Widespread inhibition of daytime ecosystem respiration

Drainage effects on carbon budgets of degraded peatlands in the north of the Netherlands

Peatlands store vast amounts of carbon (C). However, land-use-driven drainage causes peat oxidation, resulting in CO2 emission. There is a growing need for ground-truthing CO2 emission and its potential drivers to better quantify long-term emission trends in peatlands. This will help improve National Inventory Reporting and ultimately aid the design and verification of mitigation measures. To investigate regional drivers of CO2 emission, we estimated C budgets using custom-made automated chamber systems measuring CO2 concentrations corrected for carbon export and import. Chamber systems were rotated among thirteen degraded peatland pastures in Friesland (the Netherlands). These peatlands varied in water table depth (WTD), drainage-irrigation management (fixed regulated ditch water level (DWL), subsurface irrigation, furrow irrigation, or dynamic regulated DWL), and soil moisture. We investigated (1) whether drainage-irrigation management and related hydrological drivers could explain variation in C budgets, (2) how nighttime ecosystem respiration (Reconight) related to hydrological drivers, and (3) how C budgets compared with estimates from Tier 1 and Tier 2 models regularly used in National Inventory Reporting. Deep-drained peatlands largely overlapped with C budgets from shallow-drained peatlands. The variation in C budgets could not be explained with drainage-irrigation measures or annual WTD, likely because of high variation between sites. Reconightincreased from 85 to 250 kg CO2 ha-1 day-1 as the WTD dropped from 0 to 50 cm across all sites. A deeper WTD had no apparent effect on Reconight, which could be explained by the unimodal relationship we found between Reconight and soil moisture. Finally, C budgets estimated by Tier 1 emission factors and Tier 2 national models mismatched the between-site and between-year variation found in chamber-based estimated NECBs. To conclude, our study showed that shallow WTDs greatly determine C budgets and that regional C budgets, which can be accurately measure with periodic automated chamber measurements, are instrumental for model validation.

Biome-scale temperature sensitivity of ecosystem respiration revealed by atmospheric CO2 observations

The temperature sensitivity of ecosystem respiration regulates how the terrestrial carbon sink responds to a warming climate but has been difficult to constrain observationally beyond the plot scale. Here we use observations of atmospheric CO2 concentrations from a network of towers together with carbon flux estimates from state-of-the-art terrestrial biosphere models to characterize the temperature sensitivity of ecosystem respiration, as represented by the Arrhenius activation energy, over various North American biomes. We infer activation energies of 0.43 eV for North America and 0.38 eV to 0.53 eV for major biomes therein, which are substantially below those reported for plot-scale studies (approximately 0.65 eV). This discrepancy suggests that sparse plot-scale observations do not capture the spatial-scale dependence and biome specificity of the temperature sensitivity. We further show that adjusting the apparent temperature sensitivity in model estimates markedly improves their ability to represent observed atmospheric CO2 variability. This study provides observationally constrained estimates of the temperature sensitivity of ecosystem respiration directly at the biome scale and reveals that temperature sensitivities at this scale are lower than those based on earlier plot-scale studies. These findings call for additional work to assess the resilience of large-scale carbon sinks to warming.

Decoupling between carbon source and sink induced by responses of daily stem growth to water availability in subtropical urban forests

Urban forests are anticipated to offer sustainable ecosystem services, necessitating a comprehensive understanding of the ways in which trees respond to environmental changes. This study monitored stem radius fluctuations in Cinnamomum camphora and Taxodium distichum var. imbricatum trees using high-resolution dendrometers at two sites, respectively. Gross primary production (GPP) was measured using eddy-covariance techniques and aggregated to daily sums. Hourly and daily stem radius fluctuations were estimated across both species, and the responses of stems to radiation (Rg), air temperature (Tair), vapor pressure deficit (VPD), and soil humidity (SoilH) were quantified using Bayesian linear models. The diel growth patterns of the monitored trees showed similar characteristics at the species level. Results revealed that trees growth occurred primarily at night, with the lowest hourly contribution to total growth and probability for growth occurring in the afternoon. Furthermore, the Bayesian models indicated that VPD was the most important driver of daily growth and growth probability. After considering the potential constraints imposed by VPD, a modified Gompertz equation showed good performance, with R² ranging from 0.94 to 0.99 for the relationship between accumulative growth and time. Bayes-based model-independent data assimilation using advanced Markov chain Monte Carlo (MCMC) algorithms provided deeper insights into nonlinear model parameterization. Finally, the quantified relationship between GPP and stem daily growth revealed that the decoupling between carbon source and sink increased with VPD. These findings provided direct empirical evidence for VPD as a key driver of daily growth patterns and raise questions about carbon neutrality accounting under future climate change given the uncertainties induced by increased water stress limitations on carbon utilization.

It's only natural: Plant respiration in unmanaged systems

Respiration plays a key role in the terrestrial carbon cycle and is a fundamental metabolic process in all plant tissues and cells. We review respiration from the perspective of plants that grow in their natural habitat and how it is influenced by wide-ranging elements at different scales, from metabolic substrate availability to shifts in climate. Decades of field-based measurements have honed our understanding of the biological and environmental controls on leaf, root, stem, and whole-organism respiration. Despite this effort, there remain gaps in our knowledge within and across species and ecosystems, especially in more challenging-to-measure tissues like roots. Recent databases of respiration rates and associated leaf traits from species representing diverse biomes, plant functional types, and regional climates have allowed for a wider-lens view at modeling this important CO2 flux. We also re-analyze published data sets to show that maximum leaf respiration rates (Rmax) in species from around the globe are related both to leaf economic traits and environmental variables (precipitation and air temperature), but that root respiration does not follow the same latitudinal trends previously published for leaf data. We encourage the ecophysiological community to continue to expand their study of plant respiration in tissues that are difficult to measure and at the whole plant and ecosystem levels to address outstanding questions in the field.

