Coordination of Leaf Structural and Chemical Traits in Predicting Photosynthetic Capacity of Woody Plants in Subtropical Evergreen Broadleaf Forests

Coordination of Leaf Structural and Chemical Traits in Predicting Photosynthetic Capacity of Woody Plants in Subtropical Evergreen Broadleaf Forests | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Coordination of Leaf Structural and Chemical Traits in Predicting Photosynthetic Capacity of Woody Plants in Subtropical Evergreen Broadleaf Forests Manyi Li, Lei Ma, Lu Yao, Mingze Xu, Cheng Li, Yunqi Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7413523/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 5 You are reading this latest preprint version Abstract Functional plant traits are essential indicators of biodiversity conservation and ecosystem management. Understanding the relationships among leaf traits allows for the estimation of difficult-to-measure traits from those that are easier to quantify and widely distributed. This approach enhances the ability to analyze trait variation, identify scaling relationships, and address constraints in estimating plant-atmosphere carbon exchange. However, research on the correlation between photosynthetic capacity and leaf traits remains limited. Therefore, this study aims to explore the differences and correlations between leaf traits and photosynthetic capacity parameters across different growth forms. In this study, the leaf traits in 21 dominant woody species from a subtropical evergreen broad-leaved forest in southwestern China were assessed. Key photosynthetic parameters, including the maximum net photosynthetic rate, apparent quantum yield, light compensation point, and light saturation point, were investigated. Additionally, leaf traits such as leaf tissue density, leaf dry matter content, specific leaf area (SLA), leaf area, chlorophyll content, carbon content per unit leaf area, nitrogen content per unit leaf area (Narea), and carbon-to-nitrogen ratio were analyzed. Overall, no significant variations in maximum photosynthetic rate or photosynthetic quantum efficiency were observed between tree and shrub species in this forest. Shrub species exhibited greater adaptability and compensatory capacity to light conditions during photosynthesis. Using multiple linear regression models, SLA was identified as the key structural trait and Narea as the primary chemical trait for predicting the photosynthetic capacity of woody plants in this region. Nevertheless, for different growth forms, selecting optimal parameters for classification modeling in abiotic predictive models of photosynthetic capacity is recommended to improve prediction accuracy for subtropical evergreen broad-leaved forest plants. Leaf traits Photosynthetic capacity Predictive factors Optimal model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Amid intensifying global climate change and ongoing biodiversity loss, studying plant leaf functional traits and their environmental adaptability has become increasingly important (Ciais et al., 2014 ). Functional plant traits are widely utilized to evaluate biodiversity conservation efforts and ecosystem management effectiveness (Zhengbing et al., 2021 ). Accurately identifying and quantifying the relationships among these traits is crucial for understanding their roles in plant physiological and ecological processes. As the primary site of photosynthesis, leaf traits are closely correlated with various physiological functions in plants (Anderegg et al., 2018 ) and exhibit high plasticity (He et al., 2019 ; Midolo et al., 2019 ). Leaf functional traits and ecological stoichiometric characteristics provide insights into plant adaptation strategies, self-regulation mechanisms, and resource allocation patterns (Carvajal et al., 2017 ). For instance, key traits such as net photosynthetic rate, apparent quantum yield, specific leaf area (SLA), leaf dry matter content (LDMC), and nitrogen content per unit leaf area (Narea), along with the leaf economics spectrum (LES), highlight trade-offs between growth potential, resource acquisition, and defense in plants (Pan et al., 2020 ; Chen et al., 2021 ). Studies indicate that plants with high SLA and Narea generally exhibit higher photosynthetic rates and faster photosynthetic gains, while those with low SLA and Narea display the opposite trend (Reich, 2014 ; Reich et al., 2018). While net primary productivity in ecosystems is influenced by multiple factors (Zhang et al., 2019 ), LDMC has been identified as a more reliable predictor (Leigh et al., 2012 ; Michaletz et al., 2015 ). Analyzing the relationships among leaf traits allows for the inference of difficult-to-measure traits from those that are easier to quantify (Berzaghi et al., 2020 ). However, whether a universal model can quantitatively predict these relationships remains uncertain. Therefore, further research on the quantitative relationship among leaf traits is essential for understanding trait variation, scaling laws, and constraints on plant-atmosphere carbon exchange (Westoby et al., 2002 ). The interdependence of leaf traits underscores the complexity of photosynthesis as a physiological process influenced by multiple factors (Chelli et al., 2021 ), with its characteristic parameters and environmental factors jointly determining plant photosynthetic efficiency (Westoby et al., 2002 ). Light response curves, a crucial tool in physiological ecology research (Moreno-Sotomayor et al., 2002 ; Palliotti et al., 2015), illustrate the relationship between photosynthetic rate and light intensity, and they are widely utilized to evaluate plant adaptability to environmental changes. These parameters not only indicate plant resilience and stress responses but also display a pivotal role in determining plant growth and productivity (Wright et al., 2017 ), making them significant in photosynthetic trait studies (Buckley et al., 2015). Numerous leaf traits are strongly associated with photosynthesis, with variations in structural and biochemical characteristics reflecting the diversity of plant carbon assimilation capabilities. This provides a theoretical basis for predicting photosynthetic performance under varying environmental conditions. For example, some plants enhance photosynthetic rates by expanding leaf area to optimize light capture efficiency (Martinez-Garcia et al., 2023), while chlorophyll content also rises with increasing leaf area. Conversely, reducing leaf area or increasing LDMC can mitigate water loss due to transpiration (Onoda et al., 2009 ; Nalaka et al., 2018 ). Additionally, photosynthesis depends on respiratory carbon costs to sustain protein synthesis, while leaf construction and metabolic processes rely on nitrogen, with more than half of leaf nitrogen stored as proteins directly involved in the Calvin cycle (Bachofen et al., 2022 ). Narea serves as a key indicator of metabolic activity and structural costs (Dostál et al., 2020 ). Morphological traits, including leaf area (LA) and LDMC, provide insights into plant resource use efficiency and photosynthetic potential (Han et al., 2011 ), with LA variations closely correlated with light capture and carbon assimilation capacity (Milla et al., 2019). SLA, defined as the ratio of LA to leaf dry mass, directly reflects plant light capture efficiency and carbon assimilation strategies (Yang et al., 2012 ). Studies show that changes in LA and LDMC are strongly associated with plant growth forms (Luo et al., 2019 ), while large-scale research highlights plant life forms and functional types as primary factors influencing leaf trait variation (Zhan et al., 2017). However, current international research on leaf photosynthesis predominantly focuses on light reactions and carbon metabolism, with limited quantitative studies on the influence of structural factors. Most studies examine correlations between environmental conditions and leaf traits (Münzbergová et al., 2017; Kosová et al., 2022 ) or focus on individual species or traits (Thakur et al., 2023 ; Yan et al., 2023 ). Despite their crucial role in plant adaptation to environmental changes, photosynthetic characteristics remain understudied (Pierce et al., 2017 ; Heydari et al., 2023 ). Plants of different growth types adapt to their environments through distinct physiological and ecological traits, thereby enhancing resource use efficiency (Jackson et al., 2013 ; Cui et al., 2020 ). A detailed examination of leaf morphological, chemical, and physiological traits provides valuable insights into the mechanisms underlying plant adaptation to environmental changes (Albert et al., 2010 ; Cheng et al., 2021 ). The subtropical evergreen broad-leaved forests of southwestern China, characterized by diverse vegetation and favorable hydrothermal conditions, serve as the study area. This study aims to explore the variations and correlations between photosynthetic and leaf traits across different growth types and identifying key factors influencing photosynthetic capacity using 11 tree and 10 shrub species from the indicated region. The study addresses the following questions: (1) What are the differences and correlations between leaf traits and photosynthetic capacity parameters across different growth forms? (2) How do leaf structural and chemical traits influence and predict the photosynthetic capacity of woody plants, and which are the most effective predictors? We propose the following hypotheses: (1) While leaf traits and photosynthetic capacity parameters exhibit significant interspecific variations among woody plants, differences between growth forms are not substantial; (2) A two-factor linear regression model incorporating structural traits (e.g., SLA) and chemical traits (e.g., Narea) provides a more accurate prediction of photosynthetic capacity than single-factor models. SLA and Narea serve as key predictors, effectively capturing variations in photosynthetic capacity within subtropical evergreen broad-leaved forests in China. Based on these predictors, this study could offer theoretical support for enhancing carbon sequestration, maintaining ecosystem health, conserving biodiversity, and promoting sustainable management in subtropical broad-leaved forest regions. Materials and methods Study sites The study area is located in Yubei District, Chongqing, China (106°27′30′′–106°57′58′′ E, 29°34′45′′–30°07′22′′ N), with mountain elevations generally ranging between 600 and 1,000 m. The region experiences a mild climate with abundant rainfall, where light, heat, and water availability are well-synchronized, creating favorable conditions for vegetation growth. The average annual temperature is 18.6°C, and the average annual precipitation is approximately 1,100 mm, with higher rainfall in summer and lower in winter. Vegetation coverage exceeds 63%, and the forested area spans approximately 279.29 square kilometers. The region is characterized by a well-preserved ecological environment and abundant natural resources, falling within the subtropical, humid evergreen broad-leaved forest zone. The dominant tree species include Ficus concinna , Cinnamomum camphora , Michelia alba , Ficus virens , and Koelreuteria bipinnata , while dominant shrub species comprise Pyracantha fortuneana , Photinia serrulata , Pittosporum tobira , Viburnum odoratissimum , and Coriaria nepalensis . Sample Collection and Analysis The experiment began in July 2023. Based on plant community ecology research methods, the vegetation growth forms were categorized into trees and shrubs. Three 100 × 100 m plots were established within the study area, each subdivided into 20 × 20 m quadrats for community surveys. Based on the quadrat survey results, 21 dominant woody plant species were selected for the experiment (see Table 1 for detailed species information). For each species, two individuals growing under similar environmental conditions, exhibiting uniform growth vigor, and free from pests and diseases were selected within the plots. From each selected individual, three mature, south-facing leaves of similar size were randomly sampled from 1–2-year-old branches. These leaves, which were fully exposed to sunlight and in healthy condition, were used for light response curve measurements. After measurement, the sampled leaves were collected to determine leaf tissue density (LTD), LDMC, SLA, LA, chlorophyll content (SPAD), carbon content per unit leaf area (Carea), Narea, and carbon-to-nitrogen ratio (C/N). Table 1 List of 21 Common Plant Species order Species growth form 1 Ficus concinna (Miq.) Miq. tree 2 Osmanthus fragrans (Thunb.) Lour. tree 3 Elaeocarpus decipiens Hemsl. tree 4 Cinnamomum camphora (L.) presl tree 5 Michelia alba DC. tree 6 Cinnamomum japonicum Sieb. tree 7 Ficus virens Ait. var. tree 8 Cerasus serrulata (Lindl.) G. tree 9 Acer palmatum Thunb. tree 10 Jacaranda mimosifolia D. Don tree 11 Koelreuteria bipinnata Franch. tree 12 Pyracantha fortuneana (Maxim.) shrub 13 Photinia serrulata Lindl. shrub 14 Bougainvillea glabra Choisy. shrub 15 Viburnum odoratissimum Ker-Gawl. shrub 16 Pittosporum tobira (Thunb.) Ait. shrub 17 Rhododendron simsii Planch. shrub 18 Ligustrum quihoui Carr. Shrub 19 Vitex negundo L. var. cannabifolia (Sieb. et Zucc.) Hand.