Light-use Efficiency Research Articles

Optimizing trade-offs between light transmittance and intraspecific competition under varying crop layouts in a maize–soybean strip relay cropping system

Light is one of the most important environmental factors for plant growth and development. In relay cropping systems, crop layouts influence light distribution, affecting light use efficiency (LUE). However, the response of light interception, light conversion, and LUE for relay maize and relay soybean to different crop layouts remains unclear. We aimed to quantify the effect of crop layout on intraspecific and interspecific competition, light interception, light conversion, LUE, and land productivity between relay maize and relay soybean. We conducted a field experiment for four consecutive years from 2017 to 2020 in Sichuan province, China, comparing different crop layouts (bandwidth 2.0 m, row ratio 2:2; bandwidth 2.4 m, row ratio 2:3; bandwidth 2.8 m, row ratio 2:4), with sole maize and sole soybean as controls. The results showed that relay maize in the 2.0 m bandwidth layout had the largest leaf area index and plant biomass, the lowest intraspecific competitive intensity and the highest aggressiveness. Compared to a bandwidth of 2.0 m, a bandwidth of 2.8 m significantly decreased relay maize leaf area index by 11% and plant biomass by 24%, while a 2.4 m bandwidth caused roughly half these reductions. The 2.0 m bandwidth layout also significantly improved crop light interception and LUE compared to sole maize. The light interception, light interception rate, light conversion rate and LUE in relay maize all decreased significantly with increasing bandwidth, but they increased in relay soybean. The increased light transmittance to the lower and middle canopy with increasing bandwidth did not compensate for the loss of relay maize yield caused by increased intraspecific competition. However, it enhanced the yield of relay soybeans. Increasing the bandwidth by 80 cm increased the relay maize intraspecific competition by 580%, and reduced maize yield by 33%, light interception by 12%, and LUE by 18%. In contrast, the relay soybean intraspecific competition was reduced by 64%, and the soybean yield was increased by 26%, light interception by 32% and LUE by 46%. Relay cropping systems with a 2.0 m bandwidth optimize the trade-off between light transmittance and intraspecific competition of relay crops. These systems achieve the highest LUE, group yield and economic benefits, making them a recommended crop layout for the southwest regions of China. Our study offers valuable insights for developing strip relay cropping systems that maximize light utilization and contributes to the theoretical understanding of efficient sunlight use in relay cropping practices.

Photosynthetic Use Efficiency of Macroelements, Water, and Light by Lactuca sativa (L.) under Contamination of Soil with Lead

Photosynthetic Use Efficiency of Macroelements, Water, and Light by Lactuca sativa (L.) under Contamination of Soil with Lead

Stronger effects of accumulated soil moisture deficit on gross primary productivity and light use efficiency than lagged soil moisture deficit for cropland and forest

Many studies have underscored the impacts of drought on ecosystems, and some researchers reported the effects of accumulated soil moisture deficit (ASMD) on light use efficiency (LUE) in grassland. However, the potential effects of ASMD on gross primary productivity (GPP) and LUE for both cropland and forest ecosystems are still not understood. This study elucidated the effects of accumulated and lagged soil moisture deficit (ASMD and LSMD, respectively) on GPP and LUE in these two ecosystems by using observations from 10 cropland and 25 forest flux sites during drought years. The results showed that the effects of ASMD and LSMD on LUE/GPP for both cropland and forests obviously surpass the concurrent effects (CSMD). For cropland, the mean R2 between CSMD/LSMD/ASMD with LUE were 0.22, 0.47, 0.56, respectively, and were 0.29, 0.54, 0.74 with GPP, respectively. For forest, the mean R2 between CSMD/LSMD/ASMD with LUE were 0.21, 0.36, 0.46, respectively, and were 0.34, 0.63, and 0.65 with GPP, respectively. Additionally, the effects of ASMD and LSMD on LUE are more pronounced for cropland than for forests, and for both cropland and forest, the effect of ASMD is stronger than that of LSMD. This study underscores the crucial role of ASMD in influencing LUE and GPP for cropland and forests, thereby offering a theoretical foundation for incorporating ASMD into LUE models to enhance the accuracy of GPP simulations, especially during drought periods.

