Terrestrial biosphere models underestimate photosynthetic capacity and CO2 assimilation in the Arctic

Leaf stomatal configuration and photosynthetic traits jointly affect leaf water use efficiency in forests along climate gradients

Water use efficiency (WUE) represents the trade-off between carbon assimilation and water loss in plants. It remains unclear how leaf stomatal and photosynthetic traits regulate the spatial variation of leaf WUE in different natural forest ecosystems. We investigated 43 broad-leaf tree species spanning from cold-temperate to tropical forests in China. We quantified leaf WUE using leaf δ13C and measured stomatal traits, photosynthetic traits as well as maximum stomatal conductance (G w max $$ {G}_{{\mathrm{w}}_{\mathrm{max}}} $$ ) and maximum carboxylation capacity (V c max $$ {V}_{{\mathrm{c}}_{\mathrm{max}}} $$ ). We found that leaves in cold-temperate forests displayed 'fast' carbon economics, characterized by higher leaf nitrogen, Chl, specific leaf area, andV c max $$ {V}_{{\mathrm{c}}_{\mathrm{max}}} $$ , as an adaptation to the shorter growing season. However, these leaves exhibited 'slow' hydraulic traits, with larger but fewer stomata and similarG w max $$ {G}_{{\mathrm{w}}_{\mathrm{max}}} $$ , resulting in higher leaf WUE. By contrast, leaves in tropical forests had smaller and denser stomata, enabling swift response to heterogeneous light conditions. However, this stomatal configuration increased potential water loss, and coupled with their low photosynthetic capacity, led to lower WUE. Our findings contribute to understanding how plant photosynthetic and stomatal traits regulate carbon-water trade-offs across climatic gradients, advancing our ability to predict the impacts of climate changes on forest carbon and water cycles.

Carboxylation capacity is the main limitation of carbon assimilation in High Arctic shrubs

Increases in shrub height, biomass and canopy cover are key whole-plant features of warming-induced vegetation change in tundra. We investigated leaf functional traits underlying photosynthetic capacity of Arctic shrub species, particularly its main limiting processes such as mesophyll conductance. In this nutrient-limited ecosystem, we expect leaf nitrogen concentration to be the main limiting factor for photosynthesis. We measured the net photosynthetic rate at saturated light (Asat) in three Salix species throughout a glacial valley in High-Arctic tundra and used a causal approach to test relationships between leaf stomatal and mesophyll conductances (gsc, gm), carboxylation capacity (Vcmax), nitrogen and phosphorus concentration (Narea, Parea) and leaf mass ratio (LMA). Arctic Salix species showed no difference in Asat compared to a global data set, while being characterized by higher Narea, Parea and LMA. Vcmax, gsc and gm independently increased Asat, with Vcmax as its main limitation. We highlighted a nitrogen-influenced pathway for increasing photosynthesis in the two prostrate mesic habitat species. In contrast, the erect wetland habitat Salix richardsonii mainly increased Asat with increasing gsc. Overall, our study revealed high photosynthetic capacities of Arctic Salix species but contrasting regulatory pathways that may influence shrub ability to respond to environmental changes in High Arctic tundra.

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.

Dynamics of carbon and water vapor fluxes in three typical ecosystems of Heihe River Basin, Northwestern China

Understanding the dynamics of carbon and water vapor fluxes in arid inland river basin ecosystems is essential for predicting and assessing the regional carbon-water budget amid climate change. However, studies aiming to unravel the mechanisms driving the variations and coupling process of regional carbon-water budget in a changing environment in arid regions are limited. Here, we used the eddy covariance technique to analyze the relationship between CO2 and H2O fluxes in three typical ecosystems across the upper, middle, and lower reaches of an arid inland river basin in Northwestern China. Our results showed that all ecosystems acted as carbon sinks, with the alpine swamp meadow, cropland, and desert shrubland sequestrating -300.2 ± 0.01, -644.8 ± 2.9, and - 203.7 ± 22.5 g C m-2 yr-1, respectively. Air temperature (Ta) primarily controlled daily gross primary productivity (GPP) and net ecosystem CO2 exchange (NEE) in the irrigated cropland during the growing season, while soil temperature (Ts) and vapor pressure deficit (VPD) regulated these parameters in the alpine swamp meadow and desert shrubland. Additionally, Ta and net radiation (Rn) controlled daily evapotranspiration (ET) in cropland, while Ts and Rn regulated ET at other sites. Consequently, carbon and water vapor fluxes of all three ecosystems tended to be energy-limited during the growing season. The differential responses of carbon and water vapor fluxes in the upper, middle, and lower reaches of these ecosystems to biophysical factors determined their distinct coupling and variations in water use efficiency. Notably, the desert shrub ecosystem in the lower reach of the basin maintained a stable balance between carbon gain and water loss, indicating adaptation to aridity. This study provides valuable insights into the underlying mechanisms behind the changes in carbon and water vapor fluxes and water-use efficiency in arid river basin ecosystems.

