Responses of oak seedlings to increased herbivory and drought: a possible trade-off?

BACKGROUND AND AIMS: Anthropogenic disturbances are causing a co-occurring increase in biotic (ungulate herbivory) and abiotic (drought) stressors, threatening plant reproduction in oak-dominated ecosystems. However, we wonder whether herbivory could compensate for the adverse impact of drought by reducing evapotranspiration. Thus, we investigate the isolated and joint effects of herbivory and drought on oak seedlings of two contrasting Mediterranean species that differ in leaf habit and drought resistance. METHODS: California oak seedlings from the evergreen, and more drought-resistant, Quercus agrifolia and the deciduous Q. lobata (n=387) were assigned to a fully crossed factorial design with herbivory and drought as stress factors. Seedlings were assigned in a greenhouse to 3-4 clipping levels simulating herbivory and 3-4 watering levels, depending on the species. We measured survival, growth, and leaf attributes (chlorophyll, secondary metabolites, leaf area and weight) once a month (May-Sep) and harvested above- and below-ground biomass at the end of the growing season. KEY RESULTS: For both oak species, simulated herbivory enhanced seedling survival during severe drought or delayed its adverse effects, probably due to reduced transpiration resulting from herbivory-induced leaf area reduction and compensatory root growth. Seedlings from the deciduous, and less drought-resistant species, benefitted from herbivory at lower levels of water stress, suggesting different response across species. We also found complex interactions between herbivory and drought on their impact on leaf attributes. In contrast to chlorophyll content which was not affected by herbivory, anthocyanins increased with herbivory - although water stress reduced differences in anthocyanins due to herbivory. CONCLUSIONS: Herbivory seems to facilitate Mediterranean oak seedlings to withstand summer drought, potentially alleviating a key bottleneck in the oak recruitment process. Our study highlights the need to consider ontogenetic stages and species-specific traits in understanding complex relationships between herbivory and drought stressors for the persistence and restoration of multi-species oak savannas.

Growth, Morphology and Respiratory Cost Responses to Salinity in the Mangrove Plant Rhizophora Stylosa Depend on Growth Temperature.

Mangrove plants, which have evolved to inhabit tidal flats, may adjust their physiological and morphological traits to optimize their growth in saline habitats. Furthermore, the confined distribution of mangroves within warm regions suggests that warm temperature is advantageous to their growth in saline environments. We analyzed growth, morphology and respiratory responses to moderate salinity and temperature in a mangrove species, Rhizophora stylosa. The growth of R. stylosa was accelerated in moderate salinity compared with its growth in fresh water. Under warm conditions, the increased growth is accompanied by increased specific leaf area (SLA) and specific root length. Low temperature resulted in a low relative growth rate due to a low leaf area ratio and small SLA, regardless of salinity. Salinity lowered the ratio of the amounts of alternative oxidase to cytochrome c oxidase in the mitochondrial respiratory chain in leaves. Salinity enhanced the leaf respiration rate for maintenance, but under warm conditions this enhancement was compensated by a low leaf respiration rate for growth. In contrast, salinity enhanced overall leaf respiration rates at low temperature. Our results indicate that under moderate saline conditions R. stylosa leaves require warm temperatures to grow with a high rate of resource acquisition without enhancing respiratory cost.

Estimation of Leaf Area Index across Biomes and Growth Stages Combining Multiple Vegetation Indices.

The leaf area index (LAI) is a key indicator of vegetation canopy structure and growth status, crucial for global ecological environment research. The Moderate Resolution Spectral Imager-II (MERSI-II) aboard Fengyun-3D (FY-3D) covers the globe twice daily, providing a reliable data source for large-scale and high-frequency LAI estimation. VI-based LAI estimation is effective, but species and growth status impacts on the sensitivity of the VI-LAI relationship are rarely considered, especially for MERSI-II. This study analyzed the VI-LAI relationship for eight biomes in China with contrasting leaf structures and canopy architectures. The LAI was estimated by adaptively combining multiple VIs and validated using MODIS, GLASS, and ground measurements. Results show that (1) species and growth stages significantly affect VI-LAI sensitivity. For example, the EVI is optimal for broadleaf crops in winter, while the RDVI is best for evergreen needleleaf forests in summer. (2) Combining vegetation indices can significantly optimize sensitivity. The accuracy of multi-VI-based LAI retrieval is notably higher than using a single VI for the entire year. (3) MERSI-II shows good spatial-temporal consistency with MODIS and GLASS and is more sensitive to vegetation growth fluctuation. Direct validation with ground-truth data also demonstrates that the uncertainty of retrievals is acceptable (R2 = 0.808, RMSE = 0.642).