-Mazz. Shrub 20 Viburnum chinshanense Graebn. Shrub 21 Coriaria nepalensis Wall. Shrub Methods Measurement of Light Response Curves Light response curves were measured from August to September 2023, between 8:30 and 11:30 a.m. on sunny, windless days. For each plant, three mature, south-facing leaves from 1- to 2-year-old branches were selected. The leaves were fully exposed to sunlight, in healthy condition (free from diseases, pests, and damage, and with adequate water and nutrient availability), and of similar size. Measurements were conducted using the Li-6800 Portable Photosynthesis System (LI-COR Inc., USA), with three replicates per leaf. Before measurement, the leaves were exposed to saturating light intensity under a CO₂ concentration of 400 µmol CO₂ mol⁻¹ for approximately 30 min to ensure photosynthetic induction, and measurements were taken before the onset of midday photosynthesis depression. The leaf chamber temperature (Tblock) was set at 25°C, relative humidity (RH) was controlled at 65 ± 5%, and CO₂ concentration was maintained at 400 µmol CO₂ mol⁻¹. Each set of leaves was acclimated to these conditions for approximately 30 min before the measurement began. The light intensity gradient was set at 1800, 1500, 1000, 500, 250, 120, 60, 50, 30, 15, and 0 µmol m⁻² s⁻¹, with data automatically recorded via the Li-6800 system. Measurement of Leaf Traits For each tree species, the same three leaves used for the light response curve measurements were also employed to determine SPAD values, LA, SLA, LDMC, leaf thickness (LT), Carea, Narea, and C/N ratios. SPAD values were measured using a portable SPAD-502 chlorophyll meter, with three replicates per leaf and three leaves per plant, and the results were averaged. LA was determined by scanning the leaves on A4 paper and calculating their actual area using ImageJ software. The area of a single leaf was determined by dividing the total scanned area by the number of leaves. Leaf fresh weight was measured using a microbalance, after which the selected leaves were submerged in water for 12 h in the dark to reach saturation. After removing excess surface moisture with absorbent paper, the saturated fresh weight (SFW) was recorded. The leaves were then oven-dried at 105°C for 15 min to inactivate enzymes, followed by drying at 80°C until a constant weight was achieved and the dry weight (DW) was recorded. SLA was calculated as LA/DW and LDMC as DW/SFW. LT was measured utilizing a digital caliper with a precision of 0.02 mm, with five replicates per leaf, and the average value was recorded. Leaf volume was calculated as the product of LA and LT, while LTD was determined as DW/V. Finally, dried leaves were ground into a fine powder using a mortar, and their carbon and nitrogen content were analyzed using an Element analyzer (Vario Max CN Element Analyzer, Elementar, Germany). Data Analysis The light response characteristics were modeled using the modified rectangular hyperbola equation described by Ye ZP.et al.(2012): ​ \(\:{P}_{\text{n}}\text{(}\text{I:}\text{)}\text{=}\text{α}\frac{(1-\beta\:I)}{1+\gamma\:I}I-{R}_{\text{d}}\) where \(\:{P}_{\text{n}}\) represents the net photosynthetic rate, \(\:\text{α}\) is the maximum photosynthetic rate, \(\:\beta\:\) is the initial slope of the light response curve, \(\:\gamma\:\) denotes the photosynthetic active radiation, and \(\:{R}_{\text{d}}\) is the dark respiration. The measured data were initially processed employing Excel and analyzed using SPSS statistical software. One-way ANOVA was employed to assess significant differences in photosynthetic characteristics and leaf traits among the 21 woody plant species and various growth forms (α = 0.05). Correlation analyses were conducted to examine the relationships between photosynthetic characteristics and leaf traits, with P < 0.01 (**) indicating highly significant correlations and P < 0.05 (*) indicating significant correlations. Path and total effect analyses were conducted utilizing Amos to determine the driving effects of key leaf traits on photosynthetic characteristics. Single and multiple regression models were used to analyze the predictive capacity of major leaf traits on the photosynthetic capacity across different growth forms. Finally, data visualization and chart generation were conducted using Origin software. Results Analysis of Light Response Curve Parameters The analysis of photosynthetic characteristics among 21 woody plant species revealed significant interspecific variations in the light response curve parameters, including maximum net photosynthetic rate (Pnmax), apparent quantum yield (AQY), light saturation point (LSP), and light compensation point (LCP) (P < 0.05). The Pnmax ranged from 3.45 to 16.42 µmol·m⁻²·s⁻¹, while AQY varied between 10.13 and 42.90 µmol·mol⁻¹. The LSP and LCP values ranged from 453.13 to 1558.07 µmol·m⁻²·s⁻¹, and 3.69 to 9.40 µmol·m⁻²·s⁻¹, respectively (Fig. 1 ). At the growth form level, no significant differences were observed in Pnmax and AQY between shrubs and trees, while shrubs generally exhibited higher values than trees. In contrast, significant differences were observed in LSP and LCP between the two growth forms (P < 0.05), with shrubs exhibiting higher values for both parameters than those of trees. Analysis of Plant Leaf Functional Traits Figure 2 illustrates the variations in leaf traits among woody plants. Significant interspecific variations (P < 0.05) were observed in structural traits, including LTD, LA, LDMC, and SLA, alongside chemical traits, including Chlorophyll content (Chl), Carea, Narea, and C/N. These traits were significantly influenced by leaf habit. Overall, plant LTD ranged from 0.001 to 0.078 g·cm- 3 , while LA varied between 0.37 and 103.87 cm 2 . LDMC was within 0.21 to 0.82 g·g-1, and SLA spanned from 35.62 to 333.33 cm2·g-1. Among the chemical traits, Chl content exhibited a variation range of approximately 60 to 33. The Carea and Narea ranged from 66.33 to 11.22 g·m2 and 2.28 to 0.46 g·m2, respectively. The C/N ratio varied between 42.81 and 14.80. At the growth form level, functional leaf traits SLA, Chl, LDMC, and the contents of C and N did not show significant differences, following a general trend of shrubs > trees. However, LTD was significantly higher in shrubs than in trees (P < 0.01), while LA was significantly greater in trees than in shrubs (P < 0.01). Correlation Analysis Between Light Response Curve Characteristic Parameters and Leaf Traits A correlation analysis was conducted to examine the relationship between the characteristic parameters of the light response curve and leaf traits across 21 woody plant species (Fig. 3 ). The results showed that SLA, Narea, and C/N were strongly correlated with Pnmax (P < 0.01), while LTD exhibited a significant correlation with Pnmax (P < 0.05). No significant correlations were observed between Pnmax and other traits, including LDMC, LA, Chl, and Carea. LTD, Narea, and C/N exhibited strong correlations with AQY (P < 0.01), while Chl displayed a significant correlation with AQY (P < 0.05). LSP decreased as LTD and LA increased but showed a positive correlation with increasing SLA and Narea (P < 0.05). No significant correlations were observed between LSP and LDMC, while Chl, Carea, or C/N. LCP was significantly correlated only with the structural traits LA and SLA (P < 0.05) and showed no association with chemical traits. These findings suggest that the photosynthetic capacity among woody species is primarily influenced by leaf traits such as Narea, LTD, SLA, LA, and C/N. Further analysis of the correlation between light response curve parameters and leaf traits across different growth forms revealed distinct patterns. In trees, SLA, Narea, and C/N exhibited strong correlations with Pnmax (P < 0.01). AQY was strongly correlated with the chemical traits Narea and C/N (P < 0.01) and significantly correlated with the structural traits LTD and SLA (P < 0.05). SLA and Narea showed strong correlations with LSP (P < 0.01), with LSP increasing as these traits increased but decreased with higher C/N (P < 0.05). For LCP, only the structural trait LA showed a significant correlation (P < 0.05), while no significant relationships were observed with chemical traits. These findings suggest that the photosynthetic capacity of trees is primarily influenced by leaf traits such as Narea, SLA, and LA. In shrubs, the strongest correlation with Pnmax was observed for the chemical trait Narea (P < 0.01), followed by the structural trait LA (P < 0.05), with no significant association with other traits. AQY was significantly and positively correlated only with LA (P < 0.01), increasing as LA increased. LSP was significantly correlated only with LTD (P 0.05). LCP exhibited a strong correlation only with the chemical trait Narea (P < 0.01) and showed no significant association with other traits. Overall, the photosynthetic capacity of shrubs is primarily influenced by leaf traits such as Narea, LTD, and LA. In summary, the photosynthetic capacity of woody plants is influenced by multiple leaf traits, particularly Narea, SLA, and LTD. However, these factors vary between trees and shrubs. In trees, photosynthetic capacity is primarily influenced by Narea, SLA, and LA, while in shrubs, it is primarily influenced by Narea, LTD, and LA. For Pnmax, both trees and shrubs depend on both structural and chemical traits, but trees depend more on nitrogen content and specific leaf area, while shrubs are more affected by leaf area. Impact Analysis of Leaf Traits on Light Response Curve Characteristic Parameters This study elucidated the relationships between plant light response curve parameters and leaf traits through path analysis, illustrating the total effects among the variables (Fig. 4 ). The results indicated that the maximum net Pnmax in 21 plant species is directly and positively influenced by Narea and SLA but not directly influenced by LA. AQY is directly and positively influenced by Narea, while SLA and LA do not exert direct effects. LCP is directly and positively influenced by SLA but negatively influenced by LA, with no direct effect from Narea. Furthermore, Narea, SLA, and LA do not directly affect the LSP. From the perspective of various growth forms, Pnmax in trees is directly and positively influenced by Narea and SLA but negatively influenced by LA. This suggests that higher nitrogen content and SLA enhance photosynthetic capacity, whereas excessive LA may reduce photosynthetic efficiency. AQY is directly and positively affected by Narea, with no direct effect from SLA or LA. LSP is directly and positively influenced by SLA, while Narea or LA do not exert direct effects. LCP is directly and positively affected by SLA but negatively influenced by LA, with no direct effect from Narea, highlighting the role of leaf structural traits in determining the light compensation point. For shrubs, Pnmax is directly and positively influenced by Narea, SLA, and LA, but none of these traits directly affect AQY, LSP, or LCP. Path analysis revealed that the characteristic parameters of the light response curve, including Pnmax, AQY, LSP, and LCP, were significantly influenced by both structural (SLA, LA) and chemical (Narea) traits. In trees, photosynthetic capacity is directly influenced by Narea and SLA, while in shrubs, it is directly affected by Narea, SLA, and LA. To a certain extent, Narea, SLA, and LA can serve as predictive indicators of plant photosynthetic capacity based on leaf chemical and structural traits. Prediction of Photosynthetic Capacity Based on Leaf Traits Leaf structural and chemical traits play a crucial role in determining plant photosynthetic capacity. Regression analysis revealed that key leaf trait indicators may directly influence Pnmax and AQY. A linear regression was conducted on leaf traits, including Narea, SLA, LA, and LTD. Table 2 presents the results. Across all experimental conditions, the two-factor prediction models for Pnmax and AQY consistently outperformed the single-factor prediction models, highlighting the interactive effects of leaf traits on photosynthetic capacity (Fig. 5 ). For woody plants, the coefficient of determination (R²) for Pnmax was 0.46 in the single-factor model, which increased to 0.62 in the two-factor model, while for AQY, the R² values improved from 0.35 to 0.36 (Fig. 6 ). In tree species, the R² for Pnmax was 0.36 and 0.54 in the single- and two-factor models, respectively, while for AQY, the R² values were 0.53 and 0.62, respectively. For shrub, the R² for Pnmax was 0.60 in the single-factor model and slightly increased to 0.61 in the two-factor model, while for AQY, the R² values were 0.35 and 0.36, respectively. From the perspective of different growth forms, the R² values in the single-factor models for Pnmax and AQY were 0.36 and 0.53 for trees and 0.60 and 0.35 for shrubs, respectively. In the two-factor models, these values increased to 0.54 and 0.62 for trees and 0.61 and 0.36 for shrubs, respectively. Specifically, both Pnmax and AQY in woody plants and shrubs showed significant improvements in the two-factor models. However, while the Pnmax of shrubs was already relatively high in the single-factor model (R² = 0.60), its improvement in the two-factor model was minimal. This suggests that Pnmax in shrubs is predominantly influenced by a single leaf trait, while in trees, photosynthetic capacity is better predicted when multiple factors are considered. These findings suggest that two-factor models better demonstrate predictive performance. Table 2 Model Simulation Parameters for the Relationship between Plant Photosynthetic Capacity and Major Leaf Trait Indicators A B C R 2 F P P nmax Woody N area 5.43 1.05 0.46 52.42 < 0.001 N area ×SLA 5.55 0.02 -1.86 0.62 48.37 < 0.001 Tree N area 4.30 2.25 0.36 17.36 < 0.001 N area ×SLA 3.92 0.04 -2.06 0.54 17.61 < 0.001 Shrub N area 7.18 -1.23 0.60 42.70 < 0.001 N area ×LA 6.78 0.03 -1.17 0.61 21.32 < 0.001 AQY Woody N area 12.82 12.19 0.35 32.30 < 0.001 N area ×LTD 11.80 -22.39 14.83 0.36 16.85 < 0.001 Tree N area 14.57 8.80 0.53 34.76 < 0.001 N area ×SLA 13.84 0.08 0.28 0.62 24.40 < 0.001 Shrub LA 0.48 24.32 0.35 14.91 0.001 N area ×LA 2.39 0.45 21.32 0.36 7.44 0.003 Discussion Differences in Photosynthetic Characteristics Among Plants with Different Growth Forms The key parameters of the light response curve—Pnmax, AQY, LSP, and LCP—serve as essential indicators for assessing plant photosynthetic efficiency. These parameters provide insights into plant growth status and environmental adaptability (Qinglin et al., 2022 ). Pnmax specifically represents the maximum photosynthetic capacity of leaves under optimal