Short- and long-term responses of leaf day respiration to elevated atmospheric CO2

Evaluating leaf day respiration rate (RL), which is believed to differ from that in the dark (RDk), is essential for predicting global carbon cycles under climate change. Several studies have suggested that atmospheric CO2 impacts RL. However, the magnitude of such an impact and associated mechanisms remain uncertain. To explore the CO2 effect on RL, wheat (Triticum aestivum) and sunflower (Helianthus annuus) plants were grown under ambient (410 ppm) and elevated (820 ppm) CO2 mole fraction ([CO2]). RL was estimated from combined gas exchange and chlorophyll fluorescence measurements using the Kok method, the Kok-Phi method, and a revised Kok method (Kok-Cc method). We found that elevated growth [CO2] led to an 8.4% reduction in RL and a 16.2% reduction in RDk in both species, in parallel to decreased leaf N and chlorophyll contents at elevated growth [CO2]. We also looked at short-term CO2 effects during gas exchange experiments. Increased RL or RL/RDk at elevated measurement [CO2] were found using the Kok and Kok-Phi methods, but not with the Kok-Cc method. This discrepancy was attributed to the unaccounted changes in Cc in the former methods. We found that the Kok and Kok-Phi methods underestimate RL and overestimate the inhibition of respiration under low irradiance conditions of the Kok curve, and the inhibition of RL was only 6%, representing 26% of the apparent Kok effect. We found no significant long-term CO2 effect on RL/RDk, originating from a concurrent reduction in RL and RDk at elevated growth [CO2], and likely mediated by acclimation of nitrogen metabolism.

Estimating intraseasonal intrinsic water-use efficiency from high-resolution tree-ring δ13 C data in boreal Scots pine forests

Intrinsic water-use efficiency (iWUE), a key index for carbon and water balance, has been widely estimated from tree-ring δ¹³ C at annual resolution, but rarely at high-resolution intraseasonal scale. We estimated high-resolution iWUE from laser-ablation δ¹³ C analysis of tree-rings (iWUE_iso ) and compared it with iWUE derived from gas exchange (iWUE_gas ) and eddy covariance (iWUE_EC ) data for two Pinus sylvestris forests from 2002 to 2019. By carefully timing iWUE_iso via modeled tree-ring growth, iWUE_iso aligned well with iWUE_gas and iWUE_EC at intraseasonal scale. However, year-to-year patterns of iWUE_gas , iWUE_iso , and iWUE_EC were different, possibly due to distinct environmental drivers on iWUE across leaf, tree, and ecosystem scales. We quantified the modification of iWUE_iso by postphotosynthetic δ¹³ C enrichment from leaf sucrose to tree rings and by nonexplicit inclusion of mesophyll and photorespiration terms in photosynthetic discrimination model, which resulted in overestimation of iWUE_iso by up to 11% and 14%, respectively. We thus extended the application of tree-ring δ¹³ C for iWUE estimates to high-resolution intraseasonal scale. The comparison of iWUE_gas , iWUE_iso , and iWUE_EC provides important insights into physiological acclimation of trees across leaf, tree, and ecosystem scales under climate change and improves the upscaling of ecological models.

Environmental controls on the light use efficiency of terrestrial gross primary production

Gross primary production (GPP) by terrestrial ecosystems is a key quantity in the global carbon cycle. The instantaneous controls of leaf-level photosynthesis are well established, but there is still no consensus on the mechanisms by which canopy-level GPP depends on spatial and temporal variation in the environment. The standard model of photosynthesis provides a robust mechanistic representation for C₃ species; however, additional assumptions are required to "scale up" from leaf to canopy. As a consequence, competing models make inconsistent predictions about how GPP will respond to continuing environmental change. This problem is addressed here by means of an empirical analysis of the light use efficiency (LUE) of GPP inferred from eddy covariance carbon dioxide flux measurements, in situ measurements of photosynthetically active radiation (PAR), and remotely sensed estimates of the fraction of PAR (fAPAR) absorbed by the vegetation canopy. Focusing on LUE allows potential drivers of GPP to be separated from its overriding dependence on light. GPP data from over 100 sites, collated over 20 years and located in a range of biomes and climate zones, were extracted from the FLUXNET2015 database and combined with remotely sensed fAPAR data to estimate daily LUE. Daytime air temperature, vapor pressure deficit, diffuse fraction of solar radiation, and soil moisture were shown to be salient predictors of LUE in a generalized linear mixed-effects model. The same model design was fitted to site-based LUE estimates generated by 16 terrestrial ecosystem models. The published models showed wide variation in the shape, the strength, and even the sign of the environmental effects on modeled LUE. These findings highlight important model deficiencies and suggest a need to progress beyond simple "goodness of fit" comparisons of inferred and predicted carbon fluxes toward an approach focused on the functional responses of the underlying dependencies.

Advancing understanding of land-atmosphere interactions by breaking discipline and scale barriers

Vegetation and atmosphere processes are coupled through a myriad of interactions linking plant transpiration, carbon dioxide assimilation, turbulent transport of moisture, heat and atmospheric constituents, aerosol formation, moist convection, and precipitation. Advances in our understanding are hampered by discipline barriers and challenges in understanding the role of small spatiotemporal scales. In this perspective, we propose to study the atmosphere-ecosystem interaction as a continuum by integrating leaf to regional scales (multiscale) and integrating biochemical and physical processes (multiprocesses). The challenges ahead are (1) How do clouds and canopies affect the transferring and in-canopy penetration of radiation, thereby impacting photosynthesis and biogenic chemical transformations? (2) How is the radiative energy spatially distributed and converted into turbulent fluxes of heat, moisture, carbon, and reactive compounds? (3) How do local (leaf-canopy-clouds, 1 m to kilometers) biochemical and physical processes interact with regional meteorology and atmospheric composition (kilometers to 100 km)? (4) How can we integrate the feedbacks between cloud radiative effects and plant physiology to reduce uncertainties in our climate projections driven by regional warming and enhanced carbon dioxide levels? Our methodology integrates fine-scale explicit simulations with new observational techniques to determine the role of unresolved small-scale spatiotemporal processes in weather and climate models.

Solar-induced chlorophyll fluorescence imperfectly tracks the temperature response of photosynthesis in winter wheat

Solar-induced fluorescence (SIF) is a promising proxy for photosynthesis, but it is unclear whether it performs well in tracking the gross primary productivity (GPP) under different environmental conditions. In this study, we investigated the dynamics of the two parameters from October 2020 to June 2021 in field-grown winter wheat (Triticum aestivum) and found that the ability of SIF to track GPP was weakened at low temperatures. Accounting for the coupling of light and temperature at a seasonal scale, we found that SIF yield showed a lower temperature sensitivity and had a lower but broader optimal temperature range compared with light-use efficiency (LUE), although both SIF yield and LUE decreased in low-temperature conditions. The discrepancy between the temperature responses of SIF yield and GPP caused an increase in the ratio of SIF/GPP in winter, which indicated the variation in the relationship between them during this period. The results of our study highlight the impact of low temperature on the relationship between SIF and GPP and show the necessity of reconsidering the dynamics of energy distribution inside plants under changing environments.

