribute to the complexity in flowering behavior and its evolutionary consequences that are not detected in the annual model Arabidopsis thaliana. The characterization of these intriguing examples of adaptation towards an unusual phenotypic optimum will shed light on the evolutionary forces governing flowering behavior in natural populations.
The project will be co-supervised by Andrea Fulgione and George Coupland at the Max Planck Institute for Plant Breeding Research.