light conditions, while AQY reflects the photosynthetic potential of plants under low light intensity, making it a crucial metric for evaluating shade tolerance (Xu et al., 2023 ). A higher AQY value indicates an enhanced capacity for low-light utilization. This study revealed that shrubs have a higher pigment-protein content than trees, which may partly explain the differences in their photosynthetic characteristics. LSP and LCP are crucial for determining the ability of plants to utilize light intensity. Higher LSP and LCP values indicate reduced inhibition under high-light conditions. In this study, significant differences (P < 0.05) in LSP and LCP were observed between trees and shrubs, indicating that these parameters are strongly influenced by environmental factors, including soil moisture, nutrient availability, and climatic conditions. These factors may lead to different adaptive strategies between the two growth forms. Additionally, leaf morphology and structure may also affect plant performance under varying light intensities, thereby affecting LSP and LCP. While no significant differences (P > 0.05) were observed in Pnmax and AQY between trees and shrubs, this finding suggests that both growth forms exhibit comparable maximum photosynthetic rates and photosynthetic efficiencies. This similarity may reflect shared physiological mechanisms that enable efficient light utilization for growth and development. It also implies that, under certain ecological conditions, trees and shrubs may coexist and compete without a distinct dominance hierarchy. Overall, the light response curve parameters (Pnmax, AQY, LSP, and LCP) were higher in shrubs than in trees, highlighting significant differences in photosynthetic capacity and habitat adaptability between the two growth forms. The higher LSP and LCP values in shrubs suggest a greater ability to utilize high and low light intensities compared to those of trees. Shrubs can reach light saturation at higher intensities, indicating greater photosynthetic efficiency under strong light conditions and better adaptation to the high-light environment of the study area. This advantage may enable shrubs to secure a favorable position in the competition for light resources, leading to faster growth rates and stronger reproductive capacity in certain ecosystems. By adopting a more rapid growth strategy, shrubs can quickly capture and utilize light resources in the short term, while trees may depend on long-term growth and resource accumulation. In summary, shrubs in the study area exhibit superior photosynthetic efficiency and relatively faster growth rates than trees. Influence of Leaf Traits on Photosynthetic Characteristics in Plants with Different Growth Forms The photosynthetic capacity of plants is closely associated with their leaf traits (Longkang et al., 2024 ). Current research on the relationship between leaf traits and light response curve parameters has primarily focused on the effects of SLA and LA. In this study, Pnmax of trees and shrubs exhibited a highly significant positive correlation with Narea and LA. Nitrogen is an essential component of enzymes involved in plant photosynthesis, and its content directly influences photosynthetic efficiency. A larger LA enables plants to capture more solar radiation, thereby enhancing photosynthetic rates. Generally, photosynthetic rates are positively correlated with LA, as larger leaves facilitate greater light absorption, leading to higher net photosynthetic rates. Plants optimize resource allocation based on their growth environment and competitive conditions. The optimization strategies of shrubs and trees in nutrient acquisition and photosynthesis contribute to increased Narea and LA, which support higher Pnmax. However, no significant correlations were observed between LDMC and Chl with Pnmax, AQY, LSP, and LCP across different growth forms. This suggests that photosynthetic parameters may be more influenced by environmental factors, including water availability, temperature, and nutrient supply. Additionally, variation in leaf anatomical structure and stomatal characteristics may contribute to the weak correlations between LDMC, Chl, and Pnmax. Temporal fluctuations in environmental conditions may also cause these correlations to vary across different growth stages or seasons, implying short-term observations may not fully capture their underlying relationships. Pnmax exhibited a significant linear relationship with SLA, with Pnmax increasing as SLA increased, aligning with findings from Jackson et al. ( 2013 ) and Croft et al. ( 2017 ). This indicates that plants may expand LA to capture more solar radiation, thereby enhancing photosynthetic capacity. In trees, Pnmax was significantly positively correlated with SLA, suggesting that under sufficient light conditions, a larger SLA enhances light capture and promotes photosynthesis. However, this correlation was not significant in shrubs, potentially owing to the stabilization of LA in shrubs (Shihao et al., 2020 ). The LSP of woody plants exhibits a significant positive correlation with SLA and Narea but a negative correlation with C/N, highlighting the crucial role of nitrogen. A lower C/N ratio indicates a higher relative nitrogen abundance, suggesting that plants prioritize nitrogen uptake to support growth and photosynthetic functions. Conversely, a high C/N ratio suggests greater carbon allocation to structural components, which may limit photosynthetic efficiency and result in a lower LSP. Plants adjust resource allocation strategies based on environmental conditions and growth demands. Plants with high SLA and Narea typically exhibit photosynthetic capacity and growth rates, enabling them to reach light saturation more rapidly under high-light conditions. In shrubs, LSP was significantly negatively correlated with LTD, indicating that increased LTD reduces the capacity to utilize intense light. In trees, LCP was significantly negatively correlated with LA, suggesting that a larger LA improves the ability to utilize low light. In contrast, the LCP of shrubs was significantly negatively correlated with Narea, indicating that shrubs with higher nitrogen content require higher light intensity to reach light compensation under low-light conditions. This finding suggests that while high nitrogen content enhances photosynthetic capacity, it may also reduce photosynthetic efficiency under low light, reflecting the complex interplay between nitrogen and photosynthesis. The carbon and nitrogen content in plant leaves serves as an indicator of metabolic capacity and overall resource utilization. The C/N ratio also reflects plant structural resistance (Longkang et al., 2024 ). These results demonstrate a strong correlation between the Pnmax of trees and Narea. Leaf Trait Variations Across Different Growth Forms Leaf structural traits, including LTD, LDMC, SLA, and LA, serve as stable functional indicators shaped by long-term adaptation to external environmental conditions (Croft et al., 2017 ; Cheng et al., 2021 ). Additionally, leaf chemical traits, including Chl, Carea, Narea, and the C/N, indicate the nutrient uptake efficiency of plants from the external environment. SLA and LTD are key indicators of plant drought resistance, with SLA primarily associated with resource acquisition and retention and LTD related to water retention under drought stress (Wang et al., 2017 ). In this study, trees exhibited lower SLA but higher LTD, indicating thicker cell walls and more compact cell structure. This structural adaptation promotes osmolyte accumulation (Rawat et al., 2021 ), thereby enhancing drought resistance in trees. The lower SLA suggests that a greater proportion of leaf dry matter is allocated to guard cells, which helps minimize water loss and optimize photosynthetic efficiency. In contrast, shrubs exhibited higher values for LDMC, SLA, Chl, Carea, and Narea. Increased Chl enhances photosynthetic efficiency, while the elevated SLA in shrubs, often associated with smaller leaves (Riva et al., 2017 ), improves light capture. Moreover, the higher dry matter content in thicker shrub leaves reduces water evaporation and strengthens stress tolerance. The more concentrated allocation of nutrients, including nitrogen and carbon, leads to increased Carea and Narea. The LDMC reflects the resource utilization efficiency of a plant in its environment (Riva et al., 2017 ). In the study area, trees exhibited lower biomass and reduced nutrient and water demands compared to shrubs, resulting in lower LDMC. In contrast, shrubs, which are generally smaller and have shorter growth cycles, demonstrate greater efficiency in resource utilization, allowing for rapid dry matter and nutrient accumulation (Rawat et al., 2021 ). This indicates that shrubs have a higher adaptive capacity for utilizing environmental resources in the study area than trees. Influence Leaf Traits on Photosynthetic Characteristics in Plants with Different Growth Forms The photosynthetic capacity of plants is strongly correlated with their leaf traits (Longkang et al., 2024 ). Current research on the relationship between leaf traits and characteristic parameters of light response curves primarily focuses on the effects of SLA and LA. In this study, the Pnmax of trees and shrubs exhibited a highly significant positive correlation with Narea and LA. Nitrogen is a crucial component of key enzymes involved in plant photosynthesis, and its content directly affects photosynthetic efficiency. A larger LA allows plants to capture more solar radiation, thereby increasing photosynthetic rates. Generally, photosynthetic rates are positively correlated with LA, as larger leaves facilitate greater light absorption, leading to higher net photosynthetic rates. Plants optimize resource allocation in response to their growth environment and competitive conditions. The adaptive strategies of shrubs and trees in nutrient acquisition and photosynthesis promote increased Narea and LA, thereby supporting higher Pnmax. However, no significant correlations were observed between LDMC and Chl with Pnmax, AQY, LSP, and LCP across different growth forms. This suggests that these photosynthetic parameters are more influenced by environmental factors, including water availability, temperature, and nutrient supply. Additionally, leaf anatomical structure and stomatal characteristics may affect photosynthetic performance, contributing to the weak correlations observed between LDMC, Chl, and Pnmax. Photosynthetic parameters may also exhibit varying correlations across different growth stages or seasons owing to temporal fluctuations, implying short-term observations may not accurately reflect their underlying relationships. The Pnmax displayed a significant linear relationship with SLA, with Pnmax increasing as SLA increased, aligning with findings by Jackson et al. ( 2013 ) and Croft et al. ( 2017 ). This indicates that plants may expand LA to capture more solar radiation, thereby enhancing photosynthetic capacity. In trees, Pnmax was strongly positively correlated with SLA, suggesting that under adequate light conditions, a larger SLA facilitates improved light capture and promotes photosynthesis. However, this relationship was not significant in shrubs, potentially owing to the stabilization of LA in shrubs (Shihao et al., 2020 ). The LSP of woody plants exhibited a significant positive correlation with SLA and Narea but a negative correlation with the C/N, highlighting the critical role of nitrogen. A lower C/N ratio signifies a higher proportion of nitrogen relative to carbon, suggesting that plants prioritize nitrogen uptake to support growth and photosynthetic functions. Conversely, plants with a high C/N ratio may allocate more carbon to structural synthesis, which could limit photosynthetic efficiency and result in a lower LSP. Plants generally optimize resource allocation based on environmental conditions and adaptive growth strategies. Plants with high SLA and Narea typically demonstrate superior photosynthetic performance and growth rates, enabling them to reach light saturation more rapidly under high light conditions. In shrubs, LSP exhibited a significant negative correlation with LTD, indicating that higher LTD limits the ability to utilize intense light. In trees, the LCP displayed a significant negative correlation with LA, suggesting that larger LA enhances the ability to utilize low light. In contrast, the LCP of shrubs was significantly negatively correlated with Narea, indicating that shrubs with higher nitrogen content require greater light intensity to achieve light compensation under low-light conditions. This finding implies that while high nitrogen content enhances photosynthetic capacity, it may also limit photosynthetic efficiency under low-light conditions, underscoring the intricate relationship between nitrogen and photosynthesis. The carbon and nitrogen content of plant leaves reflects their metabolic capacity and overall utilization of environmental resources. The C/N ratio indicates the physical resistance strength of plants (Longkang et al., 2024 ). The findings further reveal a significant correlation between the Pnmax of trees and Narea. Prediction of Plant Photosynthetic Capacity Based on Leaf Traits The Pnmax and AQY serve as key indicators of plant photosynthetic capacity, reflecting both growth potential and resource utilization efficiency under varying environmental conditions. Examining the relationship between photosynthetic characteristics and leaf trait indices helps establish a scientific framework for evaluating photosynthetic capacity. This study, conducted in subtropical evergreen broad-leaved forests, reveals that SLA and Narea are significant predictors of plant photosynthetic capacity, with Narea demonstrating superior predictive power than that of SLA, aligning with findings by Grassi (2002) and others. Plants with high SLA and