Critical soil moisture thresholds of plant water stress in terrestrial ecosystems

Plant water stress occurs at the point when soil moisture (SM) limits transpiration, defining a critical SM threshold (θ_crit). Knowledge of the spatial distribution of θ_crit is crucial for future projections of climate and water resources. Here, we use global eddy covariance observations to quantify θ_crit and evaporative fraction (EF) regimes. Three canonical variables describe how EF is controlled by SM: the maximum EF (EF_max), θ_crit, and slope (S) between EF and SM. We find systematic differences of these three variables across biomes. Variation in θ_crit, S, and EF_max is mostly explained by soil texture, vapor pressure deficit, and precipitation, respectively, as well as vegetation structure. Dryland ecosystems tend to operate at low θ_crit and show adaptation to water deficits. The negative relationship between θ_crit and S indicates that dryland ecosystems minimize θ_crit through mechanisms of sustained SM extraction and transport by xylem. Our results further suggest an optimal adaptation of local EF-SM response that maximizes growing-season evapotranspiration and photosynthesis.

Nocturnal plant respiration is under strong non-temperature control

Most biological rates depend on the rate of respiration. Temperature variation is typically considered the main driver of daily plant respiration rates, assuming a constant daily respiration rate at a set temperature. Here, we show empirical data from 31 species from temperate and tropical biomes to demonstrate that the rate of plant respiration at a constant temperature decreases monotonically with time through the night, on average by 25% after 8 h of darkness. Temperature controls less than half of the total nocturnal variation in respiration. A new universal formulation is developed to model and understand nocturnal plant respiration, combining the nocturnal decrease in the rate of plant respiration at constant temperature with the decrease in plant respiration according to the temperature sensitivity. Application of the new formulation shows a global reduction of 4.5 -6 % in plant respiration and an increase of 7-10% in net primary production for the present-day.

Intercomparison of methods to estimate gross primary production based on CO2 and COS flux measurements

Separating the components of ecosystem-scale carbon exchange is crucial in order to develop better models and future predictions of the terrestrial carbon cycle. However, there are several uncertainties and unknowns related to current photosynthesis estimates. In this study, we evaluate four different methods for estimating photosynthesis at a boreal forest at the ecosystem scale, of which two are based on carbon dioxide (CO₂) flux measurements and two on carbonyl sulfide (COS) flux measurements. The CO₂-based methods use traditional flux partitioning and artificial neural networks to separate the net CO₂ flux into respiration and photosynthesis. The COS-based methods make use of a unique 5-year COS flux data set and involve two different approaches to determine the leaf-scale relative uptake ratio of COS and CO₂ (LRU), of which one (LRU_CAP) was developed in this study. LRU_CAP was based on a previously tested stomatal optimization theory (CAP), while LRU_PAR was based on an empirical relation to measured radiation. For the measurement period 2013-2017, the artificial neural network method gave a GPP estimate very close to that of traditional flux partitioning at all timescales. On average, the COS-based methods gave higher GPP estimates than the CO₂-based estimates on daily (23% and 7% higher, using LRU_PAR and LRU_CAP, respectively) and monthly scales (20% and 3% higher), as well as a higher cumulative sum over 3 months in all years (on average 25% and 3% higher). LRU_CAP was higher than LRU estimated from chamber measurements at high radiation, leading to underestimation of midday GPP relative to other GPP methods. In general, however, use of LRU_CAP gave closer agreement with CO₂-based estimates of GPP than use of LRUPAR. When extended to other sites, LRU_CAP may be more robust than LRU_PAR because it is based on a physiological model whose parameters can be estimated from simple measurements or obtained from the literature. In contrast, the empirical radiation relation in LRU_PAR may be more site-specific. However, this requires further testing at other measurement sites.

The physiological basis for estimating photosynthesis from Chla fluorescence

Solar-induced Chl fluorescence (SIF) offers the potential to curb large uncertainties in the estimation of photosynthesis across biomes and climates, and at different spatiotemporal scales. However, it remains unclear how SIF should be used to mechanistically estimate photosynthesis. In this study, we built a quantitative framework for the estimation of photosynthesis, based on a mechanistic light reaction model with the Chla fluorescence of Photosystem II (SIFPSII ) as an input (MLR-SIF). Utilizing 29 C3 and C4 plant species that are representative of major plant biomes across the globe, we confirmed the validity of this framework at the leaf level. The MLR-SIF model is capable of accurately reproducing photosynthesis for all C3 and C4 species under diverse light, temperature, and CO2 conditions. We further tested the robustness of the MLR-SIF model using Monte Carlo simulations, and found that photosynthesis estimates were much less sensitive to parameter uncertainties relative to the conventional Farquhar, von Caemmerer, Berry (FvCB) model because of the additional independent information contained in SIFPSII . Once inferred from direct observables of SIF, SIFPSII provides 'parameter savings' to the MLR-SIF model, compared to the mechanistically equivalent FvCB model, and thus avoids the uncertainties arising as a result of imperfect model parameterization. Our findings set the stage for future efforts to employ SIF mechanistically to improve photosynthesis estimates across a variety of scales, functional groups, and environmental conditions.

Historically inconsistent productivity and respiration fluxes in the global terrestrial carbon cycle

The terrestrial carbon cycle is a major source of uncertainty in climate projections. Its dominant fluxes, gross primary productivity (GPP), and respiration (in particular soil respiration, R_S), are typically estimated from independent satellite-driven models and upscaled in situ measurements, respectively. We combine carbon-cycle flux estimates and partitioning coefficients to show that historical estimates of global GPP and R_S are irreconcilable. When we estimate GPP based on R_S measurements and some assumptions about R_S:GPP ratios, we found the resulted global GPP values (bootstrap mean [Formula: see text] Pg C yr^-1) are significantly higher than most GPP estimates reported in the literature ([Formula: see text] Pg C yr^-1). Similarly, historical GPP estimates imply a soil respiration flux (Rs_GPP, bootstrap mean of [Formula: see text] Pg C yr^-1) statistically inconsistent with most published R_S values ([Formula: see text] Pg C yr^-1), although recent, higher, GPP estimates are narrowing this gap. Furthermore, global R_S:GPP ratios are inconsistent with spatial averages of this ratio calculated from individual sites as well as CMIP6 model results. This discrepancy has implications for our understanding of carbon turnover times and the terrestrial sensitivity to climate change. Future efforts should reconcile the discrepancies associated with calculations for GPP and Rs to improve estimates of the global carbon budget.

