The Stoichiometric Characteristics and Nutrient Investment Strategies of Dominant Lianas in a Karst Rocky Mountain Area of Northern Guangxi, China

The Stoichiometric Characteristics and Nutrient Investment Strategies of Dominant Lianas in a Karst Rocky Mountain Area of Northern Guangxi, China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Stoichiometric Characteristics and Nutrient Investment Strategies of Dominant Lianas in a Karst Rocky Mountain Area of Northern Guangxi, China Ningxin Li, Ting Chen, Xuehan Liu, Wen Li, Lizhao Qin, Chongyuan Qin, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8549239/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background and Aims Ecological stoichiometry provides a framework for understanding plant adaptation to nutrient limitation and environmental stress. Lianas play vital roles in forest structure and function, yet their nutrient strategies in karst ecosystems remain poorly understood. Methods We analyzed 13 dominant liana species from karst rocky mountains in northern Guangxi, China, measuring leaf N, P, K, Ca, Mg, and coarse ash contents and their stoichiometric ratios. Correlation and principal component analyses (PCA) were used to identify nutrient trade-offs and investment strategies. Results The results indicated that, influenced by the Karst soil substrate, leaf Ca and Mg showed strong interspecific variation, while N, P, and their ratios remained stable, indicating physiological homeostasis of metabolic nutrients. The mean N/P ratio (12.45) suggested relative nitrogen limitation, likely influenced by Ca–P antagonism. Correlation and PCA revealed a trade-off axis from "resource acquisition" to "resource conservation": N, P, and K formed a synergistic metabolic module supporting rapid growth and high metabolism, while Ca, Mg, and coarse ash reflected structural reinforcement and environmental tolerance. Further analysis demonstrated that life form is a key driver of this strategic differentiation: herbaceous lianas favored an "acquisitive" strategy, whereas woody lianas tended toward a "conservative" strategy, illustrating niche partitioning and functional complementarity. Conclusions These findings reveal the adaptive trade-offs of Karst lianas across a multi-element dimension, deepen our understanding of their functional ecology, and provide a theoretical basis for vegetation restoration and species selection in regions undergoing rocky desertification regions. stoichiometric characteristics lianas Karst nutrient strategy interspecific variation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Ecological stoichiometry has emerged as an essential research tool for integrating biological processes across scales, from individuals to ecosystems, by examining the balance of energy and multiple chemical elements (Elser el al. 2000; Feng el al. 2024). This theory centres on quantifying and investigating the elemental composition of organisms and their environments, focusing on ecosystem energy balance and the dynamic equilibrium of multiple chemical elements within organisms. This approach reveals survival strategies under resource constraints and environmental stress (Wang el al. 2021; Wang el al. 2021) Leaves serve as primary organs for photosynthesis and material metabolism. Variations in their nutrient content and stoichiometric ratios precisely reflect plants' trade-off strategies for resource acquisition, utilisation, and allocation across different habitats (Lawson el al. 2023; Sun el al. 2023). Research indicates that nitrogen is often closely correlated with plant photosynthetic capacity and growth rates, while phosphorus content is crucial for energy metabolism and genetic material synthesis (Johnson el al. 2022;Vitousek el al. 2010). Potassium primarily functions in osmoregulation, enzyme activation, and assimilate transport within plants, serving as a key element for maintaining photosynthetic and metabolic efficiency (Johnson R el al. 2022). Calcium enhances plant stress resistance mainly by strengthening cell walls and maintaining ion homeostasis (White el al. 2003; Wdowiak el al. 2024) while magnesium influences photosynthesis, enzyme activation, and nucleic acid and protein synthesis (Chen el al. 2018). However, reports on the ecological stoichiometric characteristics of K, Ga, and Mg remain scarce. Furthermore, stoichiometric ratios reflect physiological constraints in nutrient utilisation and resource trade-offs (Anthony el al. 2003), yet research has predominantly focused on C:N:P ratios (Patricia el al. 2012; Sun el al. 2019; Wang el al. 2024), with limited attention to K-related ratios. Beyond individual elements, leaf crude ash content serves as a crucial indicator of total inorganic mineral elements within plants, revealing strategic differences in resource acquisition and environmental adaptation (Cornelissen el al. 2003; Xie el al. 2023). Existing research, however, often overlooks the consideration of leaf crude ash content in ecometabolism. Karst regions, as globally distributed unique ecosystems, have garnered significant attention due to their distinctive geological landforms and hydrological processes (Xu & Zhang, 2021). Such regions feature extensively exposed carbonate bedrock, resulting in shallow soil layers, poor water retention capacity, and highly heterogeneous habitats. This frequently induces seasonal drought stress, presenting severe challenges to plant survival in this extreme environment (Xu el al. 2021; Zhang el al. 2015). Simultaneously, weathering of the parent rock enriches soils with mineral elements such as calcium and magnesium. High concentrations of Ca 2+ and Mg 2+ ions not only directly impact plant physiological processes but may also inhibit the uptake and utilisation of key nutrients like nitrogen, phosphorus, and potassium through antagonistic interactions, thereby exacerbating nutrient limitations (Bai el al. 2025). To cope with this multifaceted stress environment, karst plants have evolved diverse adaptive mechanisms through morphological adjustments, physiological responses, and chemotrophic strategies (Liu el al. 2025; Liu el al., 2021; Kotula el al. 2021; Yu el al. 2022). Lianas are widely distributed in tropical and subtropical forests, playing a pivotal role in community structure and ecological function (Schnitzer el al. 2002; Tang el al. 2012; Zhang el al. 2022). Their biomass is primarily invested in leaf and stem elongation rather than supporting structures (Van el al. 2022), exhibiting distinct nutrient utilisation strategies compared to trees and shrubs. Within the highly fragmented and heterogeneous habitats of karst landscapes, lianas acquire resources through stolons and adventitious roots in soil pockets within rock crevices. Physiological integration enables nutrient sharing between stolons, potentially resulting in chemically distinct characteristics compared to other plants (Harrison el al. 2021; Uzay el al. 2022). Previous studies indicate that compared to trees and shrubs, lianas typically possess higher N and P concentrations in their foliage, thereby enhancing their carbon sequestration capacity and water use efficiency, and strengthening their competitive advantage in forest understories (Cai el al. 2022; Tang el al. 2018). Although studies have documented varying nutrient utilisation strategies among lianas across different karst habitats (Bai el al. 2025; Zhang el al. 2020), the origins of this diversification and interspecific variation remain poorly elucidated, with relevant research still scarce. Presently, ecological chemistry research on plants has primarily focused on trees and shrubs (Gong el al. 2024; Wang el al. 2024; Zhou el al. 2024), with notably insufficient attention directed towards the important functional group of lianas. Concurrently, studies have predominantly centred on relatively stable forest ecosystems (Gong el al. 2024; Wang el al. 2023), while research on karst degraded ecosystems undergoing restoration remains scarce. The functional role and nutrient strategies of lianas within such ecosystems also require further elucidation. This study focuses on dominant lianas in typical karst limestone mountain areas of northern Guangxi. By systematically measuring N, P, K, Ca, Mg content, crude ash content, and their stoichiometric ratios in dominant liana leaves, and employing statistical methods such as analysis of variance, correlation analysis, and principal component analysis, it delves into their variation patterns, elemental relationships, and species strategy differentiation. The findings will provide new theoretical foundations and data support for understanding the adaptive strategies of lianas in extreme karst environments and their role in the restoration of degraded karst ecosystems. Materials and Methods Overview of the Study Area The study area is situated in the Daling Mountain region of Xiling Town, Gongcheng Yao Autonomous County, Guilin City, at 110°47ʹ54ʹʹ–110°48ʹ16ʹʹ E, 24°55ʹ41ʹʹ–26°55ʹ02ʹʹ N, with an average elevation of 252 m. The terrain exhibits typical karstic mountainous and hilly topography, characterised by a humid subtropical monsoon climate with concurrent rainfall and heat, abundant precipitation, The annual mean temperature is 20.1°C, with annual precipitation of 1453.1 mm, average relative humidity of 74%, and annual evaporation of 1524.0 mm. The frost-free period lasts 336 days, and the annual average sunshine duration is 1479.40 hours (Yang el al. 2014). Its characteristic karst geological structure has resulted in severe soil erosion, with exposed bedrock and shallow topsoil. The predominant vegetation types in this area consist of secondary thorny shrubland, scrubland, and commercial plum orchards, with only limited occurrences of secondary, sub-dominant evergreen-deciduous broadleaf mixed forests. However, the area boasts abundant lianas, including Causonis japonica , Phanera championii , Kadsura heteroclita , Dioscorea polystachya , Parthenocissus dalzielii , Vitis amurensis , Lonicera japonica and so forth. These species demonstrate strong adaptability to karst environments, extensively covering bare rock surfaces and serving as pioneer plants for ecological restoration and rock desertification control in the region. Sampling In July 2024, nine 20 m × 30 m plots were established within the study area, selecting representative zones with vigorous liana growth. These plots were primarily distributed across mountaintops, mid-slopes, and lower slopes. Within each plot, five 5 m × 5 m shrub quadrats and five 1 m × 1 m herbaceous quadrats were arranged diagonally. Based on the community survey, relative abundance, relative frequency, and relative dominance of each liana species were calculated, with their importance values comprehensively determined. Thirteen species with importance values exceeding the average importance value of all lianas were selected as the final subjects for this study. For