The use and misuse of V(c,max) in Earth System Models

A guide to photosynthetic gas exchange measurements: Fundamental principles, best practice and potential pitfalls

Gas exchange measurements enable mechanistic insights into the processes that underpin carbon and water fluxes in plant leaves which in turn inform understanding of related processes at a range of scales from individual cells to entire ecosytems. Given the importance of photosynthesis for the global climate discussion it is important to (a) foster a basic understanding of the fundamental principles underpinning the experimental methods used by the broad community, and (b) ensure best practice and correct data interpretation within the research community. In this review, we outline the biochemical and biophysical parameters of photosynthesis that can be investigated with gas exchange measurements and we provide step-by-step guidance on how to reliably measure them. We advise on best practices for using gas exchange equipment and highlight potential pitfalls in experimental design and data interpretation. The Supporting Information contains exemplary data sets, experimental protocols and data-modelling routines. This review is a community effort to equip both the experimental researcher and the data modeller with a solid understanding of the theoretical basis of gas-exchange measurements, the rationale behind different experimental protocols and the approaches to data interpretation.

Photosynthetic capacity in middle-aged larch and spruce acclimates independently to experimental warming and elevated CO2

Photosynthetic acclimation to both warming and elevated CO2 of boreal trees remains a key uncertainty in modelling the response of photosynthesis to future climates. We investigated the impact of increased growth temperature and elevated CO2 on photosynthetic capacity (Vcmax and Jmax) in mature trees of two North American boreal conifers, tamarack and black spruce. We show that Vcmax and Jmax at a standard temperature of 25°C did not change with warming, while Vcmax and Jmax at their thermal optima (Topt) and growth temperature (Tg) increased. Moreover, Vcmax and Jmax at either 25°C, Topt or Tg decreased with elevated CO2. The Jmax/Vcmax ratio decreased with warming when assessed at both Topt and Tg but did not significantly vary at 25°C. The Jmax/Vcmax increased with elevated CO2 at either reference temperature. We found no significant interaction between warming and elevated CO2 on all traits. If this lack of interaction between warming and elevated CO2 on the Vcmax, Jmax and Jmax/Vcmax ratio is a general trend, it would have significant implications for improving photosynthesis representation in vegetation models. However, future research is required to investigate the widespread nature of this response in a larger number of species and biomes.

Elevated CO2 concentration enhance carbon and nitrogen metabolism and biomass accumulation of Ormosiahosiei

Elevated CO2 concentrations may inhibit photosynthesis due to nitrogen deficiency, but legumes may be able to overcome this limitation and continue to grow. Our study confirms this conjecture well. First, we placed the two-year-old potted saplings of Ormosia hosiei (O. hosiei) (a leguminous tree species) in the open-top chamber (OTC) with three CO2 concentrations of 400 (CK), 600 (E1), and 800 μmol·mol-1 (E2) to simulate the elevated CO2 concentration environment. After 146 days, the light saturation point (LSP), light compensation point (LCP), apparent quantum efficiency (AQE), and dark respiration rate (Rd) of O. hosiei were increased under increasing CO2 concentration and obtain the maximum ribulose diphosphate (RuBP) carboxylation rate (Vc max) and RuBP regenerated photosynthetic electron transfer rate (Jmax) were also significantly increased under E2 treatment (P < 0.05). This results in a significant increase of the maximum assimilation rate (Amax) under elevated CO2 concentrations. Sucrose phosphate synthase (SPS) activity in sucrose metabolism increased in the leaves, more soluble sugars, starches, and sucrose was produced, but sucrose content only in leaves increased at E2, and more carbon flows to the roots. The activity of the NH4+ assimilating enzymes glutamine synthetase (GS), glutamate synthetase (GOGAT), and glutamate dehydrogenase (GDH) in the leaves of O. hosiei increases under elevated CO2 concentrations to promote nitrogen synthesis that reduces the content of ammonium nitrogen and increases the content of nitrate nitrogen. In addition, under E1 conditions, sucrose synthase (SS), direction of synthesis activity was highest and sucrose invertase (INV) activity was lowest, this means that the balance of C and N metabolism is maintained. While under E2 conditions SS activity decreased and INV activity increased, this increased C/N and nitrogen use efficiency. So, the elevated CO2 concentration promotes the accumulation of O. hosiei biomass, especially in the aboveground part, but did not have a significant effect on the accumulation of root biomass. This means that O. hosiei is able to cope under the elevated CO2 concentration without showing photosynthetic adaptation during the experimental period.

GasanalyzeR: advancing reproducible research using a new R package for photosynthesis data workflows

The analysis of photosynthetic traits has become an integral part of plant (eco-)physiology. Many of these characteristics are not directly measured, but calculated from combinations of several, more direct, measurements. The calculations of such derived variables are based on underlying physical models and may use additional constants or assumed values. Commercially available gas-exchange instruments typically report such derived variables, but the available implementations use different definitions and assumptions. Moreover, no software is currently available to allow a fully scripted and reproducible workflow that includes importing data, pre-processing and recalculating derived quantities. The R package gasanalyzer aims to address these issues by providing methods to import data from different instruments, by translating photosynthetic variables to a standardized nomenclature, and by optionally recalculating derived quantities using standardized equations. In addition, the package facilitates performing sensitivity analyses on variables or assumptions used in the calculations to allow researchers to better assess the robustness of the results. The use of the package and how to perform sensitivity analyses are demonstrated using three different examples.

The importance of species-specific and temperature-sensitive parameterisation of A/Ci models: A case study using cotton (Gossypium hirsutum L.) and the automated 'OptiFitACi' R-package

Leaf gas exchange measurements are an important tool for inferring a plant's photosynthetic biochemistry. In most cases, the responses of photosynthetic CO2 assimilation to variable intercellular CO2 concentrations (A/Ci response curves) are used to model the maximum (potential) rate of carboxylation by ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco, Vcmax) and the rate of photosynthetic electron transport at a given incident photosynthetically active radiation flux density (PAR; JPAR). The standard Farquhar-von Caemmerer-Berry model is often used with default parameters of Rubisco kinetic values and mesophyll conductance to CO2 (gm) derived from tobacco that may be inapplicable across species. To study the significance of using such parameters for other species, here we measured the temperature responses of key in vitro Rubisco catalytic properties and gm in cotton (Gossypium hirsutum cv. Sicot 71) and derived Vcmax and J2000 (JPAR at 2000 µmol m-2 s-1 PAR) from cotton A/Ci curves incrementally measured at 15°C-40°C using cotton and other species-specific sets of input parameters with our new automated fitting R package 'OptiFitACi'. Notably, parameterisation by a set of tobacco parameters produced unrealistic J2000:Vcmax ratio of <1 at 25°C, two- to three-fold higher estimates of Vcmax above 15°C, up to 2.3-fold higher estimates of J2000 and more variable estimates of Vcmax and J2000, for our cotton data compared to model parameterisation with cotton-derived values. We determined that errors arise when using a gm,25 of 2.3 mol m-2 s-1 MPa-1 or less and Rubisco CO2-affinities in 21% O2 (KC 21%O2) at 25°C outside the range of 46-63 Pa to model A/Ci responses in cotton. We show how the A/Ci modelling capabilities of 'OptiFitACi' serves as a robust, user-friendly, and flexible extension of 'plantecophys' by providing simplified temperature-sensitivity and species-specificity parameterisation capabilities to reduce variability when modelling Vcmax and J2000.

Reduced global plant respiration due to the acclimation of leaf dark respiration coupled with photosynthesis

Leaf dark respiration (Rd ) acclimates to environmental changes. However, the magnitude, controls and time scales of acclimation remain unclear and are inconsistently treated in ecosystem models. We hypothesized that Rd and Rubisco carboxylation capacity (Vcmax ) at 25°C (Rd,25 , Vcmax,25 ) are coordinated so that Rd,25 variations support Vcmax,25 at a level allowing full light use, with Vcmax,25 reflecting daytime conditions (for photosynthesis), and Rd,25 /Vcmax,25 reflecting night-time conditions (for starch degradation and sucrose export). We tested this hypothesis temporally using a 5-yr warming experiment, and spatially using an extensive field-measurement data set. We compared the results to three published alternatives: Rd,25 declines linearly with daily average prior temperature; Rd at average prior night temperatures tends towards a constant value; and Rd,25 /Vcmax,25 is constant. Our hypothesis accounted for more variation in observed Rd,25 over time (R2  = 0.74) and space (R2  = 0.68) than the alternatives. Night-time temperature dominated the seasonal time-course of Rd , with an apparent response time scale of c. 2 wk. Vcmax dominated the spatial patterns. Our acclimation hypothesis results in a smaller increase in global Rd in response to rising CO2 and warming than is projected by the two of three alternative hypotheses, and by current models.

