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Carbon isotope discrimination (Δ13C) in C3 woody plants is a key variable for the study of photosynthesis. Yet how Δ13C varies at decadal scales, and across regions, and how it is related to gross primary production (GPP), are still incompletely understood. Here we address these questions by implementing a new Δ13C modelling capability in the land-surface model JULES incorporating both photorespiratory and mesophyll-conductance fractionations. We test the ability of four leaf-internal CO2 concentration models embedded in JULES to reproduce leaf and tree-ring (TR) carbon isotopic data. We show that all the tested models tend to overestimate average Δ13C values, and to underestimate interannual variability in Δ13C. This is likely because they ignore the effects of soil water stress on stomatal behavior. Variations in post-photosynthetic isotopic fractionations across species, sites and years, may also partly explain the discrepancies between predicted and TR-derived Δ13C values. Nonetheless, the “least-cost” (Prentice) model shows the lowest biases with the isotopic measurements, and lead to improved predictions of canopy-level carbon and water fluxes. Overall, modelled Δ13C trends vary strongly between regions during the recent (1979–2016) historical period but stay nearly constant when averaged over the globe. Photorespiratory and mesophyll effects modulate the simulated global Δ13C trend by 0.0015±0.005‰ and –0.0006±0.001‰ ppm−1, respectively. These predictions contrast with previous findings based on atmospheric carbon isotope measurements. Predicted Δ13C and GPP tend to be negatively correlated in wet-humid and cold regions, and in tropical African forests, but positively related elsewhere. The negative correlation between Δ13C and GPP is partly due to the strong dominant influences of temperature on GPP and vapor pressure deficit on Δ13C in those forests. Our results demonstrate that the combined analysis of Δ13C and GPP can help understand the drivers of photosynthesis changes in different climatic regions.

Ecosystem models commonly assume that key photosynthetic traits, such as carboxylation capacity measured at a standard temperature, are constant in time. The temperature responses of modelled photosynthetic or respiratory rates then depend entirely on enzyme kinetics. Optimality considerations, however, suggest this assumption may be incorrect. The “coordination hypothesis” (that Rubisco- and electron-transport-limited rates of photosynthesis are co-limiting under typical daytime conditions) predicts, instead, that carboxylation (Vcmax) capacity should acclimate so that it increases somewhat with growth temperature but less steeply than its instantaneous response, implying that Vcmax when normalized to a standard temperature (e.g. 25 ∘C) should decline with growth temperature. With additional assumptions, similar predictions can be made for electron-transport capacity (Jmax) and mitochondrial respiration in the dark (Rdark). To explore these hypotheses, photosynthetic measurements were carried out on woody species during the warm and the cool seasons in the semi-arid Great Western Woodlands, Australia, under broadly similar light environments. A consistent proportionality between Vcmax and Jmax was found across species. Vcmax, Jmax and Rdark increased with temperature in most species, but their values standardized to 25 ∘C declined. The ci:ca ratio increased slightly with temperature. The leaf N : P ratio was lower in the warm season. The slopes of the relationships between log-transformed Vcmax and Jmax and temperature were close to values predicted by the coordination hypothesis but shallower than those predicted by enzyme kinetics.

Maximum quantum yield for leaf CO2 assimilation under limiting light conditions (Φ CO2LL) is commonly estimated as the slope of the linear regression of net photosynthetic rate against absorbed irradiance over a range of low-irradiance conditions. Methodological errors associated with this estimation have often been attributed either to light absorptance by non-photosynthetic pigments or to some data points being beyond the linear range of the irradiance response, both causing an underestimation of Φ CO2LL. We demonstrate here that a decrease in photosystem (PS) photochemical efficiency with increasing irradiance, even at very low levels, is another source of error that causes a systematic underestimation of Φ CO2LL. A model method accounting for this error was developed, and was used to estimate Φ CO2LL from simultaneous measurements of gas exchange and chlorophyll fluorescence on leaves using various combinations of species, CO2, O2, or leaf temperature levels. The conventional linear regression method under-estimated Φ CO2LL by ca. 10–15 %. Differences in the estimated Φ CO2LL among measurement conditions were generally accounted for by different levels of photorespiration as described by the Farquhar-von Caemmerer–Berry model. However, our data revealed that the temperature dependence of PSII photochemical efficiency under low light was an additional factor that should be accounted for in the model.

Nearly three decades ago Farquhar, von Caemmerer and Berry published a biochemical model for C 3 photosynthetic rates (the FvCB model). The model predicts net photosynthesis ( A ) as the minimum of the Rubisco-limited rate of CO 2 assimilation ( A c ) and the electron transport-limited rate of CO 2 assimilation ( A j ). Given its simplicity and the growing availability of the required enzyme kinetic constants, the FvCB model has been used for a wide range of studies, from analysing underlying C 3 leaf biochemistry to predicting photosynthetic fluxes of ecosystems in response to global warming. However, surprisingly, this model has seen limited use in existing crop growth models. Here we highlight the elegance, simplicity, and robustness of this model. In the light of some uncertainties with photosynthetic electron transport pathways, a recently extended FvCB model to calculate A j is summarized. Applying the FvCB-type model in crop growth models for predicting leaf photosynthesis requires a stomatal conductance ( g s ) model to be incorporated, so that intercellular CO 2 concentration ( C i ) can be estimated. In recent years great emphasis has been put on the significant drawdown of Rubisco carboxylation-site CO 2 concentration ( C c ) relative to C i . To account for this drawdown, mesophyll conductance ( g m ) for CO 2 transfer can be added. We present an analytical algorithm that incorporates a g s model and uses g m as a temperature-dependent parameter for calculating A under various environmental scenarios. Finally we discuss a C 4 -equivalent version of the FvCB model. In addition to the algorithms already elaborated for C 3 photosynthesis, most important algorithms for C 4 photosynthesis are those that capture the CO 2 concentrating mechanism and the extra ATP requirement by the C 4 cycle. Although the current estimation of the C 4 enzyme kinetic constants is less certain, applying FvCB-type models to both C 3 and C 4 crops is recommended to accurately predict the response of crop photosynthesis to multiple, interactive environmental variables.