Optimizing native vegetation establishment in urban soils: Assessing the impacts of organic amendments on specific growth parameters.

Following soil disturbances, establishing healthy roadside vegetation can reduce surface water runoff, improve soil quality, decrease erosion, and enhance landscape aesthetics. This study explores the use of organic soil amendments (OAs) as alternatives to conventional vegetation growth approaches, aiming to provide optimal compost mixing ratios for poor soils, and clarify guidelines for OAs' use in roadside projects. Three sandy loam soils and one loam soil were chosen for the study. Organic amendments included yard waste (Y), food waste (F), turkey litter and green waste-based (T) composts, and wood-derived biochar (B). Treatment applications targeted specific increases in the organic matter (OM) percentage of the soils. A selection of seven native species (grasses and forbs) in a total of 156 pots (4 control soils + 4 soils x 4 OAs x 3 application rates, all prepared in triplicates) was used for the pot study experiment. A significant correlation between electrical conductivity (soluble salts) in soil-OA blends and corresponding percent green coverage (%GC) was found. High salts from the T compost either delayed or curtailed growth. Notably, 3 out of the 4 soils amended with biochar exhibited rapid vegetation coverage during initial growth stages compared to other soil-OA blends but reduced the nitrogen (N) uptake and leaf area in black-eyed Susan (BES) plants. In contrast, N uptake was higher in the BES plants emerging from composts T, F, and Y compared to biochar. It is recommended to minimize concentrated manure-based (e.g., turkey litter) composts for roadside projects as an OM source, and alternatively, enriching wood-based biochar with nutrients when used as a soil amendment. Within the current study, composts such as F and Y were well-suited to establish healthy and long-lasting vegetation.

A moving target: trade-offs between maximizing carbon and minimizing hydraulic stress for plants in a changing climate.

Observational evidence indicates that tree leaf area may acclimate in response to changes in water availability to alleviate hydraulic stress. However, the underlying mechanisms driving leaf area changes and consequences of different leaf area allocation strategies remain unknown. Here, we use a trait-based hydraulically enabled tree model with two endmember leaf area allocation strategies, aimed at either maximizing carbon gain or moderating hydraulic stress. We examined the impacts of these strategies on future plant stress and productivity. Allocating leaf area to maximize carbon gain increased productivity with high CO2, but systematically increased hydraulic stress. Following an allocation strategy to avoid increased future hydraulic stress missed out on 26% of the potential future net primary productivity in some geographies. Both endmember leaf area allocation strategies resulted in leaf area decreases under future climate scenarios, contrary to Earth system model (ESM) predictions. Leaf area acclimation to avoid increased hydraulic stress (and potentially the risk of accelerated mortality) was possible, but led to reduced carbon gain. Accounting for plant hydraulic effects on canopy acclimation in ESMs could limit or reverse current projections of future increases in leaf area, with consequences for the carbon and water cycles, and surface energy budgets.

Spatio-temporal mapping of leaf area index in rice: spectral indices and multi-scale texture comparison derived from different sensors.

Introduction: Monitoring the leaf area index (LAI), which is directly related to the growth status of rice, helps to optimize and meet the crop's fertilizer requirements for achieving high quality, high yield, and environmental sustainability. The remote sensing technology of the unmanned aerial vehicle (UAV) has great potential in precision monitoring applications in agriculture due to its efficient, nondestructive, and rapid characteristics. The spectral information currently widely used is susceptible to the influence of factors such as soil background and canopy structure, leading to low accuracy in estimating the LAI in rice. Methods: In this paper, the RGB and multispectral images of the critical period were acquired through rice field experiments. Based on the remote sensing images above, the spectral indices and texture information of the rice canopy were extracted. Furthermore, the texture information of various images at multiple scales was acquired through resampling, which was utilized to assess the estimation capacity of LAI. Results and discussion: The results showed that the spectral indices (SI) based on RGB and multispectral imagery saturated in the middle and late stages of rice, leading to low accuracy in estimating LAI. Moreover, multiscale texture analysis revealed that the texture of multispectral images derived from the 680 nm band is less affected by resolution, whereas the texture of RGB images is resolution dependent. The fusion of spectral and texture features using random forest and multiple stepwise regression algorithms revealed that the highest accuracy in estimating LAI can be achieved based on SI and texture features (0.48 m) from multispectral imagery. This approach yielded excellent prediction results for both high and low LAI values. With the gradual improvement of satellite image resolution, the results of this study are expected to enable accurate monitoring of rice LAI on a large scale.