The Dynamic Assimilation Technique measures photosynthetic CO2 response curves with similar fidelity to steady-state approaches in half the time

The net CO2 assimilation (A) response to intercellular CO2 concentration (Ci) is a fundamental measurement in photosynthesis and plant physiology research. The conventional A/Ci protocols rely on steady-state measurements and take 15-40 min per measurement, limiting data resolution or biological replication. Additionally, there are several CO2 protocols employed across the literature, without clear consensus as to the optimal protocol or systematic biases in their estimations. We compared the non-steady-state Dynamic Assimilation Technique (DAT) protocol and the three most used CO2 protocols in steady-state measurements, and tested whether different CO2 protocols lead to systematic differences in estimations of the biochemical limitations to photosynthesis. The DAT protocol reduced the measurement time by almost half without compromising estimation accuracy or precision. The monotonic protocol was the fastest steady-state method. Estimations of biochemical limitations to photosynthesis were very consistent across all CO2 protocols, with slight differences in Rubisco carboxylation limitation. The A/Ci curves were not affected by the direction of the change of CO2 concentration but rather the time spent under triose phosphate utilization (TPU)-limited conditions. Our results suggest that the maximum rate of Rubisco carboxylation (Vcmax), linear electron flow for NADPH supply (J), and TPU measured using different protocols within the literature are comparable, or at least not systematically different based on the measurement protocol used.

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.

The effect of the vertical gradients of photosynthetic parameters on the CO2 assimilation and transpiration of a Panamanian tropical forest

Terrestrial biosphere models (TBMs) include the representation of vertical gradients in leaf traits associated with modeling photosynthesis, respiration, and stomatal conductance. However, model assumptions associated with these gradients have not been tested in complex tropical forest canopies. We compared TBM representation of the vertical gradients of key leaf traits with measurements made in a tropical forest in Panama and then quantified the impact of the observed gradients on simulated canopy-scale CO₂ and water fluxes. Comparison between observed and TBM trait gradients showed divergence that impacted canopy-scale simulations of water vapor and CO₂ exchange. Notably, the ratio between the dark respiration rate and the maximum carboxylation rate was lower near the ground than at the top-of-canopy, leaf-level water-use efficiency was markedly higher at the top-of-canopy, and the decrease in maximum carboxylation rate from the top-of-canopy to the ground was less than TBM assumptions. The representation of the gradients of leaf traits in TBMs is typically derived from measurements made within-individual plants, or, for some traits, assumed constant due to a lack of experimental data. Our work shows that these assumptions are not representative of the trait gradients observed in species-rich, complex tropical forests.

From remotely sensed solar-induced chlorophyll fluorescence to ecosystem structure, function, and service: Part I-Harnessing theory

Solar-induced chlorophyll fluorescence (SIF) is a remotely sensed optical signal emitted during the light reactions of photosynthesis. The past two decades have witnessed an explosion in availability of SIF data at increasingly higher spatial and temporal resolutions, sparking applications in diverse research sectors (e.g., ecology, agriculture, hydrology, climate, and socioeconomics). These applications must deal with complexities caused by tremendous variations in scale and the impacts of interacting and superimposing plant physiology and three-dimensional vegetation structure on the emission and scattering of SIF. At present, these complexities have not been overcome. To advance future research, the two companion reviews aim to (1) develop an analytical framework for inferring terrestrial vegetation structures and function that are tied to SIF emission, (2) synthesize progress and identify challenges in SIF research via the lens of multi-sector applications, and (3) map out actionable solutions to tackle these challenges and offer our vision for research priorities over the next 5-10 years based on the proposed analytical framework. This paper is the first of the two companion reviews, and theory oriented. It introduces a theoretically rigorous yet practically applicable analytical framework. Guided by this framework, we offer theoretical perspectives on three overarching questions: (1) The forward (mechanism) question-How are the dynamics of SIF affected by terrestrial ecosystem structure and function? (2) The inference question: What aspects of terrestrial ecosystem structure, function, and service can be reliably inferred from remotely sensed SIF and how? (3) The innovation question: What innovations are needed to realize the full potential of SIF remote sensing for real-world applications under climate change? The analytical framework elucidates that process complexity must be appreciated in inferring ecosystem structure and function from the observed SIF; this framework can serve as a diagnosis and inference tool for versatile applications across diverse spatial and temporal scales.

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.