High photosynthesis rates in Brassiceae species are mediated by leaf anatomy enabling high biochemical capacity, rapid CO2 diffusion and efficient light use.

Certain species in the Brassicaceae family exhibit high photosynthesis rates, potentially providing a valuable route toward improving agricultural productivity. However, factors contributing to their high photosynthesis rates are still unknown. We compared Hirschfeldia incana, Brassica nigra, Brassica rapa and Arabidopsis thaliana, grown under two contrasting light intensities. Hirschfeldia incana matched B. nigra and B. rapa in achieving very high photosynthesis rates under high growth-light condition, outperforming A. thaliana. Photosynthesis was relatively more limited by maximum photosynthesis capacity in H. incana and B. rapa and by mesophyll conductance in A. thaliana and B. nigra. Leaf traits such as greater exposed mesophyll specific surface enabled by thicker leaf or high-density small palisade cells contributed to the variation in mesophyll conductance among the species. The species exhibited contrasting leaf construction strategies and acclimation responses to low light intensity. High-light plants distributed Chl deeper in leaf tissue, ensuring even distribution of photosynthesis capacity, unlike low-light plants. Leaf anatomy of H. incana, B. nigra and B. rapa facilitated effective CO2 diffusion, efficient light use and provided ample volume for their high maximum photosynthetic capacity, indicating that a combination of adaptations is required to increase CO2-assimilation rates in plants.

An ecological framework to index crop yields using productivity and Ecosystem Fit: A case study from India

On the global scale, agricultural crop yields have decreased or plateaued over the last several decades. This suggests that the current focus on selecting crop varieties based on a plant’s light-use efficiency (photosynthetic and nitrogen-use-efficiency metrics) may not be sensitive to the site’s edaphic parameters, which limit growth. This study introduces a new framework to determine if crops can achieve higher yield potentials by assessing how plants adapt to the edaphic properties that impact growth, especially when contending with climate change. The new approach calculates an Ecosystem Fit index using a ratio of remotely sensed (or observed) total net primary productivity to the theoretical maximum productivity of the site. Then, it uses that index as a benchmark to judge quantitatively whether any new crop species or variety is improving potential biomass or economic yields at that specific site. It can also determine the best soil types for those crop varieties and monitor their potential adaptability relative to climate change over time. This study used a database of 356 spatially independent reference sites to develop this framework using a landcover classification of crops across 21 ecoregions and five biomes in India. It includes total net primary productivity data, theoretical maximum productivity potential, and soil and climatic data. This comparison showed that the light-use efficiency model, as intended, was not sensitive to variations in soil characteristics, temperature, or precipitation. Our framework showed significant differences in growth by soil type and precipitation and three significant productivity thresholds by soil type. The results of this study demonstrate that total crop productivity and Ecosystem Fit create a useful index for local land managers to assess growth and yield potentials across diverse edaphic landscapes and for decision-making with changing climates.

Effects of Biophysical Factors on Light Use Efficiency at Multiple Time Scales in a Chinese Cork Oak Plantation Ecosystem

Light use efficiency (LUE) characterizes the efficiency of vegetation in converting photosynthetically active radiation (PAR) into biomass energy through photosynthesis and is a critical parameter for gross primary productivity (GPP) in terrestrial ecosystems. Based on the eddy covariance measurements of a Chinese cork oak plantation ecosystem in northern China, the temporal variations in LUE were investigated, and biophysical factors were examined at time scales ranging from hours to years. Our results show that diurnal LUE first increased sharply before 8:30 and then decreased gradually until 12:00, thereafter increasing gradually and reaching the maximum value at sunset during the growing season. The daily and monthly LUE first increased and then decreased within a year and showed a substantial drop around June. The annual LUE ranged from 0.09 to 0.17 g C mol photon−1, and the multiyear mean maximal LUE was 0.30 g C mol photon−1 during 2006–2019. Only GPP (positive) and clearness index (CI) (negative) had consistent effects on LUE at different time scales, and the effects of the remaining biophysical factors on LUE were different as the time scale changed. The effects of air temperature, vapor pressure deficit, precipitation, evaporative fraction, and normalized difference vegetation index on LUE were mainly indirect (via PAR and/or GPP). When CI decreased, an increased ratio of diffuse PAR to PAR produced a more uniform irradiance in the canopy, which ultimately resulted in a higher LUE. Due to climate change in our study area, the annual LUE may decrease in the future but improving management practices may slow or even reverse this trend in the annual LUE in the studied Chinese cork oak plantation.