Leaf area estimation based on ANFIS using embedded system and PV panel.

Leaf area is one of the important parameters for plant canopy development. It is used as an indicator closely related to plant growth in several studies on plant production. However, most leaf area meters used today are costly and rely on human observations. This situation may be limiting for researchers in terms of having proper leaf area measuring devices. The reliance on human-focused measurements leads to human errors. Digital scanners and cameras, digital image processing-based estimation methods, paper weighing, grid counting, regression equations, width and height correlation models, planimeters, laser optics, and handheld scanners can be used to determine leaf area. However, some of these methods are expensive and unnecessary for simple studies. Therefore, this study aims to design and implement an embedded system with a simpler, cheaper alternative to the currently used methods and devices, minimizing human errors. The proposed embedded system serves as a tool for measuring leaf area using a photovoltaic panel (PV) and an Adaptive Neuro-Fuzzy Inference System (ANFIS). In the study, geometric shapes with known areas are used as the learning data, and real plant leaves with known areas are used in the testing process. As a result, the prediction made by ANFIS is observed to have an accuracy of R 2  = 0.99.

Improving hydrological modeling to close the gap between elevated CO2 concentration and crop response: Implications for water resources.

Rising atmospheric carbon dioxide concentrations ([CO2]) affect crop growth and the associated hydrological cycle through physiological forcing, which is mainly regulated by reducing stomatal conductance (gs) and increasing leaf area index (LAI). However, reduced gs and increased LAI can affect crop water consumption, and the overall effects need to be quantified under elevated [CO2]. Here we develop a SWAT-gs-LAI model by incorporating a nonlinear gs-CO2 equation and a missing LAI-CO2 relationship to investigate the responses of water consumption of grain maize, maize yield, and losses of water and soil to elevated [CO2] in the Upper Mississippi River Basin (UMRB; 492,000 km2). Results exhibited enhanced maize yield with decreased water consumption for increases in [CO2] from 495 ppm to 825 ppm during the historical period (1985-2014). Elevated [CO2] promoted surface runoff but suppressed sediment loss as the predominant impact of LAI-CO2 leading to enhanced surface cover. A comprehensive analysis of future climate change showed increased maize water consumption in comparison to the historical period, driven by the more pronounced effects of overall climate change rather than solely elevated [CO2]. Generally, future climate change promoted maize yield in most regions of the UMRB for three Shared Socioeconomic Pathway (SSP) scenarios. Surface runoff was shown to increase generally in the future with sediment loss increasing by an average of 0.39, 0.42, and 0.66 ton ha-1 for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. This was due to negative climatic change effects largely surpassing the positive effect of elevated [CO2], particularly in zones near the middle and lower stream. Our results underscore the crucial role of employing a physically-based model to represent crop physiological processes under elevated [CO2] conditions, improving the reliability of predictions related to crop growth and the hydrological cycle.

Plant traits mediate foliar uptake of deposited nitrogen by mature woody plants.

Increased atmospheric nitrogen (N) deposition significantly disturbs ecosystem N cycle. Although foliar interception and uptake of N deposition can provide an important alternative N supply to forest ecosystems, the mechanisms regulating foliar N uptake from wet deposition are not fully understood. Here, we selected 19 woody species with a wide range of plant traits from different functional groups and conducted a 15N isotope labelling experiment through brushing 15NH4 + and 15NO3 - solution on canopy leaves. Our findings demonstrate that leaves can directly absorb N from wet deposition within a few hours. The average leaf 15N recoveries were 10% and 28% under 15NH4 + and 15NO3 - treatments across species, respectively, while twig N recoveries were only 1%-7% of leaf N recoveries. Differences in foliar N uptake efficiency among species were closely associated with leaf traits but were little influenced by meteorological conditions or soil nutrient status. Specifically, plants with higher leaf N concentration, larger specific leaf area and lower wax concentration exhibited higher leaf N recovery. Our results indicated that tree canopies could directly absorb N from atmospheric deposition. We highlight the critical role of leaf traits in determining canopy foliar N uptake, which may consequently influence plant competition under elevated N deposition.