Wood-density has no effect on stomatal control of leaf-level water use efficiency in an Amazonian forest

Forest disturbances increase the proportion of fast-growing tree species compared to slow-growing ones. To understand their relative capacity for carbon uptake and their vulnerability to climate change, and to represent those differences in Earth system models, it is necessary to characterise the physiological differences in their leaf-level control of water use efficiency and carbon assimilation. We used wood density as a proxy for the fast-slow growth spectrum and tested the assumption that trees with a low wood density (LWD) have a lower water-use efficiency than trees with a high wood density (HWD). We selected 5 LWD tree species and 5 HWD tree species growing in the same location in an Amazonian tropical forest and measured in situ steady-state gas exchange on top-of-canopy leaves with parallel sampling and measurement of leaf mass area and leaf nitrogen content. We found that LWD species invested more nitrogen in photosynthetic capacity than HWD species, had higher photosynthetic rates and higher stomatal conductance. However, contrary to expectations, we showed that the stomatal control of the balance between transpiration and carbon assimilation was similar in LWD and HWD species and that they had the same dark respiration rates.

Seasonal trends in leaf-level photosynthetic capacity and water use efficiency in a North American Eastern deciduous forest and their impact on canopy-scale gas exchange

Vegetative transpiration (E) and photosynthetic carbon assimilation (A) are known to be seasonally dynamic, with changes in their ratio determining the marginal water use efficiency (WUE). Despite an understanding that stomata play a mechanistic role in regulating WUE, it is still unclear how stomatal and nonstomatal processes influence change in WUE over the course of the growing season. As a result, limited understanding of the primary physiological drivers of seasonal dynamics of canopy WUE remains one of the largest uncertainties in earth system model projections of carbon and water exchange in temperate deciduous forest ecosystems. We investigated seasonal patterns in leaf-level physiological, hydraulic, and anatomical properties, including the seasonal progress of the stomatal slope parameter (g1 ; inversely proportional to WUE) and the maximum carboxylation rate (Vcmax ). Vcmax and g1 were seasonally variable; however, their patterns were not temporally synchronized. g1 generally showed an increasing trend until late in the season, while Vcmax peaked during the midsummer months. Seasonal progression of Vcmax was primarily driven by changes in leaf structural, and anatomical characteristics, while seasonal changes in g1 were most strongly related to changes in Vcmax and leaf hydraulics. Using a seasonally variable Vcmax and g1 to parameterize a canopy-scale gas exchange model increased seasonally aggregated A and E by 3% and 16%, respectively.

Soil nitrogen fertilization reduces relative leaf nitrogen allocation to photosynthesis

The connection between soil nitrogen availability, leaf nitrogen, and photosynthetic capacity is not perfectly understood. Because these three components tend to be positively related over large spatial scales, some posit that soil nitrogen positively drives leaf nitrogen, which positively drives photosynthetic capacity. Alternatively, others posit that photosynthetic capacity is primarily driven by above-ground conditions. Here, we examined the physiological responses of a non-nitrogen-fixing plant (Gossypium hirsutum) and a nitrogen-fixing plant (Glycine max) in a fully factorial combination of light by soil nitrogen availability to help reconcile these competing hypotheses. Soil nitrogen stimulated leaf nitrogen in both species, but the relative proportion of leaf nitrogen used for photosynthetic processes was reduced under elevated soil nitrogen in all light availability treatments due to greater increases in leaf nitrogen content than chlorophyll and leaf biochemical process rates. Leaf nitrogen content and biochemical process rates in G. hirsutum were more responsive to changes in soil nitrogen than those in G. max, probably due to strong G. max investments in root nodulation under low soil nitrogen. Nonetheless, whole-plant growth was significantly enhanced by increased soil nitrogen in both species. Light availability consistently increased relative leaf nitrogen allocation to leaf photosynthesis and whole-plant growth, a pattern that was similar between species. These results suggest that the leaf nitrogen-photosynthesis relationship varies under different soil nitrogen levels and that these species preferentially allocated more nitrogen to plant growth and non-photosynthetic leaf processes, rather than photosynthesis, as soil nitrogen increased.

Comparative physiology of canopy tree leaves in evergreen and deciduous forests in lowland Thailand

The typical seasonally dry forests in Southeast Asia are the mixed deciduous forest (MDF), dry dipterocarp (deciduous) forest (DDF), and dry evergreen forest (DEF). We obtained 21 physiological traits in the top/sunlit leaves of 107, 65 and 51 tree species in MDF, DEF and DDF, respectively. Approximately 70%, 95% and 95% of canopy tree species which consist of MDF, DEF and DDF are sampled, respectively. Light-saturated photosynthetic rates (Asat) exhibit a positive correlation with foliar nitrogen (N) and phosphorus (P) on leaf mass and area bases across tree species. Decreased leaf mass-based P reduces the positive slope of the mass-based N and Asat relationship across species and habitats. The differences in nutrient and water use and leaf habits are well matched to the variation in soil properties among the forest types, highlighting the reliability of this comprehensive database for revealing the mechanism of niche segregation based on edaphic factors.

Optimized leaf storage and photosynthetic nitrogen trade-off promote synergistic increases in photosynthetic rate and photosynthetic nitrogen use efficiency

A coordinated increase in the photosynthetic rate (A) and photosynthetic nitrogen use efficiency (PNUE) is an effective strategy for improving crop yield and nitrogen (N) utilization efficiency. PNUE tends to decrease with increasing N levels, but there are natural variations. Consequently, leaf functional N partitioning in Brassica napus genotypes under different N rates was measured to explore the optimized N allocation model for synchronously increasing A and PNUE values. The results showed that genotypes whose PNUE increased with increasing N supply (PNUE-I) produced an approximate A value with a relatively low leaf N content, owing to reduced storage N (Nstore ) and close photosynthetic N (Npsn ) content. Partial least squares path modeling showed that A was dominated by the Npsn content, and PNUE was directly influenced by A and Nstore . The A value increased with the Npsn content until the Npsn content exceeded the threshold value. The boundary line of PNUE varied with the Npsn and Nstore proportions, indicating that the optimum Npsn and Nstore proportions were 51.6% and 40.3%, respectively. The Nstore proportion of PNUE-I was closer to the thresholds and benefited from lower increments in Rubisco content and nonprotein form storage N content with improved N supply. Optimized Nstore and Npsn trade-off by regulating increments in Nstore content with increased N supply, thereby promoting coordinated increases in A and PNUE.

Seasonal dynamics of photosynthetic nitrogen content and partitioning in deciduous forests

Nitrogen allocated to the photosynthetic apparatus and its partitioning into different photosynthetic components is crucial for understanding plant carbon gain and plant productivity. It is known that photosynthetic nitrogen content and partitioning are controlled by both environmental and vegetation factors and have versatile and dynamic responses. However, such responses are greatly simplified in most current gas exchange models, in which only a prescribed relationship is commonly applied to describe the effect of nitrogen on photosynthesis and with limited model performance. While within-canopy variation at a specific time in leaf photosynthetic nitrogen content and partitioning has been studied previously, far less attention has been paid to the seasonal dynamics of photosynthetic nitrogen content and partitioning, which is especially critical to deciduous forests. In this study, we integrated long-term field observations in deciduous forests in Japan to determine seasonal patterns of photosynthetic nitrogen content and partitioning (rubisco, electron transport, and light capture) and to examine how photosynthetic nitrogen content and partitioning varied seasonally in deciduous forest canopies growing at different altitudes. The results demonstrated that there were remarkable seasonal variations in both photosynthetic nitrogen content and partitioning in deciduous forests along the altitudinal gradient. Moreover, photosynthetic nitrogen use efficiency was well explained by nitrogen partitioning rather than total leaf nitrogen. These results suggest that seasonal patterns of nitrogen partitioning should be integrated into ecosystem models to accurately project emergent properties of ecosystem productivity on local, regional, and global scales.