each species, randomly select 5–8 healthy mature plants within the plot. Collect 20–30 fully expanded, disease and pest-free mature leaves from each plant. Where insufficient individuals were present within the plot, additional specimens were collected from the surrounding area. Leaves from the same species were pooled to form a single replicate sample, with five replicates collected per species. Samples were placed in resealable bags and stored in a portable cooler to the laboratory. Table 1 Thirteen dominant lianas species Species Family Genus Life form 1.Causonis japonica Vitaceae Causonis Herbaceous lianas 2.Phanera championii Fabaceae Phanera Woody lianas 3.Trichosanthes kirilowii Cucurbitaceae Trichosanthes Herbaceous lianas 4.Parthenocissus dalzielii Vitaceae Parthenocissus Woody lianas 5.Smilax glaucochina Smilacaceae Smilax Woody lianas 6.Trachelospermum jasminoides Apocynaceae Trachelospermum Woody lianas 7.Kadsura heteroclita Schisandraceae Kadsura Woody lianas 8.Dioscorea polystachya Dioscoreaceae Dioscorea Herbaceous lianas 9.Ipomoea obscura Convolvulaceae Ipomoea Herbaceous lianas 10.Akebia trifoliata Lardizabalaceae Akebia Woody lianas 11.Lonicera japonica Caprifoliaceae Lonicera Woody lianas 12.Vitis amurensis Vitaceae Vitis Woody lianas 13.Mallotus repandus Euphorbiaceae Mallotus Woody lianas Experimental Methods Fresh leaf samples brought back to the laboratory were killed at 105°C for 30 minutes, then dried in an oven at 70°C to constant weight. The dried leaf samples were pulverised using a plant grinder and sieved through a 100-mesh screen. The resulting powdered samples were placed in sealed bags and stored in a desiccator for subsequent chemical analysis. Plant leaf N content was determined using a SEAL flow analyser (Auto Analyser 3, Germany). P and K content was measured by inductively coupled plasma atomic emission spectroscopy (Iris Advantage 1000, Thermo Jarrell Ash, Franklin, USA). Ca and Mg contents were determined by atomic absorption spectrophotometry (Hitachi Z2000 atomic absorption spectrophotometer, Japan). Crude ash content in leaves was measured using the direct ashing method. Data Processing and Analysis Data processing and statistical analysis were performed using Excel 2016 and SPSS21.0, while plotting was conducted with Origin 2021 software. Normality of distribution for nutrient data was assessed via the Kolmogorov-Smirnov test. For normally distributed data, arithmetic means were used to describe population characteristics; for non-normally distributed data, geometric means were employed. One-way ANOVA and LSD multiple comparisons were employed to test the significance of inter-species differences in the chemical metrics of leaves from 13 species. Pearson correlation analysis examined relationships between leaf nutrient element concentrations and their chemical ratios. Finally, principal component analysis (PCA) was employed to reduce the dimensionality of the six leaf nutrient element datasets across the 13 species. This aimed to identify the primary gradients driving species differentiation in nutrient strategies and to explore niche differentiation among lianas of different life forms within a multidimensional nutrient space. Results Distribution and Variation Characteristics of Leaf Nutrient Element Content Analysis of the overall distribution of leaf nutrient contents across 13 dominant lianas, revealed the following: Leaf nitrogen content ranged from 12.0 to 32.0 g·kg -1 , with a concentration between 20.0 and 24.0 g·kg -1 . Leaf phosphorus content ranged from 1.0 to 3.0 g·kg -1 , with 40% of samples distributed between 2.0 and 2.5 g·kg -1 . Leaf potassium content ranged from 10.0 to 24.0 g·kg -1 , primarily concentrated between 12.5 and 15.0 g·kg -1 (approximately 45%). Leaf calcium content exhibited the widest range at 10.0–55.0 g·kg -1 , predominantly distributed between 20.0 and 30.0 g·kg -1 . Leaf magnesium ranged from 3.0 to 10.0 g·kg -1 , with approximately 60% of samples falling within (4.0–6.0 g·kg -1 ). Leaf crude ash content ranged from 4.0 to 16.0%, with a concentration between 7.0 and 10.0%. The skewness values for all nutrient indicators in lianas plant leaves from this region were less than1, indicating a near-normal distribution. Consequently, arithmetic means were employed for overall characterisation. The average leaf contents of N, P, K, Ca, and Mg in lianas plants from this region were 23.13 g·kg -1 , 1.93 g·kg -1 , 12.75 g·kg -1 , 32.64 g·kg -1 , and 4.16 g·kg -1 . respectively, with an average crude ash content of 10.65%. Regarding the coefficient of variation (CV), all indicators exhibited moderate variability. Among these, LMC exhibited the highest coefficient of variation (47.60%), followed by LCaC (39.56%), while N showed the lowest CV (18.13%). This indicates that elements closely linked to geological background (Mg, Ca) demonstrate significantly greater inter-species variation than those involved in core metabolic processes (N). The mean leaf N/P, N/K, and P/K ratios were 12.45, 1.90, and 0.15, respectively, with relatively low coefficients of variation (17.24%–20.47%). These ratios exhibited weak variability and minimal differences in CV, suggesting they may be subject to strong physiological constraints and demonstrate high stability. Table 2 The nutrient content (mean±standard error) and variability of liana leaves Variables Mean ± SE Minimum Maximum Interspecific variation/% Intraspecific variation/% Leaf N Concentration (LNC, g·kg -1 ) 23.13±4.18 14.10 31.32 18.13 6.88 Leaf P Concentration (LPC, g·kg -1 ) 1.93±0.53 1.00 2.97 27.56 6.75 Leaf K Concentration (LKC, g·kg -1 ) 12.75±3.87 7.06 23.06 30.30 7.75 Leaf Ca Concentration (LCaC, g·kg -1 ) 32.64±12.73 13.68 54.16 39.56 3.53 Leaf Mg concentration (LMC, g·kg -1 ) 4.16±1.95 1.27 9.01 47.60 9.33 Leaf Coarse Ash (LCA, %) 10.65±2.71 7.07 15.68 25.56 3.30 Mean - - - 31.45 6.26 Inter-species Variability in Leaf Nutrient Content Single-factor analysis of variance (ANOVA) revealed extremely significant interspecific differences ( P < 0.001) among the 13 lianas species for N, P, K, Ca, Mg, and crude ash content (as shown in the figure). Specifically, Trichosanthes kirilowii exhibited the highest N (29.18 g·kg -1 ) and Mg (8.74 g·kg -1 ) contents, while Akebia trifoliata displayed the lowest N (15.20 g·kg -1 ) and Mg (1.38 g·kg -1 ) contents. Causonis japonica exhibited the highest P content (2.82 g·kg -1 ), while Akebia trifoliata had the lowest (1.11 g·kg -1 ). Dioscorea polystachya displayed the highest K content (21.96 g·kg -1 ), whereas Akebia trifoliata , Parthenocissus dalzielii and Phanera championii showed significantly lower K levels. Notably, Akebia trifoliata exhibited the highest Ca content (53.31 g·kg -1 ) among all species, whereas Lonicera japonica had the lowest (14.32 g·kg -1 ). These pronounced interspecific differences reflect the highly differentiated nutrient acquisition and allocation strategies developed by different species under karst stress conditions. At the species level, significant variations in N, P, K, Ca, Mg and crude ash content were observed across different species, reflecting plants' trade-off strategies between resource acquisition and nutrient demands to adapt to nutrient limitations and fluctuations within karst. For instance, the leaves of Akebia trifoliata exhibit elevated Ca and crude ash content, yet lower N, P, K, and Mg concentrations, whereas Trichosanthes kirilowi i possesses higher N and Mg levels. Regarding intraspecific variation, significant differences ( P < 0.05) were also observed in leaf chemical composition within each species. For instance, high intraspecific variation was noted for K (CV=12.82%) in Phanera championii and Mg (CV=18.21%) in Smilax glaucochina , while Ca in Causonis japonica (CV=1.02%) and P in Trichosanthes kirilowii (CV=4.07%) exhibited lower intraspecific variation, reflecting species-specific responses to habitat heterogeneity. Correlation Analysis of Leaf Nutrient Elements Pearson correlation analysis revealed complex interrelationships among leaf nutrient elements. LNC exhibited extremely significant positive correlations with both LPC and LKC ( P < 0.001), indicating that nitrogen accumulation typically accompanies the accumulation of other key metabolic elements, collectively serving plant growth and photosynthesis. LPC also exhibited highly significant positive correlations with LKC and LMC, while LKC and LMC similarly showed significant positive correlations ( P < 0.05), confirming the physiological synergy among these four elements: N, P, K, and Mg. Notably, however, LCaC showed no significant correlation with LNC, LPC, or LKC and even exhibited a negative trend with LPC and LKC, albeit not statistically significant. This suggests Ca accumulation may follow distinct regulatory pathways from other metabolic elements and involve trade-offs. Regarding stoichiometric ratios, LNC exhibited a highly significant positive correlation with N/P, while LPC showed a highly significant negative correlation with N/P, indicating that the N/P ratio is strongly constrained by P content. Furthermore, LPC displayed a significant negative correlation with N/K but a significant positive correlation with P/K. Principal Component Analysis Based on Leaf Nutrient Elements This study conducted a principal component analysis on the leaf nutrient content of 13 dominant lianas species. Results indicated that PC1 and PC2 collectively explained 83.3% of the total variance, effectively reflecting the primary differentiation patterns of leaf nutrients. PC1 (54.2%) served as the primary gradient distinguishing species nutrient strategies. Its negative axis showed significant correlations with leaf Ca, Mg content, and crude ash, reflecting investment in structural support and tolerance to high calcium stress. The positive axis correlated significantly with leaf N, P, and K, representing core nutrients required for rapid growth and metabolic activity. This axis reveals differentiation among lianas along a "resource conservation–resource acquisition" continuum, with negatively polarised species tending towards conservative investment for environmental stress tolerance, while positively polarised species prioritise rapid resource acquisition and utilisation. PC2 (29.1%) represents secondary differentiation, with the positive pole correlated with matrix elements such as Ca and Mg, and the negative pole with biogeochemical elements like N, P, and K, reflecting species trade-offs between matrix and cycling element utilisation. Regarding species life form distribution: Woody lianas predominantly occupy the negative region of the PC1 axis, exhibiting a conservative strategy characterised by high calcium and magnesium tolerance. This aligns with their extended life cycles and adaptation to karst environments with elevated calcium stress. Herbaceous lianas cluster in the positive region of PC1, exhibiting a resource-acquisition strategy