Next Article in Journal
The Mechanisms of Sphagneticola trilobata Invasion as One of the Most Aggressive Invasive Plant Species
Previous Article in Journal
The Diversity Pattern of Two Endangered Dung Beetles in China Under the Influence of Climate Change
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Leaf Stoichiometric Characteristics of Three Dominant Plant Species in the Water–Land Ecotone

College of Ecological Engineering, Guizhou University of Engineering Science, Bijie 551700, China
*
Author to whom correspondence should be addressed.
Diversity 2025, 17(10), 697; https://doi.org/10.3390/d17100697
Submission received: 13 August 2025 / Revised: 1 October 2025 / Accepted: 3 October 2025 / Published: 4 October 2025
(This article belongs to the Section Plant Diversity)

Abstract

Ecological stoichiometry, as a discipline investigating plant elemental coupling mechanisms, has become a research focus across various ecosystems. However, few studies have examined plant stoichiometric characteristics in the water–land ecotone of plateau karst lake wetlands. It remains unclear whether foliar nutrient contents and stoichiometric ratios in this transitional zone vary with flooding intensity. This study established three sampling gradients (near-water area, middle area, and far-water area) within the water–land ecotone of Caohai Lake wetland in Guizhou Plateau, measuring nutrient concentrations along with their stoichiometric ratios in leaves of three dominant plant species. The results revealed significant interspecific differences in leaf nitrogen (N), phosphorus (P), potassium (K), calcium (Ca) concentrations and N:P ratios among the three dominant species, while significant spatial variations were observed in N concentration and the C:N ratio across sampling locations. Correlation analysis demonstrated significant positive relationships among leaf N, P, and K concentrations, all showing negative correlations with C concentration. Phragmites australis exhibited significantly lower C:N and N:P ratios compared to Scirpus validus and Juncus effusus, suggesting its growth advantage through rapid nutrient acquisition. This species may serve as an efficient phytoremediator for N and P absorption from both soil and water. These findings provide valuable references for vegetation selection in constructed wetlands.

1. Introduction

Ecological stoichiometry is an emerging interdisciplinary field that investigates the proportional relationships of elemental composition between organisms and their environments [1,2]. By quantitatively analyzing key elemental ratios such as carbon (C), nitrogen (N), and phosphorus (P), this discipline not only reveals the chemical nature of organism–environment interactions but also provides crucial quantitative methodologies for understanding fundamental ecological processes, including biogeochemical cycling, organismal adaptation, and ecosystem functional maintenance [3,4,5,6]. As the primary site for nutrient uptake and utilization, energy conversion, and substance synthesis in plants, leaves possess distinct elemental allocation capabilities compared to other storage organs [7,8]. Consequently, research on the ecological stoichiometric characteristics of plant leaves has become a focal point in ecological studies, with extensive investigations conducted across various ecosystems and plant types [1,2,9,10,11].
Carbon (C), N, and P serve as fundamental macronutrients that play critical roles in various plant biological processes, including growth regulation, developmental progression, photosynthetic activities, and physiological metabolism [12]. Specially, C serves as the fundamental element of organisms, while N and P participate in critical biological processes including protease synthesis for metabolic regulation, genetic information transfer, and energy storage/release [3,13]. Additionally, other elements such as potassium (K) and calcium (Ca) play specialized roles in metabolic regulation—K and Ca are, respectively, involved in drought resistance of plant leaves and cell wall stabilization [14]. Numerous studies have demonstrated that the stoichiometric relationships among C, N, and P concentrations in plant leaves reflect the plants’ adaptive strategies, regulatory mechanisms, and environmental response patterns [3,15,16]. The ratios of C:N and C:P ratios are closely associated with carbon sequestration capacity, N/P use efficiency, and growth rates of plants [3,15,17]. Furthermore, the N:P ratio serves as an indicator of nutrient limitation, revealing potential nutrient deficiencies or reflecting the physiological status of plants during growth [5,15,18].
Wetlands, recognized as one of the four major terrestrial ecosystems, play pivotal roles in biogeochemical cycling, energy flow, climate modulation, hydrological regulation, and water purification [19]. Notably, lacustrine wetlands exert profound influences on global climate change through their carbon sequestration capacity [20]. However, irrational anthropogenic exploitation has precipitated the widespread shrinkage and disappearance of lake wetlands globally [21]. The land–water ecotone, serving as a transitional interface between aquatic and terrestrial ecosystems, encompasses riparian zones, wetlands, estuaries, and coastal areas [19,22]. Its ecological significance manifests in three primary aspects: (1) sustaining species diversity, (2) mediating hydrological processes and water quality, and (3) maintaining ecological equilibrium via carbon sinks and landform stabilization [19,23,24]. Nevertheless, intensified human disturbances have triggered environmental degradation in these ecotones, severely compromising their ecological functions and threatening wetland security [25]. Consequently, investigating the spatiotemporal dynamics of vegetation patterns and soil nutrient distribution in land–water ecotones constitutes a fundamental scientific prerequisite for formulating effective wetland conservation strategies and sustainable management frameworks.
Located in Weining County, northwestern Guizhou Province, Caohai Lake is a typical karst plateau freshwater lake wetland [26]. It plays a crucial ecological role in regulating water cycles and maintaining species diversity within wetland ecosystems, particularly in providing essential habitats and breeding grounds for waterfowl and migratory birds [26,27]. In recent years, excessive development and anthropogenic disturbances have led to unpredictable alterations in the hydrological conditions of the Caohai Lake wetland ecosystem, resulting in severe vegetation degradation and significant impairment of ecosystem functions [28,29,30]. Consequently, an in-depth investigation into the ecological stoichiometric characteristics and their correlations of plants in the ecotone of Caohai Lake is imperative. This study aims to identify the limiting factors affecting plant growth in the wetland, thereby providing a theoretical foundation for scientifically understanding ecotone ecosystems, protecting wetland environments, and sustaining their ecological functions. Previous research in the Caohai Lake wetland has revealed a declining trend in soil nutrient content with reduced flooding intensity [31]. Given the close relationship between plant nutrient content and soil nutrient availability, we hypothesize that plant nutrient concentrations also decrease as flooding intensity diminishes.

