Plant Photosynthetic Responses Research Articles

Integrating leaf spectral and water status information to effectively track chlorophyll a fluorescence parameters during dehydration.

Monitoring changes in chlorophyll a (ChlFa) fluorescence during dehydration can provide insights into plant photosynthetic responses to climate change challenges, which are predicted to increase drought frequency. However, the limited knowledge of how ChlFa parameters respond to water deficit hinders the exploration of the photochemical mechanism of the photosynthetic process and the simulation of photosynthetic fluorescence models. Furthermore, how to track such responses of ChlFa parameters, especially at large scales, remains a challenge. In this study, we attempted to use spectral information reflected from leaves to follow the dynamic response patterns of ChlFa parameters of seven species under prolonged dehydration. The results showed that the investigated ChlFa parameters exhibited significant changes as dehydration progressed, with considerable variability among the different species as well as under different water conditions. This study also demonstrated that the integration of both spectral and water content information can provide an effective method for tracking ChlFa parameters during dehydration, explaining over 90% of the total variance in the measured ChlFa parameters. Collectively, these results should serve as a valuable reference for predicting the response of ChlFa parameters to dehydration and offer a potential method for estimating ChlFa parameters under drought conditions.

Utilizing Hyperspectral Reflectance and Machine Learning Algorithms for Non-Destructive Estimation of Chlorophyll Content in Citrus Leaves

To address the demands of precision agriculture and the measurement of plant photosynthetic response and nitrogen status, it is necessary to employ advanced methods for estimating chlorophyll content quickly and non-destructively at a large scale. Therefore, we explored the utilization of both linear regression and machine learning methodology to improve the prediction of leaf chlorophyll content (LCC) in citrus trees through the analysis of hyperspectral reflectance data in a field experiment. And the relationship between phenology and LCC estimation was also tested in this study. The LCC of citrus tree leaves at five growth seasons (May, June, August, October, and December) were measured alongside measurements of leaf hyperspectral reflectance. The measured LCC data and spectral parameters were used for evaluating LCC using univariate linear regression (ULR), multivariate linear regression (MLR), random forest regression (RFR), K-nearest neighbor regression (KNNR), and support vector regression (SVR). The results revealed the following: the MLR and machine learning models (RFR, KNNR, SVR), in both October and December, performed well in LCC estimation with a coefficient of determination (R2) greater than 0.70. In August, the ULR model performed the best, achieving an R2 of 0.69 and root mean square error (RMSE) of 8.92. However, the RFR model demonstrated the highest predictive power for estimating LCC in May, June, October, and December. Furthermore, the prediction accuracy was the best with the RFR model with parameters VOG2 and Carte4 in October, achieving an R2 of 0.83 and RMSE of 6.67. Our findings revealed that using just a few spectral parameters can efficiently estimate LCC in citrus trees, showing substantial promise for implementation in large-scale orchards.

Phytotoxic effects of polyethylene microplastics combined with cadmium on the photosynthetic performance of maize (Zea mays L.)

Microplastics (MPs) and cadmium (Cd) has attracted increasing attention due to their combined toxicity to terrestrial vegetation. Photosynthesis which utilizes light energy to synthesize organic substances is crucial for crop production. However, the plant photosynthetic response to the joint toxicity of MPs and Cd is still unknown. Here, we studied the effects of polyethylene (PE) MPs on the photosynthetic performance of two maize cultivars Xianyu 335 (XY) and Zhengdan 958 (ZD) grown in a Cd contaminated soil. Results showed that the leaf Cd concentration in XY and ZD reached 26.1 and 31.9 μg g−1, respectively. PE-MPs did not influence the leaf Cd content, but posed direct and negative effects on photosynthesis by increasing the malondialdehyde content, reducing the chlorophyll content, inhibiting photosynthetic capacity, disrupting the PSII donor side, blocking electron transfer in different photosystems, and suppressing the oxidation and reduction states of PSI. Transcriptomic analysis revealed that the inhibitory effect of combined PE-MPs and Cd on maize photosynthesis was attributed to suppressed expression of the genes encoding PSII, PSI, F-type ATPase, cytochrome b6/f complex, and electron transport between PSII and PSI. Using WGCNA, we identified a MEturquoise module highly correlated with photosynthetic traits. Hub genes bridging carbohydrate metabolism, amino acid metabolism, lipid metabolism, and translation provided the molecular mechanisms of PE-MPs and Cd tolerance in maize plants. The comprehensive information on the phytotoxicity mechanisms of Cd stress in the presence or absence of PE-MPs on the photosynthesis of maize is helpful for cloning Cd and PE-MP resistance genes in the future.

