Leaf morphological classification reveals structure–function continuum in Neosinocalamus affinis: integrating non-destructive area estimation with functional trait variation across geographic gradients

Leaf morphological classification reveals structure–function continuum in Neosinocalamus affinis: integrating non-destructive area estimation with functional trait variation across geographic gradients | 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 Article Leaf morphological classification reveals structure–function continuum in Neosinocalamus affinis: integrating non-destructive area estimation with functional trait variation across geographic gradients Miao Liu, Chunju Cai, Guanglu Liu, Xiaopeng Shi, Shuguang Li, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9042305/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background Leaf area is a critical functional trait governing photosynthetic capacity in bamboo ecosystems. However, the relationship between leaf morphological structure and multiple functional traits remains poorly understood in clump-forming bamboo species. This study investigated how leaf shape variation, quantified by the length-to-width (L/W) ratio, influences both allometric area estimation and a suite of leaf functional traits in Neosinocalamus affinis (Rendle) Keng f., an ecologically and economically important bamboo species in subtropical China. Results A dataset of 4,497 leaves from 38 sites across seven provinces, and 162 site-level observations of 13 leaf functional traits (leaf area, SLA, LDMC, leaf thickness, SPAD, Fv/Fm, and C:N:P stoichiometry) were analyzed. Leaves were classified into broader (L/W ≤ 5.5), elliptic (5.5 7.5) categories. The classified fitting model significantly outperformed Montgomery equation and multiple linear regression (R² = 0.886, RMSE = 3.72 cm², lowest AIC = 3560.3). The Montgomery parameter (k) increased systematically from broader (0.450) to slender leaves (0.659). This classification also captured significant functional trait variation: broader leaves exhibited higher SLA (408.8 vs. 335.1 cm²/g), higher nitrogen (30.93 vs. 26.88 g/kg), and lower LDMC (29.9% vs. 35.6%) compared to slender leaves. A strong SLA–LDMC trade-off (r = − 0.850) and N–P coordination (r = 0.590) were consistent with the leaf economics spectrum. Conclusions Morphological classification based on L/W ratio provides a unified framework that simultaneously improves leaf area estimation and reveals functional trait coordination. Leaf shape reflects fundamental resource investment trade-offs. The validated model combined with functional trait characterization provides an integrated tool for bamboo ecosystem assessment. Biological sciences/Ecology Earth and environmental sciences/Ecology Biological sciences/Plant sciences Bamboo Leaf economics spectrum Leaf functional traits Morphological classification Non-destructive estimation Neosinocalamus affinis Specific leaf area Stoichiometry Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Leaves are the primary organs for photosynthesis, transpiration, and gas exchange, fundamentally determining plant productivity and ecological functions (Ewert, 2004; Koyama et al., 2022). Leaf area represents a critical functional trait that governs light interception, photosynthetic efficiency, carbon assimilation, and water-energy balance (Shi et al., 2019a; Sun et al., 2023a; Xu et al., 2024). However, leaf area does not function in isolation—it is coordinated with structural, chemical, and physiological traits that collectively define the leaf economics spectrum (LES), a universal framework describing trade-offs between resource acquisition and conservation strategies (Wright et al., 2004; Reich, 2014). Understanding how leaf morphological structure relates to this broader functional trait network is essential for predicting ecophysiological strategies and environmental adaptability (Lin et al., 2020a; Fu et al., 2025; Li et al., 2024a). The leaf economics spectrum posits that leaf traits are coordinated along a continuum from ‘fast-return’ strategies (high SLA, high nitrogen, low LDMC) to ‘slow-return’ strategies (low SLA, low nitrogen, high LDMC) (Wright et al., 2004; Westoby et al., 2002). While the LES has been validated across global datasets, its manifestation within single species exhibiting high morphological plasticity remains less explored (Bruelheide et al., 2024; Wang et al., 2022b). Non-destructive leaf area estimation based on leaf length and width has been widely developed (Yu et al., 2020; Mu et al., 2024; Li et al., 2022b). The Montgomery equation, LA = k × L × W, is the most common approach (Montgomery, 1911; Shi et al., 2019a), but assumes constant proportionality across all leaf shapes, which may be violated in species with high morphological plasticity (Williams and Martinson, 2003; Ma et al., 2022). Classified fitting approaches that stratify leaves by shape categories have proven more successful for variable species (Wang et al., 2015; Yu et al., 2021; Lin et al., 2020a). A critical but unaddressed question is whether morphological categories that improve area estimation also capture meaningful variation in other functional traits. If the L/W ratio reflects systematic differences in construction costs, nutrient investment, and photosynthetic characteristics, then morphological classification would bridge allometric modeling and leaf functional ecology within a unified analytical framework. Bamboos cover approximately 31.47 million hectares globally (Song et al., 2020). Neosinocalamus affinis (Rendle) Keng f., commonly known as Cizhu, is one of the most important clump-forming bamboo species in southwestern China, distributed across Sichuan, Chongqing, Guizhou, and Yunnan provinces at elevations of 350–1,500 m (Ma et al., 2021; Wu et al., 2023). Its characteristically narrow, lanceolate leaves with high and variable L/W ratios provide an ideal model system for investigating structure-function relationships. The objectives were: ( 1 ) to develop and validate a morphology-category-specific non-destructive leaf area estimation model; ( 2 ) to characterize functional trait variation (SLA, LDMC, leaf thickness, SPAD, chlorophyll fluorescence, C:N:P stoichiometry) across L/W-defined categories; ( 3 ) to evaluate whether morphological classification captures leaf economics spectrum patterns; and ( 4 ) to assess environmental drivers of morpho-functional trait variation. We hypothesize that: (H1) classified fitting will improve area estimation accuracy; (H2) broader leaves will exhibit ‘fast-return’ and slender leaves ‘slow-return’ economics; and (H3) the Montgomery k will increase with leaf slenderness. Methods Plant material and sampling design Leaf samples of Neosinocalamus affinis were collected from 38 geographically distinct sites across seven provinces in southern China during 2023–2024 growing seasons (May–September; Table 1 ). Sites spanned elevations of 37–1,854 m, with MAT of 9.9–20.6°C and MAP of 651–1,559 mm. At each site, 3–8 mature clumps were randomly selected (≥ 10 m apart). Only fully expanded, healthy leaves were included. A total of 4,497 leaves were collected for leaf area estimation, and site-level means (n = 162) were computed for functional trait analyses. Table 1 Summary of sampling sites for Neosinocalamus affinis leaf collection across seven provinces in China (38 sites total; representative sites shown). Sampling Site Coordinates Elev. (m) n Mean LA (cm²) Changning, Sichuan 104°47'E, 28°20'N 379–436 152 21.3 ± 14.1 Bijiang, Guizhou 108°56'E, 27°26'N 419–439 139 17.5 ± 8.0 Baoxing, Sichuan 102°48'E, 30°12'N 949–1004 110 26.3 ± 10.3 Changning, Yunnan 99°24'E, 24°31'N 1700–1786 146 24.6 ± 7.0 Chishui, Guizhou 105°28'E, 28°18'N 344–499 158 21.8 ± 13.2 Daguan, Yunnan 103°30'E, 27°28'N 1057–1309 113 23.9 ± 5.5 Kaijiang, Sichuan 107°31'E, 31°02'N 425–450 111 26.1 ± 11.8 Kunming, Yunnan 102°25'E, 25°01'N 1854 30 24.6 ± 7.5 Longshan, Hunan 109°11'E, 29°08'N 344–455 162 22.6 ± 8.2 Lushan, Sichuan 102°33'E, 30°04'N 590–723 163 23.3 ± 11.2 Luoyang, Henan 112°14'E, 34°23'N 129 30 3.7 ± 1.3 Muchuan, Sichuan 103°32'E, 28°33'N 711–770 165 21.9 ± 9.5 Neijiang, Sichuan 105°07'E, 28°21'N 315–349 179 23.9 ± 11.0 Nayong, Guizhou 105°11'E, 26°48'N 1512–1571 149 25.3 ± 7.1 Pengan, Sichuan 106°10'E, 30°49'N 282–295 151 30.8 ± 7.7 Qingchuan, Sichuan 105°14'E, 32°22'N 553–594 168 30.8 ± 5.6 Qingshen, Sichuan 103°33'E, 29°29'N 393–450 173 32.7 ± 17.2 Santai, Sichuan 104°31'E, 30°49'N 380–390 167 18.6 ± 6.1 Sinan, Guizhou 108°06'E, 27°27'N 381–446 177 23.2 ± 7.9 Songtao, Guizhou 108°48'E, 28°04'N 389–451 174 26.8 ± 12.3 Tongjiang, Sichuan 107°15'E, 31°41'N 327–365 169 20.7 ± 11.1 Wangcang, Sichuan 106°23'E, 32°12'N 424–533 145 19.7 ± 7.0 Wulong, Chongqing 107°27'E, 29°10'N 253–943 133 29.5 ± 8.4 Wusheng, Sichuan 106°16'E, 30°16'N 193–295 167 20.7 ± 9.2 Huaan, Fujian 117°19'E, 25°00'N 219 30 22.3 ± 4.1 Honghe, Yunnan 102°15'E, 23°10'N 1379 22 29.8 ± 10.5 Longli, Guizhou 106°33'E, 26°11'N 1235–1266 150 17.1 ± 6.2 Luodian, Guizhou 106°27'E, 25°22'N 853–939 90 17.0 ± 4.0 Shuangjiang, Yunnan 99°26'E, 23°15'N 1314–1328 57 26.7 ± 7.8 Xiangtan, Hunan 112°25'E, 27°30'N 64–74 59 20.5 ± 5.3 Yongan, Fujian 117°12'E, 26°02'N 230 29 22.8 ± 5.5 Yubei, Chongqing 106°17'E, 29°24'N 173–230 58 27.3 ± 7.9 Yanhe, Guizhou 108°13'E, 28°18'N 470–517 168 24.0 ± 11.2 Yiyang, Hunan 112°10'E, 28°18'N 37–49 88 19.1 ± 7.0 Yongzhou, Hunan 111°27'E, 26°13'N 428 30 30.2 ± 9.5 Pingwu, Sichuan 104°29'E, 32°07'N 664–689 86 20.1 ± 7.0 Pengzhou, Sichuan 103°29'E, 31°03'N 748–870 158 20.5 ± 8.8 Zhenfeng, Guizhou 105°23'E, 25°12'N 940–1003 41 16.7 ± 5.3 All sites (total) — 37–1854 4497 23.3 ± 10.5 LA = leaf area. Values are means ± standard deviation. Plant material was formally identified by Miao