How light competition between plants affects trait optimization and vegetation-atmosphere feedbacks

Abstract

How plants respond to climate change is of major concern, as plants will strongly impact future ecosystem functioning, food production and climate. Competition between plants for resources is an important selective force. As a result competition through natural selection determines vegetation functioning and thereby strongly influence plant responses to climate change and associated atmospheric interactions. In this thesis I analysed how vegetation structure and functioning is influenced by climate change and defined to which extent these effects are modified by competition, in order to identify how strong the associated feedbacks with the atmosphere are. We first focussed on leaf level responses to climate change when N allocation within the photosynthetic machinery is optimized. And showed that the simulated increase in leaf photosynthesis with CO2 elevation was considerably higher when N allocation responded optimally than when this allocation was fixed. A similar but weaker trend was found for warming. Additionally, we showed that even small time lags between environmental change and trait optimization had a large negative effect on the simulated photosynthetic performance. Next, a canopy model was developed and different degrees of light competition between neighbouring plants through canopy mixing were considered. We found that when competition was considered in this model, the optimal response shifts to producing larger leaf areas, but with lower stomatal conductance and associated vegetation transpiration than when competition was not considered. Furthermore, only when competition was considered were model predictions on the effects of the traits observed by a large number of Free Air CO2 Enrichment (FACE) experiments as well as the seasonal dynamics of LAI accurately. Besides, our experimental results from plants originating from naturally elevated CO2 and from ambient CO2 also showed the importance of competition, as changes in CO2 favoured the genotypes that were more competitive and not the ones that had the highest performance. Finally, we used a coupled vegetation-atmosphere model and included plant competition. We showed when competition is considered the model accurately predicts a number of atmospheric state variables and vegetation responses of the diurnal data from Ameriflux Bondville site over a growing season. Additionally, including competition also resulted in a less negative net ecosystem exchange of CO2, and resulting in an increased atmospheric CO2 concentration. In conclusion, the results presented in this thesis show the importance of considering the time scale of leaf-level responses to environmental changes in optimization models. In addition, we showed that plants respond mostly on the short term time scale to elevated CO2 and not on the long term time scale. More generally, the results underline the importance of plant competition on vegetation functioning and its influence on atmospheric processes. Furthermore, our results strongly imply that the negative feedback of plants extra carbon uptake under elevated CO2 would be less strong. From the results in my thesis I can thus conclude that when attempting to predict vegetation responses to climate change it is very important to consider competition between plants as it strongly modifies these responses.