characterised by high nitrogen, phosphorus, and potassium content, reflecting their rapid growth and regeneration survival strategy. In summary, principal component analysis not only effectively distinguishes the nutrient investment strategies of different lianas but also reveals that life form is a key factor driving this strategic differentiation. Discussion Leaf elemental stoichiometric characteristics and their variability The stoichiometric characteristics of plant leaves result from the interaction between genetic traits and the environment, reflecting both constraints on environmental nutrient supply and internal physiological regulation within plants (Liu el al. 2014; Sterner el al. 20002). The N content in lianas leaves within this study area (23.13 g·kg - ¹) exceeds both global and Chinese terrestrial plant averages (Han el al. 2011; Xie el al. 2023), as well as that of terrestrial plants in other karst regions (Liu el al. 2014; Wu el al. 2021), though it remains comparable to lianas species in Guizhou's karst terrain (22.86 g·kg -1 ) (Bai el al. 2025). Concurrently, the P content in lianas leaves (1.93 g·kg -1 ) in this region differs minimally from global-scale findings (1.99 g·kg -1 ) (Xie el al. 2023), yet it surpasses the average levels observed in Chinese terrestrial plants and other karst region lianas (Bai el al. 2025; Han el al. 2011; Wu el al. 2021)，This characteristic of leaf N and P content may be attributed to frequent agricultural activities in the study area, the nitrogen-fixing capacity of certain lianas, and rhizosphere regulation enhancing phosphorus utilisation efficiency. In this study, the leaf K content of karst lianas (12.75 g·kg -1 ) was consistent with other karst studies (Wu el al. 2021; Zhao el al. 2025), both being below the Chinese average (15.1 g·kg -1 ) (Han el al. 2011), This is attributed to the extremely low soil K content in karst regions, which struggles to provide sustained plant supply. Furthermore, the high Ca and Mg background in karst soils exerts an antagonistic effect on potassium uptake, leading to generally low leaf K content. This finding aligns with the conclusions of Wan et al (Wan el al. 2025). The leaf Ca content in this study (32.64 g·kg -1 ) was significantly higher than global (10–20 g·kg -1 ) and Chinese averages (12.5 g·kg -1 ) (Han el al. 2011; Xie el al. 2023), and exceeded most non-karst ecosystems (Wang el al. 2024; Shi el al. 2024), consistent with the generally elevated Ca content observed in other karst plants (Bai el al. 2025; McLaughlin & Wimmer 1999 ; Wan el al. 2025). This is attributed to the release of substantial Ca²⁺ from weathered carbonate parent rock, with plants typically exhibiting passive uptake under high-Ca soil conditions (McLaughlin & Wimmer 1999). Furthermore, certain species adaptively utilise Ca to enhance cell wall stability, increase leaf mechanical strength, and improve drought tolerance (Karley el al. 2009). In contrast, Mg content (4.16 g·kg -1 ) falls within the global average range (2–5 g·kg -1 ) (Han el al. 2011; Xie el al. 2023), though it exhibits significant interspecific variation, indicating divergent Mg uptake and utilisation strategies among species. The average leaf crude ash content was 10.65%, exceeding previous studies (7.67–8.5%) (Abifarin el al. 2021; Manisha el al. 2018). This may relate to the enrichment of mineral elements such as Ca and Mg in karst soils and the robust resource acquisition capacity of lianas (McLaughlin & Wimmer 1999). Variation in plant functional compounds across and within species constitutes a crucial prerequisite for species coexistence and community assembly, more accurately reflecting species responses to environmental change and resource competition (Güsewell 2004; Violle el al. 2012). Karst lianas leaf nutrient content exhibited moderate variation (31.45%), with interspecific variation (31.45%) exceeding intraspecific variation (6.25%). indicating that under the highly heterogeneous karst environment, different species exhibit distinct nutrient acquisition strategies and have developed relatively stable nutrient profiles. This aligns with previous studies on other plant functional groups in karst regions (Wang el al. 2023; Wu el al. 2021). A particularly crucial finding is that elements derived from the geological matrix (Mg and Ca) exhibited significantly higher coefficients of variation than N, which is dominated by biogeochemical cycling. Mg exhibited the highest coefficient of variation (47.60%), followed by Ca (39.56%), while N showed the lowest (18.13%). This disparity suggests distinct nutrient strategies among species primarily concerning calcium and magnesium: Some species may enhance structural stability or photosynthetic capacity by accumulating Ca and Mg, while others limit uptake to avoid potential ionic toxicity effects (Frans 2009; McLaughlin & Wimmer 1999 ). Nitrogen exhibited the lowest variability despite high leaf N content, a key element for maintaining plant photosynthesis and metabolism. This aligns with " the Stability of Limiting Elements Hypothesis " proposed in related studies (Han el al. 2011; Yan el al. 2015). This hypothesis suggests that plants selectively regulate nutrient composition to minimise fluctuations in highly abundant, critical, and relatively limited elements, thereby maintaining stable function in vital organs. This indicates that lianas growth in this region is more likely to be nitrogen-limited. Compared to elemental concentrations, the stoichiometric ratios N/P, N/K, and P/K exhibited low variability both interspecifically and intraspecifically. This supports the notion that element ratios better reflect intrinsic physiological constraints and nutrient utilisation stability than individual element concentrations (Güsewell 2004). In this study, the average leaf N/P ratio for lianas was 12.45, which is below the global terrestrial plant average (13.8) (Reich & Oleksyn 2004) ,and falls within the N-limiting threshold range (N/P < 14) (Koerselman & Arthur 1996), further confirming that dominant lianas species in this region face N limitation. This finding aligns with studies indicating N limitation during early stages of succession in karst restoration ecosystems (Yan el al. 2018; Zhang el al. 2015). During land degradation or initial vegetation recovery, slow soil organic matter accumulation and limited biological nitrogen fixation capacity render N bioavailability the primary bottleneck constraining plant growth. Additionally, we assessed the N/K and K/P ratios (derived from P/K conversion) in plant leaves using threshold ranges proposed by Venterink (Venterink el al. 2003). Results indicated N/K values below 2.1 and K/P values above 3.5, confirming that K-element limitation does not constrain lianas species in this region. Synergy and Trade-offs Among Leaf Nutrient Elements Plant nutrient utilisation strategies do not target individual elements in isolation but involve complex trade-offs and synergies among multiple elements (Güsewell 2004). Correlation analyses in this study revealed significant overall synergistic relationships among leaf nutrient elements in dominant lianas species. Leaf N exhibited extremely significant positive correlations with P, K, and Mg, consistent with previous regional and global-scale plant chemometric studies (Bai el al. 2025; Güsewell 2004; Han el al. 2011). This indicates that these key elements, closely linked to metabolism and growth, exhibit a tendency towards coupled accumulation within plants. Specifically, elements such as N, P, and K often show synergistic increases in "resource-acquiring" species to support higher photosynthetic efficiency and growth rates. This characteristic is also widely confirmed in the global "leaf economic spectrum" (Reich el al. 2014; Zheng el al. 2023). Notably, leaf Ca exhibits negative correlations with N, P, and K, particularly with P and K, though these relationships are not statistically significant. This phenomenon may stem partly from Ca 2+ in karst soils readily precipitating and immobilising P, reducing its availability, and competing with K ions during rhizosphere uptake and transmembrane transport, thereby creating potential antagonism at the foliar level (Bai el al. 2025; Wan el al. 2025). However, on the other hand, N, P, and K, being essential metabolic elements, are strictly regulated by plant physiological homeostasis (Güsewell 2004). Furthermore, significant atmospheric nitrogen deposition and exogenous nutrient inputs from agricultural activities within the regionpartially alleviate nutrient stress under high Ca conditions (Zheng el al. 2023), thereby weakening the negative correlation. Furthermore, PCA analysis indicates woody lianas favour a high-Ca strategy, whereas herbaceous lianas adopt a high-N, -P, -K approach. This suggests that strategic differences between life forms may statistically offset each other, diminishing the significance of the negative correlation between Ca and P or K. Overall, this trend still reflects the potential trade-off between structural element investment and metabolic element investment in lianas plants within karst environments (Reich el al. 2014). Differentiation in Nutrient Investment Strategies Coexisting species typically achieve coexistence through niche differentiation to reduce resource competition, with divergence in nutrient investment strategies constituting a key dimension of niche differentiation (Jonathan 2004). The PCA results in this study visually demonstrate the differentiation in leaf nutrient investment strategies among dominant lianas species. This differentiation represents a key evolutionary mechanism developed by species to achieve ecological niche partitioning for resource utilisation and long-term stable coexistence within the extreme stress and heterogeneous karst environment. The first principal component (PC1) represents the core "resource acquisition-resource conservation" gradient. The positive axis correlates with rapid growth-related N, P, and K, while the negative axis correlates with structure and stress tolerance-related Ca, Mg, and crude ash content. Species distribution along this gradient reflects their strategic trade-offs between "rapid growth" and "structural maintenance" (Reich el al. 2014). We observed that this strategic differentiation is highly coupled with plant life forms. For instance, woody lianas cluster along the negative axis of PC1, exhibiting "conservative" traits characterised by high Ca, Mg, and crude ash content. These species exhibit longer lifespans and more durable foliage, enhancing cell wall and tissue structure through Ca and Mg accumulation (Cai el al. 2009). This boosts stress resistance and long-term survival capacity, facilitating sustained existence in nutrient-poor habitats. In contrast, herbaceous lianas predominantly occupy the positive axis of PC1, exhibiting an "acquisition-type" profile characterised by high