Narea typically achieve higher Pnmax and AQY values, indicating superior resource acquisition and utilization efficiency (Scheepens et al., 2010 ). A high SLA, characterized by thin, lightweight leaf structures, enhances light capture and CO2 absorption, thereby promoting photosynthesis. High Narea reflects a greater allocation of nitrogen to leaves, a critical component of photosynthetic enzymes, including Rubisco, directly boosting photosynthetic efficiency. Additionally, plants with high SLA and Narea allocate fewer resources to leaf construction and protective tissues (Pietsch et al., 2014), allowing more investment in growth and photosynthetic activity, thus gaining competitive advantages. However, the predictive power of SLA and Narea exhibit limitations across varying plant growth forms. In shrubs, the combined influence of Narea and LA plays a more dominant role in predicting photosynthetic capacity, while SLA exhibits a weaker correlation. This may be attributed to the ability of shrubs to optimize leaf spatial distribution, such as increasing LA, to enhance photosynthetic efficiency rather than relying on SLA. The short growth cycles and high metabolic rates of shrubs, along with their flexible resource utilization strategies, may further weaken the relationship between SLA and Narea. Our findings indicate that photosynthetic capacity prediction models incorporating SLA, LA, and Narea offer improved accuracy in simulating hydrological processes in subtropical evergreen broad-leaved forests. These indices not only serve as indicators of photosynthetic capacity but are also closely associated with water use efficiency. For instance, plants with high SLA and Narea exhibit higher transpiration rates, influencing water absorption and distribution, thereby affecting ecosystem hydrodynamics. Integrating these indices into predictive models enables a more comprehensive evaluation of plant functional role within ecosystems, providing a scientific foundation for ecological management. In conclusion, SLA and Narea are key leaf trait indices for evaluating plant photosynthetic capacity, though their predictive power is affected by various factors across different plant growth forms. Future research should examine the relationships between additional leaf trait indices and photosynthetic capacity to enhance predictive models, ultimately contributing to a more comprehensive theoretical framework for plant functional studies. Conclusion This study elucidates the similarities and variations in photosynthetic characteristics between arborescent and shrub species in subtropical evergreen broad-leaved forests. The results demonstrate that while no significant variations were observed in Pmax and quantum yield of photosynthesis (Φ) between the two plant types (P > 0.05), shrub species exhibited greater photosynthetic acclimation capacity and enhanced light compensation ability. The trait-based analysis identified Narea and SLA as the most effective predictors of photosynthetic capacity, with a multiple linear regression model (R2 = 0.62) outperforming the single-factor linear regression model (R2 = 0.46) in predictive accuracy. These findings provide deeper insights into the relationship between leaf traits and photosynthetic performance, offering novel perspectives for research in plant photosynthetic physiology. However, considering the diversity of plant species and habitats, future studies should include larger sample sizes and broader geographical coverage to validate the generalizability of these conclusions. Moreover, further research is needed to investigate the mechanistic effects of various environmental factors on plant photosynthetic characteristics. Declarations Acknowledgements: This work was supported by 1.Open Subject Funding of CQORS-2024-2 2.Natural Science Foundation of Chongqing (No:CSTB2022NSCQ-MSX1121) 3.Performance Incentive and Guidance Projects of Chongqing Scientific Research Institution(No:CSTB2024JXJL-YFX0037,CSTB2024JXJL-YFX0053) Declaration of competing interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. CRediT authorship contribution statement Manyi Li: Conceptualization, Methodology, Writing original draft. Cheng Li: Funding acquisition, Project administration, Writing original draft. Lei Ma: Software, Writing–review & editing. Lu Yao: Data curation, Methodology, Visualization. Mingze Xu: Software, Writing – review & editing. Yunqi Wang: Methodology, Writing – original draft. References Albert, C. H., et al. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7413523","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":511885899,"identity":"ce4e2508-6fc1-44dc-985c-c80fda30e834","order_by":0,"name":"Manyi Li","email":"","orcid":"","institution":"Chongqing Institute of Geology and Mineral Resources","correspondingAuthor":false,"prefix":"","firstName":"Manyi","middleName":"","lastName":"Li","suffix":""},{"id":511885900,"identity":"10ec7eb2-8412-469d-bb8c-f3d9bd8ad7f9","order_by":1,"name":"Lei Ma","email":"","orcid":"","institution":"Chongqing Institute of Geology and Mineral Resources","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Ma","suffix":""},{"id":511885901,"identity":"98174ccc-92cc-4f62-85f5-da8ec14b5a36","order_by":2,"name":"Lu Yao","email":"","orcid":"","institution":"Beijing Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Yao","suffix":""},{"id":511885902,"identity":"d7e5978c-86ab-448a-b318-39f83c5325d3","order_by":3,"name":"Mingze Xu","email":"","orcid":"","institution":"Chongqing Institute of Geology and Mineral Resources","correspondingAuthor":false,"prefix":"","firstName":"Mingze","middleName":"","lastName":"Xu","suffix":""},{"id":511885903,"identity":"6785a45e-b876-43ab-952d-77e6d90886a0","order_by":4,"name":"Cheng Li","email":"","orcid":"","institution":"Chongqing Institute of Geology and Mineral Resources","correspondingAuthor":false,"prefix":"","firstName":"Cheng","middleName":"","lastName":"Li","suffix":""},{"id":511885904,"identity":"321768a6-d91c-4ccd-acc4-a134ae2c565c","order_by":5,"name":"Yunqi Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYDADNvYeBoMKEOsA0Vp4zjAYnCFJC4NEDgMDUVoMjp89/Jq37Y5sn+TbAwUH2xjk+G4kMH4uwKflTF6aNW/bM+M26bwEA6AWY8kbCczSM/BoMTuQY2bM23Y4sU06x8D4YxtD4oYbCWzMPPi0nH8D1SJ5xgBkSz1hLTdyjB+DtUjwgLUkGBDSYn/jjRnjnHOHjdt4gH45cE7CcOaZh83S+LRI9ucYf3hTdlh2fvvZYwYHymzk+Y4nH/yMTwsQsEkBFTA2ABkGwNhhgLDxA+aPPyDKmB8QUjoKRsEoGAUjEwAAurNR1xP34bQAAAAASUVORK5CYII=","orcid":"","institution":"Beijing Forestry University","correspondingAuthor":true,"prefix":"","firstName":"Yunqi","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-08-20 05:13:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7413523/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7413523/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91404933,"identity":"d00ca659-829b-416b-ad89-36ca0d5e87c5","added_by":"auto","created_at":"2025-09-16 07:39:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":138777,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of interspecific differences and growth form variations in plant photosynthetic characteristic parameters\u003c/p\u003e\n\u003cp\u003e**, significant differences between growth forms (P\u0026lt;0.05); ***, highly significant differences between growth forms (P\u0026lt;0.01)\u003c/p\u003e","description":"","filename":"Picture1.png","url":"https://assets-eu.researchsquare.com/files/rs-7413523/v1/1169b582830e60b977a92bdc.png"},{"id":91404813,"identity":"71e7bef8-04fc-47d7-80c7-cecb183b8c81","added_by":"auto","created_at":"2025-09-16 07:39:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":174652,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of interspecific and growth form differences in plant leaf traits\u003c/p\u003e\n\u003cp\u003e**, significant differences between growth forms (P\u0026lt;0.05); ***, highly significant differences between growth forms (P\u0026lt;0.01)\u003c/p\u003e","description":"","filename":"Picture2.png","url":"https://assets-eu.researchsquare.com/files/rs-7413523/v1/0ee53c6ccef91de685d6b363.png"},{"id":91405514,"identity":"7c9b21cc-0a5e-4b95-9c44-67cc77b550a0","added_by":"auto","created_at":"2025-09-16 07:47:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":358110,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation Analysis Between Leaf Traits and Photosynthetic Characteristics Parameters\u003c/p\u003e","description":"","filename":"Picture3.png","url":"https://assets-eu.researchsquare.com/files/rs-7413523/v1/0a967d647b9ebeabcd9f8963.png"},{"id":91404859,"identity":"4454606e-0578-4dac-aa30-53c19cb990d2","added_by":"auto","created_at":"2025-09-16 07:39:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":735621,"visible":true,"origin":"","legend":"\u003cp\u003ePathways Driving the Influence of Leaf Traits on Photosynthetic Characteristics Parameters\u003c/p\u003e\n\u003cp\u003e**, P \u0026lt; 0.01; ***, P \u0026lt; 0.001; ns, P \u0026gt; 0.05; Blue and green arrows indicate positive and negative driving effects, respectively.\u003c/p\u003e","description":"","filename":"Picture4.png","url":"https://assets-eu.researchsquare.com/files/rs-7413523/v1/059043cd23c701ea05b570b3.png"},{"id":91404717,"identity":"73d10f91-3208-4738-b2e2-18faf4505cb6","added_by":"auto","created_at":"2025-09-16 07:39:26","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":190407,"visible":true,"origin":"","legend":"\u003cp\u003eProcess-based Prediction of Maximum Photosynthetic Rate (Pnmax) via Major Leaf Traits\u003c/p\u003e","description":"","filename":"Picture5.png","url":"https://assets-eu.researchsquare.com/files/rs-7413523/v1/0bae4b05c92a9208fba580ba.png"},{"id":91404904,"identity":"d78551ef-d1b9-4da6-a923-e67d51dd7aa7","added_by":"auto","created_at":"2025-09-16 07:39:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":198680,"visible":true,"origin":"","legend":"\u003cp\u003eProcess-based Prediction of Apparent Quantum Yield (AQY) via Major Leaf Traits\u003c/p\u003e","description":"","filename":"Picture6.png","url":"https://assets-eu.researchsquare.com/files/rs-7413523/v1/e9d996ec8627367c225473ae.png"},{"id":91405517,"identity":"6d8a6d18-7c5a-4853-9df2-f077fa2426bb","added_by":"auto","created_at":"2025-09-16 07:47:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2553619,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7413523/v1/51fcea97-4583-400c-bf85-fb1ebcb8c29f.pdf"}],"financialInterests":"","formattedTitle":"Coordination of Leaf Structural and Chemical Traits in Predicting Photosynthetic Capacity of Woody Plants in Subtropical Evergreen Broadleaf Forests","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAmid intensifying global climate change and ongoing biodiversity loss, studying plant leaf functional traits and their environmental adaptability has become increasingly important (Ciais et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Functional plant traits are widely utilized to evaluate biodiversity conservation efforts and ecosystem management effectiveness (Zhengbing et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Accurately identifying and quantifying the relationships among these traits is crucial for understanding their roles in plant physiological and ecological processes. As the primary site of photosynthesis, leaf traits are closely correlated with various physiological functions in plants (Anderegg et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and exhibit high plasticity (He et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Midolo et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Leaf functional traits and ecological stoichiometric characteristics provide insights into plant adaptation strategies, self-regulation mechanisms, and resource allocation patterns (Carvajal et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For instance, key traits such as net photosynthetic rate, apparent quantum yield, specific leaf area (SLA), leaf dry matter content (LDMC), and nitrogen content per unit leaf area (Narea), along with the leaf economics spectrum (LES), highlight trade-offs between growth potential, resource acquisition, and defense in plants (Pan et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Studies indicate that plants with high SLA and Narea generally exhibit higher photosynthetic rates and faster photosynthetic gains, while those with low SLA and Narea display the opposite trend (Reich, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Reich et al., 2018). While net primary productivity in ecosystems is influenced by multiple factors (Zhang et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), LDMC has been identified as a more reliable predictor (Leigh et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Michaletz et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Analyzing the relationships among leaf traits allows for the inference of difficult-to-measure traits from those that are easier to quantify (Berzaghi et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, whether a universal model can quantitatively predict these relationships remains uncertain. Therefore, further research on the quantitative relationship among leaf traits is essential for understanding trait variation, scaling laws, and constraints on plant-atmosphere carbon exchange (Westoby et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe interdependence of leaf traits underscores the complexity of photosynthesis as a physiological process influenced by multiple factors (Chelli et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), with its characteristic parameters and environmental factors jointly determining plant photosynthetic efficiency (Westoby et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Light response curves, a crucial tool in physiological ecology research (Moreno-Sotomayor et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Palliotti et al., 2015), illustrate the relationship between photosynthetic rate and light intensity, and they are widely utilized to evaluate plant adaptability to environmental changes. These parameters not only indicate plant resilience and stress responses but also display a pivotal role in determining plant growth and productivity (Wright et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), making them significant in photosynthetic trait studies (Buckley et al., 2015).