Stomatal conductance, not biochemistry, drives low temperature acclimation of photosynthesis in Populus balsamifera, regardless of nitrogen availability

Low-temperature thermal acclimation may require adjustments to N and water use to sustain photosynthesis because of slow enzyme functioning and high water viscosity. However, understanding of photosynthetic acclimation to temperatures below 11 °C is limited. We acclimated Populus balsamifera to 6 °C and 10 °C (6A and 10A, respectively) and provided the trees with either high or low N fertilizer. We measured net CO₂ assimilation (A_net ), stomatal conductance (g_s ), maximum rates of Rubisco carboxylation (V_cmax ), electron transport (J_max ) and dark respiration (R_d ) at leaf temperatures of 2, 6, 10, 14 and 18 °C, along with leaf N concentrations. The 10A trees had higher A_net than the 6A trees at warmer leaf temperatures, which was correlated with higher g_s in the 10A trees. The instantaneous temperature responses of V_cmax , J_max and R_d were similar for trees from both acclimation temperatures. While soil N availability increased leaf N concentrations, this had no effect on acclimation of photosynthesis or respiration. Our results indicate that acclimation below 11 °C occurred primarily through changes in stomatal conductance, not photosynthetic biochemistry, and was unaffected by short-term N supply. Thermal acclimation of stomatal conductance should therefore be a priority for future carbon cycle model development.

Assessing dynamic vegetation model parameter uncertainty across Alaskan arctic tundra plant communities

As the Arctic region moves into uncharted territory under a warming climate, it is important to refine the terrestrial biosphere models (TBMs) that help us understand and predict change. One fundamental uncertainty in TBMs relates to model parameters, configuration variables internal to the model whose value can be estimated from data. We incorporate a version of the Terrestrial Ecosystem Model (TEM) developed for arctic ecosystems into the Predictive Ecosystem Analyzer (PEcAn) framework. PEcAn treats model parameters as probability distributions, estimates parameters based on a synthesis of available field data, and then quantifies both model sensitivity and uncertainty to a given parameter or suite of parameters. We examined how variation in 21 parameters in the equation for gross primary production influenced model sensitivity and uncertainty in terms of two carbon fluxes (net primary productivity and heterotrophic respiration) and two carbon (C) pools (vegetation C and soil C). We set up different parameterizations of TEM across a range of tundra types (tussock tundra, heath tundra, wet sedge tundra, and shrub tundra) in northern Alaska, along a latitudinal transect extending from the coastal plain near Utqiaġvik to the southern foothills of the Brooks Range, to the Seward Peninsula. TEM was most sensitive to parameters related to the temperature regulation of photosynthesis. Model uncertainty was mostly due to parameters related to leaf area, temperature regulation of photosynthesis, and the stomatal responses to ambient light conditions. Our analysis also showed that sensitivity and uncertainty to a given parameter varied spatially. At some sites, model sensitivity and uncertainty tended to be connected to a wider range of parameters, underlining the importance of assessing tundra community processes across environmental gradients or geographic locations. Generally, across sites, the flux of net primary productivity (NPP) and pool of vegetation C had about equal uncertainty, while heterotrophic respiration had higher uncertainty than the pool of soil C. Our study illustrates the complexity inherent in evaluating parameter uncertainty across highly heterogeneous arctic tundra plant communities. It also provides a framework for iteratively testing how newly collected field data related to key parameters may result in more effective forecasting of Arctic change.

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.

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.

Seasonal trends in photosynthesis and leaf traits in scarlet oak

Understanding seasonal variation in photosynthesis is important for understanding and modeling plant productivity. Here, we used shotgun sampling to examine physiological, structural and spectral leaf traits of upper canopy, sun-exposed leaves in Quercus coccinea Münchh (scarlet oak) across the growing season in order to understand seasonal trends, explore the mechanisms underpinning physiological change and investigate the impact of extrapolating measurements from a single date to the whole season. We tested the hypothesis that photosynthetic rates and capacities would peak at the summer solstice, i.e., at the time of peak photoperiod. Contrary to expectations, our results reveal a late-season peak in both photosynthetic capacity and rate before the expected sharp decrease at the start of senescence. This late-season maximum occurred after the higher summer temperatures and vapor pressure deficit and was correlated with the recovery of leaf water content and increased stomatal conductance. We modeled photosynthesis at the top of the canopy and found that the simulated results closely tracked the maximum carboxylation capacity of Rubisco. For both photosynthetic capacity and modeled top-of-canopy photosynthesis, the maximum value was therefore not observed at the summer solstice. Rather, in each case, the measurements at and around the solstice were close to the overall seasonal mean, with values later in the season leading to deviations from the mean by up to 41 and 52%, respectively. Overall, we found that the expected Gaussian pattern of photosynthesis was not observed. We conclude that an understanding of species- and environment-specific changes in photosynthesis across the season is essential for correct estimation of seasonal photosynthetic capacity.