Plant photosynthetic responses under drought stress: Effects and management

AbstractBalanced photosynthesis is essential for improved plant survival and agricultural benefits in terms of biomass and yield. Photosynthesis is the hub of energy metabolism in plants; however, drought stress (DS) strongly perturbs photosynthetic efficiency due to biochemical and diffusive limitations that reduce key photosynthetic components and close stomata. This review describes photosynthetic responses, chloroplast retrograde signalling, and genetic imprints that curtail DS damage to photosynthetic machinery. While stomatal closure, disrupted photosynthetic systems, over‐reduced electron transport rates (ETR), partial hindrance of the Calvin cycle, and reduced pigment contents strongly affect the repertoire of photosynthetic processes under DS, chloroplast retrograde signalling also has a plausible role in preserving photosynthetic capacity. Progress in agronomic, genetic engineering approaches and isoprene regulation would help to rescue photosynthetic apparatus under DS.

Assessment of different salt concentrations on the growth and phytochemical change of the ice plants

The ice plant (Mesembryanthemum crystallinum L.) has become a halophyte model to study the plant photosynthetic responses C3 photosynthesis to crassulacean acid metabolism (CAM), which is accelerated by salt stress. However, this adaptive mechanism improves water use efficiency, and water stress tolerance is still poorly known. This study examined the effect of individual and mixture of NaCl and CaCl2 concentrations on morphological parameters and bioactive component contents of ice plants in a plant factory system. Eight salt treatments individually and a combination of sodium chloride (NaCl) and calcium chloride (CaCl2), and Hoagland solutions were applied after the transplanting of ice plants. Morphological parameters like the number of leaves and lateral stems, leaf chlorophyll content (SPAD value), fresh weight, and dry weight of shoots and roots were measured at the adult stage. Concurrently, in the juvenile phase, the area of a canopy was evaluated using an image processing technique in HSV (hue, saturation, value) colour space. Correspondingly, ice plant secondary metabolites such as cations, anions, and radical scavenging activity were assayed in the adult phase correlated to the salt stress. The effects of salt stress on the growth of ice plants and secondary metabolite production were analysed using completely randomized block designs through the variance by one-way ANOVA with a significance level of p < 0.05. This study demonstrated that 400 mM CaCl2 (T4) enhanced the biomass and high sodium (Na+) and calcium (Ca2+) accumulations, and 200 mM CaCl2 (T3) accelerated the potassium (K+), magnesium (Mg2+), phosphate (PO43−), and sulfate (SO42−) accumulations. Moreover, NaCl 400 mM (T1) and combination of 100 mM NaCl and 300 mM CaCl2 (T5) positively influenced the chloride (Cl−) deposition and combination of 200 mM NaCl and 200 mM CaCl2 (T6) improved nitrate (NO3–) accretion. Furthermore, 100 mM CaCl2 (T2) exhibited the highest antioxidant activity in ice plants grown under the plant factory system.

A Model of C4 Photosynthetic Acclimation Based on Least‐Cost Optimality Theory Suitable for Earth System Model Incorporation

AbstractEmpirical studies have shown that plant photosynthetic responses to environmental change can vary over time due to acclimation, but acclimation responses are often not included in Earth System Models. Photosynthetic least cost theory can be used to develop models of photosynthetic acclimation that are simple and testable. The theory is based on the idea that plants will acclimate to minimize the ratio of carbon costs to photosynthetic assimilation rate (Prentice et al., 2014, https://doi.org/10.1111/ele.12211). Formulations of this theory have been developed for C3 plants, but not C4 plants, which account for over 20% of global photosynthesis and are over‐represented among widely grown crops. Here, we use photosynthetic least cost theory to derive a model for C4 photosynthetic acclimation to above‐ground abiotic conditions. We then compare our model's responses to a similar model of C3 photosynthetic acclimation and find that C4 photosynthesis has the highest simulated advantage over C3 photosynthesis in hot, dry, and low CO2 environments. We find that this advantage predicts C4 abundance globally, but that the shallower CO2 response of C4 as compared to C3 photosynthesis will reduce C4 plant competitiveness under future conditions, despite higher temperatures. We also show that an acclimated model predicts similar or faster rates of C4 under all conditions than a model that does not consider acclimation, suggesting that Earth System Models (ESMs) are underestimating future C4 carbon uptake by not including acclimation. Our model is designed for easy incorporation into such ESMs.