Physiological characteristics of ornamental caladiums (Caladium x hortulanum Birdsey, Araceae Juss.) through leaf colour diversity

This work aims to analyze the physiological characteristics of caladium ornamental plants with different leaf colors. Ten leaf colors, i.e., dark maroon, maroon, red, pink, white, pinkish young green, yellow, lime green, pinkish dark green, and dark green, were arranged in a completely randomized design and subjected to colorimetric red–green–blue (RGB) and portable photosynthesis measurement. Caladium leaf color, defined by the colorimetric RGB value, significantly impacted leaf physiological properties. Heatmap analysis apparently clustered caladiums into dark-colored group and light-colored ones. Dark-colored caladiums, as indicated by lower RGB values, exhibited higher photosynthesis rate, stomatal conductance, and water use efficiency (WUE). In contrast, light-colored caladium show a higher RGB, leaf temperature and light use efficiency (LUE) than the dark ones. By considering their leaf color-based physiological adaptability, this work recommends moderate sunlight and adequate water conditions for growing light-colored caladiums, while dark caladiums potentially tolerate excess light and water scarcity. Further research, including quantitative pigment analysis, is necessary to verify this hypothesis.

Light-use Efficiency and Fruit Dry Matter Content Affect Fresh Yield of Sweet Pepper

Improving the productivity of sweet pepper (Capsicum annuum) is essential to meeting the increasing global demand. This can be partially accomplished by investigating and determining high-yield traits, thereby enabling the selection or breeding of high-yield plants. Therefore, this study aimed to determine the high-yield traits of sweet pepper by analyzing its yield components. We analyzed yield components of commercially available cultivars (red and yellow) that were hydroponically grown in a greenhouse (e.g., total fruit fresh weight, fruit dry weight, fruit dry matter content, total dry matter production, and light-use efficiency) using Pearson’s correlation coefficient (r). Our results showed the following: the total fruit fresh weight was positively and negatively correlated with the fruit dry weight (r = 0.83; P < 0.001) and fruit dry matter content (r = –0.70; P < 0.001), respectively; the fruit dry weight was positively correlated with the total dry matter production (r = 0.50; P < 0.01), and the total dry matter production was positively correlated with the light-use efficiency (r = 0.93; P < 0.001); and the cultivars with the high total fruit fresh weight were characterized by the notably low fruit dry matter content and high light-use efficiency (e.g., ‘Gialte’). In conclusion, high-yielding sweet peppers are characterized by a low fruit dry matter content and high light-use efficiency.

Impact of water stress on Mediterranean oak savanna grasslands productivity: Implications for on-farm grazing management

This study analyzes the productivity of grasslands in a Mediterranean oak-savanna ecosystem, focusing on its linkages with water availability. In these water-controlled ecosystems, grassland environmental preservation and sustainable management depend on quantitatively understanding these links. Productivity and water stress were modeled in southern Spain (2001–2018), integrating meteorological information and MODIS sensor data into a light-use efficiency model and a surface energy balance. The results provided valuable insights into how grasslands behaved during droughts at different spatiotemporal scales. During the most significant droughts, 2004/2005 and 2011/2012, aerial biomass production was reduced by 42 % and 67 %, respectively. The spatial analysis identified the central east side of the region, with low slopes and moderate tree cover, as the most productive area. The biomass production time series classification identified four distinct trends, all showing shifted relationships with similar slopes between production and anomalies of relative evapotranspiration. The seasonal analysis highlighted the importance of autumn, accounting for nearly 30 % of the annual biomass production, which was essential in years with spring water deficits. The proposed methodology provides on-farm grassland production curves depending on water availability (max-mean-min range with a mean error of 15.5 %). Together with weather forecast data, this could help farmers decide on the optimal level of management intensification and stocking rate. Although the regional specificity may limit the study’s direct applicability, this scheme offers valuable metrics that could be adapted to other areas under water scarcity conditions.