\u003c/p\u003e\u003cp\u003eNumerous leaf traits are strongly associated with photosynthesis, with variations in structural and biochemical characteristics reflecting the diversity of plant carbon assimilation capabilities. This provides a theoretical basis for predicting photosynthetic performance under varying environmental conditions. For example, some plants enhance photosynthetic rates by expanding leaf area to optimize light capture efficiency (Martinez-Garcia et al., 2023), while chlorophyll content also rises with increasing leaf area. Conversely, reducing leaf area or increasing LDMC can mitigate water loss due to transpiration (Onoda et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Nalaka et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Additionally, photosynthesis depends on respiratory carbon costs to sustain protein synthesis, while leaf construction and metabolic processes rely on nitrogen, with more than half of leaf nitrogen stored as proteins directly involved in the Calvin cycle (Bachofen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Narea serves as a key indicator of metabolic activity and structural costs (Dost\u0026aacute;l et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Morphological traits, including leaf area (LA) and LDMC, provide insights into plant resource use efficiency and photosynthetic potential (Han et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), with LA variations closely correlated with light capture and carbon assimilation capacity (Milla et al., 2019). SLA, defined as the ratio of LA to leaf dry mass, directly reflects plant light capture efficiency and carbon assimilation strategies (Yang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Studies show that changes in LA and LDMC are strongly associated with plant growth forms (Luo et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), while large-scale research highlights plant life forms and functional types as primary factors influencing leaf trait variation (Zhan et al., 2017). However, current international research on leaf photosynthesis predominantly focuses on light reactions and carbon metabolism, with limited quantitative studies on the influence of structural factors. Most studies examine correlations between environmental conditions and leaf traits (M\u0026uuml;nzbergov\u0026aacute; et al., 2017; Kosov\u0026aacute; et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) or focus on individual species or traits (Thakur et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yan et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite their crucial role in plant adaptation to environmental changes, photosynthetic characteristics remain understudied (Pierce et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Heydari et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Plants of different growth types adapt to their environments through distinct physiological and ecological traits, thereby enhancing resource use efficiency (Jackson et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Cui et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). A detailed examination of leaf morphological, chemical, and physiological traits provides valuable insights into the mechanisms underlying plant adaptation to environmental changes (Albert et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Cheng et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe subtropical evergreen broad-leaved forests of southwestern China, characterized by diverse vegetation and favorable hydrothermal conditions, serve as the study area. This study aims to explore the variations and correlations between photosynthetic and leaf traits across different growth types and identifying key factors influencing photosynthetic capacity using 11 tree and 10 shrub species from the indicated region. The study addresses the following questions: (1) What are the differences and correlations between leaf traits and photosynthetic capacity parameters across different growth forms? (2) How do leaf structural and chemical traits influence and predict the photosynthetic capacity of woody plants, and which are the most effective predictors? We propose the following hypotheses: (1) While leaf traits and photosynthetic capacity parameters exhibit significant interspecific variations among woody plants, differences between growth forms are not substantial; (2) A two-factor linear regression model incorporating structural traits (e.g., SLA) and chemical traits (e.g., Narea) provides a more accurate prediction of photosynthetic capacity than single-factor models. SLA and Narea serve as key predictors, effectively capturing variations in photosynthetic capacity within subtropical evergreen broad-leaved forests in China. Based on these predictors, this study could offer theoretical support for enhancing carbon sequestration, maintaining ecosystem health, conserving biodiversity, and promoting sustainable management in subtropical broad-leaved forest regions.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eStudy sites\u003c/h2\u003e\u003cp\u003eThe study area is located in Yubei District, Chongqing, China (106\u0026deg;27\u0026prime;30\u0026prime;\u0026prime;\u0026ndash;106\u0026deg;57\u0026prime;58\u0026prime;\u0026prime; E, 29\u0026deg;34\u0026prime;45\u0026prime;\u0026prime;\u0026ndash;30\u0026deg;07\u0026prime;22\u0026prime;\u0026prime; N), with mountain elevations generally ranging between 600 and 1,000 m. The region experiences a mild climate with abundant rainfall, where light, heat, and water availability are well-synchronized, creating favorable conditions for vegetation growth. The average annual temperature is 18.6\u0026deg;C, and the average annual precipitation is approximately 1,100 mm, with higher rainfall in summer and lower in winter. Vegetation coverage exceeds 63%, and the forested area spans approximately 279.29 square kilometers. The region is characterized by a well-preserved ecological environment and abundant natural resources, falling within the subtropical, humid evergreen broad-leaved forest zone. The dominant tree species include \u003cem\u003eFicus concinna\u003c/em\u003e, \u003cem\u003eCinnamomum camphora\u003c/em\u003e, \u003cem\u003eMichelia alba\u003c/em\u003e, \u003cem\u003eFicus virens\u003c/em\u003e, and \u003cem\u003eKoelreuteria bipinnata\u003c/em\u003e, while dominant shrub species comprise \u003cem\u003ePyracantha fortuneana\u003c/em\u003e, \u003cem\u003ePhotinia serrulata\u003c/em\u003e, \u003cem\u003ePittosporum tobira\u003c/em\u003e, \u003cem\u003eViburnum odoratissimum\u003c/em\u003e, and \u003cem\u003eCoriaria nepalensis\u003c/em\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSample Collection and Analysis\u003c/h2\u003e\u003cp\u003eThe experiment began in July 2023. Based on plant community ecology research methods, the vegetation growth forms were categorized into trees and shrubs. Three 100 \u0026times; 100 m plots were established within the study area, each subdivided into 20 \u0026times; 20 m quadrats for community surveys. Based on the quadrat survey results, 21 dominant woody plant species were selected for the experiment (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for detailed species information). For each species, two individuals growing under similar environmental conditions, exhibiting uniform growth vigor, and free from pests and diseases were selected within the plots. From each selected individual, three mature, south-facing leaves of similar size were randomly sampled from 1\u0026ndash;2-year-old branches. These leaves, which were fully exposed to sunlight and in healthy condition, were used for light response curve measurements. After measurement, the sampled leaves were collected to determine leaf tissue density (LTD), LDMC, SLA, LA, chlorophyll content (SPAD), carbon content per unit leaf area (Carea), Narea, and carbon-to-nitrogen ratio (C/N).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eList of 21 Common Plant Species\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eorder\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSpecies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003egrowth form\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eFicus concinna\u003c/em\u003e (Miq.) Miq.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003etree\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eOsmanthus fragrans\u003c/em\u003e (Thunb.) Lour.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003etree\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eElaeocarpus decipiens\u003c/em\u003e Hemsl.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003etree\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCinnamomum camphora\u003c/em\u003e (L.) presl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003etree\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eMichelia alba\u003c/em\u003e DC.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003etree\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCinnamomum japonicum\u003c/em\u003e Sieb.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003etree\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eFicus virens\u003c/em\u003e Ait. var.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003etree\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCerasus serrulata\u003c/em\u003e (Lindl.) G.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003etree\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eAcer palmatum\u003c/em\u003e Thunb.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003etree\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eJacaranda mimosifolia\u003c/em\u003e D. Don\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003etree\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eKoelreuteria bipinnata\u003c/em\u003e Franch.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003etree\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePyracantha fortuneana\u003c/em\u003e (Maxim.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eshrub\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePhotinia serrulata\u003c/em\u003e Lindl.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eshrub\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eBougainvillea glabra\u003c/em\u003e Choisy.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eshrub\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eViburnum odoratissimum\u003c/em\u003e Ker-Gawl.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eshrub\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePittosporum tobira\u003c/em\u003e (Thunb.) Ait.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eshrub\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eRhododendron simsii\u003c/em\u003e Planch.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eshrub\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eLigustrum quihoui\u003c/em\u003e Carr.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eShrub\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eVitex negundo\u003c/em\u003e L. \u003cem\u003evar. cannabifolia\u003c/em\u003e (Sieb. et Zucc.) Hand.-Mazz.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eShrub\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eViburnum chinshanense\u003c/em\u003e Graebn.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eShrub\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCoriaria nepalensis\u003c/em\u003e Wall.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eShrub\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMethods\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eMeasurement of Light Response Curves\u003c/h2\u003e\u003cp\u003eLight response curves were measured from August to September 2023, between 8:30 and 11:30 a.m. on sunny, windless days. For each plant, three mature, south-facing leaves from 1- to 2-year-old branches were selected. The leaves were fully exposed to sunlight, in healthy condition (free from diseases, pests, and damage, and with adequate water and nutrient availability), and of similar size. Measurements were conducted using the Li-6800 Portable Photosynthesis System (LI-COR Inc., USA), with three replicates per leaf. Before measurement, the leaves were exposed to saturating light intensity under a CO₂ concentration of 400 \u0026micro;mol CO₂ mol⁻\u0026sup1; for approximately 30 min to ensure photosynthetic induction, and measurements were taken before the onset of midday photosynthesis depression. The leaf chamber temperature (Tblock) was set at 25\u0026deg;C, relative humidity (RH) was controlled at 65\u0026thinsp;\u0026plusmn;\u0026thinsp;5%, and CO₂ concentration was maintained at 400 \u0026micro;mol CO₂ mol⁻\u0026sup1;. Each set of leaves was acclimated to these conditions for approximately 30 min before the measurement began. The light intensity gradient was set at 1800, 1500, 1000, 500, 250, 120, 60, 50, 30, 15, and 0 \u0026micro;mol m⁻\u0026sup2; s⁻\u0026sup1;, with data automatically recorded via the Li-6800 system.