Liu (co-author) at the International Center for Bamboo and Rattan, Beijing, China. Voucher specimens have been deposited in the herbarium of the Key Laboratory of National Forestry and Grassland Administration for Bamboo & Rattan Science and Technology (herbarium code: ICBR). The voucher numbers are ICBR-Neosinocalamus-2023001 to 2023038, corresponding to each of the 38 sampling sites. Leaf morphological and functional trait measurements Morphological parameters measured: ( 1 ) leaf length (L, cm) along the midrib; ( 2 ) maximum leaf width (W, cm); ( 3 ) leaf perimeter (P, cm); ( 4 ) actual leaf area (LA, cm²). Length and width: digital calipers (Mitutoyo, ± 0.01 mm). Leaf area: LI-3000C portable meter (LI-COR Biosciences). Additional functional traits: ( 5 ) leaf thickness (LT, mm) by digital micrometer; ( 6 ) fresh weight (FW, g) and ( 7 ) dry weight (DW, g) after 65°C/72 h; ( 8 ) SLA = LA/DW (cm²/g); ( 9 ) LDMC = DW/FW × 100 (%); ( 10 ) SPAD chlorophyll index (SPAD-502Plus, Konica Minolta, 3 readings averaged); ( 11 ) chlorophyll fluorescence: Fv/Fm, Fo, Fm, Fv/Fo, Qp, NPQ (after 30 min dark adaptation); ( 12 ) leaf C (TC), N (TN), P (TP) concentrations (g/kg) by elemental analysis and spectrophotometry; stoichiometric ratios C/N, N/P, C/P calculated. Environmental variables: elevation, slope, MAT, MAP, maximum/minimum monthly temperatures, soil moisture content (SWC), and soil bulk density (SBD). Leaf shape classification Based on L/W ratio, leaves were classified into: ( 1 ) broader (L/W ≤ 5.5); ( 2 ) elliptic (5.5 7.5). Thresholds were determined by frequency distribution analysis and cluster analysis (Fig. 1 ). Statistical analysis The 4,497 leaves were partitioned into training (70%, n = 3,147) and testing (30%, n = 1,350) subsets. Three models were evaluated: Model 1 (Montgomery: LA = k(L×W)^b), Model 2 (MLR: LA = a + b₁L + b₂W + b₃(L×W)), Model 3 (Classified: separate LA = a + b(L×W) per category). Performance: R², RMSE, MAE, AIC, BIC. Functional traits compared by one-way ANOVA with Tukey HSD. Pearson correlation matrix for 12 traits. Environmental correlations assessed. Python 3.10 (NumPy, SciPy, Pandas, scikit-learn). α = 0.05. Results Leaf morphological characteristics and variation The 4,497 N. affinis leaves exhibited substantial morphological variation (Table 2 ). Leaf area ranged 62-fold from 1.98 to 122.82 cm² (mean ± SD = 23.33 ± 10.47 cm², CV = 44.9%). The L/W ratio averaged 6.65 ± 1.90 (range: 0.15–18.52, CV = 28.6%). Broader leaves comprised 29.6% (n = 1,330), elliptic 43.1% (n = 1,939), and slender 27.3% (n = 1,228) (Fig. 1 ). Table 2 Descriptive statistics of leaf morphological parameters for Neosinocalamus affinis (n = 4,497). Parameter Min Max Mean SD CV (%) Leaf area (cm²) 1.98 122.82 23.33 10.47 44.9 Leaf length (cm) 1.77 29.97 15.23 3.94 25.9 Leaf width (cm) 0.81 12.50 2.38 0.69 28.8 L/W ratio 0.15 18.52 6.65 1.90 28.6 L × W (cm²) 3.12 193.19 37.43 17.36 46.4 SD = standard deviation; CV = coefficient of variation. Leaf area model performance comparison All three models demonstrated significant predictive ability, but the classified fitting model (Model 3) consistently outperformed alternatives (Table 3 ; Fig. 2 ). Model 3 achieved R² = 0.886, RMSE = 3.72 cm², and MAE = 2.51 cm², with lowest AIC (3560.3) and BIC (3591.6), and ΔAIC > 300 vs. Model 1. The slope coefficient (k) increased systematically from broader to slender leaves (0.450 → 0.568 → 0.659), while the intercept decreased (3.89 → 2.61 → 0.19). Table 3 Performance comparison of three leaf area prediction models on independent test dataset (n = 1,350). Model Equation R² RMSE MAE AIC BIC Montgomery LA = 0.867(L×W)^0.907 0.856 4.19 2.81 3873.0 3883.4 MLR LA = − 1.94 + 0.71L − 0.79W + 0.43(L×W) 0.875 3.91 2.68 3686.8 3707.7 Classified* See text 0.886 3.72 2.51 3560.3 3591.6 *Broader (L/W ≤ 5.5): LA = 0.450(L×W) + 3.89; Elliptic (5.5 7.5): LA = 0.659(L×W) + 0.19. RMSE and MAE in cm². Model validation and residual analysis The classified model produced unbiased predictions (predicted LA = 0.997 × observed LA + 0.096, R² = 0.886). Residuals were normally distributed (mean = 0.003, SD = 3.72 cm²) with homoscedastic distribution. Only 2.1% of observations had errors > 10 cm² (Fig. 3 ). Functional trait variation across leaf shape categories The three categories exhibited systematically different functional trait profiles (Table 4 ; Fig. 4 ). SLA declined from broader (408.8 ± 199.2) to slender (335.1 ± 159.2 cm²/g). LDMC increased from broader (29.9 ± 11.2%) to slender (35.6 ± 8.5%, F = 3.18, P < 0.05). TN was significantly higher in broader leaves (30.93 ± 7.75 g/kg) than elliptic (26.55 ± 4.20) and slender (26.88 ± 4.89, F = 7.93, P < 0.001). TP showed a similar pattern (F = 5.21, P < 0.01). C/N was lowest in broader leaves (14.57, F = 3.00, P < 0.05). SPAD and Fv/Fm showed less pronounced variation. Table 4 Leaf functional traits across three morphological categories of N. affinis (site-level data). Trait Broader (L/W ≤ 5.5) Elliptic (5.5 7.5) F n 25 111 26 — LA (cm²) 19.9 ± 6.9 24.0 ± 6.8 23.5 ± 6.8 3.76* SLA (cm²/g) 408.8 ± 199.2 359.4 ± 123.5 335.1 ± 159.2 1.80 LDMC (%) 29.9 ± 11.2 33.9 ± 7.7 35.6 ± 8.5 3.18* LT (mm) 0.098 ± 0.010 0.102 ± 0.019 0.109 ± 0.022 2.43 SPAD 38.1 ± 4.7 39.7 ± 4.1 39.8 ± 5.0 1.40 Fv/Fm 0.765 ± 0.046 0.779 ± 0.071 0.777 ± 0.049 0.47 TN (g/kg) 30.93 ± 7.75 26.55 ± 4.20 26.88 ± 4.89 7.93*** TP (g/kg) 3.43 ± 0.84 2.99 ± 0.50 3.02 ± 0.81 5.21** C/N 14.57 ± 2.97 16.09 ± 2.77 15.94 ± 2.88 3.00* N/P 9.10 ± 1.20 9.00 ± 1.49 9.22 ± 1.99 0.27 LDW (g) 0.058 ± 0.031 0.076 ± 0.037 0.080 ± 0.039 2.89 Mean ± SD. SLA=specific leaf area; LDMC=leaf dry matter content; LT=leaf thickness; TN=total nitrogen; TP=total phosphorus; LDW=leaf dry weight. *P < 0.05; **P < 0.01; ***P < 0.001. Leaf functional trait correlation network Pearson correlation analysis among 12 traits revealed a structured coordination network (Table 5 ; Fig. 5 ). The strongest correlation was SLA vs. LDMC (r = − 0.850). SLA was also negatively correlated with LDW (r = − 0.652) and LT (r = − 0.246). TN and TP were positively correlated (r = 0.590), and TN strongly negatively with C/N (r = − 0.914). SPAD correlated with TN (r = 0.295) and C/N (r = − 0.318). Leaf area correlated strongly with dimensions (L: r = 0.910; W: r = 0.774; LDW: r = 0.779) but weakly with nutrients. The L/W ratio correlated negatively with TN (r = − 0.239) and TP (r = − 0.186), positively with LDMC (r = 0.221). Table 5 Pearson correlation matrix of key leaf functional traits (n = 162). Bold: |r| > 0.30. LA LA LL LW L/W SLA LT LDMC SPAD TN TP C/N LDW — 0.91 0.77 0.26 -0.22 0.51 0.21 0.45 0.00 -0.07 -0.06 0.78 LL 0.91 — 0.61 0.57 -0.26 0.47 0.30 0.43 -0.13 -0.18 0.04 0.74 LW 0.77 0.61 — -0.29 -0.17 0.31 0.14 0.35 0.07 -0.05 -0.07 0.61 L/W 0.26 0.57 -0.29 — -0.13 0.21 0.22 0.16 -0.24 -0.19 0.15 0.23 SLA -0.22 -0.26 -0.17 -0.13 — -0.25 -0.85 -0.12 0.03 0.08 0.01 -0.65 LT 0.51 0.47 0.31 0.21 -0.25 — 0.03 0.19 -0.04 -0.03 0.04 0.59 LDMC 0.21 0.30 0.14 0.22 -0.85 0.03 — 0.10 -0.04 -0.07 -0.03 0.57 SPAD 0.45 0.43 0.35 0.16 -0.12 0.19 0.10 — 0.30 0.00 -0.32 0.34 TN 0.00 -0.13 0.07 -0.24 0.03 -0.04 -0.04 0.30 — 0.59 -0.91 0.02 TP -0.07 -0.18 -0.05 -0.19 0.08 -0.03 -0.07 0.00 0.59 — -0.53 -0.05 C/N -0.06 0.04 -0.07 0.15 0.01 0.04 -0.03 -0.32 -0.91 -0.53 — -0.08 LDW 0.78 0.74 0.61 0.23 -0.65 0.59 0.57 0.34 0.02 -0.05 -0.08 — LA=leaf area; LL=leaf length; LW=leaf width; SLA=specific leaf area; LT=leaf thickness; LDMC=leaf dry matter content; TN=total nitrogen; TP=total phosphorus; LDW=leaf dry weight. Bold: |r|>0.30. Environmental drivers of leaf morpho-functional variation Environmental correlations revealed weak but differential responses. MAP was positively associated with LA (r = 0.173) and L/W (r = 0.123). SBD was positively associated with LA (r = 0.198) and SLA (r = 0.165) but negatively with L/W (r = − 0.228) and LDMC (r = − 0.249). Elevation showed no significant direct effects on leaf morphological traits (Table 6 ). Table 6 Pearson correlations between environmental variables and key leaf functional traits (n = 162). Bold: |r| > 0.15. Env. variable LA L/W SLA LDMC SPAD TN Fv/Fm Elevation 0.006 −0.056 0.049 −0.053 0.141 −0.034 0.007 MAT 0.048 −0.095 −0.138 0.056 0.018 0.147 0.033 MAP 0.173 0.123 −0.067 0.060 −0.020 −0.007 −0.001 SWC −0.090 0.041 0.103 −0.025 −0.093 −0.153 −0.111 SBD 0.198 −0.228 0.165 −0.249 −0.006 −0.079 0.032 MAT=mean annual temperature; MAP=mean annual precipitation; SWC=soil water content; SBD=soil bulk density. Bold: |r|>0.15. Discussion Morphological classification bridges allometry and functional ecology This study demonstrates that L/W-based morphological classification provides a unified framework that simultaneously improves leaf area estimation and reveals meaningful functional trait coordination in N. affinis . The systematic increase in Montgomery k from broader (0.450) through elliptic (0.568) to slender leaves (0.659) confirms H1 and H3, revealing a continuous ‘morphological efficiency spectrum.’ As leaves become more elongated, their outline approaches the bounding rectangle (L × W), yielding higher k values (Shi et al., 2019b; Schrader et al., 2021). Our k values (0.450–0.659) are lower than other bamboo species (typically 0.68–0.75), reflecting the extreme narrowness of N. affinis leaves (mean L/W = 6.65), underscoring the need for species-level allometric models (Yu et al., 2021; Lin et al., 2020a). Leaf shape reflects intraspecific economics spectrum position Hypothesis H2 is largely supported. Broader leaves (higher SLA: 408.8 cm²/g, higher TN: 30.93 g/kg, lower LDMC: 29.9%, lower C/N: 14.57) align with the ‘fast-return’ end of the leaf economics spectrum (Wright et al., 2004; Reich, 2014; Westoby et al., 2002). Slender leaves (lower SLA: 335.1 cm²/g, lower TN: 26.88 g/kg, higher LDMC: 35.6%) align with the ‘slow-return’ end. The strong SLA–LDMC trade-off (r = − 0.850) is consistent with global patterns (Wright et al., 2004), demonstrating this fundamental trade-off operates at the intraspecific level. The N/P ratio (mean = 9.05) suggests nitrogen limitation across most sites (Güsewell, 2004). The conceptual model (Fig. 6 ) integrates these findings. Independence of leaf size and chemical composition Leaf area showed near-zero correlation with nutrient concentrations (TN: r = − 0.003; TP: r = − 0.069), contrasting with interspecific patterns (Wright et al., 2004). Within N. affinis , size and chemical composition represent independent variation dimensions, consistent with findings that intraspecific trait variation is structured differently from interspecific variation (Messier et al., 2017; Siefert et al., 2015). This implies leaf area and nutrient dynamics need independent prediction in bamboo ecosystem modeling. Environmental modulation and broader implications Weak environmental correlations suggest that N. affinis leaf traits respond more to local microsite conditions and genetic factors than broad climatic gradients. The positive MAP–LA association (r = 0.173) is consistent with expectations that wetter conditions support larger leaves (Wang et al., 2022b). The ‘Morphotype-Specific Functional Trait Framework’ can be generalized to other species with leaf shape plasticity, offering: ( 1 ) improved allometric accuracy; ( 2 ) interpretable parameters; and ( 3 ) a bridge between structural classification and physiological trait networks (Huxley et al., 2023; Li et al., 2024a). Limitations Functional trait analyses used site-level data (n = 162) rather than individual-leaf level. The model requires validation beyond the seven sampled provinces. The cross-sectional design cannot distinguish genetic from plastic variation. Future studies should incorporate common garden experiments and molecular approaches. Conclusions This study demonstrates that L/W-based morphological classification in Neosinocalamus affinis simultaneously: ( 1 ) achieves superior leaf area estimation (R² = 0.886) with k increasing from 0.450 to 0.659; ( 2 ) reveals functional trait differentiation consistent with the leaf economics spectrum (broader = fast-return, slender = slow-return); ( 3 ) identifies the SLA–LDMC trade-off (r = − 0.850) and N–P coordination (r = 0.590) at the intraspecific level; and ( 4 ) demonstrates independence of leaf size and chemical composition axes. Leaf shape reflects fundamental resource investment trade-offs. The integrated framework provides a practical tool for non-destructive ecosystem assessment and links structural allometry with functional ecology in bamboo. Declarations Ethics approval and consent to participate Leaf sampling was conducted on public lands with permission from the respective local forestry authorities. No specific permits were required for the collection of Neosinocalamus affinis leaves, as this species is not protected under Chinese regulations. All sampling activities complied with local, provincial, and national guidelines. Consent for publication Not applicable. Availability of data and materials The complete dataset (n = 4,497 leaves; n = 162 functional trait observations) is available from the corresponding author upon reasonable request and will be deposited in a public repository upon acceptance. Competing interests The authors declare no competing interests. Funding National Key R&D Program of China (2021YFD2200501). Authors’ contributions ML: conceptualization, formal analysis, original draft. CC: methodology. GL: review & editing. XS, SL: data curation. SF: funding, supervision. All authors approved the final manuscript. Acknowledgements We thank field assistants at all sampling sites. References Bruelheide, H. et al. Global leaf trait database (TRY) v6. Glob Chang. 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Evol. 7 , 9731–9738 (2017). Shi, P. et al. Leaf area–length allometry. Trees 33 , 1073–1085 (2019a). Shi, P. et al. Leaf area and L×W proportionality. Forests 10 , 178 (2019b). Siefert, A. et al. Intraspecific trait variation. Ecol. Lett. 18 , 1406–1419 (2015). Song, X. et al. Grain for Green and soil carbon. Sci. Rep. 10 , 12232 (2020). Sun, J. et al. Leaf carbon capture in bamboo forest. Front. Plant. Sci. 14 , 1137487 (2023a). Sun, J. et al. Overwintering of bamboo leaves. Ecol. Evol. 13 , e10476 (2023b). Wang, H. et al. Leaf traits as climate adaptations. J. Ecol. 110 , 1344–1355 (2022b). Wang, Y. N. et al. Cassava leaf area model. Chin. J. Crops . 36 , 1025–1029 (2015). Westoby, M. et al. Plant ecological strategies. Annu. Rev. Ecol. Syst. 33 , 125–159 (2002). Williams, L. III & Martinson, T. E. Leaf area estimation of grapevines. Sci. Hortic. 98 , 493–498 (2003). Wright, I. J. et al. The worldwide leaf economics spectrum. Nature 428 , 821–827 (2004). Wu, C. et al. 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Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 31 Mar, 2026 Reviews received at journal 30 Mar, 2026 Reviews received at journal 24 Mar, 2026 Reviews received at journal 20 Mar, 2026 Reviewers agreed at journal 15 Mar, 2026 Reviewers agreed at journal 14 Mar, 2026 Reviewers agreed at journal 13 Mar, 2026 Reviewers invited by journal 13 Mar, 2026 Editor invited by journal 12 Mar, 2026 Editor assigned by journal 06 Mar, 2026 Submission checks completed at journal 06 Mar, 2026 First submitted to journal 05 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-9042305","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":606273800,"identity":"9596bb3e-2023-4183-a1df-5dc9e34a9ae3","order_by":0,"name":"Miao Liu","email":"","orcid":"","institution":"International Center for Bamboo and Rattan","correspondingAuthor":false,"prefix":"","firstName":"Miao","middleName":"","lastName":"Liu","suffix":""},{"id":606273801,"identity":"c0a715bd-e650-48ae-83c4-e621cae33906","order_by":1,"name":"Chunju Cai","email":"","orcid":"","institution":"International Center for Bamboo and Rattan","correspondingAuthor":false,"prefix":"","firstName":"Chunju","middleName":"","lastName":"Cai","suffix":""},{"id":606273803,"identity":"2aec7e95-b8f9-4623-bd30-eba7803c3016","order_by":2,"name":"Guanglu Liu","email":"","orcid":"","institution":"International Center for Bamboo and Rattan","correspondingAuthor":false,"prefix":"","firstName":"Guanglu","middleName":"","lastName":"Liu","suffix":""},{"id":606273805,"identity":"08ffcaf4-4e07-43de-a611-f31a509178ac","order_by":3,"name":"Xiaopeng Shi","email":"","orcid":"","institution":"International Center for Bamboo and Rattan","correspondingAuthor":false,"prefix":"","firstName":"Xiaopeng","middleName":"","lastName":"Shi","suffix":""},{"id":606273806,"identity":"d5f4b422-fec0-4d15-aa31-6fd8d88af770","order_by":4,"name":"Shuguang Li","email":"","orcid":"","institution":"International Center for Bamboo and Rattan","correspondingAuthor":false,"prefix":"","firstName":"Shuguang","middleName":"","lastName":"Li","suffix":""},{"id":606273807,"identity":"5766162b-1f6e-4819-bd27-3fa31b8158e3","order_by":5,"name":"Shaohui Fan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIiWNgGAWjYBAC9gYGBmaGCgkGA6K18BwAaTlDshbGNgZStLCfPfy5cJ6FvLn0AcYPPxjs8ghr4clLk565TcJwZ18Cs2QPQ3IxQS32DDlmzLzbJBIMzjAwSDMwHEhsIGgL/xvjz7xzwFqYfxOnRSLHQJq3AayFjUhbJN6YSfMcA/qlh7HNsscgmRiH5Rh/5qmpkzfnYT5840eFHWEtSICxgZTYGQWjYBSMglGADwAAMOcw7bLtWQMAAAAASUVORK5CYII=","orcid":"","institution":"International Center for Bamboo and Rattan","correspondingAuthor":true,"prefix":"","firstName":"Shaohui","middleName":"","lastName":"Fan","suffix":""}],"badges":[],"createdAt":"2026-03-05 16:08:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9042305/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9042305/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104744862,"identity":"b4ef07c9-7eec-4b38-b12a-72a646478295","added_by":"auto","created_at":"2026-03-16 17:22:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":64117,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of L/W ratio for 4,497 \u003cem\u003eN. affinis\u003c/em\u003e leaves (A) and schematic leaf shape categories (B).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9042305/v1/8e1e5966974f07b84c67423b.png"},{"id":104744868,"identity":"77bc8eba-c0ce-4f8a-8767-cb281ca32dbd","added_by":"auto","created_at":"2026-03-16 17:22:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":85134,"visible":true,"origin":"","legend":"\u003cp\u003eObserved vs. predicted leaf area for (A) Montgomery equation, (B) MLR, (C) Classified fitting. Dashed lines: 1:1 relationship. Colors: blue=broader, green=elliptic, orange=slender.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9042305/v1/61bec16ab061f0e5d1bf3e8d.png"},{"id":104782908,"identity":"732c1e69-63f4-4473-a5fe-0f6d243219c8","added_by":"auto","created_at":"2026-03-17 07:57:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":75548,"visible":true,"origin":"","legend":"\u003cp\u003eResidual diagnostics: (A) Residuals vs. fitted values; (B) Histogram of residuals; (C) Q-Q plot.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9042305/v1/165e397c7724553ae38272da.png"},{"id":104808584,"identity":"8245631b-0b1b-472d-be0b-d5b27fc829a6","added_by":"auto","created_at":"2026-03-17 12:38:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":118649,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional traits across morphological categories. (A) SLA; (B) LDMC; (C) Leaf nitrogen; (D) Leaf phosphorus; (E) C/N; (F) SPAD. *P\u0026lt;0.05; **P\u0026lt;0.01; ***P\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9042305/v1/fb7d21d57786f3aa6bc7b077.png"},{"id":104782823,"identity":"b7f25d1a-ca87-4086-8c73-f79593b48dad","added_by":"auto","created_at":"2026-03-17 07:57:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":145724,"visible":true,"origin":"","legend":"\u003cp\u003eTrait correlation network. (A) Heatmap; (B) Network diagram (|r|\u0026gt;0.30). Node colors: blue=morphological, green=structural, orange=chemical.