Physiological traits, crop growth, and grain quality of quinoa in response to deficit irrigation and planting methods.

Climate change has become a concern, emphasizing the need for the development of crops tolerant to drought. Therefore, this study is designed to explore the physiological characteristics of quinoa that enable it to thrive under drought and other extreme stress conditions by investigating the combined effects of irrigation water levels (100%, 75%, and 50% of quinoa's water requirements, WR as I1, I2 and I3) and different planting methods (basin, on-ridge, and in-furrow as P1, P2 and P3) on quinoa's physiological traits and gas exchange. Results showed that quinoa's yield is lowest with on-ridge planting and highest in the in-furrow planting method. Notably, the seed protein concentrations in I2 and I3 did not significantly differ but they were 25% higher than those obtained in I1, which highlighted the possibility of using a more effective irrigation method without compromising the seed quality. On the other hand, protein yield (PY) was lowest in P2 (mean of I1 and I2 as 257 kg ha-1) and highest in P3 (mean of I1 and I2 as 394 kg ha-1, 53% higher). Interestingly, PY values were not significantly different in I1 and I2, but they were lower significantly in I3 by 28%, 27% and 20% in P1, P2, and P3, respectively. Essential plant characteristics including plant height, stem diameter, and panicle number were 6.1-16.7%, 6.4-24.5%, and 18.4-36.5% lower, respectively, in I2 and I3 than those in I1. The highest Leaf Area Index (LAI) value (5.34) was recorded in the in-furrow planting and I1, while the lowest value was observed in the on-ridge planting method and I3 (3.47). In I3, leaf temperature increased by an average of 2.5-3 oC, particularly during the anthesis stage. The results also showed that at a similar leaf water potential (LWP) higher yield and dry matter were obtained in the in-furrow planting compared to those obtained in the basin and on-ridge planting methods. The highest stomatal conductance (gs) value was observed within the in-furrow planting method and full irrigation (I1P3), while the lowest values were obtained in the on-ridge and 50%WR (I3P2). Finally, photosynthesis rate (An) reduction with diminishing LWP was mild, providing insights into quinoa's adaptability to drought. In conclusion, considering the thorough evaluation of all the measured parameters, the study suggests using the in-furrow planting method with a 75%WR as the best approach for growing quinoa in arid and semi-arid regions to enhance production and resource efficiency.

Estimation of winter wheat LAI based on color indices and texture features of RGB images taken by UAV.

BACKGROUND: Leaf area index (LAI) is an important indicator for assessing plant growth and development, and is also closely related to photosynthesis in plants. The realization of rapid accurate estimation of crop LAI plays an important role in guiding farmland production. In study, the UAV-RGB technology was used to estimate LAI based on 65 winter wheat varieties at different fertility periods, the wheat varieties including farm varieties, main cultivars, new lines, core germplasm and foreign varieties. Color indices (CIs) and texture features were extracted from RGB images to determine their quantitative link to LAI. RESULTS: The results revealed that among the extracted image features, LAI exhibited a significant positive correlation with CIs (r = 0.801), whereas there was a significant negative correlation with texture features (r = -0.783). Furthermore, the visible atmospheric resistance index, the green-red vegetation index, the modified green-red vegetation index in the CIs, and the mean in the texture features demonstrated a strong correlation with the LAI with r > 0.8. With reference to the model input variables, the backpropagation neural network (BPNN) model of LAI based on the CIs and texture features (R2 = 0.730, RMSE = 0.691, RPD = 1.927) outperformed other models constructed by individual variables. CONCLUSION: This study offers a theoretical basis and technical reference for precise monitor on winter wheat LAI based on consumer-level UAVs. The BPNN model, incorporating CIs and texture features, proved to be superior in estimating LAI, and offered a reliable method for monitoring the growth of winter wheat. © 2024 Society of Chemical Industry.

Phosphorus High-rate application through band placement improved cotton productivity under arid climate.