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMeasurement of Leaf Traits\u003c/h3\u003e\n\u003cp\u003eFor each tree species, the same three leaves used for the light response curve measurements were also employed to determine SPAD values, LA, SLA, LDMC, leaf thickness (LT), Carea, Narea, and C/N ratios. SPAD values were measured using a portable SPAD-502 chlorophyll meter, with three replicates per leaf and three leaves per plant, and the results were averaged. LA was determined by scanning the leaves on A4 paper and calculating their actual area using ImageJ software. The area of a single leaf was determined by dividing the total scanned area by the number of leaves. Leaf fresh weight was measured using a microbalance, after which the selected leaves were submerged in water for 12 h in the dark to reach saturation. After removing excess surface moisture with absorbent paper, the saturated fresh weight (SFW) was recorded. The leaves were then oven-dried at 105\u0026deg;C for 15 min to inactivate enzymes, followed by drying at 80\u0026deg;C until a constant weight was achieved and the dry weight (DW) was recorded. SLA was calculated as LA/DW and LDMC as DW/SFW. LT was measured utilizing a digital caliper with a precision of 0.02 mm, with five replicates per leaf, and the average value was recorded. Leaf volume was calculated as the product of LA and LT, while LTD was determined as DW/V. Finally, dried leaves were ground into a fine powder using a mortar, and their carbon and nitrogen content were analyzed using an Element analyzer (Vario Max CN Element Analyzer, Elementar, Germany).\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eThe light response characteristics were modeled using the modified rectangular hyperbola equation described by Ye ZP.et al.(2012):\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e​\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{\\text{n}}\\text{(}\\text{I:}\\text{)}\\text{=}\\text{\u0026alpha;}\\frac{(1-\\beta\\:I)}{1+\\gamma\\:I}I-{R}_{\\text{d}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/h2\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{P}_{\\text{n}}\\)\u003c/span\u003e\u003c/span\u003e represents the net photosynthetic rate, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{\u0026alpha;}\\)\u003c/span\u003e\u003c/span\u003e is the maximum photosynthetic rate, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e is the initial slope of the light response curve, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\gamma\\:\\)\u003c/span\u003e\u003c/span\u003e denotes the photosynthetic active radiation, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{\\text{d}}\\)\u003c/span\u003e\u003c/span\u003e is the dark respiration.\u003c/p\u003e\u003cp\u003eThe measured data were initially processed employing Excel and analyzed using SPSS statistical software. One-way ANOVA was employed to assess significant differences in photosynthetic characteristics and leaf traits among the 21 woody plant species and various growth forms (α\u0026thinsp;=\u0026thinsp;0.05). Correlation analyses were conducted to examine the relationships between photosynthetic characteristics and leaf traits, with P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 (**) indicating highly significant correlations and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (*) indicating significant correlations. Path and total effect analyses were conducted utilizing Amos to determine the driving effects of key leaf traits on photosynthetic characteristics. Single and multiple regression models were used to analyze the predictive capacity of major leaf traits on the photosynthetic capacity across different growth forms. Finally, data visualization and chart generation were conducted using Origin software.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eAnalysis of Light Response Curve Parameters\u003c/h2\u003e\n \u003cp\u003eThe analysis of photosynthetic characteristics among 21 woody plant species revealed significant interspecific variations in the light response curve parameters, including maximum net photosynthetic rate (Pnmax), apparent quantum yield (AQY), light saturation point (LSP), and light compensation point (LCP) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The Pnmax ranged from 3.45 to 16.42 \u0026micro;mol\u0026middot;m⁻\u0026sup2;\u0026middot;s⁻\u0026sup1;, while AQY varied between 10.13 and 42.90 \u0026micro;mol\u0026middot;mol⁻\u0026sup1;. The LSP and LCP values ranged from 453.13 to 1558.07 \u0026micro;mol\u0026middot;m⁻\u0026sup2;\u0026middot;s⁻\u0026sup1;, and 3.69 to 9.40 \u0026micro;mol\u0026middot;m⁻\u0026sup2;\u0026middot;s⁻\u0026sup1;, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eAt the growth form level, no significant differences were observed in Pnmax and AQY between shrubs and trees, while shrubs generally exhibited higher values than trees. In contrast, significant differences were observed in LSP and LCP between the two growth forms (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with shrubs exhibiting higher values for both parameters than those of trees.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eAnalysis of Plant Leaf Functional Traits\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the variations in leaf traits among woody plants. Significant interspecific variations (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were observed in structural traits, including LTD, LA, LDMC, and SLA, alongside chemical traits, including Chlorophyll content (Chl), Carea, Narea, and C/N. These traits were significantly influenced by leaf habit.\u003c/p\u003e\n \u003cp\u003eOverall, plant LTD ranged from 0.001 to 0.078 g\u0026middot;cm-\u003csup\u003e3\u003c/sup\u003e, while LA varied between 0.37 and 103.87 cm\u003csup\u003e2\u003c/sup\u003e. LDMC was within 0.21 to 0.82 g\u0026middot;g-1, and SLA spanned from 35.62 to 333.33 cm2\u0026middot;g-1. Among the chemical traits, Chl content exhibited a variation range of approximately 60 to 33. The Carea and Narea ranged from 66.33 to 11.22 g\u0026middot;m2 and 2.28 to 0.46 g\u0026middot;m2, respectively. The C/N ratio varied between 42.81 and 14.80.\u003c/p\u003e\n \u003cp\u003eAt the growth form level, functional leaf traits SLA, Chl, LDMC, and the contents of C and N did not show significant differences, following a general trend of shrubs\u0026thinsp;\u0026gt;\u0026thinsp;trees. However, LTD was significantly higher in shrubs than in trees (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while LA was significantly greater in trees than in shrubs (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eCorrelation Analysis Between Light Response Curve Characteristic Parameters and Leaf Traits\u003c/h2\u003e\n \u003cp\u003eA correlation analysis was conducted to examine the relationship between the characteristic parameters of the light response curve and leaf traits across 21 woody plant species (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The results showed that SLA, Narea, and C/N were strongly correlated with Pnmax (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while LTD exhibited a significant correlation with Pnmax (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No significant correlations were observed between Pnmax and other traits, including LDMC, LA, Chl, and Carea. LTD, Narea, and C/N exhibited strong correlations with AQY (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while Chl displayed a significant correlation with AQY (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). LSP decreased as LTD and LA increased but showed a positive correlation with increasing SLA and Narea (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No significant correlations were observed between LSP and LDMC, while Chl, Carea, or C/N. LCP was significantly correlated only with the structural traits LA and SLA (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and showed no association with chemical traits. These findings suggest that the photosynthetic capacity among woody species is primarily influenced by leaf traits such as Narea, LTD, SLA, LA, and C/N.\u003c/p\u003e\n \u003cp\u003eFurther analysis of the correlation between light response curve parameters and leaf traits across different growth forms revealed distinct patterns. In trees, SLA, Narea, and C/N exhibited strong correlations with Pnmax (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). AQY was strongly correlated with the chemical traits Narea and C/N (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and significantly correlated with the structural traits LTD and SLA (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). SLA and Narea showed strong correlations with LSP (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with LSP increasing as these traits increased but decreased with higher C/N (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For LCP, only the structural trait LA showed a significant correlation (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while no significant relationships were observed with chemical traits. These findings suggest that the photosynthetic capacity of trees is primarily influenced by leaf traits such as Narea, SLA, and LA.\u003c/p\u003e\n \u003cp\u003eIn shrubs, the strongest correlation with Pnmax was observed for the chemical trait Narea (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), followed by the structural trait LA (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with no significant association with other traits. AQY was significantly and positively correlated only with LA (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), increasing as LA increased. LSP was significantly correlated only with LTD (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with higher LTD associated with lower LSP, while no significant correlations were observed with chemical traits such as Chl, Carea, Narea, or C/N (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). LCP exhibited a strong correlation only with the chemical trait Narea (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and showed no significant association with other traits. Overall, the photosynthetic capacity of shrubs is primarily influenced by leaf traits such as Narea, LTD, and LA.\u003c/p\u003e\n \u003cp\u003eIn summary, the photosynthetic capacity of woody plants is influenced by multiple leaf traits, particularly Narea, SLA, and LTD. However, these factors vary between trees and shrubs. In trees, photosynthetic capacity is primarily influenced by Narea, SLA, and LA, while in shrubs, it is primarily influenced by Narea, LTD, and LA. For Pnmax, both trees and shrubs depend on both structural and chemical traits, but trees depend more on nitrogen content and specific leaf area, while shrubs are more affected by leaf area.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eImpact Analysis of Leaf Traits on Light Response Curve Characteristic Parameters\u003c/h2\u003e\n \u003cp\u003eThis study elucidated the relationships between plant light response curve parameters and leaf traits through path analysis, illustrating the total effects among the variables (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The results indicated that the maximum net Pnmax in 21 plant species is directly and positively influenced by Narea and SLA but not directly influenced by LA. AQY is directly and positively influenced by Narea, while SLA and LA do not exert direct effects. LCP is directly and positively influenced by SLA but negatively influenced by LA, with no direct effect from Narea. Furthermore, Narea, SLA, and LA do not directly affect the LSP.\u003c/p\u003e\n \u003cp\u003eFrom the perspective of various growth forms, Pnmax in trees is directly and positively influenced by Narea and SLA but negatively influenced by LA. This suggests that higher nitrogen content and SLA enhance photosynthetic capacity, whereas excessive LA may reduce photosynthetic efficiency. AQY is directly and positively affected by Narea, with no direct effect from SLA or LA. LSP is directly and positively influenced by SLA, while Narea or LA do not exert direct effects. LCP is directly and positively affected by SLA but negatively influenced by LA, with no direct effect from Narea, highlighting the role of leaf structural traits in determining the light compensation point. For shrubs, Pnmax is directly and positively influenced by Narea, SLA, and LA, but none of these traits directly affect AQY, LSP, or LCP.\u003c/p\u003e\n \u003cp\u003ePath analysis revealed that the characteristic parameters of the light response curve, including Pnmax, AQY, LSP, and LCP, were significantly influenced by both structural (SLA, LA) and chemical (Narea) traits. In trees, photosynthetic capacity is directly influenced by Narea and SLA, while in shrubs, it is directly affected by Narea, SLA, and LA. To a certain extent, Narea, SLA, and LA can serve as predictive indicators of plant photosynthetic capacity based on leaf chemical and structural traits.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003ePrediction of Photosynthetic Capacity Based on Leaf Traits\u003c/h2\u003e\n \u003cp\u003eLeaf structural and chemical traits play a crucial role in determining plant photosynthetic capacity. Regression analysis revealed that key leaf trait indicators may directly influence Pnmax and AQY. A linear regression was conducted on leaf traits, including Narea, SLA, LA, and LTD. Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results.\u003c/p\u003e\n \u003cp\u003eAcross all experimental conditions, the two-factor prediction models for Pnmax and AQY consistently outperformed the single-factor prediction models, highlighting the interactive effects of leaf traits on photosynthetic capacity (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). For woody plants, the coefficient of determination (R\u0026sup2;) for Pnmax was 0.46 in the single-factor model, which increased to 0.62 in the two-factor model, while for AQY, the R\u0026sup2; values improved from 0.35 to 0.36 (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). In tree species, the R\u0026sup2; for Pnmax was 0.36 and 0.54 in the single- and two-factor models, respectively, while for AQY, the R\u0026sup2; values were 0.53 and 0.62, respectively. For shrub, the R\u0026sup2; for Pnmax was 0.60 in the single-factor model and slightly increased to 0.61 in the two-factor model, while for AQY, the R\u0026sup2; values were 0.35 and 0.36, respectively. From the perspective of different growth forms, the R\u0026sup2; values in the single-factor models for Pnmax and AQY were 0.36 and 0.53 for trees and 0.60 and 0.35 for shrubs, respectively. In the two-factor models, these values increased to 0.54 and 0.62 for trees and 0.61 and 0.36 for shrubs, respectively.