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9042305/v1/1cf15564672826ac5cd14b75.png"},{"id":104783048,"identity":"51b69c74-ea11-4298-bcd4-8c5513176666","added_by":"auto","created_at":"2026-03-17 07:58:08","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":98624,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual model of the morphological-functional continuum in \u003cem\u003eN. affinis\u003c/em\u003e. L/W ratio relates to both Montgomery k (morphological efficiency) and leaf economics spectrum position.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9042305/v1/e9b6b32c7deb182f77c221af.png"},{"id":104835480,"identity":"8564957c-33b0-4904-b390-164a5855516a","added_by":"auto","created_at":"2026-03-17 17:45:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1881040,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9042305/v1/36b44e48-bbf8-4a4f-919d-8070b5b28921.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Leaf morphological classification reveals structure–function continuum in Neosinocalamus affinis: integrating non-destructive area estimation with functional trait variation across geographic gradients","fulltext":[{"header":"Background","content":"\u003cp\u003eLeaves are the primary organs for photosynthesis, transpiration, and gas exchange, fundamentally determining plant productivity and ecological functions (Ewert, 2004; Koyama et al., 2022). Leaf area represents a critical functional trait that governs light interception, photosynthetic efficiency, carbon assimilation, and water-energy balance (Shi et al., 2019a; Sun et al., 2023a; Xu et al., 2024). However, leaf area does not function in isolation\u0026mdash;it is coordinated with structural, chemical, and physiological traits that collectively define the leaf economics spectrum (LES), a universal framework describing trade-offs between resource acquisition and conservation strategies (Wright et al., 2004; Reich, 2014). Understanding how leaf morphological structure relates to this broader functional trait network is essential for predicting ecophysiological strategies and environmental adaptability (Lin et al., 2020a; Fu et al., 2025; Li et al., 2024a).\u003c/p\u003e \u003cp\u003eThe leaf economics spectrum posits that leaf traits are coordinated along a continuum from \u0026lsquo;fast-return\u0026rsquo; strategies (high SLA, high nitrogen, low LDMC) to \u0026lsquo;slow-return\u0026rsquo; strategies (low SLA, low nitrogen, high LDMC) (Wright et al., 2004; Westoby et al., 2002). While the LES has been validated across global datasets, its manifestation within single species exhibiting high morphological plasticity remains less explored (Bruelheide et al., 2024; Wang et al., 2022b).\u003c/p\u003e \u003cp\u003eNon-destructive leaf area estimation based on leaf length and width has been widely developed (Yu et al., 2020; Mu et al., 2024; Li et al., 2022b). The Montgomery equation, LA\u0026thinsp;=\u0026thinsp;k \u0026times; L \u0026times; W, is the most common approach (Montgomery, 1911; Shi et al., 2019a), but assumes constant proportionality across all leaf shapes, which may be violated in species with high morphological plasticity (Williams and Martinson, 2003; Ma et al., 2022). Classified fitting approaches that stratify leaves by shape categories have proven more successful for variable species (Wang et al., 2015; Yu et al., 2021; Lin et al., 2020a).\u003c/p\u003e \u003cp\u003eA critical but unaddressed question is whether morphological categories that improve area estimation also capture meaningful variation in other functional traits. If the L/W ratio reflects systematic differences in construction costs, nutrient investment, and photosynthetic characteristics, then morphological classification would bridge allometric modeling and leaf functional ecology within a unified analytical framework.\u003c/p\u003e \u003cp\u003eBamboos cover approximately 31.47\u0026nbsp;million hectares globally (Song et al., 2020). \u003cem\u003eNeosinocalamus affinis\u003c/em\u003e (Rendle) Keng f., commonly known as Cizhu, is one of the most important clump-forming bamboo species in southwestern China, distributed across Sichuan, Chongqing, Guizhou, and Yunnan provinces at elevations of 350\u0026ndash;1,500 m (Ma et al., 2021; Wu et al., 2023). Its characteristically narrow, lanceolate leaves with high and variable L/W ratios provide an ideal model system for investigating structure-function relationships.\u003c/p\u003e \u003cp\u003eThe objectives were: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) to develop and validate a morphology-category-specific non-destructive leaf area estimation model; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) to characterize functional trait variation (SLA, LDMC, leaf thickness, SPAD, chlorophyll fluorescence, C:N:P stoichiometry) across L/W-defined categories; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) to evaluate whether morphological classification captures leaf economics spectrum patterns; and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) to assess environmental drivers of morpho-functional trait variation. We hypothesize that: (H1) classified fitting will improve area estimation accuracy; (H2) broader leaves will exhibit \u0026lsquo;fast-return\u0026rsquo; and slender leaves \u0026lsquo;slow-return\u0026rsquo; economics; and (H3) the Montgomery k will increase with leaf slenderness.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant material and sampling design\u003c/h2\u003e \u003cp\u003eLeaf samples of \u003cem\u003eNeosinocalamus affinis\u003c/em\u003e were collected from 38 geographically distinct sites across seven provinces in southern China during 2023\u0026ndash;2024 growing seasons (May\u0026ndash;September; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Sites spanned elevations of 37\u0026ndash;1,854 m, with MAT of 9.9\u0026ndash;20.6\u0026deg;C and MAP of 651\u0026ndash;1,559 mm. At each site, 3\u0026ndash;8 mature clumps were randomly selected (\u0026ge;\u0026thinsp;10 m apart). Only fully expanded, healthy leaves were included. A total of 4,497 leaves were collected for leaf area estimation, and site-level means (n\u0026thinsp;=\u0026thinsp;162) were computed for functional trait analyses.\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\u003eSummary of sampling sites for \u003cem\u003eNeosinocalamus affinis\u003c/em\u003e leaf collection across seven provinces in China (38 sites total; representative sites shown).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSampling Site\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoordinates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElev. (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean LA (cm\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChangning, Sichuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104\u0026deg;47'E, 28\u0026deg;20'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e379\u0026ndash;436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e21.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBijiang, Guizhou\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108\u0026deg;56'E, 27\u0026deg;26'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e419\u0026ndash;439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e17.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaoxing, Sichuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102\u0026deg;48'E, 30\u0026deg;12'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e949\u0026ndash;1004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e26.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChangning, Yunnan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99\u0026deg;24'E, 24\u0026deg;31'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1700\u0026ndash;1786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e24.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChishui, Guizhou\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105\u0026deg;28'E, 28\u0026deg;18'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e344\u0026ndash;499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e21.8\u0026thinsp;\u0026plusmn;\u0026thinsp;13.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaguan, Yunnan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103\u0026deg;30'E, 27\u0026deg;28'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1057\u0026ndash;1309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e23.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKaijiang, Sichuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107\u0026deg;31'E, 31\u0026deg;02'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e425\u0026ndash;450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e26.1\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKunming, Yunnan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102\u0026deg;25'E, 25\u0026deg;01'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e24.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLongshan, Hunan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109\u0026deg;11'E, 29\u0026deg;08'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e344\u0026ndash;455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e22.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLushan, Sichuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102\u0026deg;33'E, 30\u0026deg;04'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e590\u0026ndash;723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e23.