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

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

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.

Short-term variation in leaf-level water use efficiency in a tropical forest.

The representation of stomatal regulation of transpiration and CO2 assimilation is key to forecasting terrestrial ecosystem responses to global change. Given its importance in determining the relationship between forest productivity and climate, accurate and mechanistic model representation of the relationship between stomatal conductance (gs ) and assimilation is crucial. We assess possible physiological and mechanistic controls on the estimation of the g1 (stomatal slope, inversely proportional to water use efficiency) and g0 (stomatal intercept) parameters, using diurnal gas exchange surveys and leaf-level response curves of six tropical broadleaf evergreen tree species. g1 estimated from ex situ response curves averaged 50% less than g1 estimated from survey data. While g0 and g1 varied between leaves of different phenological stages, the trend was not consistent among species. We identified a diurnal trend associated with g1 and g0 that significantly improved model projections of diurnal trends in transpiration. The accuracy of modeled gs can be improved by accounting for variation in stomatal behavior across diurnal periods, and between measurement approaches, rather than focusing on phenological variation in stomatal behavior. Additional investigation into the primary mechanisms responsible for diurnal variation in g1 will be required to account for this phenomenon in land-surface models.

Late-day measurement of excised branches results in uncertainty in the estimation of two stomatal parameters derived from response curves in Populus deltoides Bartr. × Populus nigra L.

Many terrestrial biosphere models depend on an understanding of the relationship between stomatal conductance and photosynthesis. However, unlike the measurement of photosynthetic parameters, such as the maximum carboxylation capacity, where standard methods (e.g., CO2 response or ACi curves) are widely accepted, a consensus method for empirically measuring parameters representing stomatal response has not yet emerged. Most models of stomatal response to environment represent stomatal conductance as being bounded by a lower intercept parameter (g0), and linearly scaled based on a multivariate term described by the stomatal slope parameter (g1). Here we employ the widely used Unified Stomatal Optimization model, to test whether g1 and g0 parameters are impacted by the choice of measurement method, either on an intact branch or a cut branch segment stored in water. We measured paired stomatal response curves on intact and excised branches of a hybrid poplar clone (Populus deltoides Bartr. × Populus nigra L. OP367), measured twice over a diurnal period. We found that predawn branch excision did not significantly affect measured g0 and g1 when measured within 4 h of excision. Measurement in the afternoon resulted in significantly higher values of g1 and lower values of g0, with values changing by 55% and 56%, respectively. Excision combined with afternoon measurement resulted in a marked effect on parameter estimates, with g1 increasing 89% from morning to afternoon and a 25% lower g1 for cut branches than those measured in situ. We also show that in hybrid poplar the differences in parameter estimates obtained from plants measured under different conditions can directly impact models of canopy function, reducing modeled transpiration by 18% over a simulated 12.5-h period. Although these results are only for a single isohydric woody species, our findings suggest that stomatal optimality parameters may not remain constant throughout the day.

An improved representation of the relationship between photosynthesis and stomatal conductance leads to more stable estimation of conductance parameters and improves the goodness-of-fit across diverse data sets.

Stomata play a central role in surface-atmosphere exchange by controlling the flux of water and CO2 between the leaf and the atmosphere. Representation of stomatal conductance (gsw ) is therefore an essential component of models that seek to simulate water and CO2 exchange in plants and ecosystems. For given environmental conditions at the leaf surface (CO2 concentration and vapor pressure deficit or relative humidity), models typically assume a linear relationship between gsw and photosynthetic CO2 assimilation (A). However, measurement of leaf-level gsw response curves to changes in A are rare, particularly in the tropics, resulting in only limited data to evaluate this key assumption. Here, we measured the response of gsw and A to irradiance in six tropical species at different leaf phenological stages. We showed that the relationship between gsw and A was not linear, challenging the key assumption upon which optimality theory is based-that the marginal cost of water gain is constant. Our data showed that increasing A resulted in a small increase in gsw at low irradiance, but a much larger increase at high irradiance. We reformulated the popular Unified Stomatal Optimization (USO) model to account for this phenomenon and to enable consistent estimation of the key conductance parameters g0 and g1 . Our modification of the USO model improved the goodness-of-fit and reduced bias, enabling robust estimation of conductance parameters at any irradiance. In addition, our modification revealed previously undetectable relationships between the stomatal slope parameter g1 and other leaf traits. We also observed nonlinear behavior between A and gsw in independent data sets that included data collected from attached and detached leaves, and from plants grown at elevated CO2 concentration. We propose that this empirical modification of the USO model can improve the measurement of gsw parameters and the estimation of plant and ecosystem-scale water and CO2  fluxes.