N, P, and K. These species feature short life cycles and rapid growth rates, favouring a "rapid investment-return" strategy to swiftly complete growth and reproduction whilst occupying ecological niches in frequently disturbed environments (Reich el al. 2014). This advantage is significantly amplified during transient periods of nutrient enrichment and within microhabitats. The distinctive stoichiometric patterns observed in karst lianas are largely shaped by the underlying soil geochemistry. In carbonate-derived soils, abundant Ca 2+ and Mg 2+ ions can precipitate with phosphate, reducing P bioavailability, while also competing with K + and NH 4+ for exchange sites. These ionic interactions impose strong constraints on nutrient uptake and allocation, leading to species-specific adjustments in leaf nutrient composition. Such soil-driven nutrient imbalances may explain the co-occurrence of high Ca-Mg accumulation and relatively low P concentration in liana leaves, as well as the trade-off axis between metabolic (N, P, K) and structural (Ca, Mg) elements revealed by PCA (Valladares el al. 2015). Conclusions This study examined the leaf stoichiometric traits and nutrient investment strategies of 13 dominant liana species in secondary karst shrublands of northern Guangxi. Results showed marked interspecific variation in Ca and Mg, while N, P, and their ratios remained relatively stable, reflecting metabolic homeostasis; overall, the community exhibited relative nitrogen limitation. The study revealed a trade-off axis from acquisitive to conservative strategies in karst lianas: high N, P, and K supported rapid growth and metabolism, whereas high Ca and Mg enhanced structural support and stress tolerance. Life form was a key driver, with herbaceous lianas favoring acquisitive strategies and woody lianas adopting conservative ones. Such differentiation promotes resource complementarity and community stability. These findings advance understanding of liana nutrient ecology and inform species selection for karst ecosystem restoration. Declarations Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Funding This study was supported by Guangxi Key Research and Development Programme Project (Gui Ke AB24010024, Gui Ke AB22080057); Basic Research Operating Expenses Project of Guangxi Institute of Botany (Gui Zhi Ye 23007). Conflict of interest statement The authors have no conflicts of interest to declare that are relevant to the contents of this article. Author contributions Ningxin Li, Xiankun Li and Shuhua Lu conceived and designed the study. Ningxin Li, Ting Chen, Lizhao Qin and Wen Li performed the experiments and collected the data. Ningxin Li, Ting Chen, Xuehan Liu and Chongyuan Qin analysed the data. Ningxin Li, Ting Chen and Xuehan Liu developed the figures and prepared the manuscript. All authors contributed to editing the manuscript and gave final approval for publication. Shuhua Lu reviewed and edited the manuscript. All authors participated in data interpretation and manuscript revision. 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BMC Plant Biol 24: 325. https://doi.org/10.1186/s12870-024-05010-x Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 02 Mar, 2026 Reviewers invited by journal 21 Jan, 2026 Editor invited by journal 14 Jan, 2026 Editor assigned by journal 14 Jan, 2026 First submitted to journal 07 Jan, 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8549239","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":578043989,"identity":"b481bca8-2ea6-4091-8cf8-9a5d3bb4de13","order_by":0,"name":"Ningxin 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2","display":"","copyAsset":false,"role":"figure","size":158944,"visible":true,"origin":"","legend":"\u003cp\u003eStatistical characteristics of leaf elements content. N:Sample number; AM: Arithmetic mean; GM: Geometricalmean; Min: Minimum; Max: Maximum; Skewness; Kurtosis; CV: Coefficient\u0026nbsp;of\u0026nbsp;variation,\u0026nbsp;CV≤ 20%\u0026nbsp;is\u0026nbsp;weak\u0026nbsp;variability,\u0026nbsp;20% \u0026lt; CV \u0026lt; 50%\u0026nbsp;is\u0026nbsp;medium\u0026nbsp;variability,\u0026nbsp;CV≥ 50%\u0026nbsp;is\u0026nbsp;strong\u0026nbsp;variability-y;\u0026nbsp;\u003cem\u003eP\u003c/em\u003e\u003csub\u003e(K-S)\u003c/sub\u003e:\u0026nbsp;Kolmogorov-Smirnov test.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8549239/v1/5d5c32da0a3978013999647a.jpg"},{"id":100970131,"identity":"e6915a52-3b84-4cde-a16b-b2979cd9b381","added_by":"auto","created_at":"2026-01-23 09:56:54","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":178498,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in ecological stoichiometric characteristics of leaf elements. Different letters above the columns represent significant differences (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), the data upon letters indicate intra-species coefficient of variation, which in bracket represent the average coefficient of variation within the species; The abscissa numbers 1–13 represent the species, from left to right: 1-Causonis japonica; 2-Phanera championii; 3-Trichosanthes kirilowii; 4-Parthenocissus dalzielii; 5-Smilax glaucochina;6-Trachelospermum jasminoides; 7-Kadsura heteroclita; 8-Dioscorea polystachya; 9-Ipomoea obscura; 10-Akebia trifoliata; 11-Lonicera japonica; 12-Vitis amurensis; 13-Mallotus repandus.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8549239/v1/4deb4295ce6e3e21c1cc0ab5.jpg"},{"id":101203197,"identity":"9993d2e1-75a8-4ed1-b1bd-146c84eaa42c","added_by":"auto","created_at":"2026-01-27 09:39:03","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":88065,"visible":true,"origin":"","legend":"\u003cp\u003ePearson correlation heat map of \u0026nbsp;\u0026nbsp;leaf element ecological stoichiometric characteristics.\u003c/p\u003e\n\u003cp\u003e** \u0026nbsp;\u0026nbsp;indicates very significant correlation (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01); * indicates \u0026nbsp;\u0026nbsp;significant correlation (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). LNC, Leaf N content; LPC, Leaf \u0026nbsp;\u0026nbsp;P content; LKC, Leaf K content; LCaC, Leaf Ca content; LMC, Leaf Mg content; \u0026nbsp;\u0026nbsp;LCA, Leaf coarse ash; N:P, nitrogen-phosphorus ratio; N:K, nitrogen-potassium \u0026nbsp;\u0026nbsp;ratio; P:K, Phosphorus-potassium ratio; The same below.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8549239/v1/a993fa06f30aa3ae3f1d1c7c.jpg"},{"id":100970132,"identity":"658c21f7-7744-4bd5-b56f-9ead72e3d635","added_by":"auto","created_at":"2026-01-23 09:56:54","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":62215,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis of leaf element among 13dominant lianas in a karst area in Northern Guangxi. The red circle represents woody lianas, the blue triangle represents herbaceous lianas. See the text for trait abbreviations.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8549239/v1/0fa3e5a1f08bd4dd9d1d3385.jpg"},{"id":101296683,"identity":"16867dcd-dfe4-489b-a85c-7921b7babf74","added_by":"auto","created_at":"2026-01-28 09:18:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1439381,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8549239/v1/8dee5b00-8f26-4efc-9c66-b436d781b20b.pdf"}],"financialInterests":"","formattedTitle":"The Stoichiometric Characteristics and Nutrient Investment Strategies of Dominant Lianas in a Karst Rocky Mountain Area of Northern Guangxi, China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEcological stoichiometry has emerged as an essential research tool for integrating biological processes across scales, from individuals to ecosystems, by examining the balance of energy and multiple chemical elements (Elser el al. 2000; Feng el al. 2024). This theory centres on quantifying and investigating the elemental composition of organisms and their environments, focusing on ecosystem energy balance and the dynamic equilibrium of multiple chemical elements within organisms. This approach reveals survival strategies under resource constraints and environmental stress (Wang el al. 2021; Wang el al. 2021) Leaves serve as primary organs for photosynthesis and material metabolism. Variations in their nutrient content and stoichiometric ratios precisely reflect plants\u0026apos; trade-off strategies for resource acquisition, utilisation, and allocation across different habitats (Lawson el al. 2023; Sun el al. 2023). Research indicates that nitrogen is often closely correlated with plant photosynthetic capacity and growth rates, while phosphorus content is crucial for energy metabolism and genetic material synthesis (Johnson el al. 2022;Vitousek el al. 2010). Potassium primarily functions in osmoregulation, enzyme activation, and assimilate transport within plants, serving as a key element for maintaining photosynthetic and metabolic efficiency (Johnson R el al. 2022). Calcium enhances plant stress resistance mainly by strengthening cell walls and maintaining ion homeostasis (White el al. 2003; Wdowiak el al. 2024) while magnesium influences photosynthesis, enzyme activation, and nucleic acid and protein synthesis (Chen el al. 2018). However, reports on the ecological stoichiometric characteristics of K, Ga, and Mg remain scarce. Furthermore, stoichiometric ratios reflect physiological constraints in nutrient utilisation and resource trade-offs (Anthony el al. 2003), yet research has predominantly focused on C:N:P ratios (Patricia el al. 2012; Sun el al. 2019; Wang el al. 2024), with limited attention to K-related ratios. Beyond individual elements, leaf crude ash content serves as a crucial indicator of total inorganic mineral elements within plants, revealing strategic differences in resource acquisition and environmental adaptation (Cornelissen el al. 2003; Xie el al. 2023). Existing research, however, often overlooks the consideration of leaf crude ash content in ecometabolism.\u003c/p\u003e\n\u003cp\u003eKarst regions, as globally distributed unique ecosystems, have garnered significant attention due to their distinctive geological landforms and hydrological processes (Xu \u0026amp; Zhang, 2021). Such regions feature extensively exposed carbonate bedrock, resulting in shallow soil layers, poor water retention capacity, and highly heterogeneous habitats. This frequently induces seasonal drought stress, presenting severe challenges to plant survival in this extreme environment (Xu el al. 2021; Zhang el al. 2015). Simultaneously, weathering of the parent rock enriches soils with mineral elements such as calcium and magnesium. High concentrations of Ca\u003csup\u003e2+ \u003c/sup\u003eand Mg\u003csup\u003e2+\u003c/sup\u003e ions not only directly impact plant physiological processes but may also inhibit the uptake and utilisation of key nutrients like nitrogen, phosphorus, and potassium through antagonistic interactions, thereby exacerbating nutrient limitations (Bai el al. 2025). To cope with this multifaceted stress environment, karst plants have evolved diverse adaptive mechanisms through morphological adjustments, physiological responses, and chemotrophic strategies (Liu el al. 2025; Liu el al., 2021; Kotula el al. 2021; Yu el al. 2022).