Estimating Maize Crop Height and Aboveground Biomass Using Multi-Source Unmanned Aerial Vehicle Remote Sensing and Optuna-Optimized Ensemble Learning Algorithms

Accurately assessing maize crop height (CH) and aboveground biomass (AGB) is crucial for understanding crop growth and light-use efficiency. Unmanned aerial vehicle (UAV) remote sensing, with its flexibility and high spatiotemporal resolution, has been widely applied in crop phenotyping studies. Traditional canopy height models (CHMs) are significantly influenced by image resolution and meteorological factors. In contrast, the accumulated incremental height (AIH) extracted from point cloud data offers a more accurate estimation of CH. In this study, vegetation indices and structural features were extracted from optical imagery, nadir and oblique photography, and LiDAR point cloud data. Optuna-optimized models, including random forest regression (RFR), light gradient boosting machine (LightGBM), gradient boosting decision tree (GBDT), and support vector regression (SVR), were employed to estimate maize AGB. Results show that AIH99 has higher accuracy in estimating CH. LiDAR demonstrated the highest accuracy, while oblique photography and nadir photography point clouds were slightly less accurate. Fusion of multi-source data achieved higher estimation accuracy than single-sensor data. Embedding structural features can mitigate spectral saturation, with R2 ranging from 0.704 to 0.939 and RMSE ranging from 0.338 to 1.899 t/hm2. During the entire growth cycle, the R2 for LightGBM and RFR were 0.887 and 0.878, with an RMSE of 1.75 and 1.76 t/hm2. LightGBM and RFR also performed well across different growth stages, while SVR showed the poorest performance. As the amount of nitrogen application gradually decreases, the accumulation and accumulation rate of AGB also gradually decrease. This high-throughput crop-phenotyping analysis method offers advantages, such as speed and high accuracy, providing valuable references for precision agriculture management in maize fields.

The applicability of a SIF-based mechanistic model for estimating GPP at the canopy scale

Mechanistically linking gross primary productivity (GPP) and sun-induced chlorophyll fluorescence (SIF) is an essential step to unleash the full potential of SIF for remote sensing-based predictions of GPP across biomes, climates, and spatiotemporal scales. The latest SIF-based mechanistic light response model that includes the fraction of open photosystem II reaction centers as key parameter (qMLR-SIF model), can accurately reproduce leaf-scale photosynthesis under various conditions. However, it remains unclear to what extent the qMLR-SIF model is suitable for estimating GPP at larger scales such as the canopy scale. Therefore, canopy-scale data of tower-based far-red SIF, GPP and key environmental variables from 10 study sites were collected to analyze the SIF-GPP relationship and to compare the qMLR-SIF model with the widely used Farquhar, von Caemmerer, Berry (FvCB) model and with a light use efficiency (LUE) model for different plant functional types (PFTs), photosynthetic pathways (C3 and C4), and temporal scales (hourly, daily and 4-day). Results showed that the nonlinear SIF-GPP relationship existed in all PFTs and the degree of linearity increased at larger temporal scales. The qMLR-SIF model exhibited wide applicability to quantify canopy GPP for different PFTs (R2 = 0.55–0.80, RMSE = 2.72–11.03 μmol CO2 m-2 s-1), photosynthetic pathways (R2 = 0.70–0.78, RMSE = 5.29–9.05 μmol CO2 m-2 s-1) and temporal scales (R2 = 0.82–0.97, RMSE = 3.42–8.32 μmol CO2 m-2s-1). Compared with the two other models, the qMLR-SIF model performed best overall, which is mainly due to its simpler model structure and the mechanistic link between SIF and photosynthesis. Particularly, the qMLR-SIF model could more accurately estimate GPP in C4 species, with higher R2 (0.78) and lower RMSE (8.46 μmol CO2 m-2s-1). These findings highlight the advantages of the qMLR-SIF model in GPP estimation at the canopy scale, showing its potential in applications at regional and global scales.