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuoyang, Henan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112\u0026deg;14'E, 34\u0026deg;23'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuchuan, Sichuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103\u0026deg;32'E, 28\u0026deg;33'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e711\u0026ndash;770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e21.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeijiang, Sichuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105\u0026deg;07'E, 28\u0026deg;21'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e315\u0026ndash;349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e23.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNayong, Guizhou\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105\u0026deg;11'E, 26\u0026deg;48'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1512\u0026ndash;1571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e25.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePengan, Sichuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106\u0026deg;10'E, 30\u0026deg;49'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e282\u0026ndash;295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e30.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQingchuan, Sichuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105\u0026deg;14'E, 32\u0026deg;22'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e553\u0026ndash;594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e30.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQingshen, Sichuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103\u0026deg;33'E, 29\u0026deg;29'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e393\u0026ndash;450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e32.7\u0026thinsp;\u0026plusmn;\u0026thinsp;17.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSantai, Sichuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104\u0026deg;31'E, 30\u0026deg;49'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e380\u0026ndash;390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e18.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSinan, Guizhou\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108\u0026deg;06'E, 27\u0026deg;27'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e381\u0026ndash;446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e23.2\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSongtao, Guizhou\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108\u0026deg;48'E, 28\u0026deg;04'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e389\u0026ndash;451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e26.8\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTongjiang, Sichuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107\u0026deg;15'E, 31\u0026deg;41'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e327\u0026ndash;365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e20.7\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWangcang, Sichuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106\u0026deg;23'E, 32\u0026deg;12'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e424\u0026ndash;533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e19.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWulong, Chongqing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107\u0026deg;27'E, 29\u0026deg;10'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e253\u0026ndash;943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e29.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWusheng, Sichuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106\u0026deg;16'E, 30\u0026deg;16'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e193\u0026ndash;295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e20.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuaan, Fujian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117\u0026deg;19'E, 25\u0026deg;00'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e22.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHonghe, Yunnan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102\u0026deg;15'E, 23\u0026deg;10'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e29.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLongli, Guizhou\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106\u0026deg;33'E, 26\u0026deg;11'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1235\u0026ndash;1266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e17.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuodian, Guizhou\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106\u0026deg;27'E, 25\u0026deg;22'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e853\u0026ndash;939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e17.0\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShuangjiang, Yunnan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99\u0026deg;26'E, 23\u0026deg;15'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1314\u0026ndash;1328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e26.7\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXiangtan, Hunan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112\u0026deg;25'E, 27\u0026deg;30'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64\u0026ndash;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e20.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYongan, Fujian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e117\u0026deg;12'E, 26\u0026deg;02'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e22.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYubei, Chongqing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106\u0026deg;17'E, 29\u0026deg;24'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e173\u0026ndash;230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e27.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYanhe, Guizhou\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108\u0026deg;13'E, 28\u0026deg;18'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e470\u0026ndash;517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e24.0\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYiyang, Hunan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112\u0026deg;10'E, 28\u0026deg;18'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e19.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYongzhou, Hunan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111\u0026deg;27'E, 26\u0026deg;13'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e30.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePingwu, Sichuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104\u0026deg;29'E, 32\u0026deg;07'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e664\u0026ndash;689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e20.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePengzhou, Sichuan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e103\u0026deg;29'E, 31\u0026deg;03'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e748\u0026ndash;870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e20.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhenfeng, Guizhou\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105\u0026deg;23'E, 25\u0026deg;12'N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e940\u0026ndash;1003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e16.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAll sites (total)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e37\u0026ndash;1854\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4497\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e23.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eLA\u0026thinsp;=\u0026thinsp;leaf area. Values are means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation.\u003c/em\u003e \u003c/p\u003e \u003cp\u003ePlant material was formally identified by Miao Liu (co-author) at the International Center for Bamboo and Rattan, Beijing, China. Voucher specimens have been deposited in the herbarium of the Key Laboratory of National Forestry and Grassland Administration for Bamboo \u0026amp; Rattan Science and Technology (herbarium code: ICBR). The voucher numbers are ICBR-Neosinocalamus-2023001 to 2023038, corresponding to each of the 38 sampling sites.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLeaf morphological and functional trait measurements\u003c/h3\u003e\n\u003cp\u003eMorphological parameters measured: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) leaf length (L, cm) along the midrib; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) maximum leaf width (W, cm); (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) leaf perimeter (P, cm); (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) actual leaf area (LA, cm\u0026sup2;). Length and width: digital calipers (Mitutoyo, \u0026plusmn;\u0026thinsp;0.01 mm). Leaf area: LI-3000C portable meter (LI-COR Biosciences).\u003c/p\u003e \u003cp\u003eAdditional functional traits: (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) leaf thickness (LT, mm) by digital micrometer; (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) fresh weight (FW, g) and (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) dry weight (DW, g) after 65\u0026deg;C/72 h; (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) SLA\u0026thinsp;=\u0026thinsp;LA/DW (cm\u0026sup2;/g); (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) LDMC\u0026thinsp;=\u0026thinsp;DW/FW \u0026times; 100 (%); (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) SPAD chlorophyll index (SPAD-502Plus, Konica Minolta, 3 readings averaged); (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) chlorophyll fluorescence: Fv/Fm, Fo, Fm, Fv/Fo, Qp, NPQ (after 30 min dark adaptation); (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) leaf C (TC), N (TN), P (TP) concentrations (g/kg) by elemental analysis and spectrophotometry; stoichiometric ratios C/N, N/P, C/P calculated.