The plant-available soil phosphorus rate and methods for applying phosphatic fertilizer and soil P-fixation capacity are critical factors for lower cotton productivity in Southern Punjab, Pakistan. Hence, a two-year study was conducted in Central Cotton Research Institute (CCRI), Multan, Pakistan, to examine the effects of various P rates and application methods on cotton crop output during the growing seasons of 2014 and 2015. Phosphorus was applied in four rates (0, 40, 80, and 120 kg ha-1 P2O5) using broadcast, band application, and fertigation methods. Results indicated that the impact of P rates was statistically significant on plant height, the number of nodes, monopodial and sympodial branches, leaf area index, harvest index, and seed cotton yield. The greater P application (120 kg P2O5 ha-1) had a better effect on cotton productivity than the lower application rates (0, 40, and 80 kg P2O5 ha-1). The band application responded better on nodes plant-1, sympodial branches plant-1, boll weight, leaf area index, lint yield, and harvest during the growing season 2015. Therefore, by adopting the band application coupled with 120 kg P2O5 ha-1 rather than the conventional method of broadcast, productivity of cotton crops could be increased.

A New Spectral Index for Monitoring Leaf Area Index of Winter Oilseed Rape (Brassica napus L.) under Different Coverage Methods and Nitrogen Treatments.

The leaf area index (LAI) is a crucial physiological indicator of crop growth. This paper introduces a new spectral index to overcome angle effects in estimating the LAI of crops. This study quantitatively analyzes the relationship between LAI and multi-angle hyperspectral reflectance from the canopy of winter oilseed rape (Brassica napus L.) at various growth stages, nitrogen application levels and coverage methods. The angular stability of 16 traditional vegetation indices (VIs) for monitoring the LAI was tested under nine view zenith angles (VZAs). These multi-angle VIs were input into machine learning models including support vector machine (SVM), eXtreme gradient boosting (XGBoost), and Random Forest (RF) to determine the optimal monitoring strategy. The results indicated that the back-scattering direction outperformed the vertical and forward-scattering direction in terms of monitoring the LAI. In the solar principal plane (SPP), EVI-1 and REP showed angle stability and high accuracy in monitoring the LAI. Nevertheless, this relationship was influenced by experimental conditions and growth stages. Compared with traditional VIs, the observation perspective insensitivity vegetation index (OPIVI) had the highest correlation with the LAI (r = 0.77-0.85). The linear regression model based on single-angle OPIVI was most accurate at -15° (R2 = 0.71). The LAI monitoring achieved using a multi-angle OPIVI-RF model had the higher accuracy, with an R2 of 0.77 and with a root mean square error (RMSE) of 0.38 cm2·cm-2. This study provides valuable insights for selecting VIs that overcome the angle effect in future drone and satellite applications.

G × E × M analysis to define allometric rules between leaves and stems in wheat.

Allometric rules provide insights into the structure-function relationships across species and scales and are commonly used in ecology. The fields of agronomy, plant phenotyping and modeling also need simplifications such as allometric rules to reconcile data at different temporal and spatial levels (organs/canopy). This paper explores the variations in relationships for wheat regarding (i) the distribution of crop green area between leaves and stems, and (ii) the allocation of above-ground biomass between leaves and stems during the vegetative period, using a large dataset covering different years, countries, genotypes and management practices. Our results show that the relationship between leaf and stem area was linear, genotype-specific, and sensitive to radiation. The relationship between leaf and stem biomass depended on genotype and nitrogen fertilization. The mass per area, associating area and biomass for both leaf and stem, varied strongly by developmental stage and was significantly affected by environment and genotype. These allometric rules were evaluated with satisfactory performance, and their potential use is discussed with regard to current phenotyping techniques and plant/crop models. Our results enable the definition of models and minimum datasets required for characterizing diversity panels and making predictions in various G × E × M contexts.

Investigation of canopy interception characteristics in slope protection grasses: A laboratory experiment.