\u003c/p\u003e\n\u003cp\u003eLianas are widely distributed in tropical and subtropical forests, playing a pivotal role in community structure and ecological function (Schnitzer el al. 2002; Tang el al. 2012; Zhang el al. 2022). Their biomass is primarily invested in leaf and stem elongation rather than supporting structures (Van el al. 2022), exhibiting distinct nutrient utilisation strategies compared to trees and shrubs. Within the highly fragmented and heterogeneous habitats of karst landscapes, lianas acquire resources through stolons and adventitious roots in soil pockets within rock crevices. Physiological integration enables nutrient sharing between stolons, potentially resulting in chemically distinct characteristics compared to other plants (Harrison el al. 2021; Uzay el al. 2022). Previous studies indicate that compared to trees and shrubs, lianas typically possess higher N and P concentrations in their foliage, thereby enhancing their carbon sequestration capacity and water use efficiency, and strengthening their competitive advantage in forest understories (Cai el al. 2022; Tang el al. 2018). Although studies have documented varying nutrient utilisation strategies among lianas across different karst habitats (Bai el al. 2025; Zhang el al. 2020), the origins of this diversification and interspecific variation remain poorly elucidated, with relevant research still scarce.\u003c/p\u003e\n\u003cp\u003ePresently, ecological chemistry research on plants has primarily focused on trees and shrubs (Gong el al. 2024; Wang el al. 2024; Zhou el al. 2024), with notably insufficient attention directed towards the important functional group of lianas. Concurrently, studies have predominantly centred on relatively stable forest ecosystems (Gong el al. 2024; Wang el al. 2023), while research on karst degraded ecosystems undergoing restoration remains scarce. The functional role and nutrient strategies of lianas within such ecosystems also require further elucidation. This study focuses on dominant lianas in typical karst limestone mountain areas of northern Guangxi. By systematically measuring N, P, K, Ca, Mg content, crude ash content, and their stoichiometric ratios in dominant liana leaves, and employing statistical methods such as analysis of variance, correlation analysis, and principal component analysis, it delves into their variation patterns, elemental relationships, and species strategy differentiation. The findings will provide new theoretical foundations and data support for understanding the adaptive strategies of lianas in extreme karst environments and their role in the restoration of degraded karst ecosystems.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003ch3\u003eOverview of the Study Area\u003c/h3\u003e\n\u003cp\u003eThe study area is situated in the Daling Mountain region of Xiling Town, Gongcheng Yao Autonomous County, Guilin City, at 110\u0026deg;47ʹ54ʹʹ\u0026ndash;110\u0026deg;48ʹ16ʹʹ E, 24\u0026deg;55ʹ41ʹʹ\u0026ndash;26\u0026deg;55ʹ02ʹʹ N, with an average elevation of 252 m. The terrain exhibits typical karstic mountainous and hilly topography, characterised by a humid subtropical monsoon climate with concurrent rainfall and heat, abundant precipitation, The annual mean temperature is 20.1\u0026deg;C, with annual precipitation of 1453.1 mm, average relative humidity of 74%, and annual evaporation of 1524.0 mm. The frost-free period lasts 336 days, and the annual average sunshine duration is 1479.40 hours (Yang el al. 2014). Its characteristic karst geological structure has resulted in severe soil erosion, with exposed bedrock and shallow topsoil.\u003c/p\u003e\n\u003cp\u003eThe predominant vegetation types in this area consist of secondary thorny shrubland, scrubland, and commercial plum orchards, with only limited occurrences of secondary, sub-dominant evergreen-deciduous broadleaf mixed forests. However, the area boasts abundant lianas, including \u003cem\u003eCausonis japonica\u003c/em\u003e, \u003cem\u003ePhanera championii\u003c/em\u003e, \u003cem\u003eKadsura heteroclita\u003c/em\u003e, \u003cem\u003eDioscorea polystachya\u003c/em\u003e, \u003cem\u003eParthenocissus dalzielii\u003c/em\u003e, \u003cem\u003eVitis amurensis\u003c/em\u003e, \u003cem\u003eLonicera japonica\u0026nbsp;\u003c/em\u003eand so forth. These species demonstrate strong adaptability to karst environments, extensively covering bare rock surfaces and serving as pioneer plants for ecological restoration and rock desertification control in the region.\u003c/p\u003e\n\u003ch3\u003eSampling\u003c/h3\u003e\n\u003cp\u003eIn July 2024, nine 20 m \u0026times; 30 m plots were established within the study area, selecting representative zones with vigorous liana growth. These plots were primarily distributed across mountaintops, mid-slopes, and lower slopes. Within each plot, five 5 m \u0026times; 5 m shrub quadrats and five 1 m \u0026times; 1 m herbaceous quadrats were arranged diagonally. Based on the community survey, relative abundance, relative frequency, and relative dominance of each liana species were calculated, with their importance values comprehensively determined. Thirteen species with importance values exceeding the average importance value of all lianas were selected as the final subjects for this study.\u003c/p\u003e\n\u003cp\u003eFor each species, randomly select 5\u0026ndash;8 healthy mature plants within the plot. Collect 20\u0026ndash;30 fully expanded, disease and pest-free mature leaves from each plant. Where insufficient individuals were present within the plot, additional specimens were collected from the surrounding area. Leaves from the same species were pooled to form a single replicate sample, with five replicates collected per species. Samples were placed in resealable bags and stored in a portable cooler to the laboratory.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eThirteen dominant lianas species\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"98%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003eSpecies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eFamily\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003eGenus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eLife form\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e1.Causonis japonica\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cem\u003eVitaceae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003eCausonis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eHerbaceous lianas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e2.Phanera championii\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cem\u003eFabaceae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003ePhanera\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eWoody lianas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e3.Trichosanthes kirilowii\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cem\u003eCucurbitaceae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003eTrichosanthes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eHerbaceous lianas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e4.Parthenocissus dalzielii\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cem\u003eVitaceae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003eParthenocissus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eWoody lianas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e5.Smilax glaucochina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cem\u003eSmilacaceae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003eSmilax\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eWoody lianas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e6.Trachelospermum jasminoides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cem\u003eApocynaceae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003eTrachelospermum\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eWoody lianas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e7.Kadsura heteroclita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cem\u003eSchisandraceae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003eKadsura\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eWoody lianas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e8.Dioscorea polystachya\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cem\u003eDioscoreaceae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003eDioscorea\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eHerbaceous lianas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e9.Ipomoea obscura\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cem\u003eConvolvulaceae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003eIpomoea\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eHerbaceous lianas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e10.Akebia trifoliata\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cem\u003eLardizabalaceae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003eAkebia\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eWoody lianas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e11.Lonicera japonica\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cem\u003eCaprifoliaceae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003eLonicera\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eWoody lianas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e12.Vitis amurensis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cem\u003eVitaceae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003eVitis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eWoody lianas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e13.Mallotus repandus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cem\u003eEuphorbiaceae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cem\u003eMallotus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003eWoody lianas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003eExperimental Methods\u003c/h3\u003e\n\u003cp\u003eFresh leaf samples brought back to the laboratory were killed at 105\u0026deg;C for 30 minutes, then dried in an oven at 70\u0026deg;C to constant weight. The dried leaf samples were pulverised using a plant grinder and sieved through a 100-mesh screen. The resulting powdered samples were placed in sealed bags and stored in a desiccator for subsequent chemical analysis.\u003c/p\u003e\n\u003cp\u003ePlant leaf N content was determined using a SEAL flow analyser (Auto Analyser 3, Germany). P and K content was measured by inductively coupled plasma atomic emission spectroscopy (Iris Advantage 1000, Thermo Jarrell Ash, Franklin, USA). Ca and Mg contents were determined by atomic absorption spectrophotometry (Hitachi Z2000 atomic absorption spectrophotometer, Japan). Crude ash content in leaves was measured using the direct ashing method.