A high-density plantation of three genotypes of Populus was exposed to an elevated concentration of carbon dioxide ([CO(2)]; 550 micromol mol(-1)) from planting through canopy closure using a free-air CO(2) enrichment (FACE) technique. The FACE treatment stimulated gross primary productivity by 22 and 11% in the second and third years, respectively. Partitioning of extra carbon (C) among C pools of different turnover rates is of critical interest; thus, we calculated net ecosystem productivity (NEP) to determine whether elevated atmospheric [CO(2)] will enhance net plantation C storage capacity. Free-air CO(2) enrichment increased net primary productivity (NPP) of all genotypes by 21% in the second year and by 26% in the third year, mainly because of an increase in the size of C pools with relatively slow turnover rates (i.e., wood). In all genotypes in the FACE treatment, more new soil C was added to the total soil C pool compared with the control treatment. However, more old soil C loss was observed in the FACE treatment compared with the control treatment, possibly due to a priming effect from newly incorporated root litter. FACE did not significantly increase NEP, probably as a result of this priming effect.

The widely used steady-state model of Farquhar et al. (Planta 149: 78-90, 1980) for C-3 photosynthesis was developed on the basis of linear whole-chain (non-cyclic) electron transport. In this model, calculation of the RuBP-regeneration limited CO2-assimilation rate depends on whether it is insufficient ATP or NADPH that causes electron transport limitation. A new, generalized equation that allows co-limitation of NADPH and ATP on electron transport is presented herein. The model is based on the assumption that other thylakoid pathways (the Q-cycle, cyclic photophosphorylation, and pseudocyclic electron transport) interplay with the linear chain to co-contribute to a balanced production of NADPH and ATP as required by stromal metabolism. The original model assuming linear electron transport limited either by NADPH or by ATP, predicts quantum yields for CO2 uptake that represent the highest and the lowest values, respectively, of the range given by the new equation. The applicability of the new equation is illustrated for a number of C-3 crop species, by curve fitting to gas exchange data in the literature. In comparison with the original model, the new model enables analysis of photosynthetic regulation via the electron transport pathways in response to environmental stresses.

The conventional representation of Plant Functional Types (PFTs) in Dynamic Global Vegetation Models (DGVMs) is increasingly recognized as simplistic and lacking in predictive power. Key ecophysiological traits, including photosynthetic parameters, are typically assigned single values for each PFT while the substantial trait variation within PFTs is neglected. This includes continuous variation in response to environmental factors, and differences linked to spatial and temporal niche differentiation within communities. A much stronger empirical basis is required for the treatment of continuous plant functional trait variation in DGVMs. We analyse 431 sets of measurements of leaf and plant traits, including photosynthetic measurements, on evergreen angiosperm trees in tropical moist forests of Australia and China. Confining attention to tropical moist forests, our analysis identifies trait differences that are linked to vegetation dynamic roles. Coordination theory predicts that Rubisco- and electron-transport limited rates of photosynthesis are co-limiting under field conditions. The least-cost hypothesis predicts that air-to-leaf CO2 drawdown minimizes the combined costs per unit carbon assimilation of maintaining carboxylation and transpiration capacities. Aspects of these predictions are supported for within-community trait variation linked to canopy position, just as they are for variation along spatial environmental gradients. Trait differences among plant species occupying different structural and temporal niches may provide a basis for the ecophysiological representation of vegetation dynamics in next-generation DGVMs.

1. Leaf dry mass per unit area (LMA), carboxylation capacity (Vcmax) and leaf nitrogen per unit area (Narea) and mass (Nmass) are key traits for plant functional ecology and ecosystem modelling. There is however no consensus about how these traits are regulated, or how they should be modelled. Here we confirm that observed leaf nitrogen across species and sites can be estimated well from observed LMA and Vcmax at 25˚C (Vcmax25). We then test the hypothesis that global variations of both quantities depend on climate variables in specific ways that are predicted by leaf-level optimality theory, thus allowing both Narea to be predicted as functions of the growth environment. 2. A new global compilation of field measurements was used to quantify the empirical relationships of leaf N to Vcmax25 and LMA. Relationships of observed Vcmax25 and LMA to climate variables were estimated, and compared to independent theoretical predictions of these relationships. Soil effects were assessed by analysing biases in the theoretical predictions. 3. LMA was the most important predictor of Narea (increasing) and Nmass (decreasing). About 60% of global variation across species and sites in observed Narea, and 31% in Nmass, could be explained by observed LMA and V¬cmax25. These traits in turn were quantitatively related to climate variables, with significant partial relationships similar or indistinguishable from those predicted by optimality theory. Predicted trait values explained 21% of global variation in observed site-mean Vcmax25, 43% in LMA, and 31% in Narea. Predicted Vcmax25 was biased low on clay-rich soils but predicted LMA was biased high, with compensating effects on Narea. Narea was overpredicted on organic soils. 4. Synthesis. Global patterns of variation in observed site-mean Narea can be explained by climate-induced variations in optimal Vcmax25¬ and LMA. Leaf nitrogen should accordingly be modelled as a consequence (not a cause) of Vcmax25 and LMA, both being optimized to the environment. Nitrogen limitation of plant growth would then be modelled principally via whole-plant carbon allocation, rather than via leaf-level traits. Further research is required to better understand and model the terrestrial nitrogen and carbon cycles and their coupling.