Screening New Mungbean Varieties for Terminal Drought Tolerance

Rainfed mungbean crops in Queensland Australia frequently experience terminal drought (drought stress in the final stages of reproductive development), highlighting the importance of drought-tolerant varieties for sustainable mungbean production. Given there is limited information on the relative drought tolerance of current mungbean varieties in Australia, the study of genetic variations and mechanisms of drought tolerance in summer mungbean can provide a basis for developing drought-tolerant mungbean varieties. This study evaluated the physiological, biochemical, and phenological traits underpinning yield attributes associated with drought tolerance in selected mungbean varieties. Four new mungbean varieties (AVTMB#1 to 4) and the Australian commercial line (Jade-AU) were grown in tall (75 cm) polyvinyl chloride (PVC) lysimeters where drought stress was imposed at the early flowering stage (R1) and maintained until maturity. Drought stress significantly impacted all the varieties. Averaged across all the varieties, drought stress was associated with a reduction in stomatal conductance (gs) and photosynthetic rate (Asat) by 78% and 86%, respectively, compared to well-watered plants. Internal carbon dioxide concentration (Ci), the effective quantum yield of photosystem II (ΦPSII) and maximum light-use efficiency of light-acclimated photosystem II (PSII) centres (Fv’/Fm’) were also decreased, while excitation pressure (1-qP) increased with drought treatment. A positive correlation (r = 0.60) existed between seed yield and ΦPSII assessed at R1, while a weak correlation with Fv’/Fm’ (r = 0.24) was observed. Excitation pressure (1-qP) at the R1 stage was negatively correlated with seed yield (r = −0.66). Therefore, leaf fluorescence measures, viz., 1-qP and ΦPSII, were recommended for use in screening mungbean varieties for drought tolerance. The varieties, AVTMB#1 and AVTMB#4, respectively achieved 39 and 38% greater seed yields relative to the commercial variety, Jade-AU, under terminal drought conditions.

Cross-species transcriptomics reveals differential regulation of essential photosynthesis genes in Hirschfeldia incana.

Photosynthesis is the only yield-related trait not yet substantially improved by plant breeding. Previously, we have established H. incana as the model plant for high photosynthetic light-use efficiency (LUE). Now we aim to unravel the genetic basis of this trait in H. incana, potentially contributing to the improvement of photosynthetic LUE in other species. Here, we compare its transcriptomic response to high light with that of Arabidopsis thaliana, Brassica rapa, and Brassica nigra, 3 fellow Brassicaceae members with lower photosynthetic LUE. We built a high-light, high-uniformity growing environment, in which the plants developed normally without signs of stress. We compared gene expression in contrasting light conditions across species, utilizing a panproteome to identify orthologous proteins. In-depth analysis of 3 key photosynthetic pathways showed a general trend of lower gene expression under high-light conditions for all 4 species. However, several photosynthesis-related genes in H. incana break this trend. We observed cases of constitutive higher expression (like antenna protein LHCB8), treatment-dependent differential expression (as for PSBE), and cumulative higher expression through simultaneous expression of multiple gene copies (like LHCA6). Thus, H. incana shows differential regulation of essential photosynthesis genes, with the light-harvesting complex as the first point of deviation. The effect of these expression differences on protein abundance and turnover, and ultimately the high photosynthetic LUE phenotype is relevant for further investigation. Furthermore, this transcriptomic resource of plants fully grown under, rather than briefly exposed to, a very high irradiance, will support the development of highly efficient photosynthesis in crops.

A leaf age-dependent light use efficiency model for remote sensing the gross primary productivity seasonality over pantropical evergreen broadleaved forests.

