The operation of two decarboxylases, transamination, and partitioning of C4 metabolic processes between mesophyll and bundle sheath cells allows light capture to be balanced for the maize C4 pathway.

The C4 photosynthesis carbon-concentrating mechanism in maize (Zea mays) has two CO2 delivery pathways to the bundle sheath (BS; via malate or aspartate), and rates of phosphoglyceric acid reduction, starch synthesis, and phosphoenolpyruvate regeneration also vary between BS and mesophyll (M) cells. The theoretical partitioning of ATP supply between M and BS cells was derived for these metabolic activities from simulated profiles of light penetration across a leaf, with a potential 3-fold difference in the fraction of ATP produced in the BS relative to M (from 0.29 to 0.96). A steady-state metabolic model was tested using varying light quality to differentially stimulate M or BS photosystems. CO2 uptake, ATP production rate (JATP; derived with a low oxygen/chlorophyll fluorescence method), and carbon isotope discrimination were measured on plants under a low light intensity, which is considered to affect C4 operating efficiency. The light quality treatments did not change the empirical ATP cost of gross CO2 assimilation (JATP/GA). Using the metabolic model, measured JATP/GA was compared with the predicted ATP demand as metabolic functions were varied between M and BS. Transamination and the two decarboxylase systems (NADP-malic enzyme and phosphoenolpyruvate carboxykinase) were critical for matching ATP and reduced NADP demand in BS and M when light capture was varied under contrasting light qualities

A dynamic hydro-mechanical and biochemical model of stomatal conductance for C4 photosynthesis

C4 plants are major grain (maize, sorghum), sugar (sugarcane) and biofuel (Miscanthus) producers, and contribute ~20% to global productivity. Plants lose water through stomatal pores in order to acquire CO2 (assimilation, A), and control their carbon-for-water balance by regulating stomatal conductance (gS). The ability to mechanistically predict gS and A in response to atmospheric CO2, water availability and time is critical for simulating stomatal control of plant-atmospheric carbon and water exchange under current, past or future environmental conditions. Yet, dynamic mechanistic models for gS are lacking, especially for C4 photosynthesis. We developed and coupled a hydro-mechanical model of stomatal behaviour with a biochemical model of C4 photosynthesis, calibrated using gas exchange measurements in maize, and extended the coupled model with time- explicit functions to predict dynamic responses. We demonstrated the wider applicability of the model with three additional C4 grass species in which interspecific differences in stomatal behaviour could be accounted for by fitting a single parameter. The model accurately predicted steady-state responses of gS to light, atmospheric CO2 and O2, soil drying and evaporative demand, as well as dynamic responses to light intensity. Further analyses suggest the effect of variable leaf hydraulic conductance is negligible. Based on the model, we derived a set of equations suitable for incorporation in land surface models. Our model illuminates the processes underpinning stomatal control in C4 plants and suggests the hydraulic benefits associated with fast stomatal responses of C4 grasses may have supported the evolution of C4 photosynthesis

Acclimation of C-4 metabolism to low light in mature maize leaves could limit energetic losses during progressive shading in a crop canopy

C4 plants have a biochemical carbon-concentrating mechanism that increases CO2 concentration around Rubisco in the bundle sheath. Under low light, the activity of the carbon-concentrating mechanism generally decreases, associated with an increase in leakiness (ϕ), the ratio of CO2 retrodiffusing from the bundle sheath relative to C4 carboxylation. This increase in ϕ had been theoretically associated with a decrease in biochemical operating efficiency (expressed as ATP cost of gross assimilation, ATP/GA) under low light and, because a proportion of canopy photosynthesis is carried out by shaded leaves, potential productivity losses at field scale. Maize plants were grown under light regimes representing the cycle that leaves undergo in the canopy, whereby younger leaves initially developed under high light and were then re-acclimated to low light (600 to 100 μE·m−2·s−1 photosynthetically active radiation) for 3 weeks. Following re-acclimation, leaves reduced rates of light-respiration and reached a status of lower ϕ, effectively optimizing the limited ATP resources available under low photosynthetically active radiation. Direct estimates of respiration in the light, and ATP production rate, allowed an empirical estimate of ATP production rate relative to gross assimilation to be derived. These values were compared to modelled ATP/GA which was predicted using leakiness as the sole proxy for ATP/GA, and, using a novel comprehensive biochemical model, showing that irrespective of whether leaves are acclimated to very low or high light intensity, the biochemical efficiency of the C4 cycle does not decrease at low photosynthetically active radiation

An Excel tool for deriving key photosynthetic parameters from combined gas exchange and chlorophyll fluorescence: theory and practice.