Low phosphorus supply constrains plant responses to elevated CO2 : A meta-analysis.

Phosphorus (P) is an essential macro-nutrient required for plant metabolism and growth. Low P availability could potentially limit plant responses to elevated carbon dioxide (eCO2 ), but consensus has yet to be reached on the extent of this limitation. Here, based on data from experiments that manipulated both CO2 and P for young individuals of woody and non-woody species, we present a meta-analysis of P limitation impacts on plant growth, physiological, and morphological response to eCO2 . We show that low P availability attenuated plant photosynthetic response to eCO2 by approximately one-quarter, leading to a reduced, but still positive photosynthetic response to eCO2 compared to those under high P availability. Furthermore, low P limited plant aboveground, belowground, and total biomass responses to eCO2 , by 14.7%, 14.3%, and 12.4%, respectively, equivalent to an approximate halving of the eCO2 responses observed under high P availability. In comparison, low P availability did not significantly alter the eCO2 -induced changes in plant tissue nutrient concentration, suggesting tissue nutrient flexibility is an important mechanism allowing biomass response to eCO2 under low P availability. Low P significantly reduced the eCO2 -induced increase in leaf area by 14.3%, mirroring the aboveground biomass response, but low P did not affect the eCO2 -induced increase in root length. Woody plants exhibited stronger attenuation effect of low P on aboveground biomass response to eCO2 than non-woody plants, while plants with different mycorrhizal associations showed similar responses to low P and eCO2 interaction. This meta-analysis highlights crucial data gaps in capturing plant responses to eCO2 and low P availability. Field-based experiments with longer-term exposure of both CO2 and P manipulations are critically needed to provide ecosystem-scale understanding. Taken together, our results provide a quantitative baseline to constrain model-based hypotheses of plant responses to eCO2 under P limitation, thereby improving projections of future global change impacts.

Global response patterns of plant photosynthesis to nitrogen addition: A meta-analysis.

A mechanistic understanding of plant photosynthetic response is needed to reliably predict changes in terrestrial carbon (C) gain under conditions of chronically elevated atmospheric nitrogen (N) deposition. Here, using 2,683 observations from 240 journal articles, we conducted a global meta-analysis to reveal effects of N addition on 14 photosynthesis-related traits and affecting moderators. We found that across 320 terrestrial plant species, leaf N was enhanced comparably on mass basis (Nmass , +18.4%) and area basis (Narea , +14.3%), with no changes in specific leaf area or leaf mass per area. Total leaf area (TLA) was increased significantly, as indicated by the increases in total leaf biomass (+46.5%), leaf area per plant (+29.7%), and leaf area index (LAI, +24.4%). To a lesser extent than for TLA, N addition significantly enhanced leaf photosynthetic rate per area (Aarea , +12.6%), stomatal conductance (gs , +7.5%), and transpiration rate (E, +10.5%). The responses of Aarea were positively related with that of gs , with no changes in instantaneous water-use efficiency and only slight increases in long-term water-use efficiency (+2.5%) inferred from 13 C composition. The responses of traits depended on biological, experimental, and environmental moderators. As experimental duration and N load increased, the responses of LAI and Aarea diminished while that of E increased significantly. The observed patterns of increases in both TLA and E indicate that N deposition will increase the amount of water used by plants. Taken together, N deposition will enhance gross photosynthetic C gain of the terrestrial plants while increasing their water loss to the atmosphere, but the effects on C gain might diminish over time and that on plant water use would be amplified if N deposition persists.

Physiological Changes in Mesembryanthemum crystallinum During the C3 to CAM Transition Induced by Salt Stress.