\u003c/p\u003e \u003cp\u003eEnvironmental variables: elevation, slope, MAT, MAP, maximum/minimum monthly temperatures, soil moisture content (SWC), and soil bulk density (SBD).\u003c/p\u003e\n\u003ch3\u003eLeaf shape classification\u003c/h3\u003e\n\u003cp\u003eBased on L/W ratio, leaves were classified into: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) broader (L/W\u0026thinsp;\u0026le;\u0026thinsp;5.5); (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) elliptic (5.5\u0026thinsp;\u0026lt;\u0026thinsp;L/W\u0026thinsp;\u0026le;\u0026thinsp;7.5); (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) slender (L/W\u0026thinsp;\u0026gt;\u0026thinsp;7.5). Thresholds were determined by frequency distribution analysis and cluster analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe 4,497 leaves were partitioned into training (70%, n\u0026thinsp;=\u0026thinsp;3,147) and testing (30%, n\u0026thinsp;=\u0026thinsp;1,350) subsets. Three models were evaluated: Model 1 (Montgomery: LA\u0026thinsp;=\u0026thinsp;k(L\u0026times;W)^b), Model 2 (MLR: LA\u0026thinsp;=\u0026thinsp;a + b₁L\u0026thinsp;+\u0026thinsp;b₂W\u0026thinsp;+\u0026thinsp;b₃(L\u0026times;W)), Model 3 (Classified: separate LA\u0026thinsp;=\u0026thinsp;a + b(L\u0026times;W) per category). Performance: R\u0026sup2;, RMSE, MAE, AIC, BIC. Functional traits compared by one-way ANOVA with Tukey HSD. Pearson correlation matrix for 12 traits. Environmental correlations assessed. Python 3.10 (NumPy, SciPy, Pandas, scikit-learn). α\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLeaf morphological characteristics and variation\u003c/h2\u003e \u003cp\u003eThe 4,497 \u003cem\u003eN. affinis\u003c/em\u003e leaves exhibited substantial morphological variation (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Leaf area ranged 62-fold from 1.98 to 122.82 cm\u0026sup2; (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u0026thinsp;=\u0026thinsp;23.33\u0026thinsp;\u0026plusmn;\u0026thinsp;10.47 cm\u0026sup2;, CV\u0026thinsp;=\u0026thinsp;44.9%). The L/W ratio averaged 6.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.90 (range: 0.15\u0026ndash;18.52, CV\u0026thinsp;=\u0026thinsp;28.6%). Broader leaves comprised 29.6% (n\u0026thinsp;=\u0026thinsp;1,330), elliptic 43.1% (n\u0026thinsp;=\u0026thinsp;1,939), and slender 27.3% (n\u0026thinsp;=\u0026thinsp;1,228) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of leaf morphological parameters for \u003cem\u003eNeosinocalamus affinis\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;4,497).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCV (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeaf area (cm\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e122.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e44.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeaf length (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeaf width (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL/W ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL \u0026times; W (cm\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e193.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e46.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eSD\u0026thinsp;=\u0026thinsp;standard deviation; CV\u0026thinsp;=\u0026thinsp;coefficient of variation.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLeaf area model performance comparison\u003c/h3\u003e\n\u003cp\u003eAll three models demonstrated significant predictive ability, but the classified fitting model (Model 3) consistently outperformed alternatives (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Model 3 achieved R\u0026sup2; = 0.886, RMSE\u0026thinsp;=\u0026thinsp;3.72 cm\u0026sup2;, and MAE\u0026thinsp;=\u0026thinsp;2.51 cm\u0026sup2;, with lowest AIC (3560.3) and BIC (3591.6), and ΔAIC\u0026thinsp;\u0026gt;\u0026thinsp;300 vs. Model 1. The slope coefficient (k) increased systematically from broader to slender leaves (0.450 \u0026rarr; 0.568 \u0026rarr; 0.659), while the intercept decreased (3.89 \u0026rarr; 2.61 \u0026rarr; 0.19).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePerformance comparison of three leaf area prediction models on independent test dataset (n\u0026thinsp;=\u0026thinsp;1,350).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEquation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRMSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMAE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMontgomery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLA\u0026thinsp;=\u0026thinsp;0.867(L\u0026times;W)^0.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3873.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3883.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLA\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.94\u0026thinsp;+\u0026thinsp;0.71L\u0026thinsp;\u0026minus;\u0026thinsp;0.79W\u0026thinsp;+\u0026thinsp;0.43(L\u0026times;W)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3686.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3707.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClassified*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSee text\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3560.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3591.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e*Broader (L/W\u0026thinsp;\u0026le;\u0026thinsp;5.5): LA\u0026thinsp;=\u0026thinsp;0.450(L\u0026times;W)\u0026thinsp;+\u0026thinsp;3.89; Elliptic (5.5\u0026thinsp;\u0026lt;\u0026thinsp;L/W\u0026thinsp;\u0026le;\u0026thinsp;7.5): LA\u0026thinsp;=\u0026thinsp;0.568(L\u0026times;W)\u0026thinsp;+\u0026thinsp;2.61; Slender (L/W\u0026thinsp;\u0026gt;\u0026thinsp;7.5): LA\u0026thinsp;=\u0026thinsp;0.659(L\u0026times;W)\u0026thinsp;+\u0026thinsp;0.19. RMSE and MAE in cm\u0026sup2;.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eModel validation and residual analysis\u003c/h3\u003e\n\u003cp\u003eThe classified model produced unbiased predictions (predicted LA\u0026thinsp;=\u0026thinsp;0.997 \u0026times; observed LA\u0026thinsp;+\u0026thinsp;0.096, R\u0026sup2; = 0.886). Residuals were normally distributed (mean\u0026thinsp;=\u0026thinsp;0.003, SD\u0026thinsp;=\u0026thinsp;3.72 cm\u0026sup2;) with homoscedastic distribution. Only 2.1% of observations had errors\u0026thinsp;\u0026gt;\u0026thinsp;10 cm\u0026sup2; (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFunctional trait variation across leaf shape categories\u003c/h2\u003e \u003cp\u003eThe three categories exhibited systematically different functional trait profiles (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). SLA declined from broader (408.8\u0026thinsp;\u0026plusmn;\u0026thinsp;199.2) to slender (335.1\u0026thinsp;\u0026plusmn;\u0026thinsp;159.2 cm\u0026sup2;/g). LDMC increased from broader (29.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2%) to slender (35.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5%, F\u0026thinsp;=\u0026thinsp;3.18, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). TN was significantly higher in broader leaves (30.93\u0026thinsp;\u0026plusmn;\u0026thinsp;7.75 g/kg) than elliptic (26.55\u0026thinsp;\u0026plusmn;\u0026thinsp;4.20) and slender (26.88\u0026thinsp;\u0026plusmn;\u0026thinsp;4.89, F\u0026thinsp;=\u0026thinsp;7.93, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). TP showed a similar pattern (F\u0026thinsp;=\u0026thinsp;5.21, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). C/N was lowest in broader leaves (14.57, F\u0026thinsp;=\u0026thinsp;3.00, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). SPAD and Fv/Fm showed less pronounced variation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLeaf functional traits across three morphological categories of \u003cem\u003eN. affinis\u003c/em\u003e (site-level data).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBroader (L/W\u0026thinsp;\u0026le;\u0026thinsp;5.5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElliptic (5.5\u0026thinsp;\u0026lt;\u0026thinsp;L/W\u0026thinsp;\u0026le;\u0026thinsp;7.5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSlender (L/W\u0026thinsp;\u0026gt;\u0026thinsp;7.5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLA (cm\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.76*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSLA (cm\u0026sup2;/g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e408.8\u0026thinsp;\u0026plusmn;\u0026thinsp;199.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e359.4\u0026thinsp;\u0026plusmn;\u0026thinsp;123.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e335.1\u0026thinsp;\u0026plusmn;\u0026thinsp;159.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDMC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.18*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLT (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.098\u0026thinsp;\u0026plusmn;\u0026thinsp;0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.102\u0026thinsp;\u0026plusmn;\u0026thinsp;0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.109\u0026thinsp;\u0026plusmn;\u0026thinsp;0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSPAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFv/Fm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.765\u0026thinsp;\u0026plusmn;\u0026thinsp;0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.779\u0026thinsp;\u0026plusmn;\u0026thinsp;0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.777\u0026thinsp;\u0026plusmn;\u0026thinsp;0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTN (g/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.93\u0026thinsp;\u0026plusmn;\u0026thinsp;7.