\u003c/p\u003e\n\u003ch3\u003eData Processing and Analysis\u003c/h3\u003e\n\u003cp\u003eData processing and statistical analysis were performed using Excel 2016 and SPSS21.0, while plotting was conducted with Origin 2021 software. Normality of distribution for nutrient data was assessed via the Kolmogorov-Smirnov test. For normally distributed data, arithmetic means were used to describe population characteristics; for non-normally distributed data, geometric means were employed.\u003c/p\u003e\n\u003cp\u003eOne-way ANOVA and LSD multiple comparisons were employed to test the significance of inter-species differences in the chemical metrics of leaves from 13 species. Pearson correlation analysis examined relationships between leaf nutrient element concentrations and their chemical ratios. Finally, principal component analysis (PCA) was employed to reduce the dimensionality of the six leaf nutrient element datasets across the 13 species. This aimed to identify the primary gradients driving species differentiation in nutrient strategies and to explore niche differentiation among lianas of different life forms within a multidimensional nutrient space.\u003c/p\u003e"},{"header":"Results ","content":"\u003ch3\u003eDistribution and Variation Characteristics of Leaf Nutrient Element Content\u003c/h3\u003e\n\u003cp\u003eAnalysis of the overall distribution of leaf nutrient contents across 13 dominant lianas, revealed the following: Leaf nitrogen content ranged from 12.0 to 32.0 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e, with a concentration between 20.0 and 24.0 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e. Leaf phosphorus content ranged from 1.0 to 3.0 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e, with 40% of samples distributed between 2.0 and 2.5 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e. Leaf potassium content ranged from 10.0 to 24.0 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e, primarily concentrated between 12.5 and 15.0 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e (approximately 45%). Leaf calcium content exhibited the widest range at 10.0\u0026ndash;55.0 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e, predominantly distributed between 20.0 and 30.0 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e. Leaf magnesium ranged from 3.0 to 10.0 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e, with approximately 60% of samples falling within (4.0\u0026ndash;6.0 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e). Leaf crude ash content ranged from 4.0 to 16.0%, with a concentration between 7.0 and 10.0%.\u003c/p\u003e\n\u003cp\u003eThe skewness values for all nutrient indicators in lianas plant leaves from this region were less than1, indicating a near-normal distribution. Consequently, arithmetic means were employed for overall characterisation. The average leaf contents of N, P, K, Ca, and Mg in lianas plants from this region were 23.13 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e, 1.93 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e, 12.75 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e, 32.64 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e, and 4.16 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e. respectively, with an average crude ash content of 10.65%. Regarding the coefficient of variation (CV), all indicators exhibited moderate variability. Among these, LMC exhibited the highest coefficient of variation (47.60%), followed by LCaC (39.56%), while N showed the lowest CV (18.13%). This indicates that elements closely linked to geological background (Mg, Ca) demonstrate significantly greater inter-species variation than those involved in core metabolic processes (N).\u003c/p\u003e\n\u003cp\u003eThe mean leaf N/P, N/K, and P/K ratios were 12.45, 1.90, and 0.15, respectively, with relatively low coefficients of variation (17.24%\u0026ndash;20.47%). These ratios exhibited weak variability and minimal differences in CV, suggesting they may be subject to strong physiological constraints and demonstrate high stability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e The nutrient content (mean\u0026plusmn;standard error) and variability of liana leaves\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"101%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 225px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eInterspecific variation/%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eIntraspecific variation/%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 225px;\"\u003e\n \u003cp\u003eLeaf N Concentration (LNC, g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e23.13\u0026plusmn;4.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e14.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e31.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e18.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e6.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 225px;\"\u003e\n \u003cp\u003eLeaf P Concentration (LPC, g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1.93\u0026plusmn;0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e2.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e27.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e6.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 225px;\"\u003e\n \u003cp\u003eLeaf K Concentration (LKC, g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e12.75\u0026plusmn;3.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e7.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e23.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e30.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e7.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 225px;\"\u003e\n \u003cp\u003eLeaf Ca Concentration (LCaC, g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e32.64\u0026plusmn;12.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e13.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e54.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e39.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e3.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 225px;\"\u003e\n \u003cp\u003eLeaf Mg concentration (LMC, g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e4.16\u0026plusmn;1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e9.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e47.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e9.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 225px;\"\u003e\n \u003cp\u003eLeaf Coarse Ash (LCA, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e10.65\u0026plusmn;2.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e7.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e15.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e25.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e3.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 225px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e31.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e6.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003eInter-species Variability in Leaf Nutrient Content\u003c/h3\u003e\n\u003cp\u003eSingle-factor analysis of variance (ANOVA) revealed extremely significant interspecific differences (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) among the 13 lianas species for N, P, K, Ca, Mg, and crude ash content (as shown in the figure). Specifically, \u003cem\u003eTrichosanthes kirilowii\u003c/em\u003e exhibited the highest N (29.18 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e) and Mg (8.74 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e) contents, while \u003cem\u003eAkebia trifoliata\u003c/em\u003e displayed the lowest N (15.20 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e) and Mg (1.38 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e) contents. \u003cem\u003eCausonis japonica\u003c/em\u003e exhibited the highest P content (2.82 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e), while \u003cem\u003eAkebia trifoliata\u003c/em\u003e had the lowest (1.11 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e). \u003cem\u003eDioscorea polystachya\u003c/em\u003e displayed the highest K content (21.96 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e), whereas \u003cem\u003eAkebia trifoliata\u003c/em\u003e, \u003cem\u003eParthenocissus dalzielii\u003c/em\u003e and \u003cem\u003ePhanera championii\u003c/em\u003e showed significantly lower K levels. Notably, \u003cem\u003eAkebia trifoliata\u003c/em\u003e exhibited the highest Ca content (53.31 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e) among all species, whereas\u003cem\u003e\u0026nbsp;Lonicera japonica\u003c/em\u003e had the lowest (14.32 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e). These pronounced interspecific differences reflect the highly differentiated nutrient acquisition and allocation strategies developed by different species under karst stress conditions. At the species level, significant variations in N, P, K, Ca, Mg and crude ash content were observed across different species, reflecting plants\u0026apos; trade-off strategies between resource acquisition and nutrient demands to adapt to nutrient limitations and fluctuations within karst. For instance, the leaves of \u003cem\u003eAkebia trifoliata\u003c/em\u003e exhibit elevated Ca and crude ash content, yet lower N, P, K, and Mg concentrations, whereas \u003cem\u003eTrichosanthes kirilowi\u003c/em\u003ei possesses higher N and Mg levels.\u003c/p\u003e\n\u003cp\u003eRegarding intraspecific variation, significant differences (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) were also observed in leaf chemical composition within each species. For instance, high intraspecific variation was noted for K (CV=12.82%) in \u003cem\u003ePhanera championii\u0026nbsp;\u003c/em\u003eand Mg (CV=18.21%) in \u003cem\u003eSmilax glaucochina\u003c/em\u003e, while Ca in \u003cem\u003eCausonis japonica\u003c/em\u003e (CV=1.02%) and P in \u003cem\u003eTrichosanthes kirilowii\u003c/em\u003e (CV=4.07%) exhibited lower intraspecific variation, reflecting species-specific responses to habitat heterogeneity.\u003c/p\u003e\n\u003ch3\u003eCorrelation Analysis of Leaf Nutrient Elements\u003c/h3\u003e\n\u003cp\u003ePearson correlation analysis revealed complex interrelationships among leaf nutrient elements. LNC exhibited extremely significant positive correlations with both LPC and LKC (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), indicating that nitrogen accumulation typically accompanies the accumulation of other key metabolic elements, collectively serving plant growth and photosynthesis. LPC also exhibited highly significant positive correlations with LKC and LMC, while LKC and LMC similarly showed significant positive correlations (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), confirming the physiological synergy among these four elements: N, P, K, and Mg. Notably, however, LCaC showed no significant correlation with LNC, LPC, or LKC and even exhibited a negative trend with LPC and LKC, albeit not statistically significant. This suggests Ca accumulation may follow distinct regulatory pathways from other metabolic elements and involve trade-offs.