Salt stress impedes plant growth and development, and leads to yield loss. Recently, a halophyte species Mesembryanthemum crystallinum has become a model to study plant photosynthetic responses to salt stress. It has an adaptive mechanism of shifting from C3 photosynthesis to crassulacean acid metabolism (CAM) photosynthesis under stresses, which greatly enhances water usage efficiency and stress tolerance. In this study, we focused on investigating the morphological and physiological changes [e.g., leaf area, stomatal movement behavior, gas exchange, leaf succulence, and relative water content (RWC)] of M. crystallinum during the C3 to CAM photosynthetic transition under salt stress. Our results showed that in M. crystallinum seedlings, CAM photosynthesis was initiated after 6 days of salt treatment, the transition takes place within a 3-day period, and plants became mostly CAM in 2 weeks. This result defined the transition period of a facultative CAM plant, laid a foundation for future studies on identifying the molecular switches responsible for the transition from C3 to CAM, and contributed to the ultimate goal of engineering CAM characteristics into C3 crops.

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Effects of simulated N deposition on photosynthesis and productivity of key plants from different functional groups of alpine meadow on Qinghai-Tibetan plateau

Nitrogen (N) deposition may alter physiological process of plants in grassland ecosystem. However, little is known about the response mechanism of individual plants in alpine regions to N deposition. We conducted a field experiment, and three treatments including 0 kg Nha-1year-1 (CK), 8 kgNha-1year-1 (Low N), and 72 kg N ha-1 year-1 (High N) were established to simulate N deposition in alpine meadow of Qinghai-Tibetan plateau. Our objectives were to determine the influence of N deposition on photosynthesis of different functional types of herbage species in alpine meadow, and finally characterize the links of plant productivity and photosynthesis with soil nutrients. The results showed that responses of alpine plants were species-specific under N deposition. Compared with grass species Agropyron cristatum and forb species Thalictrum aquilegifolium, the sedge species Carex melanantha was much more sensitive to N deposition; a lower N load (8 kgNha-1year-1) can cause a negative effect on its photosynthesis and productivity. Additionally, N deposition can promote plant N uptake and significantly decreased the C (carbon)/N (nitrogen) ratio. Compared with CK and low N deposition, high N deposition inhibited the photosynthesis and growth of the forb species Thalictrum aquilegifolium and sedge species Carex melanantha. In all three functional types of herbage species, the grass species A.cristatum tended to show a much higher photosynthetic capacity and better growth potential; thus, suggesting that grass species A.cristatum will be a more adaptative alpine plants under N deposition. Our findings suggested that plant photosynthetic responses to N deposition were species-specific, low N deposition was not beneficial for all the herbage species, and N deposition may change plant composition by the differential photosynthetic responses among species in alpine grassland. Plant composition shift to grass-dorminant in alpine regions might be attributed to a much higher photosynthetic potential and N use efficiency of grass species.

A Random Forest Machine Learning Approach for the Retrieval of Leaf Chlorophyll Content in Wheat

Developing rapid and non-destructive methods for chlorophyll estimation over large spatial areas is a topic of much interest, as it would provide an indirect measure of plant photosynthetic response, be useful in monitoring soil nitrogen content, and offer the capacity to assess vegetation structural and functional dynamics. Traditional methods of direct tissue analysis or the use of handheld meters, are not able to capture chlorophyll variability at anything beyond point scales, so are not particularly useful for informing decisions on plant health and status at the field scale. Examining the spectral response of plants via remote sensing has shown much promise as a means to capture variations in vegetation properties, while offering a non-destructive and scalable approach to monitoring. However, determining the optimum combination of spectra or spectral indices to inform plant response remains an active area of investigation. Here, we explore the use of a machine learning approach to enhance the estimation of leaf chlorophyll (Chlt), defined as the sum of chlorophyll a and b, from spectral reflectance data. Using an ASD FieldSpec 4 Hi-Res spectroradiometer, 2700 individual leaf hyperspectral reflectance measurements were acquired from wheat plants grown across a gradient of soil salinity and nutrient levels in a greenhouse experiment. The extractable Chlt was determined from laboratory analysis of 270 collocated samples, each composed of three leaf discs. A random forest regression algorithm was trained against these data, with input predictors based upon (1) reflectance values from 2102 bands across the 400–2500 nm spectral range; and (2) 45 established vegetation indices. As a benchmark, a standard univariate regression analysis was performed to model the relationship between measured Chlt and the selected vegetation indices. Results show that the root mean square error (RMSE) was significantly reduced when using the machine learning approach compared to standard linear regression. When exploiting the entire spectral range of individual bands as input variables, the random forest estimated Chlt with an RMSE of 5.49 µg·cm−2 and an R2 of 0.89. Model accuracy was improved when using vegetation indices as input variables, producing an RMSE ranging from 3.62 to 3.91 µg·cm−2, depending on the particular combination of indices selected. In further analysis, input predictors were ranked according to their importance level, and a step-wise reduction in the number of input features (from 45 down to 7) was performed. Implementing this resulted in no significant effect on the RMSE, and showed that much the same prediction accuracy could be obtained by a smaller subset of indices. Importantly, the random forest regression approach identified many important variables that were not good predictors according to their linear regression statistics. Overall, the research illustrates the promise in using established vegetation indices as input variables in a machine learning approach for the enhanced estimation of Chlt from hyperspectral data.