Tropical and subtropical evergreen broadleaved forests (TEFs) contribute more than one-third of terrestrial gross primary productivity (GPP). However, the continental-scale leaf phenology-photosynthesis nexus over TEFs is still poorly understood to date. This knowledge gap hinders most light use efficiency (LUE) models from accurately simulating the GPP seasonality in TEFs. Leaf age is the crucial plant trait to link the dynamics of leaf phenology with GPP seasonality. Thus, here we incorporated the seasonal leaf area index of different leaf age cohorts into a widely used LUE model (i.e., EC-LUE) and proposed a novel leaf age-dependent LUE model (denoted as LA-LUE model). At the site level, the LA-LUE model (average R2 = .59, average root-mean-square error [RMSE] = 1.23 gC m-2 day-1) performs better than the EC-LUE model in simulating the GPP seasonality across the nine TEFs sites (average R2 = .18; average RMSE = 1.87 gC m-2 day-1). At the continental scale, the monthly GPP estimates from the LA-LUE model are consistent with FLUXCOM GPP data (R2 = .80; average RMSE = 1.74 gC m-2 day-1), and satellite-based GPP data retrieved from the global Orbiting Carbon Observatory-2 (OCO-2) based solar-induced chlorophyll fluorescence (SIF) product (GOSIF) (R2 = .64; average RMSE = 1.90 gC m-2 day-1) and the reconstructed TROPOspheric Monitoring Instrument SIF dataset using machine learning algorithms (RTSIF) (R2 = .78; average RMSE = 1.88 gC m-2 day-1). Typically, the estimated monthly GPP not only successfully represents the unimodal GPP seasonality near the Tropics of Cancer and Capricorn, but also captures well the bimodal GPP seasonality near the Equator. Overall, this study for the first time integrates the leaf age information into the satellite-based LUE model and provides a feasible implementation for mapping the continental-scale GPP seasonality over the entire TEFs.

Does the Daily Light Integral Influence the Sowing Density of Tomato Plug Seedlings in a Controlled Environment?

To achieve high-density tomato seedlings in a plant factory with artificial lighting, tomatoes (Solanum lycopersicum Mill. cv. “Zhongza NO.9”) were used as the experimental material. This study expected to analyze the effects of light intensity (150, 200, 250, and 300 μmol·m−2·s−1) and light time (12 and 14 h), as well as daily light integral (DLI, 10.80, 12.60, and 12.96 mol·m−2·d−1) and sowing density (50, 72, and 105 holes per tray), on seedling quality. The results indicated that biomass accumulation, seedling quality, and energy use efficiency of seedlings significantly improved with an increase in DLI. At a DLI of 12.96 mol·m−2·d−1, seedlings sown at a density of 72 holes per tray exhibited comparable growth characteristics and biomass accumulation to those sown at 50 holes per tray. However, under lower DLIs, seedlings at 50 holes per tray displayed superior growth morphology and seedling quality compared to those at 72 holes per tray. This indicates that increasing the DLI can partially mitigate the negative effects of higher sowing density on seedling quality. Light use efficiency (LUE) and energy use efficiency (EUE) were not significantly different between seedlings at 72 and 105 holes per tray but were higher than those at 50 holes per tray. Therefore, optimizing parameters such as DLI and sowing density can effectively enhance the seedling quality, spatial use efficiency, and light use efficiency in industrial seedling production. Based on the results of this study, a DLI of 12.96 mol·m−2·d−1 (achieved with a light intensity of 300 μmol·m−2·s−1 and a light time of 12 h) and sowing density of 72 holes per tray are recommended for cultivating high-quality tomato seedlings while reducing energy consumption.