Source:sink imbalance detected with leaf- and canopy-level spectroscopy in a field-grown crop

The finely tuned balance between sources and sinks determines plant resource partitioning and regulates growth and development. Understanding and measuring metabolic indicators of source or sink limitation forms a vital part of global efforts to increase crop yield for future food security. We measured metabolic profiles of Cucurbita pepo (zucchini) grown in the field under carbon sink limitation and control conditions. We demonstrate that these profiles can be measured non-destructively using hyperspectral reflectance at both leaf and canopy scales. Total non-structural carbohydrates (TNC) increased 82% in sink-limited plants; leaf mass per unit area (LMA) increased 38% and free amino acids increased 22%. Partial least-squares regression (PLSR) models link these measured functional traits with reflectance data, enabling high-throughput estimation of traits comprising the sink limitation response. Leaf- and canopy-scale models for TNC had R² values of 0.93 and 0.64 and %RMSE of 13 and 38%, respectively. For LMA, R² values were 0.91 and 0.60 and %RMSE 7 and 14%; for free amino acids, R² was 0.53 and 0.21 with %RMSE 20 and 26%. Remote sensing can enable accurate, rapid detection of sink limitation in the field at the leaf and canopy scale, greatly expanding our ability to understand and measure metabolic responses to stress.

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.

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.

Snow melt stimulates ecosystem respiration in Arctic ecosystems

Cold seasons in Arctic ecosystems are increasingly important to the annual carbon balance of these vulnerable ecosystems. Arctic winters are largely harsh and inaccessible leading historic data gaps during that time. Until recently, cold seasons have been assumed to have negligible impacts on the annual carbon balance but as data coverage increases and the Arctic warms, the cold season has been shown to account for over half of annual methane (CH₄ ) emissions and can offset summer photosynthetic carbon dioxide (CO₂ ) uptake. Freeze-thaw cycle dynamics play a critical role in controlling cold season CO₂ and CH₄ loss, but the relationship has not been extensively studied. Here, we analyze freeze-thaw processes through in situ CO₂ and CH₄ fluxes in conjunction with soil cores for physical structure and porewater samples for redox biogeochemistry. We find a movement of water toward freezing fronts in soil cores, leaving air spaces in soils, which allows for rapid infiltration of oxygen-rich snow melt in spring as shown by oxidized iron in porewater. The snow melt period coincides with rising ecosystem respiration and can offset up to 41% of the summer CO₂ uptake. Our study highlights this important seasonal process and shows spring greenhouse gas emissions are largely due to production from respiration instead of only bursts of stored gases. Further warming is projected to result in increases of snowpack and deeper thaws, which could increase this ecosystem respiration dominate snow melt period causing larger greenhouse gas losses during spring.

Climate warming alters photosynthetic responses to elevated CO2 in prairie plants

PREMISE

The impact of elevated CO₂ concentration ([CO₂ ]) and climate warming on plant productivity in dryland ecosystems is influenced strongly by soil moisture availability. We predicted that the influence of warming on the stimulation of photosynthesis by elevated [CO₂ ] in prairie plants would operate primarily through direct and indirect effects on soil water.

METHODS

We measured light-saturated photosynthesis (A_net ), stomatal conductance (g_s ), maximum Rubisco carboxylation rate (V_cmax ), maximum electron transport capacity (J_max ) and related variables in four C₃ plant species in the Prairie Heating and CO₂ Enrichment (PHACE) experiment in southeastern Wyoming. Measurements were conducted over two growing seasons that differed in the amount of precipitation and soil moisture content.

RESULTS

A_net in the C₃ subshrub Artemisia frigida and the C₃ forb Sphaeralcea coccinea was stimulated by elevated [CO₂ ] under ambient and warmed temperature treatments. Warming by itself reduced A_net in all species during the dry year, but stimulated photosynthesis in S. coccinea in the wet year. In contrast, A_net in the C₃ grass Pascopyrum smithii was not stimulated by elevated [CO₂ ] or warming under wet or dry conditions. Photosynthetic downregulation under elevated [CO₂ ] in this species countered the potential stimulatory effect under improved water relations. Warming also reduced the magnitude of CO₂ -induced down-regulation in this grass, possibly by sustaining high levels of carbon utilization.

CONCLUSIONS

Direct and indirect effects of elevated [CO₂ ] and warming on soil water was an overriding factor influencing patterns of A_net in this semi-arid temperate grassland, emphasizing the important role of water relations in driving grassland responses to global change.