Effects of nitrogen and phosphorus imbalance on photosynthetic traits of poplar Oxford clone under ozone pollution.

Ozone (O3) pollution and the availability of nitrogen (N) and phosphorus (P) in the soil both affect plant photosynthesis and chlorophyll (Chl) content, but the interaction of O3 and nutrition is unclear. We postulated that the nutritional condition changes plant photosynthetic responses to O3. An O3-sensitive poplar clone (Oxford) was subject to two N levels (N0, 0kgNha- 1; N80, 80kgNha- 1), two P levels (P0, 0kgPha- 1; P80, 80kgPha- 1) and three levels of O3 exposure (ambient concentration, AA; 1.5 × AA; 2.0 × AA) over a growing season in an O3 free air controlled exposure (FACE) facility. The daily change of leaf gas exchange and dark respiration (Rd) were investigated at mid-summer (August). Chl a fluorescence was measured three times in July, August and September. At the end of the growing season, Chl content was measured. It was found that Chl content, the maximum quantum yield (Fv/Fm), Chl a fluorescence performance index (PI) and gas exchange were negatively affected by elevated O3. Phosphorus may mitigate the O3-induced reduction of the ratio of photosynthesis to stomatal conductance, while it exacerbated the O3-induced loss of Fv/Fm. Nitrogen alleviated negative effects of O3 on Fv/Fm and PI in July. Ozone-induced loss of net photosynthetic rate was mitigated by N in medium O3 exposure (1.5 × AA). However, such a mitigation effect was not observed in the higher O3 level (2.0 × AA). Nitrogen addition exacerbated O3-induced increase of Rd suggesting an increased respiratory carbon loss in the presence of O3 and N. This may result in a further reduction of the net carbon gain for poplars exposed to O3.

Soil Moisture Stress as a Major Driver of Carbon Cycle Uncertainty

AbstractFuture projections suggest an increase in drought globally with climate change. Current vegetation models typically regulate the plant photosynthetic response to soil moisture stress through an empirical function, rather than a mechanistic response where plant water potentials respond to changes in soil water. This representation of soil moisture stress may introduce significant uncertainty into projections for the terrestrial carbon cycle. We examined the use of the soil moisture limitation function in historical and future emissions scenarios in nine Earth system models. We found that soil moisture‐limited productivity across models represented a large and uncertain component of the simulated carbon cycle, comparable to 3–286% of current global productivity. Approximately 40–80% of the intermodel variability was due to the functional form of the limitation equation alone. Our results highlight the importance of implementing mechanistic water limitation schemes in models and illuminate several avenues for improving projections of the land carbon sink.

Large sensitivity in land carbon storage due to geographical and temporal variation in the thermal response of photosynthetic capacity.

Summary Plant temperature responses vary geographically, reflecting thermally contrasting habitats and long‐term species adaptations to their climate of origin. Plants also can acclimate to fast temporal changes in temperature regime to mitigate stress. Although plant photosynthetic responses are known to acclimate to temperature, many global models used to predict future vegetation and climate–carbon interactions do not include this process.We quantify the global and regional impacts of biogeographical variability and thermal acclimation of temperature response of photosynthetic capacity on the terrestrial carbon (C) cycle between 1860 and 2100 within a coupled climate–carbon cycle model, that emulates 22 global climate models.Results indicate that inclusion of biogeographical variation in photosynthetic temperature response is most important for present‐day and future C uptake, with increasing importance of thermal acclimation under future warming. Accounting for both effects narrows the range of predictions of the simulated global land C storage in 2100 across climate projections (29% and 43% globally and in the tropics, respectively).Contrary to earlier studies, our results suggest that thermal acclimation of photosynthetic capacity makes tropical and temperate C less vulnerable to warming, but reduces the warming‐induced C uptake in the boreal region under elevated CO2.