Rubisco deactivation and chloroplast electron transport rates co-limit photosynthesis above optimal leaf temperature in terrestrial plants

Net photosynthetic CO₂ assimilation rate (A_n) decreases at leaf temperatures above a relatively mild optimum (T_opt) in most higher plants. This decline is often attributed to reduced CO₂ conductance, increased CO₂ loss from photorespiration and respiration, reduced chloroplast electron transport rate (J), or deactivation of Ribulose-1,5-bisphosphate Carboxylase Oxygenase (Rubisco). However, it is unclear which of these factors can best predict species independent declines in A_n at high temperature. We show that independent of species, and on a global scale, the observed decline in A_n with rising temperatures can be effectively accounted for by Rubisco deactivation and declines in J. Our finding that A_n declines with Rubisco deactivation and J supports a coordinated down-regulation of Rubisco and chloroplast electron transport rates to heat stress. We provide a model that, in the absence of CO₂ supply limitations, can predict the response of photosynthesis to short-term increases in leaf temperature.

Application of the rapid leaf A-Ci response (RACiR) technique: examples from evergreen broadleaved species

Using steady-state photosynthesis-intercellular CO₂ concentration (A-C_i) response curves to obtain the maximum rates of ribulose-1,5-bisphosphate carboxylase oxygenase carboxylation (V_cmax) and electron transport (J_max) is time-consuming and labour-intensive. Instead, the rapid A-C_i response (RACiR) technique provides a potential, high-efficiency method. However, efficient parameter settings of RACiR technique for evergreen broadleaved species remain unclear. Here, we used Li-COR LI-6800 to obtain the optimum parameter settings of RACiR curves for evergreen broadleaved trees and shrubs. We set 11 groups of CO₂ gradients ([CO₂]), i.e. R1 (400-1500 ppm), R2 (400-200-800 ppm), R3 (420-20-620 ppm), R4 (420-20-820 ppm), R5 (420-20-1020 ppm), R6 (420-20-1220 ppm), R7 (420-20-1520 ppm), R8 (420-20-1820 ppm), R9 (450-50-650 ppm), R10 (650-50 ppm) and R11 (650-50-650 ppm), and then compared the differences between steady-state A-C_i and RACiR curves. We found that V_cmax and J_max calculated by steady-state A-C_i and RACiR curves overall showed no significant differences across 11 [CO₂] gradients (P > 0.05). For the studied evergreens, the efficiency and accuracy of R2, R3, R4, R9 and R10 were higher than the others. Hence, we recommend that the [CO₂] gradients of R2, R3, R4, R9 and R10 could be applied preferentially for measurements when using the RACiR technique to obtain V_cmax and J_max of evergreen broadleaved species.

Do Tree Size and Tree Shade Tolerance Affect the Photosynthetic Capacity of Broad-Leaved Tree Species?

(1) Background: leaf structure traits are closely related to leaf photosynthesis, reflecting the ability of trees to obtain external resources in the process of growth. (2) Methods: We studied the morphological, chemical, anatomical, stomatal traits and maximum net photosynthetic rate of six broad-leaf species in northern temperate mixed broad-leaved Korean pine (Pinus koraiensis) forest. (3) Aim: To investigate whether there are differences in leaf structural traits of trees with different shade tolerances and different sizes and the effects of these differences on leaf photosynthetic capacity. (4) Results: the effects of leaf structure traits on leaf photosynthesis were different among trees with different shade tolerances or different sizes. Under the condition of light saturation, the net photosynthetic rate, nitrogen use efficiency, phosphorus use efficiency and stomatal conductance of shade-intolerant trees or small trees were higher than those of shade-tolerant trees or large trees. (5) Conclusions: the shade tolerance of tree species or the size of trees affect the traits of leaf structure and indirectly affect the photosynthetic ability of plants. When constructing the leaf trait-photosynthesis model, the shade tolerance and tree size of tree species should be taken into account.

Temporal instability of partial least squares regressions for estimating leaf photosynthetic traits from hyperspectral information

Partial least squares regression (PLSR) is applied increasingly often to predict plant photosynthesis from reflectance spectra. While its applicability across different areas has been examined in previous studies, its stability across time has yet to be evaluated. In this study, we assessed a series of PLSR models built upon three different band selection approaches (iterative stepwise, genetic algorithm, and uninformative variable elimination), in combination with different spectral transforms (original and first-order derivative spectra), for their stabilities in predicting the maximum carboxylation rate (Vcmax) and maximum electron transport rate (Jmax) from hyperspectral reflectance spectra at different temporal scales (seasonal and interannual). The results showed that both photosynthetic parameters can be estimated from leaf hyperspectral reflectance with moderate to good accuracy across different growing stages (R² = 0.45-0.84) and years (R² = 0.37-0.97). We further found that the iterative stepwise selection of informative bands when building PLSR models could greatly improve its predictive capacity compared with that of other PLSR models, especially those based on first-order derivative spectra. However, the selected bands of the models for both photosynthetic parameters were, unfortunately not consistent. Furthermore, we could not have identified any model with fixed spectra performed consistently across different seasonal stages and across different years. However, the blue spectral regions were popularly selected throughout the growing stages and in different years. The results demonstrate that leaf spectra-trait estimation using PLSR models varies with time and thus cast doubt over the use of a specific PLSR model to infer leaf traits across different temporal-spatial contexts. The development of a general applicable PLSR model is still in the works.

Co-elevation of atmospheric [CO2] and temperature alters photosynthetic capacity and instantaneous water use efficiency in rice cultivars in a cold-temperate region

Crop photosynthetic capacity in response to climate change likely constrains crop productivity and adaptability to changing environments, which requests the investigation on the dynamics of photosynthetic parameters over growth season among varieties, especially in cold-temperate regions. Three Japonica rice cultivars i.e., Shoubaimao (SH), Hejiang 19 (HJ); Longjing 31, (LJ). were planted under the control, e[CO₂] (700 μmol mol^-1), warming (2°C above the air temperature) and the co-elevation of [CO₂] and temperature in open-top chambers (OTC). The objective of this study is to examine the rice photosynthetic parameters, water use efficiency (WUE) and yield formation in responses to the co-elevation of [CO₂] and temperature which is the main predicted features of future climate. e[CO₂] significantly increased A_n of SH, HJ and LJ by 37%, 39% and 23% in comparison to 34%, 34% and 27% under elevated [CO₂] plus warming, respectively. However, A_n had a weak response to warming for three cultivars. [CO₂] and temperature co-elevation significantly decreased the stomatal conductance, resulting in a significant increase of the WUE. e[CO₂] significantly increased V_{c, max} , J_max and J_max /V_{c, max} . e[CO₂] significantly increased grain yield and grain number of all cultivars. The positive effect of co-elevation of [CO₂] and temperature on grain yield was less than e[CO₂]. Warming is likely to partially offset the increased photosynthetic rate caused by e[CO₂]. The [CO₂] and temperature co-elevation may be favorable to rice crop with increasing the photosynthetic ability of rice crop and improving water use efficiency. The present study provided evidence that the rice genotypic difference in photosynthetic potential under [CO₂] and temperature co-elevation. Therefore, it is crucial to explore a broader range of phenotypes and cultivars to be applied to climate change response research, advancing the knowledge that climate change impacts rice crop under the cold-temperate climate region.

Nocturnal pollutant uptake contributes significantly to the total stomatal uptake of Mangifera indica

DO₃SE (Deposition of Ozone for Stomatal Exchange), is a dry deposition model, designed to assess tropospheric ozone risk to vegetation, and is based on two alternative algorithms to estimate stomatal conductance: multiplicative and photosynthetic. The multiplicative model has been argued to perform better for leaf-level and regional-level application. In this study, we demonstrate that the photosynthetic model is superior to the multiplicative model even for leaf-level studies using measurements performed on Mangifera indica. We find that the multiplicative model overestimates the daytime stomatal conductance, when compared with measured stomatal conductance and prescribes zero conductance at night while measurements show an average conductance of 100 mmol(H₂O)m^-2s^-1 between 9 p.m. and 4 a.m. The daytime overestimation of the multiplicative model can be significantly reduced when the model is modified to include a response function for ozone-induced stomatal closure. However, nighttime pollutant uptake fluxes can only be accurately assessed with the photosynthetic model which includes the stomatal opening at night during respiration and is capable of reproducing the measured nighttime stomatal conductance. At our site, the nocturnal flux contributes 64%, 39%, 46%, and 88% of the total for NO₂ uptake in winter, summer, monsoon, and post-monsoon, respectively. For SO_2, nocturnal uptake amounts to 35%, 28%, 28%, and 44% in winter, summer, monsoon, and post-monsoon, respectively while for ozone the nighttime uptake contributes 30%, 17%, 18%, and 29% of the total stomatal uptake in winter, summer, monsoon, and post-monsoon respectively.