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.55\u0026thinsp;\u0026plusmn;\u0026thinsp;4.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.88\u0026thinsp;\u0026plusmn;\u0026thinsp;4.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.93***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP (g/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.21**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC/N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.57\u0026thinsp;\u0026plusmn;\u0026thinsp;2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.09\u0026thinsp;\u0026plusmn;\u0026thinsp;2.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.94\u0026thinsp;\u0026plusmn;\u0026thinsp;2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.00*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN/P\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.10\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDW (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.058\u0026thinsp;\u0026plusmn;\u0026thinsp;0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.076\u0026thinsp;\u0026plusmn;\u0026thinsp;0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.080\u0026thinsp;\u0026plusmn;\u0026thinsp;0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. SLA=specific leaf area; LDMC=leaf dry matter content; LT=leaf thickness; TN=total nitrogen; TP=total phosphorus; LDW=leaf dry weight. *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLeaf functional trait correlation network\u003c/h2\u003e \u003cp\u003ePearson correlation analysis among 12 traits revealed a structured coordination network (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The strongest correlation was SLA vs. LDMC (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.850). SLA was also negatively correlated with LDW (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.652) and LT (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.246). TN and TP were positively correlated (r\u0026thinsp;=\u0026thinsp;0.590), and TN strongly negatively with C/N (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.914). SPAD correlated with TN (r\u0026thinsp;=\u0026thinsp;0.295) and C/N (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.318). Leaf area correlated strongly with dimensions (L: r\u0026thinsp;=\u0026thinsp;0.910; W: r\u0026thinsp;=\u0026thinsp;0.774; LDW: r\u0026thinsp;=\u0026thinsp;0.779) but weakly with nutrients. The L/W ratio correlated negatively with TN (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.239) and TP (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.186), positively with LDMC (r\u0026thinsp;=\u0026thinsp;0.221).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePearson correlation matrix of key leaf functional traits (n\u0026thinsp;=\u0026thinsp;162). Bold: |r| \u0026gt; 0.30.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eLA\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eL/W\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSLA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLDMC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSPAD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eC/N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eLDW\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.91\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.77\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.51\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.45\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.78\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.91\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.61\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.57\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.47\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e 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align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.31\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.61\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eL/W\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.57\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSLA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-0.85\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e-0.65\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLT\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.47\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.31\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.59\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDMC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-0.85\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.57\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSPAD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.45\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.43\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e-0.32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.59\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e-0.91\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.59\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e-0.53\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC/N\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e-0.32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e-0.91\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e-0.53\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLDW\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.78\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.74\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.61\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-0.65\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.59\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.57\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eLA=leaf area; LL=leaf length; LW=leaf width; SLA=specific leaf area; LT=leaf thickness; LDMC=leaf dry matter content; TN=total nitrogen; TP=total phosphorus; LDW=leaf dry weight. Bold: |r|\u0026gt;0.30.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEnvironmental drivers of leaf morpho-functional variation\u003c/h2\u003e \u003cp\u003eEnvironmental correlations revealed weak but differential responses. MAP was positively associated with LA (r\u0026thinsp;=\u0026thinsp;0.173) and L/W (r\u0026thinsp;=\u0026thinsp;0.123). SBD was positively associated with LA (r\u0026thinsp;=\u0026thinsp;0.198) and SLA (r\u0026thinsp;=\u0026thinsp;0.165) but negatively with L/W (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.228) and LDMC (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.249). Elevation showed no significant direct effects on leaf morphological traits (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePearson correlations between environmental variables and key leaf functional traits (n\u0026thinsp;=\u0026thinsp;162). Bold: |r| \u0026gt; 0.15.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnv. variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eL/W\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSLA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLDMC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSPAD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFv/Fm\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.173\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSWC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;0.111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.198\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.165\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eMAT=mean annual temperature; MAP=mean annual precipitation; SWC=soil water content; SBD=soil bulk density. Bold: |r|\u0026gt;0.15.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMorphological classification bridges allometry and functional ecology\u003c/h2\u003e \u003cp\u003eThis study demonstrates that L/W-based morphological classification provides a unified framework that simultaneously improves leaf area estimation and reveals meaningful functional trait coordination in \u003cem\u003eN. affinis\u003c/em\u003e. The systematic increase in Montgomery k from broader (0.450) through elliptic (0.568) to slender leaves (0.659) confirms H1 and H3, revealing a continuous \u0026lsquo;morphological efficiency spectrum.\u0026rsquo; As leaves become more elongated, their outline approaches the bounding rectangle (L \u0026times; W), yielding higher k values (Shi et al., 2019b; Schrader et al., 2021). Our k values (0.450\u0026ndash;0.659) are lower than other bamboo species (typically 0.68\u0026ndash;0.75), reflecting the extreme narrowness of \u003cem\u003eN. affinis\u003c/em\u003e leaves (mean L/W\u0026thinsp;=\u0026thinsp;6.65), underscoring the need for species-level allometric models (Yu et al., 2021; Lin et al., 2020a).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLeaf shape reflects intraspecific economics spectrum position\u003c/h2\u003e \u003cp\u003eHypothesis H2 is largely supported. Broader leaves (higher SLA: 408.8 cm\u0026sup2;/g, higher TN: 30.93 g/kg, lower LDMC: 29.9%, lower C/N: 14.57) align with the \u0026lsquo;fast-return\u0026rsquo; end of the leaf economics spectrum (Wright et al., 2004; Reich, 2014; Westoby et al., 2002). Slender leaves (lower SLA: 335.1 cm\u0026sup2;/g, lower TN: 26.88 g/kg, higher LDMC: 35.6%) align with the \u0026lsquo;slow-return\u0026rsquo; end. The strong SLA\u0026ndash;LDMC trade-off (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.850) is consistent with global patterns (Wright et al., 2004), demonstrating this fundamental trade-off operates at the intraspecific level. The N/P ratio (mean\u0026thinsp;=\u0026thinsp;9.05) suggests nitrogen limitation across most sites (G\u0026uuml;sewell, 2004). The conceptual model (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) integrates these findings.