Photochemical metabolism and fruit quality of Ubá mango tree exposed to combined light and heat stress in the field

Several experiments have highlighted the complexity of stress interactions, in field conditions, involved in plant response. However, these impacts on the mechanisms involved in plant photosynthetic response remains understudied. The aim of this work was to compare the photosynthetic efficiencies and fruit quality of mango tree (Mangifera indica L.) cv. Uba harvested from plants cultivated on the east and west sides of a commercial orchard, according to the position of plants in relation to sunrise. Chlorophyll a fluorescence, was analyzed in leaves in four different periods: fruit growth phase, fruit ripening phase, post-harvest period and after plant pruning. Photoinhibitory damage was detected by the trapped energy flux and transported electron flux per reaction center during the fruit ripening phase, and by specific energy fluxes and yield quantum efficiency after plant pruning. Although high radiation caused photoinhibition on leaves from plants cultivated on the west side of the orchard, it provided sweeter fruits. In contrast to our initial hypothesis, it was verified that plants cultivated on the west side of the orchard presented better photochemical performance in periods with the greatest requirements of photoassimilates. In addition, plants demonstrated different abilities to deal with changes on photosynthetic active radiation and high temperature. This information suggests that the phenotypic plasticity of the Uba mango cultivar is considerable, which can be exploited to be used in regions with great relief variations and the combination of increased irradiance and high temperature.

Long-term measurements of chlorophyll a fluorescence using the JIP-test show that combined abiotic stresses influence the photosynthetic performance of the perennial ryegrass (Lolium perenne) in a managed temperate grassland.

Several experiments have highlighted the complexity of stress interactions involved in plant response. The impact in field conditions of combined environmental constraints on the mechanisms involved in plant photosynthetic response, however, remains understudied. In a long-term field study performed in a managed grassland, we investigated the photosynthetic apparatus response of the perennial ryegrass (Lolium perenne L.) to environmental constraints and its ability to recover and acclimatize. Frequent field measurements of chlorophyll a fluorescence (ChlF) were made in order to determine the photosynthetic performance response of a population of L. perenne. Strong midday declines in the maximum quantum yield of primary photochemistry (FV FM ) were observed in summer, when a combination of heat and high light intensity increased photosynthetic inhibition. During this period, increase in photosystem I (PSI) activity efficiency was also recorded, suggesting an increase in the photochemical pathway for de-excitation in summer. Strong climatic events (e.g. heat waves) were shown to reduce electron transport between photosystem II (PSII) and PSI. This reduction might have preserved the PSI from photo-oxidation. Periods of low soil moisture and high levels of sun irradiance increased PSII sensitivity to heat stress, suggesting increased susceptibility to combined environmental constraints. Despite the multiple inhibitions of photosynthetic functionality in summer, the L. perenne population showed increased PSII tolerance to environmental stresses in August. This might have been a response to earlier environmental constraints. It could also be linked to the selection and/or emergence of well-adapted individuals.

The wheat mutant DELLA-encoding gene (Rht-B1c) affects plant photosynthetic responses to cadmium stress