Representation of Leaf-to-Canopy Radiative Transfer Processes Improves Simulation of Far-Red Solar-Induced Chlorophyll Fluorescence in the Community Land Model Version 5

Recent advances in satellite observations of solar-induced chlorophyll fluorescence (SIF) provide a new opportunity to constrain the simulation of terrestrial gross primary productivity (GPP). Accurate representation of the processes driving SIF emission and its radiative transfer to remote sensing sensors is an essential prerequisite for data assimilation. Recently, SIF simulations have been incorporated into several land surface models, but the scaling of SIF from leaf-level to canopy-level is usually not well-represented. Here, we incorporate the simulation of far-red SIF observed at nadir into the Community Land Model version 5 (CLM5). Leaf-level fluorescence yield was simulated by a parametric simplification of the Soil Canopy-Observation of Photosynthesis and Energy fluxes model (SCOPE). And an efficient and accurate method based on escape probability is developed to scale SIF from leaf-level to top-of-canopy while taking clumping and the radiative transfer processes into account. SIF simulated by CLM5 and SCOPE agreed well at sites except one in needleleaf forest (R ² > 0.91, root-mean-square error <0.19 W⋅m^-2⋅sr^-1⋅μm^-1), and captured the day-to-day variation of tower-measured SIF at temperate forest sites (R ² > 0.68). At the global scale, simulated SIF generally captured the spatial and seasonal patterns of satellite-observed SIF. Factors including the fluorescence emission model, clumping, bidirectional effect, and leaf optical properties had considerable impacts on SIF simulation, and the discrepancies between simulate d and observed SIF varied with plant functional type. By improving the representation of radiative transfer for SIF simulation, our model allows better comparisons between simulated and observed SIF toward constraining GPP simulations.

The CO2 record at the Amazon Tall Tower Observatory: A new opportunity to study processes on seasonal and inter-annual scales

High-quality atmospheric CO₂  measurements are sparse in Amazonia, but can provide critical insights into the spatial and temporal variability of sources and sinks of CO₂ . In this study, we present the first 6 years (2014-2019) of continuous, high-precision measurements of atmospheric CO₂ at the Amazon Tall Tower Observatory (ATTO, 2.1°S, 58.9°W). After subtracting the simulated background concentrations from our observational record, we define a CO₂ regional signal ( ΔCO2obs ) that has a marked seasonal cycle with an amplitude of about 4 ppm. At both seasonal and inter-annual scales, we find differences in phase between ΔCO2obs and the local eddy covariance net ecosystem exchange (EC-NEE), which is interpreted as an indicator of a decoupling between local and non-local drivers of ΔCO2obs . In addition, we present how the 2015-2016 El Niño-induced drought was captured by our atmospheric record as a positive 2σ anomaly in both the wet and dry season of 2016. Furthermore, we analyzed the observed seasonal cycle and inter-annual variability of ΔCO2obs together with net ecosystem exchange (NEE) using a suite of modeled flux products representing biospheric and aquatic CO₂ exchange. We use both non-optimized and optimized (i.e., resulting from atmospheric inverse modeling) NEE fluxes as input in an atmospheric transport model (STILT). The observed shape and amplitude of the seasonal cycle was captured neither by the simulations using the optimized fluxes nor by those using the diagnostic Vegetation and Photosynthesis Respiration Model (VPRM). We show that including the contribution of CO₂ from river evasion improves the simulated shape (not the magnitude) of the seasonal cycle when using a data-driven non-optimized NEE product (FLUXCOM). The simulated contribution from river evasion was found to be 25% of the seasonal cycle amplitude. Our study demonstrates the importance of the ATTO record to better understand the Amazon carbon cycle at various spatial and temporal scales.

Soil respiration variation along an altitudinal gradient in the Italian Alps: Disentangling forest structure and temperature effects

On the mountains, along an elevation gradient, we generally observe an ample variation in temperature, with the associated difference in vegetation structure and composition and soil properties. With the aim of quantifying the relative importance of temperature, vegetation and edaphic properties on soil respiration (SR), we investigated changes in SR along an elevation gradient (404 to 2101 m a.s.l) in the southern slopes of the Alps in Northern Italy. We also analysed soil physicochemical properties, including soil organic carbon (SOC) and nitrogen (N) stocks, fine root C and N, litter C and N, soil bulk densities and soil pH at five forest sites, and also stand structural properties, including vegetation height, age and basal area. Our results indicated that SR rates increased with temperature in all sites, and 55–76% of SR variability was explained by temperature. Annual cumulative SR, ranging between 0.65–1.40 kg C m^-2 yr^-1, decreased along the elevation gradient, while temperature sensitivity (Q10) of SR increased with elevation. However, a high SR rate (1.27 kg C m^-2 yr^-1) and low Q10 were recorded in the mature conifer forest stand at 1731 m a.s.l., characterized by an uneven-aged structure and high dominant tree height, resulting in a nonlinear relationship between elevation and temperature. Reference SR at 10°C (SR_ref) was unrelated to elevation, but was related to tree height. A significant negative linear relationship was found between bulk density and elevation. Conversely, SOC, root C and N stock, pH, and litter mass were best fitted by nonlinear relationships with elevation. However, these parameters were not significantly correlated with SR when the effect of temperature was removed (SR_ref). These results demonstrate that the main factor affecting SR in forest ecosystems along this Alpine elevation gradient is temperature, but its regulating role can be strongly influenced by site biological characteristics, particularly vegetation type and structure, affecting litter quality and microclimate. This study also confirms that high elevation sites are rich in SOC and more sensitive to climate change, being prone to high C losses as CO₂. Furthermore, our data indicate a positive relationship between Q10 and dominant tree height, suggesting that mature forest ecosystems characterized by an uneven-age structure, high SR_ref and moderate Q10, may be more resilient.