Combined photosynthetic gas exchange and modulated fluorometres are widely used to evaluate physiological characteristics associated with phenotypic and genotypic variation, whether in response to genetic manipulation or resource limitation in natural vegetation or crops. After describing relatively simple experimental procedures, we present the theoretical background to the derivation of photosynthetic parameters, and provide a freely available Excel-based fitting tool (EFT) that will be of use to specialists and non-specialists alike. We use data acquired in concurrent variable fluorescence-gas exchange experiments, where A/Ci and light-response curves have been measured under ambient and low oxygen. From these data, the EFT derives light respiration, initial PSII (photosystem II) photochemical yield, initial quantum yield for CO2 fixation, fraction of incident light harvested by PSII, initial quantum yield for electron transport, electron transport rate, rate of photorespiration, stomatal limitation, Rubisco (ribulose 1·5-bisphosphate carboxylase/oxygenase) rate of carboxylation and oxygenation, Rubisco specificity factor, mesophyll conductance to CO2 diffusion, light and CO2 compensation point, Rubisco apparent Michaelis-Menten constant, and Rubisco CO2 -saturated carboxylation rate. As an example, a complete analysis of gas exchange data on tobacco plants is provided. We also discuss potential measurement problems and pitfalls, and suggest how such empirical data could subsequently be used to parameterize predictive photosynthetic models

A model-based approach to recovering the structure of a plant from images

We present a method for recovering the structure of a plant directly from a small set of widely-spaced images. Structure recovery is more complex than shape estimation, but the resulting structure estimate is more closely related to phenotype than is a 3D geometric model. The method we propose is applicable to a wide variety of plants, but is demonstrated on wheat. Wheat is made up of thin elements with few identifiable features, making it difficult to analyse using standard feature matching techniques. Our method instead analyses the structure of plants using only their silhouettes. We employ a generate-and-test method, using a database of manually modelled leaves and a model for their composition to synthesise plausible plant structures which are evaluated against the images. The method is capable of efficiently recovering accurate estimates of plant structure in a wide variety of imaging scenarios, with no manual intervention

C4 savanna grasses fail to maintain assimilation in drying soil under low CO2 compared with C3 trees despite lower leaf water demand

1.C4 photosynthesis evolved when grasses migrated out of contracting forests under a declining atmospheric CO2 concentration ([CO2]a) and drying climate around 30 million years ago. C4 grasses are hypothesised to benefit from improved plant–water relations in open habitats like savannas, giving advantages over C3 plants under low [CO2]a. But experimental evidence in a low CO2 environment is limited and comparisons with C3 trees are needed to understand savanna vegetation patterns. 2.To test whether stomatal conductance (gS) and CO2 assimilation (A) are maintained in drier soil for C4 grasses than C3 trees, particularly under low [CO2]a, we investigated photosynthesis and plant–water relations of three C3 tree and three C4 grass species grown at 800, 400 or 200 ppm [CO2]a over moderate wetting–drying cycles. 3.C4 grasses had a lower soil–to–leaf water potential gradient than C3 trees, especially at 200 ppm [CO2]a, indicating reduced leaf water demand relative to supply. Yet the dependence of gS and A on predawn leaf water potential (a measure of soil water availability) was greater for the C4 grasses than trees, particularly under low [CO2]a. 4.Our findings establish that gS and A are not maintained in drier soil for C4 grasses compared with C3 trees, suggesting that this mechanism was not prevailing in the expansion of C4–dominated grasslands under low [CO2]a. This inherent susceptibility to sudden decreases in soil water availability justifies why C4 grasses have not evolved a resistant xylem allowing operation under drought, but instead shut down below a water potential threshold and rapidly recover. We point to this capacity to respond to transient water availability as a key overlooked driver of C4 grass success under low [CO2]a

Insights into the deterministic skill of air quality ensembles from the analysis of AQMEII data