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eIndependence of leaf size and chemical composition\u003c/h2\u003e \u003cp\u003eLeaf area showed near-zero correlation with nutrient concentrations (TN: r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.003; TP: r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.069), contrasting with interspecific patterns (Wright et al., 2004). Within \u003cem\u003eN. affinis\u003c/em\u003e, size and chemical composition represent independent variation dimensions, consistent with findings that intraspecific trait variation is structured differently from interspecific variation (Messier et al., 2017; Siefert et al., 2015). This implies leaf area and nutrient dynamics need independent prediction in bamboo ecosystem modeling.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eEnvironmental modulation and broader implications\u003c/h2\u003e \u003cp\u003eWeak environmental correlations suggest that \u003cem\u003eN. affinis\u003c/em\u003e leaf traits respond more to local microsite conditions and genetic factors than broad climatic gradients. The positive MAP\u0026ndash;LA association (r\u0026thinsp;=\u0026thinsp;0.173) is consistent with expectations that wetter conditions support larger leaves (Wang et al., 2022b). The \u0026lsquo;Morphotype-Specific Functional Trait Framework\u0026rsquo; can be generalized to other species with leaf shape plasticity, offering: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) improved allometric accuracy; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) interpretable parameters; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) a bridge between structural classification and physiological trait networks (Huxley et al., 2023; Li et al., 2024a).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eFunctional trait analyses used site-level data (n\u0026thinsp;=\u0026thinsp;162) rather than individual-leaf level. The model requires validation beyond the seven sampled provinces. The cross-sectional design cannot distinguish genetic from plastic variation. Future studies should incorporate common garden experiments and molecular approaches.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study demonstrates that L/W-based morphological classification in \u003cem\u003eNeosinocalamus affinis\u003c/em\u003e simultaneously: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) achieves superior leaf area estimation (R\u0026sup2; = 0.886) with k increasing from 0.450 to 0.659; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) reveals functional trait differentiation consistent with the leaf economics spectrum (broader\u0026thinsp;=\u0026thinsp;fast-return, slender\u0026thinsp;=\u0026thinsp;slow-return); (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) identifies the SLA\u0026ndash;LDMC trade-off (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.850) and N\u0026ndash;P coordination (r\u0026thinsp;=\u0026thinsp;0.590) at the intraspecific level; and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) demonstrates independence of leaf size and chemical composition axes. Leaf shape reflects fundamental resource investment trade-offs. The integrated framework provides a practical tool for non-destructive ecosystem assessment and links structural allometry with functional ecology in bamboo.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLeaf sampling was conducted on public lands with permission from the respective local forestry authorities. No specific permits were required for the collection of \u003cem\u003eNeosinocalamus affinis\u003c/em\u003e leaves, as this species is not protected under Chinese regulations. All sampling activities complied with local, provincial, and national guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe complete dataset (n = 4,497 leaves; n = 162 functional trait observations) is available from the corresponding author upon reasonable request and will be deposited in a public repository upon acceptance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNational Key R\u0026amp;D Program of China (2021YFD2200501).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eML: conceptualization, formal analysis, original draft. CC: methodology. GL: review \u0026amp; editing. XS, SL: data curation. SF: funding, supervision. All authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank field assistants at all sampling sites.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBruelheide, H. et al. Global leaf trait database (TRY) v6. \u003cem\u003eGlob Chang. Biol.\u003c/em\u003e \u003cb\u003e30\u003c/b\u003e, e17067 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, S. et al. Status of bamboo industry in China. \u003cem\u003eWorld Res.\u003c/em\u003e \u003cb\u003e35\u003c/b\u003e, 1\u0026ndash;8 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEveringham, S. E. et al. 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Phytol\u003c/em\u003e. \u003cb\u003e237\u003c/b\u003e, 392\u0026ndash;407 (2023).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Bamboo, Leaf economics spectrum, Leaf functional traits, Morphological classification, Non-destructive estimation, Neosinocalamus affinis, Specific leaf area, Stoichiometry","lastPublishedDoi":"10.21203/rs.3.rs-9042305/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9042305/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eLeaf area is a critical functional trait governing photosynthetic capacity in bamboo ecosystems. However, the relationship between leaf morphological structure and multiple functional traits remains poorly understood in clump-forming bamboo species. This study investigated how leaf shape variation, quantified by the length-to-width (L/W) ratio, influences both allometric area estimation and a suite of leaf functional traits in \u003cem\u003eNeosinocalamus affinis\u003c/em\u003e (Rendle) Keng f., an ecologically and economically important bamboo species in subtropical China.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA dataset of 4,497 leaves from 38 sites across seven provinces, and 162 site-level observations of 13 leaf functional traits (leaf area, SLA, LDMC, leaf thickness, SPAD, Fv/Fm, and C:N:P stoichiometry) were analyzed. Leaves were classified into broader (L/W\u0026thinsp;\u0026le;\u0026thinsp;5.5), elliptic (5.5\u0026thinsp;\u0026lt;\u0026thinsp;L/W\u0026thinsp;\u0026le;\u0026thinsp;7.5), and slender (L/W\u0026thinsp;\u0026gt;\u0026thinsp;7.5) categories. The classified fitting model significantly outperformed Montgomery equation and multiple linear regression (R\u0026sup2; = 0.886, RMSE\u0026thinsp;=\u0026thinsp;3.72 cm\u0026sup2;, lowest AIC\u0026thinsp;=\u0026thinsp;3560.3). The Montgomery parameter (k) increased systematically from broader (0.450) to slender leaves (0.659). This classification also captured significant functional trait variation: broader leaves exhibited higher SLA (408.8 vs. 335.1 cm\u0026sup2;/g), higher nitrogen (30.93 vs. 26.88 g/kg), and lower LDMC (29.9% vs. 35.6%) compared to slender leaves. A strong SLA\u0026ndash;LDMC trade-off (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.850) and N\u0026ndash;P coordination (r\u0026thinsp;=\u0026thinsp;0.590) were consistent with the leaf economics spectrum.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eMorphological classification based on L/W ratio provides a unified framework that simultaneously improves leaf area estimation and reveals functional trait coordination. Leaf shape reflects fundamental resource investment trade-offs. The validated model combined with functional trait characterization provides an integrated tool for bamboo ecosystem assessment.\u003c/p\u003e","manuscriptTitle":"Leaf morphological classification reveals structure–function continuum in Neosinocalamus affinis: integrating non-destructive area estimation with functional trait variation across geographic gradients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-16 17:22:17","doi":"10.21203/rs.3.rs-9042305/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-31T08:37:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-30T16:43:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-25T02:57:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-21T03:34:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177933383690526258136314205240644820686","date":"2026-03-15T06:47:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"337712783136795491536120142447438838582","date":"2026-03-14T13:14:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"249782715327159728591645481219227259802","date":"2026-03-13T06:56:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-13T06:46:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-12T13:08:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-06T07:35:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-06T07:26:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-05T15:57:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f5a913db-23a6-4cb8-9eca-1f819876efb3","owner":[],"postedDate":"March 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":64518830,"name":"Biological sciences/Ecology"},{"id":64518831,"name":"Earth and environmental sciences/Ecology"},{"id":64518832,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2026-05-12T08:28:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-16 17:22:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9042305","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9042305","identity":"rs-9042305","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}