The Role of Synthetic Biology in Atmospheric Greenhouse Gas Reduction: Prospects and Challenges

The long atmospheric residence time of CO2 creates an urgent need to add atmospheric carbon drawdown to CO2 regulatory strategies. Synthetic and systems biology (SSB), which enables manipulation of cellular phenotypes, offers a powerful approach to amplifying and adding new possibilities to current land management practices aimed at reducing atmospheric carbon. The participants (in attendance: Christina Agapakis, George Annas, Adam Arkin, George Church, Robert Cook-Deegan, Charles DeLisi, Dan Drell, Sheldon Glashow, Steve Hamburg, Henry Jacoby, Henry Kelly, Mark Kon, Todd Kuiken, Mary Lidstrom, Mike MacCracken, June Medford, Jerry Melillo, Ron Milo, Pilar Ossorio, Ari Patrinos, Keith Paustian, Kristala Jones Prather, Kent Redford, David Resnik, John Reilly, Richard J. Roberts, Daniel Segre, Susan Solomon, Elizabeth Strychalski, Chris Voigt, Dominic Woolf, Stan Wullschleger, and Xiaohan Yang) identified a range of possibilities by which SSB might help reduce greenhouse gas concentrations and which might also contribute to environmental sustainability and adaptation. These include, among other possibilities, engineering plants to convert CO2 produced by respiration into a stable carbonate, designing plants with an increased root-to-shoot ratio, and creating plants with the ability to self-fertilize. A number of serious ecological and societal challenges must, however, be confronted and resolved before any such application can be fully assessed, realized, and deployed.

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 .

The response of stomatal conductance to seasonal drought in tropical forests

Stomata regulate CO₂ uptake for photosynthesis and water loss through transpiration. The approaches used to represent stomatal conductance (g_s ) in models vary. In particular, current understanding of drivers of the variation in a key parameter in those models, the slope parameter (i.e. a measure of intrinsic plant water-use-efficiency), is still limited, particularly in the tropics. Here we collected diurnal measurements of leaf gas exchange and leaf water potential (Ψ_leaf ), and a suite of plant traits from the upper canopy of 15 tropical trees in two contrasting Panamanian forests throughout the dry season of the 2016 El Niño. The plant traits included wood density, leaf-mass-per-area (LMA), leaf carboxylation capacity (V_c,max ), leaf water content, the degree of isohydry, and predawn Ψ_leaf . We first investigated how the choice of four commonly used leaf-level g_s models with and without the inclusion of Ψ_leaf as an additional predictor variable influence the ability to predict g_s , and then explored the abiotic (i.e. month, site-month interaction) and biotic (i.e. tree-species-specific characteristics) drivers of slope parameter variation. Our results show that the inclusion of Ψ_leaf did not improve model performance and that the models that represent the response of g_s to vapor pressure deficit performed better than corresponding models that respond to relative humidity. Within each g_s model, we found large variation in the slope parameter, and this variation was attributable to the biotic driver, rather than abiotic drivers. We further investigated potential relationships between the slope parameter and the six available plant traits mentioned above, and found that only one trait, LMA, had a significant correlation with the slope parameter (R²  = 0.66, n = 15), highlighting a potential path towards improved model parameterization. This study advances understanding of g_s dynamics over seasonal drought, and identifies a practical, trait-based approach to improve modeling of carbon and water exchange in tropical forests.

How do vascular plants perform photosynthesis in extreme environments? An integrative ecophysiological and biochemical story

In this work, we review the physiological and molecular mechanisms that allow vascular plants to perform photosynthesis in extreme environments, such as deserts, polar and alpine ecosystems. Specifically, we discuss the morpho/anatomical, photochemical and metabolic adaptive processes that enable a positive carbon balance in photosynthetic tissues under extreme temperatures and/or severe water-limiting conditions in C₃ species. Nevertheless, only a few studies have described the in situ functioning of photoprotection in plants from extreme environments, given the intrinsic difficulties of fieldwork in remote places. However, they cover a substantial geographical and functional range, which allowed us to describe some general trends. In general, photoprotection relies on the same mechanisms as those operating in the remaining plant species, ranging from enhanced morphological photoprotection to increased scavenging of oxidative products such as reactive oxygen species. Much less information is available about the main physiological and biochemical drivers of photosynthesis: stomatal conductance (g_s ), mesophyll conductance (g_m ) and carbon fixation, mostly driven by RuBisCO carboxylation. Extreme environments shape adaptations in structures, such as cell wall and membrane composition, the concentration and activation state of Calvin-Benson cycle enzymes, and RuBisCO evolution, optimizing kinetic traits to ensure functionality. Altogether, these species display a combination of rearrangements, from the whole-plant level to the molecular scale, to sustain a positive carbon balance in some of the most hostile environments on Earth.

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.

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.