\u003c/p\u003e\n \u003cp\u003eSpecifically, both Pnmax and AQY in woody plants and shrubs showed significant improvements in the two-factor models. However, while the Pnmax of shrubs was already relatively high in the single-factor model (R\u0026sup2; = 0.60), its improvement in the two-factor model was minimal. This suggests that Pnmax in shrubs is predominantly influenced by a single leaf trait, while in trees, photosynthetic capacity is better predicted when multiple factors are considered. These findings suggest that two-factor models better demonstrate predictive performance.\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eModel Simulation Parameters for the Relationship between Plant Photosynthetic Capacity and Major Leaf Trait Indicators\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003enmax\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eWoody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003csub\u003earea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003csub\u003earea\u003c/sub\u003e\u0026times;SLA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003csub\u003earea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003csub\u003earea\u003c/sub\u003e\u0026times;SLA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eShrub\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003csub\u003earea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003csub\u003earea\u003c/sub\u003e\u0026times;LA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eAQY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eWoody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003csub\u003earea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003csub\u003earea\u003c/sub\u003e\u0026times;LTD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-22.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003csub\u003earea\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003csub\u003earea\u003c/sub\u003e\u0026times;SLA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eShrub\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003csub\u003earea\u003c/sub\u003e\u0026times;LA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eDifferences in Photosynthetic Characteristics Among Plants with Different Growth Forms\u003c/h2\u003e\u003cp\u003eThe key parameters of the light response curve\u0026mdash;Pnmax, AQY, LSP, and LCP\u0026mdash;serve as essential indicators for assessing plant photosynthetic efficiency. These parameters provide insights into plant growth status and environmental adaptability (Qinglin et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Pnmax specifically represents the maximum photosynthetic capacity of leaves under optimal light conditions, while AQY reflects the photosynthetic potential of plants under low light intensity, making it a crucial metric for evaluating shade tolerance (Xu et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A higher AQY value indicates an enhanced capacity for low-light utilization. This study revealed that shrubs have a higher pigment-protein content than trees, which may partly explain the differences in their photosynthetic characteristics. LSP and LCP are crucial for determining the ability of plants to utilize light intensity. Higher LSP and LCP values indicate reduced inhibition under high-light conditions. In this study, significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in LSP and LCP were observed between trees and shrubs, indicating that these parameters are strongly influenced by environmental factors, including soil moisture, nutrient availability, and climatic conditions. These factors may lead to different adaptive strategies between the two growth forms. Additionally, leaf morphology and structure may also affect plant performance under varying light intensities, thereby affecting LSP and LCP. While no significant differences (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) were observed in Pnmax and AQY between trees and shrubs, this finding suggests that both growth forms exhibit comparable maximum photosynthetic rates and photosynthetic efficiencies. This similarity may reflect shared physiological mechanisms that enable efficient light utilization for growth and development. It also implies that, under certain ecological conditions, trees and shrubs may coexist and compete without a distinct dominance hierarchy.\u003c/p\u003e\u003cp\u003eOverall, the light response curve parameters (Pnmax, AQY, LSP, and LCP) were higher in shrubs than in trees, highlighting significant differences in photosynthetic capacity and habitat adaptability between the two growth forms. The higher LSP and LCP values in shrubs suggest a greater ability to utilize high and low light intensities compared to those of trees. Shrubs can reach light saturation at higher intensities, indicating greater photosynthetic efficiency under strong light conditions and better adaptation to the high-light environment of the study area. This advantage may enable shrubs to secure a favorable position in the competition for light resources, leading to faster growth rates and stronger reproductive capacity in certain ecosystems. By adopting a more rapid growth strategy, shrubs can quickly capture and utilize light resources in the short term, while trees may depend on long-term growth and resource accumulation. In summary, shrubs in the study area exhibit superior photosynthetic efficiency and relatively faster growth rates than trees.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eInfluence of Leaf Traits on Photosynthetic Characteristics in Plants with Different Growth Forms\u003c/h2\u003e\u003cp\u003eThe photosynthetic capacity of plants is closely associated with their leaf traits (Longkang et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Current research on the relationship between leaf traits and light response curve parameters has primarily focused on the effects of SLA and LA. In this study, Pnmax of trees and shrubs exhibited a highly significant positive correlation with Narea and LA. Nitrogen is an essential component of enzymes involved in plant photosynthesis, and its content directly influences photosynthetic efficiency. A larger LA enables plants to capture more solar radiation, thereby enhancing photosynthetic rates. Generally, photosynthetic rates are positively correlated with LA, as larger leaves facilitate greater light absorption, leading to higher net photosynthetic rates.\u003c/p\u003e\u003cp\u003ePlants optimize resource allocation based on their growth environment and competitive conditions. The optimization strategies of shrubs and trees in nutrient acquisition and photosynthesis contribute to increased Narea and LA, which support higher Pnmax. However, no significant correlations were observed between LDMC and Chl with Pnmax, AQY, LSP, and LCP across different growth forms. This suggests that photosynthetic parameters may be more influenced by environmental factors, including water availability, temperature, and nutrient supply. Additionally, variation in leaf anatomical structure and stomatal characteristics may contribute to the weak correlations between LDMC, Chl, and Pnmax. Temporal fluctuations in environmental conditions may also cause these correlations to vary across different growth stages or seasons, implying short-term observations may not fully capture their underlying relationships.\u003c/p\u003e\u003cp\u003ePnmax exhibited a significant linear relationship with SLA, with Pnmax increasing as SLA increased, aligning with findings from Jackson et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Croft et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This indicates that plants may expand LA to capture more solar radiation, thereby enhancing photosynthetic capacity. In trees, Pnmax was significantly positively correlated with SLA, suggesting that under sufficient light conditions, a larger SLA enhances light capture and promotes photosynthesis. However, this correlation was not significant in shrubs, potentially owing to the stabilization of LA in shrubs (Shihao et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe LSP of woody plants exhibits a significant positive correlation with SLA and Narea but a negative correlation with C/N, highlighting the crucial role of nitrogen. A lower C/N ratio indicates a higher relative nitrogen abundance, suggesting that plants prioritize nitrogen uptake to support growth and photosynthetic functions. Conversely, a high C/N ratio suggests greater carbon allocation to structural components, which may limit photosynthetic efficiency and result in a lower LSP. Plants adjust resource allocation strategies based on environmental conditions and growth demands. Plants with high SLA and Narea typically exhibit photosynthetic capacity and growth rates, enabling them to reach light saturation more rapidly under high-light conditions. In shrubs, LSP was significantly negatively correlated with LTD, indicating that increased LTD reduces the capacity to utilize intense light. In trees, LCP was significantly negatively correlated with LA, suggesting that a larger LA improves the ability to utilize low light. In contrast, the LCP of shrubs was significantly negatively correlated with Narea, indicating that shrubs with higher nitrogen content require higher light intensity to reach light compensation under low-light conditions. This finding suggests that while high nitrogen content enhances photosynthetic capacity, it may also reduce photosynthetic efficiency under low light, reflecting the complex interplay between nitrogen and photosynthesis. The carbon and nitrogen content in plant leaves serves as an indicator of metabolic capacity and overall resource utilization. The C/N ratio also reflects plant structural resistance (Longkang et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These results demonstrate a strong correlation between the Pnmax of trees and Narea.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eLeaf Trait Variations Across Different Growth Forms\u003c/h2\u003e\u003cp\u003eLeaf structural traits, including LTD, LDMC, SLA, and LA, serve as stable functional indicators shaped by long-term adaptation to external environmental conditions (Croft et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Cheng et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, leaf chemical traits, including Chl, Carea, Narea, and the C/N, indicate the nutrient uptake efficiency of plants from the external environment. SLA and LTD are key indicators of plant drought resistance, with SLA primarily associated with resource acquisition and retention and LTD related to water retention under drought stress (Wang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this study, trees exhibited lower SLA but higher LTD, indicating thicker cell walls and more compact cell structure. This structural adaptation promotes osmolyte accumulation (Rawat et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), thereby enhancing drought resistance in trees. The lower SLA suggests that a greater proportion of leaf dry matter is allocated to guard cells, which helps minimize water loss and optimize photosynthetic efficiency. In contrast, shrubs exhibited higher values for LDMC, SLA, Chl, Carea, and Narea. Increased Chl enhances photosynthetic efficiency, while the elevated SLA in shrubs, often associated with smaller leaves (Riva et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), improves light capture. Moreover, the higher dry matter content in thicker shrub leaves reduces water evaporation and strengthens stress tolerance. The more concentrated allocation of nutrients, including nitrogen and carbon, leads to increased Carea and Narea.\u003c/p\u003e\u003cp\u003eThe LDMC reflects the resource utilization efficiency of a plant in its environment (Riva et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In the study area, trees exhibited lower biomass and reduced nutrient and water demands compared to shrubs, resulting in lower LDMC. In contrast, shrubs, which are generally smaller and have shorter growth cycles, demonstrate greater efficiency in resource utilization, allowing for rapid dry matter and nutrient accumulation (Rawat et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This indicates that shrubs have a higher adaptive capacity for utilizing environmental resources in the study area than trees.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eInfluence Leaf Traits on Photosynthetic Characteristics in Plants with Different Growth Forms\u003c/h2\u003e\u003cp\u003eThe photosynthetic capacity of plants is strongly correlated with their leaf traits (Longkang et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Current research on the relationship between leaf traits and characteristic parameters of light response curves primarily focuses on the effects of SLA and LA. In this study, the Pnmax of trees and shrubs exhibited a highly significant positive correlation with Narea and LA. Nitrogen is a crucial component of key enzymes involved in plant photosynthesis, and its content directly affects photosynthetic efficiency. A larger LA allows plants to capture more solar radiation, thereby increasing photosynthetic rates. Generally, photosynthetic rates are positively correlated with LA, as larger leaves facilitate greater light absorption, leading to higher net photosynthetic rates.