Quantifying high-temperature stress on soybean canopy photosynthesis: The unique role of sun-induced chlorophyll fluorescence

High temperature and accompanying high vapor pressure deficit often stress plants without causing distinctive changes in plant canopy structure and consequential spectral signatures. Sun-induced chlorophyll fluorescence (SIF), because of its mechanistic link with photosynthesis, may better detect such stress than remote sensing techniques relying on spectral reflectance signatures of canopy structural changes. However, our understanding about physiological mechanisms of SIF and its unique potential for physiological stress detection remains less clear. In this study, we measured SIF at a high-temperature experiment, Temperature Free-Air Controlled Enhancement, to explore the potential of SIF for physiological investigations. The experiment provided a gradient of soybean canopy temperature with 1.5, 3.0, 4.5, and 6.0°C above the ambient canopy temperature in the open field environments. SIF yield, which is normalized by incident radiation and the fraction of absorbed photosynthetically active radiation, showed a high correlation with photosynthetic light use efficiency (r = 0.89) and captured dynamic plant responses to high-temperature conditions. SIF yield was affected by canopy structural and plant physiological changes associated with high-temperature stress (partial correlation r = 0.60 and -0.23). Near-infrared reflectance of vegetation, only affected by canopy structural changes, was used to minimize the canopy structural impact on SIF yield and to retrieve physiological SIF yield (ΦF ) signals. ΦF further excludes the canopy structural impact than SIF yield and indicates plant physiological variability, and we found that ΦF outperformed SIF yield in responding to physiological stress (r = -0.37). Our findings highlight that ΦF sensitively responded to the physiological downregulation of soybean gross primary productivity under high temperature. ΦF , if reliably derived from satellite SIF, can support monitoring regional crop growth and different ecosystems' vegetation productivity under environmental stress and climate change.

Productive wetlands restored for carbon sequestration quickly become net CO2 sinks with site-level factors driving uptake variability

Inundated wetlands can potentially sequester substantial amounts of soil carbon (C) over the long-term because of slow decomposition and high primary productivity, particularly in climates with long growing seasons. Restoring such wetlands may provide one of several effective negative emission technologies to remove atmospheric CO2 and mitigate climate change. However, there remains considerable uncertainty whether these heterogeneous ecotones are consistent net C sinks and to what degree restoration and management methods affect C sequestration. Since wetland C dynamics are largely driven by climate, it is difficult to draw comparisons across regions. With many restored wetlands having different functional outcomes, we need to better understand the importance of site-specific conditions and how they change over time. We report on 21 site-years of C fluxes using eddy covariance measurements from five restored fresh to brackish wetlands in a Mediterranean climate. The wetlands ranged from 3 to 23 years after restoration and showed that several factors related to restoration methods and site conditions altered the magnitude of C sequestration by affecting vegetation cover and structure. Vegetation established within two years of re-flooding but followed different trajectories depending on design aspects, such as bathymetry-determined water levels, planting methods, and soil nutrients. A minimum of 55% vegetation cover was needed to become a net C sink, which most wetlands achieved once vegetation was established. Established wetlands had a high C sequestration efficiency (i.e. the ratio of net to gross ecosystem productivity) comparable to upland ecosystems but varied between years undergoing boom-bust growth cycles and C uptake strength was susceptible to disturbance events. We highlight the large C sequestration potential of productive inundated marshes, aided by restoration design and management targeted to maximise vegetation extent and minimise disturbance. These findings have important implications for wetland restoration, policy, and management practitioners.

Science to Commerce: A Commercial-Scale Protocol for Carbon Trading Applied to a 28-Year Record of Forest Carbon Monitoring at the Harvard Forest

Forest carbon sequestration offset protocols have been employed for more than 20 years with limited success in slowing deforestation and increasing forest carbon trading volume. Direct measurement of forest carbon flux improves quantification for trading but has not been applied to forest carbon research projects with more than 600 site installations worldwide. In this study, we apply carbon accounting methods, scaling hours to decades to 28-years of scientific CO2 eddy covariance data for the Harvard Forest (US-Ha1), located in central Massachusetts, USA and establishing commercial carbon trading protocols and applications for similar sites. We illustrate and explain transactions of high-frequency direct measurement for CO2 net ecosystem exchange (NEE, gC m−2 year−1) that track and monetize ecosystem carbon dynamics in contrast to approaches that rely on forest mensuration and growth models. NEE, based on eddy covariance methodology, quantifies loss of CO2 by ecosystem respiration accounted for as an unavoidable debit to net carbon sequestration. Retrospective analysis of the US-Ha1 NEE times series including carbon pricing, interval analysis, and ton-year exit accounting and revenue scenarios inform entrepreneur, investor, and landowner forest carbon commercialization strategies. CO2 efflux accounts for ~45% of the US-Ha1 NEE, an error of ~466% if excluded; however, the decades-old coupled human and natural system remains a financially viable net carbon sink. We introduce isoflux NEE for t13C16O2 and t12C18O16O to directly partition and quantify daytime ecosystem respiration and photosynthesis, creating new soil carbon commerce applications and derivative products in contrast to undifferentiated bulk soil carbon pool approaches. Eddy covariance NEE methods harmonize and standardize carbon commerce across diverse forest applications including, a New England, USA regional eddy covariance network, the Paris Agreement, and related climate mitigation platforms.

Emerging approaches to measure photosynthesis from the leaf to the ecosystem

Measuring photosynthesis is critical for quantifying and modeling leaf to regional scale productivity of managed and natural ecosystems. This review explores existing and novel advances in photosynthesis measurements that are certain to provide innovative directions in plant science research. First, we address gas exchange approaches from leaf to ecosystem scales. Leaf level gas exchange is a mature method but recent improvements to the user interface and environmental controls of commercial systems have resulted in faster and higher quality data collection. Canopy chamber and micrometeorological methods have also become more standardized tools and have an advanced understanding of ecosystem functioning under a changing environment and through long time series data coupled with community data sharing. Second, we review proximal and remote sensing approaches to measure photosynthesis, including hyperspectral reflectance- and fluorescence-based techniques. These techniques have long been used with aircraft and orbiting satellites, but lower-cost sensors and improved statistical analyses are allowing these techniques to become applicable at smaller scales to quantify changes in the underlying biochemistry of photosynthesis. Within the past decade measurements of chlorophyll fluorescence from earth-orbiting satellites have measured Solar Induced Fluorescence (SIF) enabling estimates of global ecosystem productivity. Finally, we highlight that stronger interactions of scientists across disciplines will benefit our capacity to accurately estimate productivity at regional and global scales. Applying the multiple techniques outlined in this review at scales from the leaf to the globe are likely to advance understanding of plant functioning from the organelle to the ecosystem.

Reconciling carbon-cycle processes from ecosystem to global scales

Understanding carbon (C) dynamics from ecosystem to global scales remains a challenge. Although expansion of global carbon dioxide (CO₂) observatories makes it possible to estimate C-cycle processes from ecosystem to global scales, these estimates do not necessarily agree. At the continental US scale, only 5% of C fixed through photosynthesis remains as net ecosystem exchange (NEE), but ecosystem measurements indicate that only 2% of fixed C remains in grasslands, whereas as much as 30% remains in needleleaf forests. The wet and warm Southeast has the highest gross primary productivity and the relatively wet and cool Midwest has the highest NEE, indicating important spatial mismatches. Newly available satellite and atmospheric data can be combined in innovative ways to identify potential C loss pathways to reconcile these spatial mismatches. Independent datasets compiled from terrestrial and aquatic environments can now be combined to advance C-cycle science across the land-water interface.

