Dataset Information

Climate forcing due to optimization of maximal leaf conductance in subtropical vegetation under rising CO2.

ABSTRACT: Plant physiological adaptation to the global rise in atmospheric CO(2) concentration (CO(2)) is identified as a crucial climatic forcing. To optimize functioning under rising CO(2), plants reduce the diffusive stomatal conductance of their leaves (g(s)) dynamically by closing stomata and structurally by growing leaves with altered stomatal densities and pore sizes. The structural adaptations reduce maximal stomatal conductance (g(smax)) and constrain the dynamic responses of g(s). Here, we develop and validate models that simulate structural stomatal adaptations based on diffusion of CO(2) and water vapor through stomata, photosynthesis, and optimization of carbon gain under the constraint of a plant physiological cost of water loss. We propose that the ongoing optimization of g(smax) is eventually limited by species-specific limits to phenotypic plasticity. Our model reproduces observed structural stomatal adaptations and predicts that adaptation will continue beyond double CO(2). Owing to their distinct stomatal dimensions, angiosperms reach their phenotypic response limits on average at 740 ppm and conifers on average at 1,250 ppm CO(2). Further, our simulations predict that doubling today's CO(2) will decrease the annual transpiration flux of subtropical vegetation in Florida by ≈60 W·m(-2). We conclude that plant adaptation to rising CO(2) is altering the freshwater cycle and climate and will continue to do so throughout this century.

SUBMITTER: de Boer HJ

PROVIDER: S-EPMC3053960 | biostudies-literature | 2011 Mar

REPOSITORIES: biostudies-literature

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Climate forcing due to optimization of maximal leaf conductance in subtropical vegetation under rising CO2.

de Boer Hugo Jan HJ Lammertsma Emmy I EI Wagner-Cremer Friederike F Dilcher David L DL Wassen Martin J MJ Dekker Stefan C SC

Proceedings of the National Academy of Sciences of the United States of America 20110217 10

Plant physiological adaptation to the global rise in atmospheric CO(2) concentration (CO(2)) is identified as a crucial climatic forcing. To optimize functioning under rising CO(2), plants reduce the diffusive stomatal conductance of their leaves (g(s)) dynamically by closing stomata and structurally by growing leaves with altered stomatal densities and pore sizes. The structural adaptations reduce maximal stomatal conductance (g(smax)) and constrain the dynamic responses of g(s). Here, we devel ...[more]

PMID: 21330553

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Project description:Forests play a crucial role in the global carbon (C) cycle by storing and sequestering a substantial amount of C in the terrestrial biosphere. Due to temporal dynamics in climate and vegetation activity, there are significant regional variations in carbon dioxide (CO2) fluxes between the biosphere and atmosphere in forests that are affecting the global C cycle. Current forest CO2 flux dynamics are controlled by instantaneous climate, soil, and vegetation conditions, which carry legacy effects from disturbances and extreme climate events. Our level of understanding from the legacies of these processes on net CO2 fluxes is still limited due to their complexities and their long-term effects. Here, we combined remote sensing, climate, and eddy-covariance flux data to study net ecosystem CO2 exchange (NEE) at 185 forest sites globally. Instead of commonly used non-dynamic statistical methods, we employed a type of recurrent neural network (RNN), called Long Short-Term Memory network (LSTM) that captures information from the vegetation and climate's temporal dynamics. The resulting data-driven model integrates interannual and seasonal variations of climate and vegetation by using Landsat and climate data at each site. The presented LSTM algorithm was able to effectively describe the overall seasonal variability (Nash-Sutcliffe efficiency, NSE = 0.66) and across-site (NSE = 0.42) variations in NEE, while it had less success in predicting specific seasonal and interannual anomalies (NSE = 0.07). This analysis demonstrated that an LSTM approach with embedded climate and vegetation memory effects outperformed a non-dynamic statistical model (i.e. Random Forest) for estimating NEE. Additionally, it is shown that the vegetation mean seasonal cycle embeds most of the information content to realistically explain the spatial and seasonal variations in NEE. These findings show the relevance of capturing memory effects from both climate and vegetation in quantifying spatio-temporal variations in forest NEE.

Dataset Information

Climate forcing due to optimization of maximal leaf conductance in subtropical vegetation under rising CO2.

Publications

Climate forcing due to optimization of maximal leaf conductance in subtropical vegetation under rising CO2.

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