Using network connectance and autonomy analyses to uncover patterns of photosynthetic responses in tropical woody species

Daily courses of leaf gas exchange and chlorophyll fluorescence in forest gap and understorey environments were used to build photosynthetic networks in two pioneers and two late-successional species. Photochemical and gas exchange networks were linked to each other by the relationship between electron transport rate and net CO2 assimilation. Global network connectance (Cg), which represents the mean strength of connections within a given network, was calculated in the photochemical and gas exchange networks for both functional groups and environments. Autonomy in relation to environmental fluctuations was estimated considering the mean correlation between environmental and physiological data. Cg was consistently higher in plants under gap condition. High daily-amplitude of environmental variables in the gap induced strong connectance in photochemical and gas exchange networks regardless of functional group. Gap scenario demands network modulation with higher level of control than understorey, which would be attained by strong connections among components of photochemical and gas exchange networks. This would allow fine and fast tuning adjustments when facing highly variable and demanding environmental conditions throughout a day. As a consequence of this highly variable environment, both functional groups showed lower autonomy in the gap, where higher coupling between leaf physiology and environmental fluctuations was evident. Our results suggest that high plant-environment coupling demands high network connectance. Contrastingly, Cg was lower (especially in photochemical network) under forest understorey, promoting autonomy in a more stable environment. Our results indicate that there is a conservative pattern of photosynthesis control based on network modulation and environmental coupling. This suggests that changes in network connectance may not be specific of a functional group but rather a more general response to environmental fluctuations, strongly related to system stability. We consider this information crucial in understanding how complex adaptive systems deal with environmental fluctuations.