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

Higher global gross primary productivity under future climate with more advanced representations of photosynthesis

Gross primary productivity (GPP) is the key determinant of land carbon uptake, but its representation in terrestrial biosphere models (TBMs) does not reflect our latest physiological understanding. We implemented three empirically well supported but often omitted mechanisms into the TBM CABLE-POP: photosynthetic temperature acclimation, explicit mesophyll conductance, and photosynthetic optimization through redistribution of leaf nitrogen. We used the RCP8.5 climate scenario to conduct factorial model simulations characterizing the individual and combined effects of the three mechanisms on projections of GPP. Simulated global GPP increased more strongly (up to 20% by 2070-2099) in more comprehensive representations of photosynthesis compared to the model lacking the three mechanisms. The experiments revealed non-additive interactions among the mechanisms as combined effects were stronger than the sum of the individual effects. The modeled responses are explained by changes in the photosynthetic sensitivity to temperature and CO2 caused by the added mechanisms. Our results suggest that current TBMs underestimate GPP responses to future CO2 and climate conditions.

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.

The evolution, complexity and diversity of models of long-term forest dynamics

To assess the impacts of climate change on vegetation from stand to global scales, models of forest dynamics that include tree demography are needed. Such models are now available for 50 years, but the currently existing diversity of model formulations and its evolution over time are poorly documented. This hampers systematic assessments of structural uncertainties in model-based studies.We conducted a meta-analysis of 28 models, focusing on models that were used in the past five years for climate change studies. We defined 52 model attributes in five groups (basic assumptions, growth, regeneration, mortality and soil moisture) and characterized each model according to these attributes. Analyses of model complexity and diversity included hierarchical cluster analysis and redundancy analysis.Model complexity evolved considerably over the past 50 years. Increases in complexity were largest for growth processes, while complexity of modelled establishment processes increased only moderately. Model diversity was lowest at the global scale, and highest at the landscape scale. We identified five distinct clusters of models, ranging from very simple models to models where specific attribute groups are rendered in a complex manner and models that feature high complexity across all attributes.Most models in use today are not balanced in the level of complexity with which they represent different processes. This is the result of different model purposes, but also reflects legacies in model code, modelers' preferences, and the 'prevailing spirit of the epoch'. The lack of firm theories, laws and 'first principles' in ecology provides high degrees of freedom in model development, but also results in high responsibilities for model developers and the need for rigorous model evaluation. Synthesis. The currently available model diversity is beneficial: convergence in simulations of structurally different models indicates robust projections, while convergence of similar models may convey a false sense of certainty. The existing model diversity-with the exception of global models-can be exploited for improved projections based on multiple models. We strongly recommend balanced further developments of forest models that should particularly focus on establishment and mortality processes, in order to provide robust information for decisions in ecosystem management and policymaking.

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.

Mathematical Modeling to Estimate Photosynthesis: A State of the Art

Photosynthesis is a process that indicates the productivity of crops. The estimation of this variable can be achieved through methods based on mathematical models. Mathematical models are usually classified as empirical, mechanistic, and hybrid. To mathematically model photosynthesis, it is essential to know: the input/output variables and their units; the modeling to be used based on its classification (empirical, mechanistic, or hybrid); existing measurement methods and their invasiveness; the validation shapes and the plant species required for experimentation. Until now, a collection of such information in a single reference has not been found in the literature, so the objective of this manuscript is to analyze the most relevant mathematical models for the photosynthesis estimation and discuss their formulation, complexity, validation, number of samples, units of the input/output variables, and invasiveness in the estimation method. According to the state of the art reviewed here, 67% of the photosynthesis measurement models are mechanistic, 13% are empirical and 20% hybrid. These models estimate gross photosynthesis, net photosynthesis, photosynthesis rate, biomass, or carbon assimilation. Therefore, this review provides an update on the state of research and mathematical modeling of photosynthesis.

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.

Inference of photosynthetic capacity parameters from chlorophyll a fluorescence is affected by redox state of PSII reaction centers

Solar-induced chlorophyll fluorescence (SIF) has been used to infer photosynthetic capacity parameters (e.g., the maximum carboxylation rate V_cmax , and the maximum electron transport rate J_max ). However, the precise mechanism and practical utility of such approach under dynamic environments remain unclear. We used the balance between the light and carbon reactions to derive theoretical equations relating chlorophyll a fluorescence (ChlF) emission and photosynthetic capacity parameters, and formulated testable hypotheses regarding the dynamic relationships between the true total ChlF emitted from PSII (SIF_PSII ) and V_cmax and J_max . We employed concurrent measurements of gas exchanges and ChlF parameters for 15 species from six biomes to test the formulated hypotheses across species, temperatures, and limitation state of carboxylation. Our results revealed that SIF_PSII alone is incapable of informing the variations in V_cmax and J_max across species, even when SIF_PSII is determined under the same environmental conditions. In contrast, the product of SIF_PSII and the fraction of open PSII reactions q_L , which indicates the redox state of PSII, is a strong predictor of both V_cmax and J_max , although their precise relationships vary somewhat with environmental conditions. Our findings suggest the redox state of PSII strongly influences the relationship between SIF_PSII and V_cmax and J_max .

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 (R_dark25), the maximum rate of carboxylation by the enzyme Rubisco (V_cmax25), the maximum rate of electron transport (J_max25), and the triose phosphate utilization rate (T_p25), 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 V_cmax25, J_max25, T_p25 and R_dark25 (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.

Principles of resilient coding for plant ecophysiologists

Plant ecophysiology is founded on a rich body of physical and chemical theory, but it is challenging to connect theory with data in unambiguous, analytically rigorous and reproducible ways. Custom scripts written in computer programming languages (coding) enable plant ecophysiologists to model plant processes and fit models to data reproducibly using advanced statistical techniques. Since many ecophysiologists lack formal programming education, we have yet to adopt a unified set of coding principles and standards that could make coding easier to learn, use and modify. We identify eight principles to help in plant ecophysiologists without much programming experience to write resilient code: (i) standardized nomenclature, (ii) consistency in style, (iii) increased modularity/extensibility for easier editing and understanding, (iv) code scalability for application to large data sets, (v) documented contingencies for code maintenance, (vi) documentation to facilitate user understanding; (vii) extensive tutorials and (viii) unit testing and benchmarking. We illustrate these principles using a new R package, {photosynthesis}, which provides a set of analytical and simulation tools for plant ecophysiology. Our goal with these principles is to advance scientific discovery in plant ecophysiology by making it easier to use code for simulation and data analysis, reproduce results and rapidly incorporate new biological understanding and analytical tools.

Converging towards a common representation of large-scale photosynthesis

Much ecological research has focused on determining how different environmental factors limit photosynthesis. Far less attention is placed on how to model the transition between limitations accurately as drivers change. Whether such changes are modelled as a single switch or there is an intermediate period of co-limitation can have a substantial impact on the estimated levels of photosynthesis.