\u003c/p\u003e\n\u003cp\u003eRegarding stoichiometric ratios, LNC exhibited a highly significant positive correlation with N/P, while LPC showed a highly significant negative correlation with N/P, indicating that the N/P ratio is strongly constrained by P content. Furthermore, LPC displayed a significant negative correlation with N/K but a significant positive correlation with P/K.\u003c/p\u003e\n\u003ch3\u003ePrincipal Component Analysis Based on Leaf Nutrient Elements\u003c/h3\u003e\n\u003cp\u003eThis study conducted a principal component analysis on the leaf nutrient content of 13 dominant lianas species. Results indicated that PC1 and PC2 collectively explained 83.3% of the total variance, effectively reflecting the primary differentiation patterns of leaf nutrients.\u003c/p\u003e\n\u003cp\u003ePC1 (54.2%) served as the primary gradient distinguishing species nutrient strategies. Its negative axis showed significant correlations with leaf Ca, Mg content, and crude ash, reflecting investment in structural support and tolerance to high calcium stress. The positive axis correlated significantly with leaf N, P, and K, representing core nutrients required for rapid growth and metabolic activity. This axis reveals differentiation among lianas along a \u0026quot;resource conservation\u0026ndash;resource acquisition\u0026quot; continuum, with negatively polarised species tending towards conservative investment for environmental stress tolerance, while positively polarised species prioritise rapid resource acquisition and utilisation. PC2 (29.1%) represents secondary differentiation, with the positive pole correlated with matrix elements such as Ca and Mg, and the negative pole with biogeochemical elements like N, P, and K, reflecting species trade-offs between matrix and cycling element utilisation.\u003c/p\u003e\n\u003cp\u003eRegarding species life form distribution: Woody lianas predominantly occupy the negative region of the PC1 axis, exhibiting a conservative strategy characterised by high calcium and magnesium tolerance. This aligns with their extended life cycles and adaptation to karst environments with elevated calcium stress. Herbaceous lianas cluster in the positive region of PC1, exhibiting a resource-acquisition strategy characterised by high nitrogen, phosphorus, and potassium content, reflecting their rapid growth and regeneration survival strategy. In summary, principal component analysis not only effectively distinguishes the nutrient investment strategies of different lianas but also reveals that life form is a key factor driving this strategic differentiation.\u003c/p\u003e"},{"header":"Discussion","content":"\u003ch3\u003eLeaf elemental stoichiometric characteristics and their variability\u003c/h3\u003e\n\u003cp\u003eThe stoichiometric characteristics of plant leaves result from the interaction between genetic traits and the environment, reflecting both constraints on environmental nutrient supply and internal physiological regulation within plants (Liu el al. 2014; Sterner el al. 20002). The N content in lianas leaves within this study area (23.13 g\u0026middot;kg\u003csup\u003e-\u003c/sup\u003e\u0026sup1;) exceeds both global and Chinese terrestrial plant averages (Han el al. 2011; Xie el al. 2023), as well as that of terrestrial plants in other karst regions (Liu el al. 2014; Wu el al. 2021), though it remains comparable to lianas species in Guizhou\u0026apos;s karst terrain (22.86 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e) (Bai el al. 2025). Concurrently, the P content in lianas leaves (1.93 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e) in this region differs minimally from global-scale findings (1.99 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e) (Xie el al. 2023), yet it surpasses the average levels observed in Chinese terrestrial plants and other karst region lianas (Bai el al. 2025; Han el al. 2011; Wu el al. 2021)，This characteristic of leaf N and P content may be attributed to frequent agricultural activities in the study area, the nitrogen-fixing capacity of certain lianas, and rhizosphere regulation enhancing phosphorus utilisation efficiency. In this study, the leaf K content of karst lianas (12.75 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e) was consistent with other karst studies (Wu el al. 2021; Zhao el al. 2025), both being below the Chinese average (15.1 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e) (Han el al. 2011), This is attributed to the extremely low soil K content in karst regions, which struggles to provide sustained plant supply. Furthermore, the high Ca and Mg background in karst soils exerts an antagonistic effect on potassium uptake, leading to generally low leaf K content. This finding aligns with the conclusions of Wan et al (Wan el al. 2025). The leaf Ca content in this study (32.64 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e) was significantly higher than global (10\u0026ndash;20 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e) and Chinese averages (12.5 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e) (Han el al. 2011; Xie el al. 2023), and exceeded most non-karst ecosystems (Wang el al. 2024; Shi el al. 2024), consistent with the generally elevated Ca content observed in other karst plants (Bai el al. 2025; \u003cu\u003eMcLaughlin \u0026amp; Wimmer 1999\u003c/u\u003e; Wan el al. 2025). This is attributed to the release of substantial Ca\u0026sup2;⁺ from weathered carbonate parent rock, with plants typically exhibiting passive uptake under high-Ca soil conditions (McLaughlin \u0026amp; Wimmer 1999). Furthermore, certain species adaptively utilise Ca to enhance cell wall stability, increase leaf mechanical strength, and improve drought tolerance (Karley el al. 2009). In contrast, Mg content (4.16 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e) falls within the global average range (2\u0026ndash;5 g\u0026middot;kg\u003csup\u003e-1\u003c/sup\u003e) (Han el al. 2011; Xie el al. 2023), though it exhibits significant interspecific variation, indicating divergent Mg uptake and utilisation strategies among species. The average leaf crude ash content was 10.65%, exceeding previous studies (7.67\u0026ndash;8.5%) (Abifarin el al. 2021; Manisha el al. 2018). This may relate to the enrichment of mineral elements such as Ca and Mg in karst soils and the robust resource acquisition capacity of lianas (McLaughlin \u0026amp; Wimmer 1999).\u003c/p\u003e\n\u003cp\u003eVariation in plant functional compounds across and within species constitutes a crucial prerequisite for species coexistence and community assembly, more accurately reflecting species responses to environmental change and resource competition (G\u0026uuml;sewell 2004; Violle el al. 2012). Karst lianas leaf nutrient content exhibited moderate variation (31.45%), with interspecific variation (31.45%) exceeding intraspecific variation (6.25%). indicating that under the highly heterogeneous karst environment, different species exhibit distinct nutrient acquisition strategies and have developed relatively stable nutrient profiles. This aligns with previous studies on other plant functional groups in karst regions (Wang el al. 2023; Wu el al. 2021). A particularly crucial finding is that elements derived from the geological matrix (Mg and Ca) exhibited significantly higher coefficients of variation than N, which is dominated by biogeochemical cycling. Mg exhibited the highest coefficient of variation (47.60%), followed by Ca (39.56%), while N showed the lowest (18.13%). This disparity suggests distinct nutrient strategies among species primarily concerning calcium and magnesium: Some species may enhance structural stability or photosynthetic capacity by accumulating Ca and Mg, while others limit uptake to avoid potential ionic toxicity effects (Frans 2009; McLaughlin \u0026amp; Wimmer 1999 ). Nitrogen exhibited the lowest variability despite high leaf N content, a key element for maintaining plant photosynthesis and metabolism. This aligns with \u0026quot; the Stability of Limiting Elements Hypothesis \u0026quot; proposed in related studies (Han el al. 2011; Yan el al. 2015). This hypothesis suggests that plants selectively regulate nutrient composition to minimise fluctuations in highly abundant, critical, and relatively limited elements, thereby maintaining stable function in vital organs. This indicates that lianas growth in this region is more likely to be nitrogen-limited.\u003c/p\u003e\n\u003cp\u003eCompared to elemental concentrations, the stoichiometric ratios N/P, N/K, and P/K exhibited low variability both interspecifically and intraspecifically. This supports the notion that element ratios better reflect intrinsic physiological constraints and nutrient utilisation stability than individual element concentrations (G\u0026uuml;sewell 2004). In this study, the average leaf N/P ratio for lianas was 12.45, which is below the global terrestrial plant average (13.8) (Reich \u0026amp; Oleksyn 2004) ,and falls within the N-limiting threshold range (N/P \u0026lt; 14) (Koerselman \u0026amp; Arthur 1996), further confirming that dominant lianas species in this region face N limitation. This finding aligns with studies indicating N limitation during early stages of succession in karst restoration ecosystems (Yan el al. 2018; Zhang el al. 2015). During land degradation or initial vegetation recovery, slow soil organic matter accumulation and limited biological nitrogen fixation capacity render N bioavailability the primary bottleneck constraining plant growth. Additionally, we assessed the N/K and K/P ratios (derived from P/K conversion) in plant leaves using threshold ranges proposed by Venterink (Venterink el al. 2003). Results indicated N/K values below 2.1 and K/P values above 3.5, confirming that K-element limitation does not constrain lianas species in this region.\u003c/p\u003e\n\u003ch3\u003eSynergy and Trade-offs Among Leaf Nutrient Elements\u003c/h3\u003e\n\u003cp\u003ePlant nutrient utilisation strategies do not target individual elements in isolation but involve complex trade-offs and synergies among multiple elements (G\u0026uuml;sewell 2004). Correlation analyses in this study revealed significant overall synergistic relationships among leaf nutrient elements in dominant lianas species. Leaf N exhibited extremely significant positive correlations with P, K, and Mg, consistent with previous regional and global-scale plant chemometric studies (Bai el al. 2025; G\u0026uuml;sewell 2004; Han el al. 2011). This indicates that these key elements, closely linked to metabolism and growth, exhibit a tendency towards coupled accumulation within plants. Specifically, elements such as N, P, and K often show synergistic increases in \u0026quot;resource-acquiring\u0026quot; species to support higher photosynthetic efficiency and growth rates. This characteristic is also widely confirmed in the global \u0026quot;leaf economic spectrum\u0026quot; (Reich el al. 2014; Zheng el al. 2023).