Thirty years of forest productivity in a mountainous landscape: The Yin and Yang of topography

AbstractWe measured light‐related patterns of primary productivity within a topographically complex Oregon watershed over a 30‐year period. Second‐growth conifer densities were experimentally altered in 1981. Plots receiving at least 3434 MJ m−2 over a 6‐month growing season averaged 40% greater aboveground net primary productivity (ANPP) than those receiving less light (p = 0.000). Unthinned stands potentially built enough LAI to compensate for low light, but risked mortality that exceeded resilience. The two light levels acted as basins of attraction for other physiological and ecological processes, including size–density relationships and limiting foliar nutrients. Initial (1981) LAI and the irradiation step (above or below 3434 MJ m−2) explained 60% of variation in a 30‐year ANPP. Irradiation within each light group did not affect ANPP. At high irradiation, foliar N/Ca and slope steepness (both negative) explained 58% of the variation in residuals from the initial models, while at low irradiation on north, east, and west aspects, 83% of residual variation was explained by foliar Mg (+), understory cover (+), and 30‐year mortality (−). Light use efficiency (LUE) of fully stocked stands correlated with LAI and foliar N/K. Results suggest that understory influence on tree foliar N (+ or −) enhances ANPP by regulating critical nutrient ratios. Mortality reduced or eliminated differences among thinning levels, which did not vary at low light and only between unthinned and heavily thinned at high light. Values associated with relatively open forests (biodiversity, resilience) may be attained without large sacrifice of long‐term carbon sinks. In our study, light interacts with topography to produce nonlinear dynamics in which small changes in irradiation can have large consequences. Reduced sunlight has been suggested as a geoengineering option to combat global warming. Ecological changes out of proportion to lowered irradiation are a distinct possibility, including sharp reductions in terrestrial carbon sinks.

Simple Mechanism for Optimal Light-Use Efficiency of Photosynthesis Inspired by Giant Clams

In photosymbiotic giant clams, vertical columns of single-celled algae absorb sunlight that has first been forward scattered from a superficial layer of light-scattering cells called iridocytes. In principle, this arrangement could lead to a highly efficient system but it has been unclear how to calculate a productivity denominator to normalize the performance of the system. Inspired by the geometry observed in the clam, we have created an analytical model that calculates the idealized performance of a system with a geometry similar to the clam. In our model, photosynthesis-irradiance behavior obeys that of algal cells isolated from clams. Using a standard rate of eight photons of photosynthetically active radiation required to create one molecule of O2, we find that a fixed geometry of the “light-dilution” strategy employed by the clams can reach a quantum efficiency of 43% relative to the solar resource in intense tropical sunlight. In comparing the performance of the model to published photosynthesis-irradiance relations of living clams, we have observed that the living system easily exceeds the performance of the static model. Therefore, we have next considered a model in which the system geometry changes dynamically to optimize the quantum efficiency as a function of the solar irradiance. In this scenario, with changes in irradiance typical of a sunny tropical day, the performance of the model was consistent with that of large mature living clams and had a quantum efficiency of 67%. We also show that a similar dynamic modulation of the clam-tissue geometry could plausibly occur in the living animals. We have considered the possibility that efficiency gains in the living system could also occur via further optimization of per-cell absorbance of multiply scattered light within the highly absorbing system. However, a numerical model of radiative transfer within clam tissue that captures realistic multiple scattering has not located efficiency gains relative to the simpler single-pass analytical model. Therefore, we infer that additional resource efficiency over the dynamic, large-clam-like model would require nontrivial organization among cells at small length scales. We also observe that boreal spruce forests coupled to atmospheric haze may realize the same scale-invariant scattering-and-absorbance strategy as the clams but at a different, larger, length scale. Given these results, our model may demonstrate the maximum realizable light-use efficiency of a large photosynthetic system relative to the solar resource. The general principles here also readily generalize to any photosynthetic cell type or organic photoconversion material and solar-irradiance regime. They could therefore provide inspiration both for engineering novel efficient photoconversion processes and materials and inform optimal land-use estimates for efficient industrial biomass production. Published by the American Physical Society 2024

Validation of Gross Primary Production Estimated by Remote Sensing for the Ecosystems of Doñana National Park through Improvements in Light Use Efficiency Estimation