No evidence for triose phosphate limitation of light-saturated leaf photosynthesis under current atmospheric CO2 concentration

The triose phosphate utilization (TPU) rate has been identified as one of the processes that can limit terrestrial plant photosynthesis. However, we lack a robust quantitative assessment of TPU limitation of photosynthesis at the global scale. As a result, TPU, and its potential limitation of photosynthesis, is poorly represented in terrestrial biosphere models (TBMs). In this study, we utilized a global data set of photosynthetic CO₂ response curves representing 141 species from tropical rainforests to Arctic tundra. We quantified TPU by fitting the standard biochemical model of C₃ photosynthesis to measured photosynthetic CO₂ response curves and characterized its instantaneous temperature response. Our results demonstrate that TPU does not limit leaf photosynthesis at the current ambient atmospheric CO₂ concentration. Furthermore, our results showed that the light-saturated photosynthetic rates of plants growing in cold environments are not more often limited by TPU than those of plants growing in warmer environments. In addition, our study showed that the instantaneous temperature response of TPU is distinct from temperature response of the maximum rate of Rubisco carboxylation. The new formulations of the temperature response of TPU derived in this study may prove useful in quantifying the biochemical limits to terrestrial plant photosynthesis and improve the representation of plant photosynthesis in TBMs.

Diverse photosynthetic capacity of global ecosystems mapped by satellite chlorophyll fluorescence measurements

Photosynthetic capacity is often quantified by the Rubisco-limited photosynthetic capacity (i.e. maximum carboxylation rate, V_cmax). It is a key plant functional trait that is widely used in Earth System Models for simulation of the global carbon and water cycles. Measuring V_cmax is time-consuming and laborious; therefore, the spatiotemporal distribution of V_cmax is still poorly understood due to limited measurements of V_cmax. In this study, we used a data assimilation approach to map the spatial variation of V_cmax for global terrestrial ecosystems from a 11-year-long satellite-observed solar-induced chlorophyll fluorescence (SIF) record. In this SIF-derived V_cmax map, the mean V_cmax value for each plant function type (PFT) is found to be comparable to a widely used N-derived V_cmax dataset by Kattge et al. (2009). The gradient of V_cmax along PFTs is clearly revealed even without land cover information as an input. Large seasonal and spatial variations of V_cmax are found within each PFT, especially for diverse crop rotation systems. The distribution of major crop belts, characterized with high V_cmax values, is highlighted in this V_cmax map. Legume plants are characterized with high V_cmax values. This V_cmax map also clearly illustrates the emerging soybean revolution in South America where V_cmax is the highest among the world. The gradient of V_cmax in Amazon is found to follow the transition of soil types with different soil N and P contents. This study suggests that satellite-observed SIF is powerful in deriving the important plant functional trait, i.e. V_cmax, for global climate change studies.

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.

The "one-point method" for estimating maximum carboxylation capacity of photosynthesis: A cautionary tale

The maximum carboxylation capacity of Rubisco, V_c,max , is an important photosynthetic parameter that is key to accurate estimation of carbon assimilation. The gold-standard technique for determining V_c,max is to derive V_c,max from the initial slope of an A-C_i curve (the response of photosynthesis, A, to intercellular CO₂ concentration, C_i ). Accurate estimates of V_c,max derived from an alternative and rapid "one-point" measurement of photosynthesis could greatly accelerate data collection and model parameterization. We evaluated the practical application of the one-point method in six species measured under standard conditions (saturating irradiance and 400 μmol CO₂ mol^-1 ) and under conditions that would increase the likelihood for successful estimation of V_c,max : (a) ensuring Rubisco-limited A by measuring at 300 μmol CO₂ mol^-1 and (b) allowing time for acclimation to saturating irradiance prior to measurement. The one-point method significantly underestimated V_c,max in four of the six species, providing estimates 21%-32% below fitted values. We identified ribulose-1,5-bisphosphate-limited A, light acclimation, and the use of an assumed respiration rate as factors that limited the effective use of the one-point method to accurately estimate V_c,max . We conclude that the one-point method requires a species-specific understanding of its application, is often unsuccessful, and must be used with caution.

Terrestrial biosphere models may overestimate Arctic CO2 assimilation if they do not account for decreased quantum yield and convexity at low temperature

How terrestrial biosphere models (TBMs) represent leaf photosynthesis and its sensitivity to temperature are two critical components of understanding and predicting the response of the Arctic carbon cycle to global change. We measured the effect of temperature on the response of photosynthesis to irradiance in six Arctic plant species and determined the quantum yield of CO₂ fixation ( ϕCO2 ) and the convexity factor (θ). We also determined leaf absorptance (α) from measured reflectance to calculate ϕCO2 on an absorbed light basis ( ϕCO2.a ) and enabled comparison with nine TBMs. The mean ϕCO2.a was 0.045 mol CO₂  mol^-1 absorbed quanta at 25°C and closely agreed with the mean TBM parameterisation (0.044), but as temperature decreased measured ϕCO2.a diverged from TBMs. At 5°C measured ϕCO2.a was markedly reduced (0.025) and 60% lower than TBM estimates. The θ also showed a significant reduction between 25°C and 5°C. At 5°C θ was 38% lower than the common model parameterisation of 0.7. These data show that TBMs are not accounting for observed reductions in ϕCO2.a and θ that can occur at low temperature. Ignoring these reductions in ϕCO2.a and θ could lead to a marked (45%) overestimation of CO₂ assimilation at subsaturating irradiance and low temperature.