Gas exchange analysers exhibit large measurement error driven by internal thermal gradients

Portable gas exchange analysers provide critical data for understanding plant-atmosphere carbon and water fluxes, and for parameterising Earth system models that forecast climate change effects and feedbacks. We characterised temperature measurement errors in the Li-Cor LI-6400XT and LI-6800, and estimated downstream errors in derived quantities, including stomatal conductance (g_sw ) and leaf intercellular CO₂ concentration (C_i ). The LI-6400XT exhibited air temperature errors (differences between reported air temperature and air temperature measured near the leaf) up to 7.2°C, leaf temperature errors up to 5.3°C, and relative errors in g_sw and C_i that increased as temperatures departed from ambient. This caused errors in leaf-to-air temperature relationships, assimilation-temperature curves and CO₂ response curves. Temperature dependencies of maximum Rubisco carboxylation rate (V_cmax ) and maximum RuBP regeneration rate (J_max ) showed errors of 12% and 35%, respectively. These errors are likely to be idiosyncratic and may differ among machines and environmental conditions. The LI-6800 exhibited much smaller errors. Earth system model predictions may be erroneous, as much of their parametrisation data were measured on the LI-6400XT system, depending on the methods used. We make recommendations for minimising errors and correcting data in the LI-6400XT. We also recommend transitioning to the LI-6800 for future data collection.

Convergence in phosphorus constraints to photosynthesis in forests around the world

Tropical forests take up more carbon (C) from the atmosphere per annum by photosynthesis than any other type of vegetation. Phosphorus (P) limitations to C uptake are paramount for tropical and subtropical forests around the globe. Yet the generality of photosynthesis-P relationships underlying these limitations are in question, and hence are not represented well in terrestrial biosphere models. Here we demonstrate the dependence of photosynthesis and underlying processes on both leaf N and P concentrations. The regulation of photosynthetic capacity by P was similar across four continents. Implementing P constraints in the ORCHIDEE-CNP model, gross photosynthesis was reduced by 36% across the tropics and subtropics relative to traditional N constraints and unlimiting leaf P. Our results provide a quantitative relationship for the P dependence for photosynthesis for the front-end of global terrestrial C models that is consistent with canopy leaf measurements.

Phosphorus (P) limitation is pervasive in tropical forests. Here the authors analyse the dependence of photosynthesis on leaf N and P in tropical forests, and show that incorporating leaf P constraints in a terrestrial biosphere model enhances its predictive power.

Climate windows of opportunity for plant expansion during the Phanerozoic

Earth's long-term climate may have profoundly influenced plant evolution. Local climatic factors, including water availability, light, and temperature, play a key role in plant physiology and growth, and have fluctuated substantially over geological time. However, the impact of these key climate variables on global plant biomass across the Phanerozoic has not yet been established. Linking climate and dynamic vegetation modelling, we identify two key 'windows of opportunity' during the Ordovician and Jurassic-Paleogene capable of supporting dramatic expansions of potential plant biomass. These conditions are driven by continental dispersion, paleolatitude of continental area and a lack of glaciation, allowing for an intense hydrological cycle and greater water availability. These windows coincide with the initial expansion of land plants and the later angiosperm radiation. Our findings suggest that the timing and expansion of habitable space for plants played an important role in plant evolution and diversification.

Using hyperspectral leaf reflectance to estimate photosynthetic capacity and nitrogen content across eastern cottonwood and hybrid poplar taxa

Eastern cottonwood (Populus deltoides W. Bartram ex Marshall) and hybrid poplars are well-known bioenergy crops. With advances in tree breeding, it is increasingly necessary to find economical ways to identify high-performing Populus genotypes that can be planted under different environmental conditions. Photosynthesis and leaf nitrogen content are critical parameters for plant growth, however, measuring them is an expensive and time-consuming process. Instead, these parameters can be quickly estimated from hyperspectral leaf reflectance if robust statistical models can be developed. To this end, we measured photosynthetic capacity parameters (Rubisco-limited carboxylation rate (Vcmax), electron transport-limited carboxylation rate (Jmax), and triose phosphate utilization-limited carboxylation rate (TPU)), nitrogen per unit leaf area (Narea), and leaf reflectance of seven taxa and 62 genotypes of Populus from two study plantations in Mississippi. For statistical modeling, we used least absolute shrinkage and selection operator (LASSO) and principal component analysis (PCA). Our results showed that the predictive ability of LASSO and PCA models was comparable, except for Narea in which LASSO was superior. In terms of model interpretability, LASSO outperformed PCA because the LASSO models needed 2 to 4 spectral reflectance wavelengths to estimate parameters. The LASSO models used reflectance values at 758 and 935 nm for estimating Vcmax (R2 = 0.51 and RMSPE = 31%) and Jmax (R2 = 0.54 and RMSPE = 32%); 687, 746, and 757 nm for estimating TPU (R2 = 0.56 and RMSPE = 31%); and 304, 712, 921, and 1021 nm for estimating Narea (R2 = 0.29 and RMSPE = 21%). The PCA model also identified 935 nm as a significant wavelength for estimating Vcmax and Jmax. Therefore, our results suggest that hyperspectral leaf reflectance modeling can be used as a cost-effective means for field phenotyping and rapid screening of Populus genotypes because of its capacity to estimate these physicochemical parameters.

Estimating peanut and soybean photosynthetic traits using leaf spectral reflectance and advance regression models

MAIN CONCLUSION

By combining hyperspectral signatures of peanut and soybean, we predicted Vcmax and Jmax with 70 and 50% accuracy. The PLS was the model that better predicted these photosynthetic parameters. One proposed key strategy for increasing potential crop stability and yield centers on exploitation of genotypic variability in photosynthetic capacity through precise high-throughput phenotyping techniques. Photosynthetic parameters, such as the maximum rate of Rubisco catalyzed carboxylation (Vc,max) and maximum electron transport rate supporting RuBP regeneration (Jmax), have been identified as key targets for improvement. The primary techniques for measuring these physiological parameters are very time-consuming. However, these parameters could be estimated using rapid and non-destructive leaf spectroscopy techniques. This study compared four different advanced regression models (PLS, BR, ARDR, and LASSO) to estimate Vc,max and Jmax based on leaf reflectance spectra measured with an ASD FieldSpec4. Two leguminous species were tested under different controlled environmental conditions: (1) peanut under different water regimes at normal atmospheric conditions and (2) soybean under high [CO2] and high night temperature. Model sensitivities were assessed for each crop and treatment separately and in combination to identify strengths and weaknesses of each modeling approach. Regardless of regression model, robust predictions were achieved for Vc,max (R2 = 0.70) and Jmax (R2 = 0.50). Field spectroscopy shows promising results for estimating spatial and temporal variations in photosynthetic capacity based on leaf and canopy spectral properties.

Reducing model uncertainty of climate change impacts on high latitude carbon assimilation

The Arctic-Boreal Region (ABR) has a large impact on global vegetation-atmosphere interactions and is experiencing markedly greater warming than the rest of the planet, a trend that is projected to continue with anticipated future emissions of CO₂ . The ABR is a significant source of uncertainty in estimates of carbon uptake in terrestrial biosphere models such that reducing this uncertainty is critical for more accurately estimating global carbon cycling and understanding the response of the region to global change. Process representation and parameterization associated with gross primary productivity (GPP) drives a large amount of this model uncertainty, particularly within the next 50 years, where the response of existing vegetation to climate change will dominate estimates of GPP for the region. Here we review our current understanding and model representation of GPP in northern latitudes, focusing on vegetation composition, phenology, and physiology, and consider how climate change alters these three components. We highlight challenges in the ABR for predicting GPP, but also focus on the unique opportunities for advancing knowledge and model representation, particularly through the combination of remote sensing and traditional boots-on-the-ground science.