Canopy interception significantly affects hydrological processes such as infiltration, runoff and evapotranspiration. Research on grass canopy interception remains limited, and the experimental methods employed differ substantially. To thoroughly investigate the canopy interception characteristics of grass and clarify the methodological differences, five commonly utilized slope protection grass species in temperate regions were cultivated in a laboratory setting, and their canopy interception characteristics were experimentally investigated using the water-balance method (WBM), the water-wiping method (WWM) and the water-immersion method (WIM), respectively. The results showed that the WBM is more accurate for measuring canopy interception in grass, whereas both the WWM and the WIM underestimate grass canopy interception capacity. The canopy interception capacity measured by the WBM was 1.61-2.09 times higher than that of the WWM and 1.93-3.47 times higher than that of the WIM. Grey correlation analysis of the eight evaluated factors indicated that leaf area is the most influential factor affecting canopy interception in grass, followed by rainfall amount, dry mass, rainfall intensity, canopy projection area, leaf contact angle, fresh weight, and average height. There is a negative power function relationship between the interception ratio and the rainfall amount. With increasing rainfall intensity, the canopy interception capacity initially increases and then decreases, peaking at rainfall intensities of 15 to 20 mm/h. Leaf contact angle is a key quantifiable parameter that explains the differences in canopy interception among different grass species, and the canopy interception per unit leaf area decreases as the leaf contact angle increases. This study demonstrates that the WBM provides the most accurate measurements of grass canopy interception compared to the WWM and WIM, and highlights the leaf contact angle as a key factor in explaining interspecies differences. These findings could enhance the understanding of grass canopy interception and guide the selection of experimental methods.

Morphological and Pigment Responses to Far-Red and Photosynthetically Active Radiation in an Olive Cultivar Suitable for Super-High-Density Orchards.

Plant density is increasing in modern olive orchards to improve yields and facilitate mechanical harvesting. However, greater density can reduce light quantity and modify its quality. The objective was to evaluate plant morphology, biomass, and photosynthetic pigments under different red/far-red ratios and photosynthetically active radiation (PAR) combinations in an olive cultivar common to super-high-density orchards. In a greenhouse, young olive trees (cv. Arbequina) were exposed to low (L) or high (H) PAR with or without lateral FR supplementation (L+FR, L-FR, H+FR, H-FR) using neutral-density shade cloth and FR light-emitting diode (LED) modules. Total plant and individual organ biomass were much lower in plants under low PAR than under high PAR, with no response to +FR supplementation. In contrast, several plant morphological traits, such as main stem elongation, individual leaf area, and leaf angle, did respond to both low PAR and +FR. Total chlorophyll content decreased with +FR when PAR was low, but not when PAR was high (i.e., a significant FR*PAR interaction). When evaluating numerous plant traits together, a greater response to +FR under low PAR than under high PAR appeared to occur. These findings suggest that consideration of light quality in addition to quantity facilitates a fuller understanding of olive tree responses to a light environment. The +FR responses found here could lead to changes in hedgerow architecture and light distribution within the hedgerow.

Comparison of five equations in describing the variation of leaf area distributions of Alangium chinense (Lour.) Harms.

Previous studies have validated a performance equation (PE) and its generalized version (GPE) in describing the rotated and right-shifted Lorenz curves of organ size (e.g., leaf area and fruit volume) distributions of herbaceous plants. Nevertheless, there are still two questions that have not been adequately addressed by prior work: (i) whether the PE and GPE apply to woody plant species and (ii) how do the PE and GPE perform in comparison with other Lorenz equations when fitting data. To address these deficiencies, we measured the lamina length and width of each leaf on 60 Alangium chinense saplings to compare the performance of the PE and GPE with three other Lorenz equations in quantifying the inequality of leaf area distributions across individual trees. Leaf area is shown to be the product of a proportionality coefficient (k) and leaf length and width. To determine the numerical value of k, we scanned 540 leaves to obtain the leaf area empirically. Using the estimated k, the leaf areas of 60 A. chinense saplings were calculated. Using these data, the two performance equations and three other Lorenz equations were then compared and assessed using the root-mean-square error (RMSE) and Akaike information criterion (AIC). The PE and GPE were found to be valid in describing the rotated and right-shifted Lorenz curves of the A. chinense leaf area distributions, and GPE has the lowest RMSE and AIC values. This work validates the GPE as the best model in gauging variations in leaf area of the woody species.

Characteristics, Relationships, and Anatomical Basis of Leaf Hydraulic Traits and Economic Traits in Temperate Desert Shrub Species.