\u003c/p\u003e\n\u003cp\u003eNotably, leaf Ca exhibits negative correlations with N, P, and K, particularly with P and K, though these relationships are not statistically significant. This phenomenon may stem partly from Ca\u003csup\u003e2+ \u003c/sup\u003ein karst soils readily precipitating and immobilising P, reducing its availability, and competing with K ions during rhizosphere uptake and transmembrane transport, thereby creating potential antagonism at the foliar level (Bai el al. 2025; Wan el al. 2025). However, on the other hand, N, P, and K, being essential metabolic elements, are strictly regulated by plant physiological homeostasis (G\u0026uuml;sewell 2004). Furthermore, significant atmospheric nitrogen deposition and exogenous nutrient inputs from agricultural activities within the regionpartially alleviate nutrient stress under high Ca conditions (Zheng el al. 2023), thereby weakening the negative correlation. Furthermore, PCA analysis indicates woody lianas favour a high-Ca strategy, whereas herbaceous lianas adopt a high-N, -P, -K approach. This suggests that strategic differences between life forms may statistically offset each other, diminishing the significance of the negative correlation between Ca and P or K. Overall, this trend still reflects the potential trade-off between structural element investment and metabolic element investment in lianas plants within karst environments (Reich el al. 2014).\u003c/p\u003e\n\u003ch3\u003eDifferentiation in Nutrient Investment Strategies\u003c/h3\u003e\n\u003cp\u003eCoexisting species typically achieve coexistence through niche differentiation to reduce resource competition, with divergence in nutrient investment strategies constituting a key dimension of niche differentiation (Jonathan 2004). The PCA results in this study visually demonstrate the differentiation in leaf nutrient investment strategies among dominant lianas species. This differentiation represents a key evolutionary mechanism developed by species to achieve ecological niche partitioning for resource utilisation and long-term stable coexistence within the extreme stress and heterogeneous karst environment.\u003c/p\u003e\n\u003cp\u003eThe first principal component (PC1) represents the core \u0026quot;resource acquisition-resource conservation\u0026quot; gradient. The positive axis correlates with rapid growth-related N, P, and K, while the negative axis correlates with structure and stress tolerance-related Ca, Mg, and crude ash content. Species distribution along this gradient reflects their strategic trade-offs between \u0026quot;rapid growth\u0026quot; and \u0026quot;structural maintenance\u0026quot; (Reich el al. 2014). We observed that this strategic differentiation is highly coupled with plant life forms. For instance, woody lianas cluster along the negative axis of PC1, exhibiting \u0026quot;conservative\u0026quot; traits characterised by high Ca, Mg, and crude ash content. These species exhibit longer lifespans and more durable foliage, enhancing cell wall and tissue structure through Ca and Mg accumulation (Cai el al. 2009). This boosts stress resistance and long-term survival capacity, facilitating sustained existence in nutrient-poor habitats. In contrast, herbaceous lianas predominantly occupy the positive axis of PC1, exhibiting an \u0026quot;acquisition-type\u0026quot; profile characterised by high N, P, and K. These species feature short life cycles and rapid growth rates, favouring a \u0026quot;rapid investment-return\u0026quot; strategy to swiftly complete growth and reproduction whilst occupying ecological niches in frequently disturbed environments (Reich el al. 2014). This advantage is significantly amplified during transient periods of nutrient enrichment and within microhabitats.\u003c/p\u003e\n\u003cp\u003eThe distinctive stoichiometric patterns observed in karst lianas are largely shaped by the underlying soil geochemistry. In carbonate-derived soils, abundant Ca\u003csup\u003e2+\u003c/sup\u003e and Mg\u003csup\u003e2+\u003c/sup\u003e ions can precipitate with phosphate, reducing P bioavailability, while also competing with K\u003csup\u003e+ \u003c/sup\u003eand NH\u003csup\u003e4+\u003c/sup\u003e for exchange sites. These ionic interactions impose strong constraints on nutrient uptake and allocation, leading to species-specific adjustments in leaf nutrient composition. Such soil-driven nutrient imbalances may explain the co-occurrence of high Ca-Mg accumulation and relatively low P concentration in liana leaves, as well as the trade-off axis between metabolic (N, P, K) and structural (Ca, Mg) elements revealed by PCA (Valladares el al. 2015).\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study examined the leaf stoichiometric traits and nutrient investment strategies of 13 dominant liana species in secondary karst shrublands of northern Guangxi. Results showed marked interspecific variation in Ca and Mg, while N, P, and their ratios remained relatively stable, reflecting metabolic homeostasis; overall, the community exhibited relative nitrogen limitation. The study revealed a trade-off axis from acquisitive to conservative strategies in karst lianas: high N, P, and K supported rapid growth and metabolism, whereas high Ca and Mg enhanced structural support and stress tolerance. Life form was a key driver, with herbaceous lianas favoring acquisitive strategies and woody lianas adopting conservative ones. Such differentiation promotes resource complementarity and community stability. These findings advance understanding of liana nutrient ecology and inform species selection for karst ecosystem restoration.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003ePublisher\u0026rsquo;s Note\u003c/strong\u003e Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Guangxi Key Research and Development Programme Project (Gui Ke AB24010024, Gui Ke AB22080057); Basic Research Operating Expenses Project of Guangxi Institute of Botany (Gui Zhi Ye 23007).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare that are relevant to the contents of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNingxin Li, Xiankun Li and Shuhua Lu conceived and designed the study. Ningxin Li, Ting Chen, Lizhao Qin and Wen Li performed the experiments and collected the data. Ningxin Li, Ting Chen, Xuehan Liu and Chongyuan Qin analysed the data. Ningxin Li, Ting Chen and Xuehan Liu developed the figures and prepared the manuscript. All authors contributed to editing the manuscript and gave final approval for publication. Shuhua Lu reviewed and edited the manuscript. All authors participated in data interpretation and manuscript revision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in this study are included in the article. The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003eAbifarin TO, Otunola GA, Afolayan, AJ (2021) Nutritional composition and antinutrient content of Heteromorpha arborescens (Spreng.) 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BMC Plant Biol 24: 325. https://doi.org/10.1186/s12870-024-05010-x\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"stoichiometric characteristics, lianas, Karst, nutrient strategy, interspecific variation","lastPublishedDoi":"10.21203/rs.3.rs-8549239/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8549239/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eBackground and Aims\u003c/em\u003eEcological stoichiometry provides a framework for understanding plant adaptation to nutrient limitation and environmental stress. Lianas play vital roles in forest structure and function, yet their nutrient strategies in karst ecosystems remain poorly understood.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMethods\u003c/em\u003e We analyzed 13 dominant liana species from karst rocky mountains in northern Guangxi, China, measuring leaf N, P, K, Ca, Mg, and coarse ash contents and their stoichiometric ratios. Correlation and principal component analyses (PCA) were used to identify nutrient trade-offs and investment strategies.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eResults\u003c/em\u003e The results indicated that, influenced by the Karst soil substrate, leaf Ca and Mg showed strong interspecific variation, while N, P, and their ratios remained stable, indicating physiological homeostasis of metabolic nutrients. The mean N/P ratio (12.45) suggested relative nitrogen limitation, likely influenced by Ca–P antagonism. Correlation and PCA revealed a trade-off axis from \"resource acquisition\" to \"resource conservation\": N, P, and K formed a synergistic metabolic module supporting rapid growth and high metabolism, while Ca, Mg, and coarse ash reflected structural reinforcement and environmental tolerance. Further analysis demonstrated that life form is a key driver of this strategic differentiation: herbaceous lianas favored an \"acquisitive\" strategy, whereas woody lianas tended toward a \"conservative\" strategy, illustrating niche partitioning and functional complementarity.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConclusions\u003c/em\u003e These findings reveal the adaptive trade-offs of Karst lianas across a multi-element dimension, deepen our understanding of their functional ecology, and provide a theoretical basis for vegetation restoration and species selection in regions undergoing rocky desertification regions.\u003c/p\u003e","manuscriptTitle":"The Stoichiometric Characteristics and Nutrient Investment Strategies of Dominant Lianas in a Karst Rocky Mountain Area of Northern Guangxi, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-23 09:56:49","doi":"10.21203/rs.3.rs-8549239/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-03-02T08:53:06+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-21T10:44:17+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Plant and Soil","date":"2026-01-14T21:23:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-14T14:30:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant and Soil","date":"2026-01-08T03:56:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"15256f5f-e0b3-4bb4-8044-c095c4e7c9b7","owner":[],"postedDate":"January 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-23T09:56:49+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-23 09:56:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8549239","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8549239","identity":"rs-8549239","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}