Changes in carbon and nitrogen metabolism during seawater-induced mortality of Picea sitchensis trees

Increasing seawater exposure is causing mortality of coastal forests, yet the physiological response associated with seawater-induced tree mortality, particularly in non-halophytes, is poorly understood. We investigated the shifts in carbon and nitrogen (N) metabolism of mature Sitka-spruce trees that were dying after an ecosystem-scale manipulation of tidal seawater exposure. Soil porewater salinity and foliar ion concentrations increased after seawater exposure and were strongly correlated with the percentage of live foliated crown (PLFC; e.g., crown 'greenness', a measure of progression to death). Co-occurring with decreasing PLFC was decreasing photosynthetic capacity, N-investment into photosynthesis, N-resorption efficiency and non-structural carbohydrate (soluble sugars and starch) concentrations, with the starch reserves depleted to near zero when PLFC dropped below 5%. Combined with declining PLFC, these changes subsequently decreased total carbon gain and thus exacerbated the carbon starvation process. This study suggests that an impairment in carbon and N metabolism during the mortality process after seawater exposure is associated with the process of carbon starvation, and provides critical knowledge necessary to predict sea-level rise impacts on coastal forests.

Spectroscopy outperforms leaf trait relationships for predicting photosynthetic capacity across different forest types

Leaf trait relationships are widely used to predict ecosystem function in terrestrial biosphere models (TBMs), in which leaf maximum carboxylation capacity (V_c,max ), an important trait for modelling photosynthesis, can be inferred from other easier-to-measure traits. However, whether trait-V_c,max relationships are robust across different forest types remains unclear. Here we used measurements of leaf traits, including one morphological trait (leaf mass per area), three biochemical traits (leaf water content, area-based leaf nitrogen content, and leaf chlorophyll content), one physiological trait (V_c,max ), as well as leaf reflectance spectra, and explored their relationships within and across three contrasting forest types in China. We found weak and forest type-specific relationships between V_c,max and the four morphological and biochemical traits (R²  ≤ 0.15), indicated by significantly changing slopes and intercepts across forest types. By contrast, reflectance spectroscopy effectively collapsed the differences in the trait-V_c,max relationships across three forest biomes into a single robust model for V_c,max (R²  = 0.77), and also accurately estimated the four traits (R²  = 0.75-0.94). These findings challenge the traditional use of the empirical trait-V_c,max relationships in TBMs for estimating terrestrial plant photosynthesis, but also highlight spectroscopy as an efficient alternative for characterising V_c,max and multitrait variability, with critical insights into ecosystem modelling and functional trait ecology.

Rubisco substitutions predicted to enhance crop performance through carbon uptake modelling

Improving the performance of the CO2-fixing enzyme Rubisco is among the targets for increasing crop yields. Here, Earth system model (ESM) representations of canopy C3 and C4 photosynthesis were combined with species-specific Rubisco parameters to quantify the consequences of bioengineering foreign Rubiscos into C3 and C4 crops under field conditions. The 'two big leaf' (sunlit/shaded) model for canopy photosynthesis was used together with species-specific Rubisco kinetic parameters including maximum rate (Kcat), Michaelis-Menten constant for CO2 at ambient atmospheric O2 (Kc21%O2), specificity for CO2 to O2 (Sc/o), and associated heat activation (Ha) values. Canopy-scale consequences of replacing native Rubiscos in wheat, maize, and sugar beet with foreign enzymes from 27 species were modelled using data from Ameriflux and Fluxnet databases. Variation among the included Rubisco kinetics differentially affected modelled carbon uptake rates, and Rubiscos from several species of C4 grasses showed the greatest potential of >50% carbon uptake improvement in wheat, and >25% improvement in sugar beet and maize. This study also reaffirms the need for data on fully characterized Rubiscos from more species, and for better parameterization of 'Vcmax' and temperature response of 'Jmax' in ESMs.

Including leaf trait information helps empirical estimation of jmax from vcmax in cool-temperate deciduous forests

Understanding the uncertainty in the parameterization of the two photosynthetic capacity parameters, leaf maximum carboxylation rate (Vcmax), and maximum electron transport rate (Jmax), is crucial for modeling and predicting carbon fluxes in terrestrial ecosystems. In gas exchange models, to date, Jmax is typically estimated from Vcmax based on a linear regression. However, recent studies have revealed that this relationship varies, dependent upon species, leaf groups, and time, so it is doubtful that the regression applies universally. Furthermore, far less is known regarding how other leaf traits affect the regression. In this study we analyzed the two key photosynthetic parameters and popularly measurable leaf traits, leaf chlorophyll concentration and leaf mass per area (LMA), of cool-temperate forest stands in Japan, aiming to construct a simple regression applicable to temperate deciduous forests, at least. The analysis was based on a long-term field dataset covering years of data for both sunlit and shaded leaves at different altitudes. Results showed that the best-fitted slope of the regression differed markedly from those previously reported, which were typically acquired from sunlit leaves. LMA had a significant effect on the regression, producing the lowest root mean square errors and the highest ratio of performance to deviation values (RPD = 2.017). Although more data are needed to validate in other ecosystems, our approach at least provides a promising way to substantially improve photosynthesis model predictions, by introducing leaf traits into the popular empirical regression of Jmax against Vcmax, and ultimately to better understand the functioning of the photosynthetic machinery.

Global variation in the fraction of leaf nitrogen allocated to photosynthesis

Plants invest a considerable amount of leaf nitrogen in the photosynthetic enzyme ribulose-1,5-bisphosphate carboxylase-oxygenase (RuBisCO), forming a strong coupling of nitrogen and photosynthetic capacity. Variability in the nitrogen-photosynthesis relationship indicates different nitrogen use strategies of plants (i.e., the fraction nitrogen allocated to RuBisCO; fLNR), however, the reason for this remains unclear as widely different nitrogen use strategies are adopted in photosynthesis models. Here, we use a comprehensive database of in situ observations, a remote sensing product of leaf chlorophyll and ancillary climate and soil data, to examine the global distribution in fLNR using a random forest model. We find global fLNR is 18.2 ± 6.2%, with its variation largely driven by negative dependence on leaf mass per area and positive dependence on leaf phosphorus. Some climate and soil factors (i.e., light, atmospheric dryness, soil pH, and sand) have considerable positive influences on fLNR regionally. This study provides insight into the nitrogen-photosynthesis relationship of plants globally and an improved understanding of the global distribution of photosynthetic potential.

Phylogenetic history of vascular plant metabolism revealed using a macroevolutionary common garden

While the fundamental biophysics of C3 photosynthesis is highly conserved across plants, substantial leaf structural and enzymatic variation translates into variability in rates of carbon assimilation. Although this variation is well documented, it remains poorly understood how photosynthetic rates evolve, and whether macroevolutionary changes are related to the evolution of leaf morphology and biochemistry. A substantial challenge in large-scale comparative studies is disentangling evolutionary adaptation from environmental acclimation. We overcome this by using a 'macroevolutionary common garden' approach in which we measured metabolic traits (Jmax and Vcmax) from 111 phylogenetically diverse species in a shared environment. We find substantial phylogenetic signal in these traits at moderate phylogenetic timescales, but this signal dissipates quickly at deeper scales. Morphological traits exhibit phylogenetic signal over much deeper timescales, suggesting that these are less evolutionarily constrained than metabolic traits. Furthermore, while morphological and biochemical traits (LMA, Narea and Carea) are weakly predictive of Jmax and Vcmax, evolutionary changes in these traits are mostly decoupled from changes in metabolic traits. This lack of tight evolutionary coupling implies that it may be incorrect to use changes in these functional traits in response to global change to infer that photosynthetic strategy is also evolving.

Global climate and nutrient controls of photosynthetic capacity

There is huge uncertainty about how global exchanges of carbon between the atmosphere and land will respond to continuing environmental change. A better representation of photosynthetic capacity is required for Earth System models to simulate carbon assimilation reliably. Here we use a global leaf-trait dataset to test whether photosynthetic capacity is quantitatively predictable from climate, based on optimality principles; and to explore how this prediction is modified by soil properties, including indices of nitrogen and phosphorus availability, measured in situ. The maximum rate of carboxylation standardized to 25 °C (Vcmax25) was found to be proportional to growing-season irradiance, and to increase-as predicted-towards both colder and drier climates. Individual species' departures from predicted Vcmax25 covaried with area-based leaf nitrogen (Narea) but community-mean Vcmax25 was unrelated to Narea, which in turn was unrelated to the soil C:N ratio. In contrast, leaves with low area-based phosphorus (Parea) had low Vcmax25 (both between and within communities), and Parea increased with total soil P. These findings do not support the assumption, adopted in some ecosystem and Earth System models, that leaf-level photosynthetic capacity depends on soil N supply. They do, however, support a previously-noted relationship between photosynthesis and soil P supply.