Тhe sensitivity to cadmium (Cd) stress of two near-isogenic wheat lines with differences at the Rht-B1 locus, Rht-B1a (tall wild type, encoding DELLA proteins) and Rht-B1c (dwarf mutant, encoding modified DELLA proteins), was investigated. The effects of 100 μM CdCl2 on plant growth, pigment content and functional activity of the photosynthetic apparatus of wheat seedlings grown on a nutrient solution were evaluated through a combination of PAM chlorophyll fluorescence, oxygen evolution, oxidation-reduction kinetics of P700 and 77 K fluorescence. The results showed that the wheat mutant (Rht-B1c) was more tolerant to Cd stress compared to the wild type (Rht-B1a), as evidenced by the lower reductions in plant growth and pigment content, lower inhibition of photosystem I (PSI) and photosystem II (PSII) photochemistry and of the oxygen evolution measured with Clark-type and Joliot-type electrodes. Furthermore, the enhanced Cd tolerance was accompanied by increased Cd accumulation within mutant plant tissues.The molecular mechanisms through which the Rht-B1c mutation improves plant tolerance to Cd stress involve structural alterations in the mutant photosynthetic membranes leading to better protection of the Mn cluster of oxygen-evolving complex and increased capacity for PSI cyclic electron transport, protecting photochemical activity of the photosynthetic apparatus under stress. This study suggests a role for the Rht-B1c-encoded DELLA proteins in protective mechanisms and tolerance of the photosynthetic apparatus in wheat plants exposed to heavy metals stress.

Plant Photosynthetic Responses During Insect Effector-Triggered Plant Susceptibility and Immunity.

Gall-inducing insects are known for altering source-sink relationships within plants. Changes in photosynthesis may contribute to this phenomenon. We investigated photosynthetic responses in wheat [Triticum aestivum L. (Poaceae: Triticeae)] seedlings attacked by the Hessian fly [Mayetiola destructor (Say) (Diptera: Cecidomyiidae], which uses a salivary effector-based strategy to induce a gall nutritive tissue in susceptible plants. Resistant plants have surveillance systems mediated by products of Resistance (R) genes. Detection of a specific salivary effector triggers downstream responses that result in a resistance that kills neonate larvae. A 2 × 2 factorial design was used to study maximum leaf photosynthetic assimilation and stomatal conductance rates. The plant treatments were-resistant or susceptible wheat lines expressing or not expressing the H13 resistance gene. The insect treatments were-no attack (control) or attack by larvae killed by H13 gene-mediated resistance. Photosynthesis was measured for the second and third leaves of the seedling, the latter being the only leaf directly attacked by larvae. We predicted effector-based attack would trigger increases in photosynthetic rates in susceptible but not resistant plants. For susceptible plants, attack was associated with increases (relative to controls) in photosynthesis for the third but not the second leaf. For resistant plants, attack was associated with increases in photosynthesis for both the second and third leaves. Mechanisms underlying the increases appeared to differ. Resistant plants exhibited responses suggesting altered source-sink relationships. Susceptible plants exhibited responses suggesting a mechanism other than altered source-sink relationships, possibly changes in water relations that contributed to increased stomatal conductance.

High temperature stress monitoring and detection using chlorophyll a fluorescence and infrared thermography in chrysanthemum (Dendranthema grandiflora)

Modern highly insulated greenhouses are more energy efficient than conventional types. Furthermore applying dynamic greenhouse climate control regimes will increase energy efficiency relatively more in modern structures. However, this combination may result in higher air and crop temperatures. Too high temperature affects the plant photosynthetic responses, resulting in a lower rate of photosynthesis. To predict and analyse physiological responses as stress indicators, two independent experiments were conducted, to detect the effect of high temperature on photosynthesis: analysing photosystem II (PSII) and stomatal conductance (gs). A combination of chlorophyll a fluorescence, gas exchange measurements and infrared thermography was applied using Chrysanthemum (Dendranthema grandiflora Tzvelev) 'Coral Charm' as a model species. Increasing temperature had a highly significant effect on PSII when the temperature exceeded 38°C for a period of 7 (±1.8) days. High temperature decreased the maximum photochemical efficiency of PSII (Fv/Fm), the conformation term for primary photochemistry (Fv/Fo) and performance index (PI), as well as increased minimal fluorescence (Fo). However, at elevated CO2 of 1000μmolmol(-1) and with a photosynthetic photon flux density (PPFD) of 800μmolm(-2)s(-1), net photosynthesis (Pn) reached its maximum at 35°C. The thermal index (IG), calculated from the leaf temperature and the temperature of a dry and wet reference leaf, showed a strong correlation with gs. We conclude that 1) chlorophyll a fluorescence and a combination of fluorescence parameters can be used as early stress indicators as well as to detect the temperature limit of PSII damage, and 2) the strong relation between gs and IG enables gs to be estimated non-invasively, which is an important first step in modelling leaf temperature to predict unfavourable growing conditions in a (dynamic) semi closed greenhouse.