Homoeostatic maintenance of nonstructural carbohydrates during the 2015-2016 El Niño drought across a tropical forest precipitation gradient

Nonstructural carbohydrates (NSCs) are essential for maintenance of plant metabolism and may be sensitive to short- and long-term climatic variation. NSC variation in moist tropical forests has rarely been studied, so regulation of NSCs in these systems is poorly understood. We measured foliar and branch NSC content in 23 tree species at three sites located across a large precipitation gradient in Panama during the 2015-2016 El Niño to examine how short- and long-term climatic variation impact carbohydrate dynamics. There was no significant difference in total NSCs as the drought progressed (leaf P = 0.32, branch P = 0.30) nor across the rainfall gradient (leaf P = 0.91, branch P = 0.96). Foliar soluble sugars decreased while starch increased over the duration of the dry period, suggesting greater partitioning of NSCs to storage than metabolism or transport as drought progressed. There was a large variation across species at all sites, but total foliar NSCs were positively correlated with leaf mass per area, whereas branch sugars were positively related to leaf temperature and negatively correlated with daily photosynthesis and wood density. The NSC homoeostasis across a wide range of conditions suggests that NSCs are an allocation priority in moist tropical forests.

Acclimation and adaptation components of the temperature dependence of plant photosynthesis at the global scale

The temperature response of photosynthesis is one of the key factors determining predicted responses to warming in global vegetation models (GVMs). The response may vary geographically, owing to genetic adaptation to climate, and temporally, as a result of acclimation to changes in ambient temperature. Our goal was to develop a robust quantitative global model representing acclimation and adaptation of photosynthetic temperature responses. We quantified and modelled key mechanisms responsible for photosynthetic temperature acclimation and adaptation using a global dataset of photosynthetic CO₂ response curves, including data from 141 C₃ species from tropical rainforest to Arctic tundra. We separated temperature acclimation and adaptation processes by considering seasonal and common-garden datasets, respectively. The observed global variation in the temperature optimum of photosynthesis was primarily explained by biochemical limitations to photosynthesis, rather than stomatal conductance or respiration. We found acclimation to growth temperature to be a stronger driver of this variation than adaptation to temperature at climate of origin. We developed a summary model to represent photosynthetic temperature responses and showed that it predicted the observed global variation in optimal temperatures with high accuracy. This novel algorithm should enable improved prediction of the function of global ecosystems in a warming climate.

Simulated projections of boreal forest peatland ecosystem productivity are sensitive to observed seasonality in leaf physiology†

We quantified seasonal CO2 assimilation capacities for seven dominant vascular species in a wet boreal forest peatland then applied data to a land surface model parametrized to the site (ELM-SPRUCE) to test if seasonality in photosynthetic parameters results in differences in simulated plant responses to elevated CO2 and temperature. We collected seasonal leaf-level gas exchange, nutrient content and stand allometric data from the field-layer community (i.e., Maianthemum trifolium (L.) Sloboda), understory shrubs (Rhododendron groenlandicum (Oeder) Kron and Judd, Chamaedaphne calyculata (L.) Moench., Kalmia polifolia Wangenh. and Vaccinium angustifolium Alton.) and overstory trees (Picea mariana (Mill.) B.S.P. and Larix laricina (Du Roi) K. Koch). We found significant interspecific seasonal differences in specific leaf area, nitrogen content (by area; Na) and photosynthetic parameters (i.e., maximum rates of Rubisco carboxylation (Vcmax25°C), electron transport (Jmax25°C) and dark respiration (Rd25°C)), but minimal correlation between foliar Na and Vcmax25°C, Jmax25°C or Rd25°C, which illustrates that nitrogen alone is not a good correlate for physiological processes such as Rubisco activity that can change seasonally in this system. ELM-SPRUCE was sensitive to the introduction of observed interspecific seasonality in Vcmax25°C, Jmax25°C and Rd25°C, leading to simulated enhancement of net primary production (NPP) using seasonally dynamic parameters as compared with use of static parameters. This pattern was particularly pronounced under simulations with higher temperature and elevated CO2, suggesting a key hypothesis to address with future empirical or observational studies as climate changes. Inclusion of species-specific seasonal photosynthetic parameters should improve estimates of boreal ecosystem-level NPP, especially if impacts of seasonal physiological ontogeny can be separated from seasonal thermal acclimation.