\u003c/p\u003e\u003cp\u003ePlants optimize resource allocation in response to their growth environment and competitive conditions. The adaptive strategies of shrubs and trees in nutrient acquisition and photosynthesis promote increased Narea and LA, thereby supporting higher Pnmax. However, no significant correlations were observed between LDMC and Chl with Pnmax, AQY, LSP, and LCP across different growth forms. This suggests that these photosynthetic parameters are more influenced by environmental factors, including water availability, temperature, and nutrient supply. Additionally, leaf anatomical structure and stomatal characteristics may affect photosynthetic performance, contributing to the weak correlations observed between LDMC, Chl, and Pnmax. Photosynthetic parameters may also exhibit varying correlations across different growth stages or seasons owing to temporal fluctuations, implying short-term observations may not accurately reflect their underlying relationships.\u003c/p\u003e\u003cp\u003eThe Pnmax displayed a significant linear relationship with SLA, with Pnmax increasing as SLA increased, aligning with findings by Jackson et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Croft et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This indicates that plants may expand LA to capture more solar radiation, thereby enhancing photosynthetic capacity. In trees, Pnmax was strongly positively correlated with SLA, suggesting that under adequate light conditions, a larger SLA facilitates improved light capture and promotes photosynthesis. However, this relationship was not significant in shrubs, potentially owing to the stabilization of LA in shrubs (Shihao et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe LSP of woody plants exhibited a significant positive correlation with SLA and Narea but a negative correlation with the C/N, highlighting the critical role of nitrogen. A lower C/N ratio signifies a higher proportion of nitrogen relative to carbon, suggesting that plants prioritize nitrogen uptake to support growth and photosynthetic functions. Conversely, plants with a high C/N ratio may allocate more carbon to structural synthesis, which could limit photosynthetic efficiency and result in a lower LSP. Plants generally optimize resource allocation based on environmental conditions and adaptive growth strategies. Plants with high SLA and Narea typically demonstrate superior photosynthetic performance and growth rates, enabling them to reach light saturation more rapidly under high light conditions. In shrubs, LSP exhibited a significant negative correlation with LTD, indicating that higher LTD limits the ability to utilize intense light. In trees, the LCP displayed a significant negative correlation with LA, suggesting that larger LA enhances the ability to utilize low light. In contrast, the LCP of shrubs was significantly negatively correlated with Narea, indicating that shrubs with higher nitrogen content require greater light intensity to achieve light compensation under low-light conditions. This finding implies that while high nitrogen content enhances photosynthetic capacity, it may also limit photosynthetic efficiency under low-light conditions, underscoring the intricate relationship between nitrogen and photosynthesis. The carbon and nitrogen content of plant leaves reflects their metabolic capacity and overall utilization of environmental resources. The C/N ratio indicates the physical resistance strength of plants (Longkang et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The findings further reveal a significant correlation between the Pnmax of trees and Narea.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003ePrediction of Plant Photosynthetic Capacity Based on Leaf Traits\u003c/h2\u003e\u003cp\u003eThe Pnmax and AQY serve as key indicators of plant photosynthetic capacity, reflecting both growth potential and resource utilization efficiency under varying environmental conditions. Examining the relationship between photosynthetic characteristics and leaf trait indices helps establish a scientific framework for evaluating photosynthetic capacity. This study, conducted in subtropical evergreen broad-leaved forests, reveals that SLA and Narea are significant predictors of plant photosynthetic capacity, with Narea demonstrating superior predictive power than that of SLA, aligning with findings by Grassi (2002) and others.\u003c/p\u003e\u003cp\u003ePlants with high SLA and Narea typically achieve higher Pnmax and AQY values, indicating superior resource acquisition and utilization efficiency (Scheepens et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). A high SLA, characterized by thin, lightweight leaf structures, enhances light capture and CO2 absorption, thereby promoting photosynthesis. High Narea reflects a greater allocation of nitrogen to leaves, a critical component of photosynthetic enzymes, including Rubisco, directly boosting photosynthetic efficiency. Additionally, plants with high SLA and Narea allocate fewer resources to leaf construction and protective tissues (Pietsch et al., 2014), allowing more investment in growth and photosynthetic activity, thus gaining competitive advantages. However, the predictive power of SLA and Narea exhibit limitations across varying plant growth forms. In shrubs, the combined influence of Narea and LA plays a more dominant role in predicting photosynthetic capacity, while SLA exhibits a weaker correlation. This may be attributed to the ability of shrubs to optimize leaf spatial distribution, such as increasing LA, to enhance photosynthetic efficiency rather than relying on SLA. The short growth cycles and high metabolic rates of shrubs, along with their flexible resource utilization strategies, may further weaken the relationship between SLA and Narea.\u003c/p\u003e\u003cp\u003eOur findings indicate that photosynthetic capacity prediction models incorporating SLA, LA, and Narea offer improved accuracy in simulating hydrological processes in subtropical evergreen broad-leaved forests. These indices not only serve as indicators of photosynthetic capacity but are also closely associated with water use efficiency. For instance, plants with high SLA and Narea exhibit higher transpiration rates, influencing water absorption and distribution, thereby affecting ecosystem hydrodynamics. Integrating these indices into predictive models enables a more comprehensive evaluation of plant functional role within ecosystems, providing a scientific foundation for ecological management.\u003c/p\u003e\u003cp\u003eIn conclusion, SLA and Narea are key leaf trait indices for evaluating plant photosynthetic capacity, though their predictive power is affected by various factors across different plant growth forms. Future research should examine the relationships between additional leaf trait indices and photosynthetic capacity to enhance predictive models, ultimately contributing to a more comprehensive theoretical framework for plant functional studies.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study elucidates the similarities and variations in photosynthetic characteristics between arborescent and shrub species in subtropical evergreen broad-leaved forests. The results demonstrate that while no significant variations were observed in Pmax and quantum yield of photosynthesis (Φ) between the two plant types (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), shrub species exhibited greater photosynthetic acclimation capacity and enhanced light compensation ability. The trait-based analysis identified Narea and SLA as the most effective predictors of photosynthetic capacity, with a multiple linear regression model (R2\u0026thinsp;=\u0026thinsp;0.62) outperforming the single-factor linear regression model (R2\u0026thinsp;=\u0026thinsp;0.46) in predictive accuracy. These findings provide deeper insights into the relationship between leaf traits and photosynthetic performance, offering novel perspectives for research in plant photosynthetic physiology. However, considering the diversity of plant species and habitats, future studies should include larger sample sizes and broader geographical coverage to validate the generalizability of these conclusions. Moreover, further research is needed to investigate the mechanistic effects of various environmental factors on plant photosynthetic characteristics.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eThis work was supported by\u003c/p\u003e\n\u003cp\u003e1.Open Subject Funding of CQORS-2024-2\u003c/p\u003e\n\u003cp\u003e2.Natural Science Foundation of\u0026nbsp;Chongqing (No:CSTB2022NSCQ-MSX1121)\u003c/p\u003e\n\u003cp\u003e3.Performance Incentive and Guidance Projects of\u0026nbsp;Chongqing Scientific Research Institution(No:CSTB2024JXJL-YFX0037,CSTB2024JXJL-YFX0053)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no known competing\u0026nbsp;financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eManyi Li: Conceptualization, Methodology, Writing original draft. Cheng Li: Funding acquisition, Project administration, Writing original draft. Lei Ma: Software, Writing\u0026ndash;review \u0026amp; editing. Lu Yao: Data curation, Methodology, Visualization. Mingze Xu: Software, Writing \u0026ndash; review \u0026amp; editing. Yunqi Wang: Methodology, Writing \u0026ndash; original draft.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlbert, C. H., et al. Intraspecific Functional Variability: Extent, Structure and Sources of Variation. Journal of Ecology 98.3 (2010): 604-613.\u003c/li\u003e\n\u003cli\u003eAnderegg, L. D. L., et al. Within-Species Patterns Challenge Our Understanding of the Leaf Economics Spectrum. Ecology Letters 21.5 (2018): 734-744.\u003c/li\u003e\n\u003cli\u003eBachofen, C., et al. Accounting for Foliar Gradients in Vcmax and Jmax Improves Estimates of Net CO2 Exchange of Forests. Agricultural and Forest Meteorology 314 (2022): 108771.\u003c/li\u003e\n\u003cli\u003eBerzaghi, F., et al. 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The New Phytologist 232.1 (2021): 134-147.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-plant-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jpre","sideBox":"Learn more about [Journal of Plant Research](http://link.springer.com/journal/10265)","snPcode":"10265","submissionUrl":"https://www.editorialmanager.com/jpre/default2.aspx","title":"Journal of Plant Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Leaf traits, Photosynthetic capacity, Predictive factors, Optimal model","lastPublishedDoi":"10.21203/rs.3.rs-7413523/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7413523/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFunctional plant traits are essential indicators of biodiversity conservation and ecosystem management. Understanding the relationships among leaf traits allows for the estimation of difficult-to-measure traits from those that are easier to quantify and widely distributed. This approach enhances the ability to analyze trait variation, identify scaling relationships, and address constraints in estimating plant-atmosphere carbon exchange. However, research on the correlation between photosynthetic capacity and leaf traits remains limited. Therefore, this study aims to explore the differences and correlations between leaf traits and photosynthetic capacity parameters across different growth forms. In this study, the leaf traits in 21 dominant woody species from a subtropical evergreen broad-leaved forest in southwestern China were assessed. Key photosynthetic parameters, including the maximum net photosynthetic rate, apparent quantum yield, light compensation point, and light saturation point, were investigated. Additionally, leaf traits such as leaf tissue density, leaf dry matter content, specific leaf area (SLA), leaf area, chlorophyll content, carbon content per unit leaf area, nitrogen content per unit leaf area (Narea), and carbon-to-nitrogen ratio were analyzed. Overall, no significant variations in maximum photosynthetic rate or photosynthetic quantum efficiency were observed between tree and shrub species in this forest. Shrub species exhibited greater adaptability and compensatory capacity to light conditions during photosynthesis. Using multiple linear regression models, SLA was identified as the key structural trait and Narea as the primary chemical trait for predicting the photosynthetic capacity of woody plants in this region. Nevertheless, for different growth forms, selecting optimal parameters for classification modeling in abiotic predictive models of photosynthetic capacity is recommended to improve prediction accuracy for subtropical evergreen broad-leaved forest plants.\u003c/p\u003e","manuscriptTitle":"Coordination of Leaf Structural and Chemical Traits in Predicting Photosynthetic Capacity of Woody Plants in Subtropical Evergreen Broadleaf Forests","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-16 07:36:44","doi":"10.21203/rs.3.rs-7413523/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2025-12-29T08:01:58+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-09-08T16:58:34+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-08T12:01:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-05T07:44:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Plant Research","date":"2025-09-03T10:12:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-plant-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jpre","sideBox":"Learn more about [Journal of Plant Research](http://link.springer.com/journal/10265)","snPcode":"10265","submissionUrl":"https://www.editorialmanager.com/jpre/default2.aspx","title":"Journal of Plant Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f33215ff-8f5d-413e-83ff-5520212e563b","owner":[],"postedDate":"September 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-03-07T10:37:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-16 07:36:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7413523","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7413523","identity":"rs-7413523","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}