Doñana National Park is located in the southwest of the Iberian Peninsula, where water scarcity is recurrent, together with a high heterogeneity in species and ecosystems. Monitoring carbon assimilation is essential to improve knowledge of global change in natural vegetation cover. In this work, a light use efficiency (LUE) model was applied to estimate gross primary production (GPP) in two ecosystems of Doñana, xeric shrub (drought resistant) and seasonal marsh (with grasslands dependent on water hydroperiod) and validated with in situ data from eddy covariance (EC) towers installed in both ecosystems. The model was applied in two ways: (1) using the fraction of absorbed photosynthetically active radiation (FAPAR) from Sentinel-2 and meteorological data from reanalysis (ERA5), and (2) using Sentinel-2 FAPAR, reanalysis solar radiation (ERA5) and the Sentinel-2 land surface water index (LSWI). In both cases and for both ecosystems, the error values are acceptable (below 1 gC/m2) and in both ecosystems the model using the LSWI gave better results (R2 of 0.8 in marshes and 0.51 in xeric shrubs). The results also show a greater influence of the water status of the system than of the meteorological variables in this area.

Differentiation in Leaf Functional Traits and Driving Factors of the Allopatric Distribution of Tetraploid and Octaploid Buddleja macrostachya in the Sino-Himalayan Region

Leaf functional traits reflect species’ adaptive strategies and habitat requirements. Examining intra-specific variations and their underlying drivers can aid in comprehending species differentiation and adaptation. Here, we investigated the leaf functional traits of Buddleja macrostachya tetraploids and octaploids across 18 sites in the Sino-Himalayan region. The habitat environmental variables were also recorded. In this study, leaf functional traits showed a considerable differentiation in both tetraploid and octaploid B. macrostachya. Redundancy analysis (RDA) revealed that the octaploid cytotypes displayed higher specific leaf area, leaf total nitrogen and phosphorus concentrations, water-use efficiency, and light-use efficiency in contrast to the tetraploid plants. These functional leaf traits exhibited different plasticity levels in both taxa. A positive link was found between habitat altitude and soil total P concentration and the geographic distribution of the B. macrostachya complex, using RDA and Pearson’s correlation. Our findings suggest that both tetraploid and octaploid B. macrostachya exhibited divergent ecological strategies, conservative and acquisitive strategies, respectively. The ecological adaptability of species within the B. macrostachya complex is enhanced by the combination of divergent ecological strategies and high phenotypic plasticity of distinct key ecological traits. Furthermore, abiotic environmental factors influenced the allopatric geographic distribution pattern of the B. macrostachya complex in the Sino-Himalayan region.

An Improved Gross Primary Production Model Considering Atmospheric CO2 Fertilization: The Qinghai–Tibet Plateau as a Case Study

Involving the effect of atmospheric CO2 fertilization is effective for improving the accuracy of estimating gross primary production (GPP) using light use efficiency (LUE) models. However, the widely used LUE model, the remote sensing-driven Carnegie–Ames–Stanford Approach (CASA) model, scarcely considers the effects of atmospheric CO2 fertilization, which causes GPP estimation uncertainties. Therefore, this study proposed an improved method for estimating GPP by integrating the atmospheric CO2 concentration into the CASA model and generated a long time series GPP dataset with high precision for the Qinghai–Tibet Plateau. The CASA model was improved by considering the impact of atmospheric CO2 on vegetation productivity and discerning variations in CO2 gradients within the canopy and leaves. A 500 m monthly GPP dataset for the Qinghai–Tibet Plateau from 2003 to 2020 was generated. The results showed that the improved GPP estimation model achieved better performances on estimating GPP (R2 = 0.68, RMSE = 406 g C/m2/year) than the original model (R2 = 0.67, RMSE = 499.32 g C/m2/year) and MODIS GPP products (R2 = 0.49, RMSE = 522.56 g C/m2/year). The GPP on the Qinghai–Tibet Plateau increased significantly with the increase in atmospheric CO2 concentration and the gradual accumulation of dry matter. The improved method can also be used for other regions and the generated GPP dataset is valuable for further understanding the ecosystem carbon cycles on the Qinghai–Tibet Plateau.