© 2016. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited. Ioannis Kioutsioukis, et al, ‘Insights into the deterministic skill of air quality ensembles from the analysis of AQMEII data’, Atmospheric Chemistry and Physics, Vol 16(24): 15629-15652, published 20 December 2016, the version of record is available at doi:10.5194/acp-16-15629-2016 Published by Copernicus Publications on behalf of the European Geosciences Union.Simulations from chemical weather models are subject to uncertainties in the input data (e.g. emission inventory, initial and boundary conditions) as well as those intrinsic to the model (e.g. physical parameterization, chemical mechanism). Multi-model ensembles can improve the forecast skill, provided that certain mathematical conditions are fulfilled. In this work, four ensemble methods were applied to two different datasets, and their performance was compared for ozone (O3), nitrogen dioxide (NO2) and particulate matter (PM10). Apart from the unconditional ensemble average, the approach behind the other three methods relies on adding optimum weights to members or constraining the ensemble to those members that meet certain conditions in time or frequency domain. The two different datasets were created for the first and second phase of the Air Quality Model Evaluation International Initiative (AQMEII). The methods are evaluated against ground level observations collected from the EMEP (European Monitoring and Evaluation Programme) and AirBase databases. The goal of the study is to quantify to what extent we can extract predictable signals from an ensemble with superior skill over the single models and the ensemble mean. Verification statistics show that the deterministic models simulate better O3 than NO2 and PM10, linked to different levels of complexity in the represented processes. The unconditional ensemble mean achieves higher skill compared to each station's best deterministic model at no more than 60 % of the sites, indicating a combination of members with unbalanced skill difference and error dependence for the rest. The promotion of the right amount of accuracy and diversity within the ensemble results in an average additional skill of up to 31 % compared to using the full ensemble in an unconditional way. The skill improvements were higher for O3 and lower for PM10, associated with the extent of potential changes in the joint distribution of accuracy and diversity in the ensembles. The skill enhancement was superior using the weighting scheme, but the training period required to acquire representative weights was longer compared to the sub-selecting schemes. Further development of the method is discussed in the conclusion.Peer reviewedFinal Published versio

A high throughput gas exchange screen for determining rates of photorespiration or regulation of C-4 activity

Large-scale research programmes seeking to characterize the C4 pathway have a requirement for a simple, high throughput screen that quantifies photorespiratory activity in C3 and C4 model systems. At present, approaches rely on model-fitting to assimilatory responses (A/C i curves, PSII quantum yield) or real-time carbon isotope discrimination, which are complicated and time-consuming. Here we present a method, and the associated theory, to determine the effectiveness of the C4 carboxylation, carbon concentration mechanism (CCM) by assessing the responsiveness of V O/V C, the ratio of RuBisCO oxygenase to carboxylase activity, upon transfer to low O2. This determination compares concurrent gas exchange and pulse-modulated chlorophyll fluorescence under ambient and low O2, using widely available equipment. Run time for the procedure can take as little as 6 minutes if plants are pre-adapted. The responsiveness of V O/V C is derived for typical C3 (tobacco, rice, wheat) and C4 (maize, Miscanthus, cleome) plants, and compared with full C3 and C4 model systems. We also undertake sensitivity analyses to determine the impact of R LIGHT (respiration in the light) and the effectiveness of the light saturating pulse used by fluorescence systems. The results show that the method can readily resolve variations in photorespiratory activity between C3 and C4 plants and could be used to rapidly screen large numbers of mutants or transformants in high throughput studies

A reporting format for leaf-level gas exchange data and metadata

Leaf-level gas exchange data support the mechanistic understanding of plant fluxes of carbon and water. These fluxes inform our understanding of ecosystem function, are an important constraint on parameterization of terrestrial biosphere models, are necessary to understand the response of plants to global environmental change, and are integral to efforts to improve crop production. Collection of these data using gas analyzers can be both technically challenging and time consuming, and individual studies generally focus on a small range of species, restricted time periods, or limited geographic regions. The high value of these data is exemplified by the many publications that reuse and synthesize gas exchange data, however the lack of metadata and data reporting conventions make full and efficient use of these data difficult. Here we propose a reporting format for leaf-level gas exchange data and metadata to provide guidance to data contributors on how to store data in repositories to maximize their discoverability, facilitate their efficient reuse, and add value to individual datasets. For data users, the reporting format will better allow data repositories to optimize data search and extraction, and more readily integrate similar data into harmonized synthesis products. The reporting format specifies data table variable naming and unit conventions, as well as metadata characterizing experimental conditions and protocols. For common data types that were the focus of this initial version of the reporting format, i.e., survey measurements, dark respiration, carbon dioxide and light response curves, and parameters derived from those measurements, we took a further step of defining required additional data and metadata that would maximize the potential reuse of those data types. To aid data contributors and the development of data ingest tools by data repositories we provided a translation table comparing the outputs of common gas exchange instruments. Extensive consultation with data collectors, data users, instrument manufacturers, and data scientists was undertaken in order to ensure that the reporting format met community needs. The reporting format presented here is intended to form a foundation for future development that will incorporate additional data types and variables as gas exchange systems and measurement approaches advance in the future. The reporting format is published in the U.S. Department of Energy's ESS-DIVE data repository, with documentation and future development efforts being maintained in a version control system