Multi-hypothesis comparison of Farquhar and Collatz photosynthesis models reveals the unexpected influence of empirical assumptions at leaf and global scales

Mechanistic photosynthesis models are at the heart of terrestrial biosphere models (TBMs) simulating the daily, monthly, annual and decadal rhythms of carbon assimilation (A). These models are founded on robust mathematical hypotheses that describe how A responds to changes in light and atmospheric CO₂ concentration. Two predominant photosynthesis models are in common usage: Farquhar (FvCB) and Collatz (CBGB). However, a detailed quantitative comparison of these two models has never been undertaken. In this study, we unify the FvCB and CBGB models to a common parameter set and use novel multi-hypothesis methods (that account for both hypothesis and parameter variability) for process-level sensitivity analysis. These models represent three key biological processes: carboxylation, electron transport, triose phosphate use (TPU) and an additional model process: limiting-rate selection. Each of the four processes comprises 1-3 alternative hypotheses giving 12 possible individual models with a total of 14 parameters. To broaden inference, TBM simulations were run and novel, high-resolution photosynthesis measurements were made. We show that parameters associated with carboxylation are the most influential parameters but also reveal the surprising and marked dominance of the limiting-rate selection process (accounting for 57% of the variation in A vs. 22% for carboxylation). The limiting-rate selection assumption proposed by CBGB smooths the transition between limiting rates and always reduces A below the minimum of all potentially limiting rates, by up to 25%, effectively imposing a fourth limitation on A. Evaluation of the CBGB smoothing function in three TBMs demonstrated a reduction in global A by 4%-10%, equivalent to 50%-160% of current annual fossil fuel emissions. This analysis reveals a surprising and previously unquantified influence of a process that has been integral to many TBMs for decades, highlighting the value of multi-hypothesis methods.

Active layer depth and soil properties impact specific leaf area variation and ecosystem productivity in a boreal forest

Specific leaf area (SLA, leaf area per unit dry mass) is a key canopy structural characteristic, a measure of photosynthetic capacity, and an important input into many terrestrial process models. Although many studies have examined SLA variation, relatively few data exist from high latitude, climate-sensitive permafrost regions. We measured SLA and soil and topographic properties across a boreal forest permafrost transition, in which dominant tree species changed as permafrost deepened from 54 to >150 cm over 75 m hillslope transects in Caribou-Poker Creeks Research Watershed, Alaska. We characterized both linear and threshold relationships between topographic and edaphic variables and SLA and developed a conceptual model of these relationships. We found that the depth of the soil active layer above permafrost was significantly and positively correlated with SLA for both coniferous and deciduous boreal tree species. Intraspecific SLA variation was associated with a fivefold increase in net primary production, suggesting that changes in active layer depth due to permafrost thaw could strongly influence ecosystem productivity. While this is an exploratory study to begin understanding SLA variation in a non-contiguous permafrost system, our results indicate the need for more extensive evaluation across larger spatial domains. These empirical relationships and associated uncertainty can be incorporated into ecosystem models that use dynamic traits, improving our ability to predict ecosystem-level carbon cycling responses to ongoing climate change.

Optimal leaf life strategies determine Vc,max dynamic during ontogeny

Leaf photosynthetic properties, for example the maximum carboxylation velocity or V_c,max , change with leaf age due to ontogenetic processes. This study introduces an optimal dynamic allocation scheme to model changes in leaf-level photosynthetic capacity as a function of leaf biochemical constraints (costs of synthesis and defence), nitrogen availability and other environmental factors (e.g. light). The model consists of a system of equations describing RuBisCO synthesis and degradation within chloroplasts, defence and ageing at leaf levels, nitrogen transfer and carbon budget at plant levels. Model results show that optimal allocation principles explained RuBisCO dynamics with leaf age. An approximated analytical solution can reproduce the basic pattern of RuBisCO and V_c,max in rice and in two tropical tree species. The model also reveals leaf life complementarities that remained unexplained in previous approaches, as the interplay between V_c,max at maturation, life span and the decline in photosynthetic capacity with age. Furthermore, it explores the role of defence, which is not implemented in current models. This framework covers some of the existing gaps in integrating multiple processes across plant organs (chloroplast, leaf and whole plant) and is a first-step towards representing mechanistically leaf ontogenetic processes into physiological and ecosystem models.

Contrasting Dependencies of Photosynthetic Capacity on Leaf Nitrogen in Early- and Late-Successional Tropical Montane Tree Species

Differences in photosynthetic capacity among tree species and tree functional types are currently assumed to be largely driven by variation in leaf nutrient content, particularly nitrogen (N). However, recent studies indicate that leaf N content is often a poor predictor of variation in photosynthetic capacity in tropical trees. In this study, we explored the relative importance of area-based total leaf N content (N_tot) and within-leaf N allocation to photosynthetic capacity versus light-harvesting in controlling the variation in photosynthetic capacity (i.e. V _cmax, J _max) among mature trees of 12 species belonging to either early (ES) or late successional (LS) groups growing in a tropical montane rainforest in Rwanda, Central Africa. Photosynthetic capacity at a common leaf temperature of 25˚C (i.e. maximum rates of Rubisco carboxylation, V _cmax25 and of electron transport, J _max25) was higher in ES than in LS species (+ 58% and 68% for V _cmax25 and J _max25, respectively). While N_tot did not significantly differ between successional groups, the photosynthetic dependency on N_tot was markedly different. In ES species, V _cmax25 was strongly and positively related to N_tot but this was not the case in LS species. However, there was no significant trade-off between relative leaf N investments in compounds maximizing photosynthetic capacity versus compounds maximizing light harvesting. Both leaf dark respiration at 25˚C (+ 33%) and, more surprisingly, apparent photosynthetic quantum yield (+ 35%) was higher in ES than in LS species. Moreover, R_d25 was positively related to N_tot for both ES and LS species. Our results imply that efforts to quantify carbon fluxes of tropical montane rainforests would be improved if they considered contrasting within-leaf N allocation and photosynthetic N_tot dependencies between species with different successional strategies.

Mechanisms underlying leaf photosynthetic acclimation to warming and elevated CO2 as inferred from least-cost optimality theory

The mechanisms responsible for photosynthetic acclimation are not well understood, effectively limiting predictability under future conditions. Least-cost optimality theory can be used to predict the acclimation of photosynthetic capacity based on the assumption that plants maximize carbon uptake while minimizing the associated costs. Here, we use this theory as a null model in combination with multiple datasets of C₃ plant photosynthetic traits to elucidate the mechanisms underlying photosynthetic acclimation to elevated temperature and carbon dioxide (CO₂ ). The model-data comparison showed that leaves decrease the ratio of the maximum rate of electron transport to the maximum rate of Rubisco carboxylation (J_max /V_cmax ) under higher temperatures. The comparison also indicated that resources used for Rubisco and electron transport are reduced under both elevated temperature and CO₂ . Finally, our analysis suggested that plants underinvest in electron transport relative to carboxylation under elevated CO₂ , limiting potential leaf-level photosynthesis under future CO₂ concentrations. Altogether, our results show that acclimation to temperature and CO₂ is primarily related to resource conservation at the leaf level. Under future, warmer, high CO₂ conditions, plants are therefore likely to use less nutrients for leaf-level photosynthesis, which may impact whole-plant to ecosystem functioning.