Shrubs are a key component of desert ecosystems, playing a crucial role in controlling desertification and promoting revegetation, yet their growth is often impeded by drought. Leaf hydraulic traits and economic traits are both involved in the process of water exchange for carbon dioxide. Exploring the characteristics, relationships, and anatomical basis of these two suites of traits is crucial to understanding the mechanism of desert shrubs adapting to the desert arid environment. However, the relationship between these two sets of traits currently remains ambiguous. This study explored the leaf hydraulic, economic, and anatomical traits of 19 desert shrub species. The key findings include the following: Relatively larger LT values and smaller SLA values were observed in desert shrubs, aligning with the "slow strategy" in the leaf economics spectrum. The relatively high P50leaf, low HSMleaf, negative TLPleaf, and positive HSMtlp values indicated that severe embolism occurs in the leaves during the dry season, while most species were able to maintain normal leaf expansion. This implies a "tolerance" leaf hydraulic strategy in response to arid stress. No significant relationship was observed between P50leaf and Kmax, indicating the absence of a trade-off between hydraulic efficiency and embolism resistance. Certain coupling relationships were observed between leaf hydraulic traits and economic traits, both of which were closely tied to anatomical structures. Out of all of the leaf traits, LT was the central trait of the leaf traits network. The positive correlation between C content and WPleaf and HSMleaf, as well as the positive correlation between N content and HSMtlp, suggested that the cost of leaf construction was synergistic with hydraulic safety. The negative correlation between SLA, P content, GCL, and SAI suggested a functional synergistic relationship between water use efficiency and gas exchange rate. In summary, this research revealed that the coupling relationship between leaf hydraulic traits and economic traits was one of the important physiological and ecological mechanisms of desert shrubs for adapting to desert habitats.

Comparison between computer recognition and manual measurement methods for the estimation of leaf area.

BACKGROUND AND AIMS: Leaf area (A) is a crucial indicator of the photosynthetic capacity of plants. The Montgomery equation (ME), which hypothesizes that A is proportional to the product of leaf length (L) and width (W), is a valid tool for non-destructively measuring A for many broadleaved plants. At present, the methods used to compute L and W for the ME can be broadly divided into two kinds: using computer recognition and measuring manually. However, the potential difference in the prediction accuracy using either method has not been thoroughly examined in previous studies. METHODS: In the present study, we measured 540 Alangium chinense leaves, 489 Liquidambar formosana leaves and 215 Liriodendron × sinoamericanum leaves, utilizing computer recognition and manual measurement methods to determine L and W. The ME was used to fit the data determined by the two methods, and the goodness of fits were compared. The prediction errors of A were analysed by examining the correlations with two leaf symmetry indices (areal ratio of the left side to the right side, and standardized index for bilateral asymmetry), as well as the leaf shape complexity index (the leaf dissection index). KEY RESULTS: The results indicate that there is a neglectable difference in the estimation of A between the two methods. This further validates that the ME is an effective method for estimating A in broadleaved tree species, including those with lobes. Additionally, leaf shape complexity significantly influenced the estimation of A. CONCLUSIONS: These results show that the use of computer recognition and manual measurement in the field are both effective and feasible, although the influence of leaf shape complexity should be considered when applying the ME to estimate A in the future.

Divergent structural leaf trait spectra in succulent versus non-succulent plant taxa.

BACKGROUND AND SCOPE: Plant functional traits are the result of natural selection to optimize carbon gain, leading to a broad spectrum of traits across environmental gradients. Among plant traits, leaf water storage capacity is paramount for plant drought resistance. We explored whether leaf-succulent taxa follow trait correlations similar to those of non-leaf-succulent taxa to evaluate whether both are similarly constrained by relationships between leaf water storage and climate. METHODS: We tested the relationships among three leaf traits related to water storage capacity and resource use strategies in 132 species comprising three primary leaf types: succulent, sclerophyllous, and leaves with rapid returns on water investment, referred to as fast return. Correlation coefficients among specific leaf area (SLA), water mass per unit of area (WMA), and saturated water content (SWC) were tested, along with relationships between leaf trait spectra and aridity determined from species occurrence records. RESULTS: Both SWC and WMA at a given SLA were ~10-fold higher in succulent leaves than in non-succulent leaves. While SWC actually increased with SLA in non-succulent leaves, no relationship was detected between SWC and SLA in succulent leaves, although WMA decreased with SLA in all leaf types. A principal component analysis (PCA) revealed that succulent taxa occupied a widely different mean trait space than either fast-return (P < 0.0001) or sclerophyllous (P < 0.0001) taxa along the first PCA axis, which explained 63 % of mean trait expression among species. However, aridity only explained 12 % of the variation in PCA1 values. This study is among the first to establish a structural leaf trait spectrum in succulent leaf taxa and quantify contrasts in leaf water storage among leaf types relative to specific leaf area. CONCLUSIONS: Trait coordination in succulent leaf taxa may not follow patterns similar to those of widely studied non-succulent taxa.