Global photosynthetic capacity is optimized to the environment

Earth system models (ESMs) use photosynthetic capacity, indexed by the maximum Rubisco carboxylation rate (V_cmax), to simulate carbon assimilation and typically rely on empirical estimates, including an assumed dependence on leaf nitrogen determined from soil fertility. In contrast, new theory, based on biochemical coordination and co‐optimization of carboxylation and water costs for photosynthesis, suggests that optimal V_cmax can be predicted from climate alone, irrespective of soil fertility. Here, we develop this theory and find it captures 64% of observed variability in a global, field‐measured V_cmax dataset for C₃ plants. Soil fertility indices explained substantially less variation (32%). These results indicate that environmentally regulated biophysical constraints and light availability are the first‐order drivers of global photosynthetic capacity. Through acclimation and adaptation, plants efficiently utilize resources at the leaf level, thus maximizing potential resource use for growth and reproduction. Our theory offers a robust strategy for dynamically predicting photosynthetic capacity in ESMs.

The nitrogen cost of photosynthesis

Global food security depends on three main cereal crops (wheat, rice and maize) achieving and maintaining high yields, as well as increasing their future yields. Fundamental to the production of this biomass is photosynthesis. The process of photosynthesis involves a large number of proteins that together account for the majority of the nitrogen in leaves. As large amounts of nitrogen are removed in the harvested grain, this needs to be replaced either from synthetic fertilizer or biological nitrogen fixation. Knowledge about photosynthetic properties of leaves in natural ecosystems is also important, particularly when we consider the potential impacts of climate change. While the relationship between nitrogen and photosynthetic capacity of a leaf differs between species, leaf nitrogen content provides a useful way to incorporate photosynthesis into models of ecosystems and the terrestrial biosphere. This review provides a generalized nitrogen budget for a C3 leaf cell and discusses the potential for improving photosynthesis from a nitrogen perspective.

Effect of growth temperature on photosynthetic capacity and respiration in three ecotypes of Eriophorum vaginatum

Ecotypic differentiation in the tussock‐forming sedge Eriophorum vaginatum has led to the development of populations that are locally adapted to climate in Alaska's moist tussock tundra. As a foundation species, E. vaginatum plays a central role in providing topographic and microclimatic variation essential to these ecosystems, but a changing climate could diminish the importance of this species. As Arctic temperatures have increased, there is evidence of adaptational lag in E. vaginatum, as locally adapted ecotypes now exhibit reduced population growth rates. Whether there is a physiological underpinning to adaptational lag is unknown. Accordingly, this possibility was investigated in reciprocal transplant gardens. Tussocks of E. vaginatum from sites separated by ~1° latitude (Coldfoot: 67°15′N, Toolik Lake: 68°37′, Sagwon: 69°25′) were transplanted into the Toolik Lake and Sagwon sites and exposed to either an ambient or an experimental warming treatment. Five tussocks pertreatment combination were measured at each garden to determine photosynthetic capacity (i.e., V_cmax and J_max) and dark respiration rate (R_d) at measurement temperatures of 15, 20, and 25°C. Photosynthetic enhancements or homeostasis were observed for all ecotypes at both gardens under increased growth temperature, indicating no negative effect of elevated temperature on photosynthetic capacity. Further, no evidence of thermal acclimation in R_d was observed for any ecotype, and there was little evidence of ecotypic variation in R_d. As such, no physiological contribution to adaptational lag was observed given the increase in growth temperature (up to ~2°C) provided by this study. Despite neutral to positive effects of increased growth temperature on photosynthesis in E. vaginatum, it appears to confer no lasting advantage to the species.

Greening of the land surface in the world's cold regions consistent with recent warming

Global ecosystem function is highly dependent on climate and atmospheric composition, yet ecosystem responses to environmental changes remain uncertain. Cold, high-latitude ecosystems in particular have experienced rapid warming¹, with poorly understood consequences^2-4. Here, we use a satellite observed proxy for vegetation cover - the fraction of absorbed photosynthetically active radiation⁵ - to identify a decline in the temperature limitation of vegetation in global ecosystems between 1982 and 2012. We quantify the spatial functional response of maximum annual vegetation cover to temperature and show that the observed temporal decline in temperature limitation is consistent with expectations based on observed recent warming. An ensemble of Earth system models from the Coupled Model Intercomparison Project (CMIP5) mischaracterized the functional response to temperature, leading to a large overestimation of vegetation cover in cold regions. We identify a 16.4% decline in the area of vegetated land that is limited by temperature over the past three decades, and suggest an expected large decline in temperature limitation under future warming scenarios. This rapid observed and expected decline in temperature limitation highlights the need for an improved understanding of other limitations to vegetation growth in cold regions^3,4,6, such as soil characteristics, species migration, recruitment, establishment, competition, and community dynamics.