Photosynthesis in a Changing Global Climate: Scaling Up and Scaling Down in Crops

Photosynthesis is the major process leading to primary production in the Biosphere. There is a total of 7000bn tons of CO2 in the atmosphere and photosynthesis fixes more than 100bn tons annually. The CO2 assimilated by the photosynthetic apparatus is the basis of crop production and, therefore, of animal and human food. This has led to a renewed interest in photosynthesis as a target to increase plant production and there is now increasing evidence showing that the strategy of improving photosynthetic traits can increase plant yield. However, photosynthesis and the photosynthetic apparatus are both conditioned by environmental variables such as water availability, temperature, [CO2], salinity, and ozone. The "omics" revolution has allowed a better understanding of the genetic mechanisms regulating stress responses including the identification of genes and proteins involved in the regulation, acclimation, and adaptation of processes that impact photosynthesis. The development of novel non-destructive high-throughput phenotyping techniques has been important to monitor crop photosynthetic responses to changing environmental conditions. This wealth of data is being incorporated into new modeling algorithms to predict plant growth and development under specific environmental constraints. This review gives a multi-perspective description of the impact of changing environmental conditions on photosynthetic performance and consequently plant growth by briefly highlighting how major technological advances including omics, high-throughput photosynthetic measurements, metabolic engineering, and whole plant photosynthetic modeling have helped to improve our understanding of how the photosynthetic machinery can be modified by different abiotic stresses and thus impact crop production.

Contrasting acclimation responses to elevated CO2 and warming between an evergreen and a deciduous boreal conifer

Rising atmospheric carbon dioxide (CO₂ ) concentrations may warm northern latitudes up to 8°C by the end of the century. Boreal forests play a large role in the global carbon cycle, and the responses of northern trees to climate change will thus impact the trajectory of future CO₂ increases. We grew two North American boreal tree species at a range of future climate conditions to assess how growth and carbon fluxes were altered by high CO₂ and warming. Black spruce (Picea mariana, an evergreen conifer) and tamarack (Larix laricina, a deciduous conifer) were grown under ambient (407 ppm) or elevated CO₂ (750 ppm) and either ambient temperatures, a 4°C warming, or an 8°C warming. In both species, the thermal optimum of net photosynthesis (T_optA ) increased and maximum photosynthetic rates declined in warm-grown seedlings, but the strength of these changes varied between species. Photosynthetic capacity (maximum rates of Rubisco carboxylation, V_cmax , and of electron transport, J_max ) was reduced in warm-grown seedlings, correlating with reductions in leaf N and chlorophyll concentrations. Warming increased the activation energy for V_cmax and J_max (E_aV and E_aJ , respectively) and the thermal optimum for J_max . In both species, the T_optA was positively correlated with both E_aV and E_aJ , but negatively correlated with the ratio of J_max /V_cmax . Respiration acclimated to elevated temperatures, but there were no treatment effects on the Q₁₀ of respiration (the increase in respiration for a 10°C increase in leaf temperature). A warming of 4°C increased biomass in tamarack, while warming reduced biomass in spruce. We show that climate change is likely to negatively affect photosynthesis and growth in black spruce more than in tamarack, and that parameters used to model photosynthesis in dynamic global vegetation models (E_aV and E_aJ ) show no response to elevated CO₂ .

Estimating photosynthetic traits from reflectance spectra: A synthesis of spectral indices, numerical inversion, and partial least square regression

The lack of efficient means to accurately infer photosynthetic traits constrains understanding global land carbon fluxes and improving photosynthetic pathways to increase crop yield. Here, we investigated whether a hyperspectral imaging camera mounted on a mobile platform could provide the capability to help resolve these challenges, focusing on three main approaches, that is, reflectance spectra-, spectral indices-, and numerical model inversions-based partial least square regression (PLSR) to estimate photosynthetic traits from canopy hyperspectral reflectance for 11 tobacco cultivars. Results showed that PLSR with inputs of reflectance spectra or spectral indices yielded an R2 of ~0.8 for predicting V cmax and J max , higher than an R2 of ~0.6 provided by PLSR of numerical inversions. Compared with PLSR of reflectance spectra, PLSR with spectral indices exhibited a better performance for predicting V cmax (R2 = 0.84 ± 0.02, RMSE = 33.8 ± 2.2 μmol m-2  s-1 ) while a similar performance for J max (R2 = 0.80 ± 0.03, RMSE = 22.6 ± 1.6 μmol m-2  s-1 ). Further analysis on spectral resampling revealed that V cmax and J max could be predicted with ~10 spectral bands at a spectral resolution of less than 14.7 nm. These results have important implications for improving photosynthetic pathways and mapping of photosynthesis across scales.

Acclimation of leaf respiration consistent with optimal photosynthetic capacity

Plant respiration is an important contributor to the proposed positive global carbon-cycle feedback to climate change. However, as a major component, leaf mitochondrial ('dark') respiration (Rd ) differs among species adapted to contrasting environments and is known to acclimate to sustained changes in temperature. No accepted theory explains these phenomena or predicts its magnitude. Here we propose that the acclimation of Rd follows an optimal behaviour related to the need to maintain long-term average photosynthetic capacity (Vcmax ) so that available environmental resources can be most efficiently used for photosynthesis. To test this hypothesis, we extend photosynthetic co-ordination theory to predict the acclimation of Rd to growth temperature via a link to Vcmax , and compare predictions to a global set of measurements from 112 sites spanning all terrestrial biomes. This extended co-ordination theory predicts that field-measured Rd and Vcmax accessed at growth temperature (Rd,tg and Vcmax,tg ) should increase by 3.7% and 5.5% per degree increase in growth temperature. These acclimated responses to growth temperature are less steep than the corresponding instantaneous responses, which increase 8.1% and 9.9% per degree of measurement temperature for Rd and Vcmax respectively. Data-fitted responses proof indistinguishable from the values predicted by our theory, and smaller than the instantaneous responses. Theory and data are also shown to agree that the basal rates of both Rd and Vcmax assessed at 25°C (Rd,25 and Vcmax,25 ) decline by ~4.4% per degree increase in growth temperature. These results provide a parsimonious general theory for Rd acclimation to temperature that is simpler-and potentially more reliable-than the plant functional type-based leaf respiration schemes currently employed in most ecosystem and land-surface models.

Sensitivity and Uncertainty Analyses of Flux-based Ecosystem Model towards Improvement of Forest GPP Simulation

An ecosystem model serves as an important tool to understand the carbon cycle in the forest ecosystem. However, the sensitivities of parameters and uncertainties of the model outputs are not clearly understood. Parameter sensitivity analysis (SA) and uncertainty analysis (UA) play a crucial role in the improvement of forest gross primary productivity GPP simulation. This study presents a global SA based on an extended Fourier amplitude sensitivity test (EFAST) method to quantify the sensitivities of 16 parameters in the Flux-based ecosystem model (FBEM). To systematically evaluate the parameters’ sensitivities, various parameter ranges, different model outputs, temporal variations of parameters sensitivity index (SI) were comprehensively explored via three experiments. Based on the numerical experiments of SA, the UA experiments were designed and performed for parameter estimation based on a Markov chain Monte Carlo (MCMC) method. The ratio of internal CO2 to air CO2 ( f C i ) , canopy quantum efficiency of photon conversion ( α q ) , maximum carboxylation rate at 25 ° C ( V m 25 ) were the most sensitive parameters for the GPP. It was also indicated that α q , E V m and Q 10 were influenced by temperature throughout the entire growth stage. The result of parameter estimation of only using four sensitive parameters (RMSE = 1.657) is very close to that using all the parameters (RMSE = 1.496). The results of SA suggest that sensitive parameters, such as f c i , α q , E V m , V m 25 strongly influence on the forest GPP simulation, and the temporal characteristics of the parameters’ SI on GPP and NEE were changed in different growth. The sensitive parameters were a major source of uncertainty and parameter estimation based on the parameter SA could lead to desirable results without introducing too great uncertainties.

Leaf Photosynthetic Capacity of Sunlit and Shaded Mature Leaves in a Deciduous Forest

A clear understanding of the dynamics of photosynthetic capacity is crucial for accurate modeling of ecosystem carbon uptake. However, such dynamical information is hardly available and has dramatically impeded our understanding of carbon cycles. Although tremendous efforts have been made in coupling the dynamic information of photosynthetic capacity into models, using “proxies” rooted from the close relationships between photosynthetic capacity and other available leaf parameters remains the popular selection. Unfortunately, no consensus has yet been reached on such “proxies”, leading them only applicable to limited cases. In this study, we aim to identify if there are close relationships between the photosynthetic capacity (represented by the maximum carboxylation rate, Vcmax) and leaf traits for mature broadleaves within a cold temperature deciduous forest. This is based on a long-term in situ dataset including leaf chlorophyll content (Chl), leaf nitrogen concentration (Narea, Nmass), leaf carbon concentration (Carea, Cmass), equivalent water thickness (EWT), leaf mass per area (LMA), and leaf gas exchange measurements from which Vcmax was derived, for both sunlit and shaded leaves during leaf mature periods from 2014 to 2019. The results show that the Vcmax values of sunlit and shaded leaves were relatively stable during these periods, and no statistically significant interannual variations occurred (p > 0.05). However, this is not applicable to specific species. Path analysis revealed that Narea was the major contributor to Vcmax for sunlit leaves (0.502), while LMA had the greatest direct relationship with Vcmax for shaded leaves (0.625). The LMA has further been confirmed as a primary proxy if no leaf type information is available. These findings provide a promising way to better understand photosynthesis and to predict carbon and water cycles in temperate deciduous forests.

