Variation in plastic responses to light results from selection in different competitive environments - A game theoretical approach using virtual plants
Phenotypic plasticity is a vital strategy for plants to deal with changing conditions by inducing phenotypes favourable in different environments. Understanding how natural selection acts on variation in phenotypic plasticity in plants is therefore a central question in ecology, but is often ignored in modelling studies. Here we present a new modelling approach that allows for the analysis of selection for variation in phenotypic plasticity as a response strategy. We assess selection for shade avoidance strategies of Arabidopsis thaliana in response to future neighbour shading signalled through a decrease in red:far-red (R:FR) ratio. For this, we used a spatially explicit 3D virtual plant model that simulates individual Arabidopsis plants competing for light in different planting densities. Plant structure and growth were determined by the organ-specific interactions with the light environment created by the vegetation structure itself. Shade avoidance plastic responses were defined by a plastic response curve relating petiole elongation and lamina growth to R:FR perceived locally. Different plasticity strategies were represented by different shapes of the response curve that expressed different levels of R:FR sensitivity. Our analyses show that the shape of the selected shade avoidance strategy varies with planting density. At higher planting densities, more sensitive response curves are selected for than at lower densities. In addition, the balance between lamina and petiole responses influences the sensitivity of the response curves selected for. Combining computational virtual plant modelling with a game theoretical analysis represents a new step towards analysing how natural selection could have acted upon variation in shade avoidance as a response strategy, which can be linked to genetic variation and underlying physiological processes.
Franca J. Bongers , Jacob C. Douma, Yoh Iwasa, Ronald Pierik, Jochem B. Evers, Niels P. R. Anten (2019) PLOS Computational Biology (online first)
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