Rapid estimation of photosynthetic leaf traits of tropical plants in diverse environmental conditions using reflectance spectroscopy.

Tropical forests are one of the main carbon sinks on Earth, but the magnitude of CO2 absorbed by tropical vegetation remains uncertain. Terrestrial biosphere models (TBMs) are commonly used to estimate the CO2 absorbed by forests, but their performance is highly sensitive to the parameterization of processes that control leaf-level CO2 exchange. Direct measurements of leaf respiratory and photosynthetic traits that determine vegetation CO2 fluxes are critical, but traditional approaches are time-consuming. Reflectance spectroscopy can be a viable alternative for the estimation of these traits and, because data collection is markedly quicker than traditional gas exchange, the approach can enable the rapid assembly of large datasets. However, the application of spectroscopy to estimate photosynthetic traits across a wide range of tropical species, leaf ages and light environments has not been extensively studied. Here, we used leaf reflectance spectroscopy together with partial least-squares regression (PLSR) modeling to estimate leaf respiration (Rdark25), the maximum rate of carboxylation by the enzyme Rubisco (Vcmax25), the maximum rate of electron transport (Jmax25), and the triose phosphate utilization rate (Tp25), all normalized to 25°C. We collected data from three tropical forest sites and included leaves from fifty-three species sampled at different leaf phenological stages and different leaf light environments. Our resulting spectra-trait models validated on randomly sampled data showed good predictive performance for Vcmax25, Jmax25, Tp25 and Rdark25 (RMSE of 13, 20, 1.5 and 0.3 µmol m-2 s-1, and R2 of 0.74, 0.73, 0.64 and 0.58, respectively). The models showed similar performance when applied to leaves of species not included in the training dataset, illustrating that the approach is robust for capturing the main axes of trait variation in tropical species. We discuss the utility of the spectra-trait and traditional gas exchange approaches for enhancing tropical plant trait studies and improving the parameterization of TBMs.

Detection of the metabolic response to drought stress using hyperspectral reflectance.

Drought is the most important limitation on crop yield. Understanding and detecting drought stress in crops is vital for improving water use efficiency through effective breeding and management. Leaf reflectance spectroscopy offers a rapid, non-destructive alternative to traditional techniques for measuring plant traits involved in a drought response. We measured drought stress in six glasshouse-grown agronomic species using physiological, biochemical, and spectral data. In contrast to physiological traits, leaf metabolite concentrations revealed drought stress before it was visible to the naked eye. We used full-spectrum leaf reflectance data to predict metabolite concentrations using partial least-squares regression, with validation R2 values of 0.49-0.87. We show for the first time that spectroscopy may be used for the quantitative estimation of proline and abscisic acid, demonstrating the first use of hyperspectral data to detect a phytohormone. We used linear discriminant analysis and partial least squares discriminant analysis to differentiate between watered plants and those subjected to drought based on measured traits (accuracy: 71%) and raw spectral data (66%). Finally, we validated our glasshouse-developed models in an independent field trial. We demonstrate that spectroscopy can detect drought stress via underlying biochemical changes, before visual differences occur, representing a powerful advance for measuring limitations on yield.

A best-practice guide to predicting plant traits from leaf-level hyperspectral data using partial least squares regression.

Partial least squares regression (PLSR) modelling is a statistical technique for correlating datasets, and involves the fitting of a linear regression between two matrices. One application of PLSR enables leaf traits to be estimated from hyperspectral optical reflectance data, facilitating rapid, high-throughput, non-destructive plant phenotyping. This technique is of interest and importance in a wide range of contexts including crop breeding and ecosystem monitoring. The lack of a consensus in the literature on how to perform PLSR means that interpreting model results can be challenging, applying existing models to novel datasets can be impossible, and unknown or undisclosed assumptions can lead to incorrect or spurious predictions. We address this lack of consensus by proposing best practices for using PLSR to predict plant traits from leaf-level hyperspectral data, including a discussion of when PLSR is applicable, and recommendations for data collection. We provide a tutorial to demonstrate how to develop a PLSR model, in the form of an R script accompanying this manuscript. This practical guide will assist all those interpreting and using PLSR models to predict leaf traits from spectral data, and advocates for a unified approach to using PLSR for predicting traits from spectra in the plant sciences.

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.

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

The maximum carboxylation capacity of Rubisco, Vc,max , is an important photosynthetic parameter that is key to accurate estimation of carbon assimilation. The gold-standard technique for determining Vc,max is to derive Vc,max from the initial slope of an A-Ci curve (the response of photosynthesis, A, to intercellular CO2 concentration, Ci ). Accurate estimates of Vc,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 Vc,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 Vc,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 Vc,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.