Historical changes in the stomatal limitation of photosynthesis: empirical support for an optimality principle

The ratio of leaf internal (c_i ) to ambient (c_a ) partial pressure of CO₂ , defined here as χ, is an index of adjustments in both leaf stomatal conductance and photosynthetic rate to environmental conditions. Measurements and proxies of this ratio can be used to constrain vegetation model uncertainties for predicting terrestrial carbon uptake and water use. We test a theory based on the least-cost optimality hypothesis for modelling historical changes in χ over the 1951-2014 period, across different tree species and environmental conditions, as reconstructed from stable carbon isotopic measurements across a global network of 103 absolutely dated tree-ring chronologies. The theory predicts optimal χ as a function of air temperature, vapour pressure deficit, c_a and atmospheric pressure. The theoretical model predicts 39% of the variance in χ values across sites and years, but underestimates the intersite variability in the reconstructed χ trends, resulting in only 8% of the variance in χ trends across years explained by the model. Overall, our results support theoretical predictions that variations in χ are tightly regulated by the four environmental drivers. They also suggest that explicitly accounting for the effects of plant-available soil water and other site-specific characteristics might improve the predictions.

From the Arctic to the tropics: multibiome prediction of leaf mass per area using leaf reflectance

Leaf mass per area (LMA) is a key plant trait, reflecting tradeoffs between leaf photosynthetic function, longevity, and structural investment. Capturing spatial and temporal variability in LMA has been a long-standing goal of ecological research and is an essential component for advancing Earth system models. Despite the substantial variation in LMA within and across Earth's biomes, an efficient, globally generalizable approach to predict LMA is still lacking. We explored the capacity to predict LMA from leaf spectra across much of the global LMA trait space, with values ranging from 17 to 393 g m^-2 . Our dataset contained leaves from a wide range of biomes from the high Arctic to the tropics, included broad- and needleleaf species, and upper- and lower-canopy (i.e. sun and shade) growth environments. Here we demonstrate the capacity to rapidly estimate LMA using only spectral measurements across a wide range of species, leaf age and canopy position from diverse biomes. Our model captures LMA variability with high accuracy and low error (R²  = 0.89; root mean square error (RMSE) = 15.45 g m^-2 ). Our finding highlights the fact that the leaf economics spectrum is mirrored by the leaf optical spectrum, paving the way for this technology to predict the diversity of LMA in ecosystems across global biomes.

Leaf photosynthetic capacity is regulated by the interaction of nitrogen and potassium through coordination of CO2 diffusion and carboxylation

Combined application of nitrogen (N) and potassium (K) fertilizer could significantly enhance crop yield. Crop yield and photosynthesis are inseparable. However, the influence of N and K interaction on photosynthesis is still not fully understood. Field and hydroponic experiments were conducted to examine the effects of N and K interaction on leaf photosynthesis characteristics and to explore the mechanisms in the hydroponic experiment. CO₂ conductance and carboxylation characteristic parameters of oilseed leaves were measured under different N and K supplies. Results indicated that detectable increases in leaf area, biomass and net photosynthetic rate (A_n ) were observed under optimal N and K supply in field and hydroponic experiments. The ratio of total CO₂ diffusion conductance to the maximum carboxylation rate (g_tot /V_cmax ) and A_n presented a linear-plateau relationship. Under insufficient N, increased K contributed to the CO₂ transmission capacity and improved the proportion of N used for carboxylation, promoting g_tot /V_cmax . However, the low V_cmax associated with N insufficiency limited the A_n . High N supply obviously accelerated V_cmax , yet K deficiency led to a reduction of g_tot , which restricted V_cmax . Synchronous increases in N and K supplementation ensured the appropriate ratio of N to K content in leaves, which simultaneously facilitated g_tot and V_cmax and preserved a g_tot /V_cmax suitable for guaranteeing CO₂ transmission and carboxylation coordination; the overall effect was increased A_n and leaf area. These results highlight the suitable N and K nutrients to coordinate CO₂ diffusion and carboxylation, thereby enhancing photosynthetic capacity and area to obtain high crop yield.

Leaf reflectance spectroscopy captures variation in carboxylation capacity across species, canopy environment and leaf age in lowland moist tropical forests

Understanding the pronounced seasonal and spatial variation in leaf carboxylation capacity (V_c,max ) is critical for determining terrestrial carbon cycling in tropical forests. However, an efficient and scalable approach for predicting V_c,max is still lacking. Here the ability of leaf spectroscopy for rapid estimation of V_c,max was tested. V_c,max was estimated using traditional gas exchange methods, and measured reflectance spectra and leaf age in leaves sampled from tropical forests in Panama and Brazil. These data were used to build a model to predict V_c,max from leaf spectra. The results demonstrated that leaf spectroscopy accurately predicts V_c,max of mature leaves in Panamanian tropical forests (R²  = 0.90). However, this single-age model required recalibration when applied to broader leaf demographic classes (i.e. immature leaves). Combined use of spectroscopy models for V_c,max and leaf age enabled construction of the V_c,max -age relationship solely from leaf spectra, which agreed with field observations. This suggests that the spectroscopy technique can capture the seasonal variability in V_c,max , assuming sufficient sampling across diverse species, leaf ages and canopy environments. This finding will aid development of remote sensing approaches that can be used to characterize V_c,max in moist tropical forests and enable an efficient means to parameterize and evaluate terrestrial biosphere models.

High-throughput field phenotyping using hyperspectral reflectance and partial least squares regression (PLSR) reveals genetic modifications to photosynthetic capacity

Spectroscopy is becoming an increasingly powerful tool to alleviate the challenges of traditional measurements of key plant traits at the leaf, canopy, and ecosystem scales. Spectroscopic methods often rely on statistical approaches to reduce data redundancy and enhance useful prediction of physiological traits. Given the mechanistic uncertainty of spectroscopic techniques, genetic modification of plant biochemical pathways may affect reflectance spectra causing predictive models to lose power. The objectives of this research were to assess over two separate years, whether a predictive model can represent natural and imposed variation in leaf photosynthetic potential for different crop cultivars and genetically modified plants, to assess the interannual capabilities of a partial least square regression (PLSR) model, and to determine whether leaf N is a dominant driver of photosynthesis in PLSR models. In 2016, a PLSR analysis of reflectance spectra coupled with gas exchange data was used to build predictive models for photosynthetic parameters including maximum carboxylation rate of Rubisco (V _c,max ), maximum electron transport rate (J _max ) and percentage leaf nitrogen ([N]). The model was developed for wild type and genetically modified plants that represent a wide range of photosynthetic capacities. Results show that hyperspectral reflectance accurately predicted V _c,max, J _max and [N] for all plants measured in 2016. Applying these PLSR models to plants grown in 2017 resulted in a strong predictive ability relative to gas exchange measurements for V _c,max, but not for J _max, and not for genotypes unique to 2017. Building a new model including data collected in 2017 resulted in more robust predictions, with R² increases of 17% for V _c,max . and 13% J _max . Plants generally have a positive correlation between leaf nitrogen and photosynthesis, however, tobacco with reduced Rubisco (SSuD) had significantly higher [N] despite much lower V _c,max. The PLSR model was able to accurately predict both lower V _c,max and higher leaf [N] for this genotype suggesting that the spectral based estimates of V _c,max and leaf nitrogen [N] are independent. These results suggest that the PLSR model can be applied across years, but only to genotypes used to build the model and that the actual mechanism measured with the PLSR technique is not directly related to leaf [N]. The success of the leaf-scale analysis suggests that similar approaches may be successful at the canopy and ecosystem scales but to use these methods across years and between genotypes at any scale, application of accurately populated physical based models based on radiative transfer principles may be required.