Is the Kok effect a respiratory phenomenon? Metabolic insight using 13 C labeling in Helianthus annuus leaves

The Kok effect is a well-known phenomenon in which the quantum yield of photosynthesis changes abruptly at low light. This effect has often been interpreted as a shift in leaf respiratory metabolism and thus used widely to measure day respiration. However, there is still no formal evidence that the Kok effect has a respiratory origin. Here, both gas exchange and isotopic labeling were carried out on sunflower leaves, using glucose that was ¹³ C-enriched at specific C-atom positions. Position-specific decarboxylation measurements and NMR analysis of metabolites were used to trace the fate of C-atoms in metabolism. Decarboxylation rates were significant at low light (including above the Kok break point) and increased with decreasing irradiance below 100 µmol photons m^-2  s^-1 . The variation in several metabolite pools such as malate, fumarate or citrate, and flux calculations suggest the involvement of several decarboxylating pathways in the Kok effect, including the malic enzyme. Our results show that day respiratory CO₂ evolution plays an important role in the Kok effect. However, the increase in the apparent quantum yield of photosynthesis below the Kok break point is also probably related to malate metabolism, which participates in maintaining photosynthetic linear electron flow.

Estimating canopy gross primary production by combining phloem stable isotopes with canopy and mesophyll conductances

Gross primary production (GPP) is a key component of the forest carbon cycle. However, our knowledge of GPP at the stand scale remains uncertain, because estimates derived from eddy covariance (EC) rely on semi-empirical modelling and the assumptions of the EC technique are sometimes not fully met. We propose using the sap flux/isotope method as an alternative way to estimate canopy GPP, termed GPP_iso/SF , at the stand scale and at daily resolution. It is based on canopy conductance inferred from sap flux and intrinsic water-use efficiency estimated from the stable carbon isotope composition of phloem contents. The GPP_iso/SF estimate was further corrected for seasonal variations in photosynthetic capacity and mesophyll conductance. We compared our estimate of GPP_iso/SF to the GPP derived from PRELES, a model parameterized with EC data. The comparisons were performed in a highly instrumented, boreal Scots pine forest in northern Sweden, including a nitrogen fertilized and a reference plot. The resulting annual and daily GPP_iso/SF estimates agreed well with PRELES, in the fertilized plot and the reference plot. We discuss the GPP_iso/SF method as an alternative which can be widely applied without terrain restrictions, where the assumptions of EC are not met.

Towards a coupled paradigm of NH3 -CO2 biosphere-atmosphere exchange modelling

Stomatal conductance, one of the major plant physiological controls within NH₃ biosphere-atmosphere exchange models, is commonly estimated from semi-empirical multiplicative schemes or simple light- and temperature-response functions. However, due to their inherent parameterization on meteorological proxy variables, instead of a direct measure of stomatal opening, they are unfit for the use in climate change scenarios and of limited value for interpreting field-scale measurements. Alternatives based on H₂ O flux measurements suffer from uncertainties in the partitioning of evapotranspiration at humid sites, as well as a potential decoupling of transpiration from stomatal opening in the presence of hygroscopic particles on leaf surfaces. We argue that these problems may be avoided by directly deriving stomatal conductance from CO₂ fluxes instead. We reanalysed a data set of NH₃ flux measurements based on CO₂ -derived stomatal conductance, confirming the hypothesis that the increasing relevance of stomatal exchange with the onset of vegetation activity caused a rapid decrease of observed NH₃ deposition velocities. Finally, we argue that developing more mechanistic representations of NH₃ biosphere-atmosphere exchange can be of great benefit in many applications. These range from model-based flux partitioning, over deposition monitoring using low-cost samplers and inferential modelling, to a direct response of NH₃ exchange to climate change.

Methane emissions reduce the radiative cooling effect of a subtropical estuarine mangrove wetland by half

The role of coastal mangrove wetlands in sequestering atmospheric carbon dioxide (CO₂ ) and mitigating climate change has received increasing attention in recent years. While recent studies have shown that methane (CH₄ ) emissions can potentially offset the carbon burial rates in low-salinity coastal wetlands, there is hitherto a paucity of direct and year-round measurements of ecosystem-scale CH₄ flux (F_CH4 ) from mangrove ecosystems. In this study, we examined the temporal variations and biophysical drivers of ecosystem-scale F_CH4 in a subtropical estuarine mangrove wetland based on 3 years of eddy covariance measurements. Our results showed that daily mangrove F_CH4 reached a peak of over 0.1 g CH₄ -C m^-2  day^-1 during the summertime owing to a combination of high temperature and low salinity, while the wintertime F_CH4 was negligible. In this mangrove, the mean annual CH₄ emission was 11.7 ± 0.4 g CH₄ -C m^-2  year^-1 while the annual net ecosystem CO₂ exchange ranged between -891 and -690 g CO₂ -C m^-2  year^-1 , indicating a net cooling effect on climate over decadal to centurial timescales. Meanwhile, we showed that mangrove F_CH4 could offset the negative radiative forcing caused by CO₂ uptake by 52% and 24% over a time horizon of 20 and 100 years, respectively, based on the corresponding sustained-flux global warming potentials. Moreover, we found that 87% and 69% of the total variance of daily F_CH4 could be explained by the random forest machine learning algorithm and traditional linear regression model, respectively, with soil temperature and salinity being the most dominant controls. This study was the first of its kind to characterize ecosystem-scale F_CH4 in a mangrove wetland with long-term eddy covariance measurements. Our findings implied that future environmental changes such as climate warming and increasing river discharge might increase CH₄ emissions and hence reduce the net radiative cooling effect of estuarine mangrove forests.

Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks

The eddy covariance (EC) technique is used to measure the net ecosystem exchange (NEE) of CO2 between ecosystems and the atmosphere, offering a unique opportunity to study ecosystem responses to climate change. NEE is the difference between the total CO2 release due to all respiration processes (RECO), and the gross carbon uptake by photosynthesis (GPP). These two gross CO2 fluxes are derived from EC measurements by applying partitioning methods that rely on physiologically based functional relationships with a limited number of environmental drivers. However, the partitioning methods applied in the global FLUXNET network of EC observations do not account for the multiple co-acting factors that modulate GPP and RECO flux dynamics. To overcome this limitation, we developed a hybrid data-driven approach based on combined neural networks (NNC-part ). NNC-part incorporates process knowledge by introducing a photosynthetic response based on the light-use efficiency (LUE) concept, and uses a comprehensive dataset of soil and micrometeorological variables as fluxes drivers. We applied the method to 36 sites from the FLUXNET2015 dataset and found a high consistency in the results with those derived from other standard partitioning methods for both GPP (R2  > .94) and RECO (R2  > .8). High consistency was also found for (a) the diurnal and seasonal patterns of fluxes and (b) the ecosystem functional responses. NNC-part performed more realistic than the traditional methods for predicting additional patterns of gross CO2 fluxes, such as: (a) the GPP response to VPD, (b) direct effects of air temperature on GPP dynamics, (c) hysteresis in the diel cycle of gross CO2 fluxes, (d) the sensitivity of LUE to the diffuse to direct radiation ratio, and (e) the post rain respiration pulse after a long dry period. In conclusion, NNC-part is a valid data-driven approach to provide GPP and RECO estimates and complementary to the existing partitioning methods.

Global evidence for the acclimation of ecosystem photosynthesis to light

Photosynthesis responds quickly to changes in light, increasing with incoming photosynthetic photon flux density (PPFD) until the leaves become light saturated. This instantaneous response to PPFD, which is widely studied and incorporated into models of photosynthesis, is overlaid on non-instantaneous photosynthetic changes resulting from the acclimation of plants to average PPFD over intermediate timescales of a week to months [Formula: see text]. Such photosynthetic light acclimation is not typically incorporated into models, due to the lack of observational constraints. Here, we use eddy covariance observations from globally distributed and automated sensor networks, along with photosynthesis estimates from nine terrestrial biosphere models (TBMs), to quantify and assess photosynthetic acclimation to light in natural environments. We also use recent theoretical developments to incorporate light acclimation in a TBM. Our results show widespread light acclimation of ecosystem photosynthesis. On average, a 1 μmol m^-2 s^-1 increase in [Formula: see text] (ten-day average PPFD) leads to a 0.031 ± 0.013 μmol C m^-2 s^-1 increase in the maximum photosynthetic assimilation rate (A_max), with croplands having stronger acclimation rates than grasslands and forests. Our analysis shows that the TBMs examined either neglect or substantially underestimate light acclimation. By updating a TBM to include photosynthetic acclimation, successfully reproducing the [Formula: see text]-A_max relationship, we provide a robust method for the incorporation of photosynthetic light acclimation in future models.

Carbon assimilation through a vertical light gradient in the canopy of invasive herbs grown under different temperature regimes is determined by leaf and whole-plant architecture

This study examined the acclimation to temperature of two globally invasive species Iris pseudacorus and Lythrum salicaria, which share the same habitat type but differ in morphology. Iris pseudacorus has long vertical leaves, allowing light penetration through the canopy, while L. salicaria has stems with small horizontal leaves, creating significant self-shading. We aimed to build a physiological understanding of how these two species respond to different growth temperatures with regard to growth and gas exchange-related traits over the canopy. Growth and gas exchange-related traits in response to low (15 °C) and high (25 °C) growth temperature regimes were compared. Plants were grown in growth chambers, and light response curves were measured with infrared gas analysers after 23-33 days at three leaf positions on each plant, following the vertical light gradient through the canopy. After 37 days of growth, above-ground biomass, photosynthetic pigments and leaf N concentration were determined. The maximum photosynthesis rate was lower in lower leaf positions but did not differ significantly between temperatures. Iris pseudacorus photosynthesis decreased with decreasing leaf position, more so than L. salicaria. This was explained by decreasing N and chlorophyll concentrations towards the leaf base in I. pseudacorus, while pigment concentrations increased towards the lower canopy in L. salicaria. Biomass, shoot height and specific leaf area increased with temperature, more so in I. pseudacorus than in L. salicaria. Light response curves revealed that L. salicaria had a higher degree of shade acclimation than I. pseudacorus, probably due to self-shading in L. salicaria. High temperature decreased C assimilation at the bottom of the canopy in L. salicaria, while C assimilation in I. pseudacorus was less affected by temperature. As vegetative growth and flowering was stimulated by temperature, the invasive potential of these species is predicted to increase under global warming.

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

The FLUXNET2015 dataset provides ecosystem-scale data on CO₂, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.

Measurement(s)	net ecosystem exchange • carbon dioxide • water • energy
Technology Type(s)	eddy covariance • measurement device
Sample Characteristic - Environment	terrestrial biome • atmosphere
Sample Characteristic - Location	Earth (planet)

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12295910

Seasonal variation in net ecosystem CO2 exchange of a Brazilian seasonally dry tropical forest

Forest ecosystems sequester large amounts of atmospheric CO₂, and the contribution from seasonally dry tropical forests is not negligible. Thus, the objective of this study was to quantify and evaluate the seasonal and annual patterns of CO₂ exchanges in the Caatinga biome, as well as to evaluate the ecosystem condition as carbon sink or source during years. In addition, we analyzed the climatic factors that control the seasonal variability of gross primary production (GPP), ecosystem respiration (R_eco) and net ecosystem CO₂ exchange (NEE). Results showed that the dynamics of the components of the CO₂ fluxes varied depending on the magnitude and distribution of rainfall and, as a consequence, on the variability of the vegetation state. Annual cumulative NEE was significantly higher (p < 0.01) in 2014 (−169.0 g C m⁻²) when compared to 2015 (−145.0 g C m⁻²) and annual NEP/GPP ratio was 0.41 in 2014 and 0.43 in 2015. Global radiation, air and soil temperature were the main factors associated with the diurnal variability of carbon fluxes. Even during the dry season, the NEE was at equilibrium and the Caatinga acted as an atmospheric carbon sink during the years 2014 and 2015.

Recovery of the CO2 sink in a remnant peatland following water table lowering

Peatland biological, physical and chemical properties change over time in response to alterations in long-term water table position. Such changes complicate our ability to predict the response of peatland carbon stocks to sustained drying. In order to better understand the effect of sustained lowering of the water table on peatland carbon dynamics, we re-visited a drainage-affected bog, repeating eddy covariance measurements of CO₂ flux after a 16-year interval. We found the ecosystem CO₂ sink to have strengthened across the intervening period, despite a deep and fluctuating water table. This was mostly due to an increase in CO₂ uptake through photosynthesis associated with increased shrub growth. We also observed a decline in CO₂ loss through ecosystem respiration. These changes could not be attributed to environmental conditions. Air temperature was the only significant contemporaneous driver of monthly anomalies in CO₂ fluxes, with higher temperatures decreasing the net CO₂ sink via increased ecosystem respiration. However, the effect of air temperature was weak in comparison to the underlying differences between time periods. Therefore, we demonstrate that for drying peatlands, long-term changes within the ecosystem can be of primary importance as drivers of CO₂ exchange. In this peatland, the ecosystem carbon sink has shown resilience to water table drawdown, with internal feedbacks leading to a recovery of the CO₂ sink after a 16-year interval.

Linking diffuse radiation and ecosystem productivity of a desert steppe ecosystem

Radiation components have distinct effects on photosynthesis. In the desert steppe ecosystem, the influence of diffuse radiation on carbon fixation has not been thoroughly explored. We examined this diffusion and its effect on ecosystem productivity was examined during the growing season from 2014 to 2015 on the basis of eddy covariance measurements of CO₂ exchange in a desert steppe ecosystem in northwest China. Our results indicated that the gross ecosystem production (GEP) and diffuse photosynthetically active radiation (PAR_dif) peaked when the clearness index (CI) was around 0.5. The maximum canopy photosynthesis (P_max) under cloudy skies (CI < 0.7) was 23.7% greater than under clear skies (CI ≥ 0.7). When the skies became cloudy in the desert steppe ecosystem, PAR_dif had a greater effect on GEP. Additionally, lower vapor pressure deficits (VPD ≤ 1 kPa), lower air temperatures (T_a ≤ 20 °C), and non-stressed water conditions (REW ≥ 0.4) were more conducive for enhanced ecosystem photosynthesis under cloudy skies than under clear skies. This may be due to the comprehensive effects of VPD and T_a on stomatal conductance. We concluded that cloudiness can influence diffuse radiation components and that diffuse radiation can increase the ecosystem production of desert steppe ecosystems in northwest China.

Gap-filling approaches for eddy covariance methane fluxes: A comparison of three machine learning algorithms and a traditional method with principal component analysis

Methane flux (FCH₄ ) measurements using the eddy covariance technique have increased over the past decade. FCH₄ measurements commonly include data gaps, as is the case with CO₂ and energy fluxes. However, gap-filling FCH₄ data are more challenging than other fluxes due to its unique characteristics including multidriver dependency, variabilities across multiple timescales, nonstationarity, spatial heterogeneity of flux footprints, and lagged influence of biophysical drivers. Some researchers have applied a marginal distribution sampling (MDS) algorithm, a standard gap-filling method for other fluxes, to FCH₄ datasets, and others have applied artificial neural networks (ANN) to resolve the challenging characteristics of FCH₄ . However, there is still no consensus regarding FCH₄ gap-filling methods due to limited comparative research. We are not aware of the applications of machine learning (ML) algorithms beyond ANN to FCH₄ datasets. Here, we compare the performance of MDS and three ML algorithms (ANN, random forest [RF], and support vector machine [SVM]) using multiple combinations of ancillary variables. In addition, we applied principal component analysis (PCA) as an input to the algorithms to address multidriver dependency of FCH₄ and reduce the internal complexity of the algorithmic structures. We applied this approach to five benchmark FCH₄ datasets from both natural and managed systems located in temperate and tropical wetlands and rice paddies. Results indicate that PCA improved the performance of MDS compared to traditional inputs. ML algorithms performed better when using all available biophysical variables compared to using PCA-derived inputs. Overall, RF was found to outperform other techniques for all sites. We found gap-filling uncertainty is much larger than measurement uncertainty in accumulated CH₄ budget. Therefore, the approach used for FCH₄ gap filling can have important implications for characterizing annual ecosystem-scale methane budgets, the accuracy of which is important for evaluating natural and managed systems and their interactions with global change processes.

Flux towers in the sky: global ecology from space

Global ecology - the study of the interactions among the Earth's ecosystems, land, atmosphere and oceans - depends crucially on global observations: this paper focuses on space-based observations of global terrestrial ecosystems. Early global ecology relied on an extrapolation of detailed site-level observations, using models of increasing complexity. Modern global ecology has been enabled largely by vegetation indices (greenness) from operational space-based imagery but current capabilities greatly expand scientific possibilities. New observations from spacecraft in orbit allowed an estimation of gross carbon fluxes, photosynthesis, biomass burning, evapotranspiration and biomass, to create virtual eddy covariance sites in the sky. Planned missions will reveal the dimensions of the diversity of life itself. These observations will improve our understanding of the global productivity and carbon storage, land use, carbon cycle-climate feedback, diversity-productivity relationships and enable improved climate forecasts. Advances in remote sensing challenge ecologists to relate information organised by biome and species to new data arrayed by pixels and develop theory to address previously unobserved scales.

Disentangling the role of photosynthesis and stomatal conductance on rising forest water-use efficiency

Forests remove about 30% of anthropogenic CO₂ emissions through photosynthesis and return almost 40% of incident precipitation back to the atmosphere via transpiration. The trade-off between photosynthesis and transpiration through stomata, the water-use efficiency (WUE), is an important driver of plant evolution and ecosystem functioning, and has profound effects on climate. Using stable carbon and oxygen isotope ratios in tree rings, we found that WUE has increased by a magnitude consistent with estimates from atmospheric measurements and model predictions. Enhanced photosynthesis was widespread, while reductions in stomatal conductance were modest and restricted to moisture-limited forests. This result points to smaller reductions in transpiration in response to increasing atmospheric CO₂, with important implications for forest–climate interactions, which remain to be explored.

Multiple lines of evidence suggest that plant water-use efficiency (WUE)—the ratio of carbon assimilation to water loss—has increased in recent decades. Although rising atmospheric CO₂ has been proposed as the principal cause, the underlying physiological mechanisms are still being debated, and implications for the global water cycle remain uncertain. Here, we addressed this gap using 30-y tree ring records of carbon and oxygen isotope measurements and basal area increment from 12 species in 8 North American mature temperate forests. Our goal was to separate the contributions of enhanced photosynthesis and reduced stomatal conductance to WUE trends and to assess consistency between multiple commonly used methods for estimating WUE. Our results show that tree ring-derived estimates of increases in WUE are consistent with estimates from atmospheric measurements and predictions based on an optimal balancing of carbon gains and water costs, but are lower than those based on ecosystem-scale flux observations. Although both physiological mechanisms contributed to rising WUE, enhanced photosynthesis was widespread, while reductions in stomatal conductance were modest and restricted to species that experienced moisture limitations. This finding challenges the hypothesis that rising WUE in forests is primarily the result of widespread, CO₂-induced reductions in stomatal conductance.