2. Materials and Methods

2.1. Study Sites

Caohai Lake is located in the southwestern part of Weining County, Bijie City, Guizhou Province (26°47′32″–26°52′52″ N, 104°10′16″–104°20′40″ E). As a karst plateau lake formed by geological processes, it features gentle shoreline topography and covers an average water area of approximately 31 km2 (ranging from 20 km2 in dry seasons to 45 km2 at peak water levels) [32]. With an average depth of 1.1 m (based on 2021–2024 monitoring data) and an elevation of 2171 m above sea level. Caohai Lake is recognized as one of China’s three major plateau freshwater lakes and the largest natural freshwater lake on the Guizhou Plateau [32]. This wetland is primarily recharged by rivers such as the Dazhong River, Maojia Haizi River, Dongshan River, and Baima River, with the Dazhong and Maojia Haizi Rivers contributing the highest discharge [33]. Caohai lake supports a complete yet fragile wetland ecosystem, distinguished by its biodiversity, climatic uniqueness, and ecological sensitivity, making it a representative subtropical plateau wetland in China [34]. The region exhibits a temperate climate with mild winters and cool summers, featuring distinct wet and dry seasons. The mean annual temperature is approximately 10.6 °C, with extremes ranging from 5.0 °C to 36.8 °C [29]. Annual precipitation averages 950 mm, peaking at 1436 mm in wet years, with 88% of rainfall concentrated between May and October [26]. The lake and its surroundings are dominated by emergent macrophytes, including Phragmites australis, Scirpus validus, and Juncus effusus [34,35].

2.2. Plot Establishment and Sampling

Preliminary vegetation surveys along the entire study area revealed distinct dominant plant communities in Caohai Lake: Phragmites australis (Cav.) Trin. ex Steud. and Scirpus validus Vahl dominated the southwestern region, while Juncus effusus L. was predominant in the eastern sector. Phragmites australis (specimen number: GZTM0036381) was collected by Guo Zhengke, identified by Zhao Houtao, and preserved in the Herbarium of Guizhou University of Traditional Chinese Medicine (GZTM). Scirpus validus (specimen number: GZTM0046577) was collected by Wang Mingchuan, identified by Wei Shenghua, and preserved in the Herbarium of Guizhou University of Traditional Chinese Medicine (GZTM). Juncus effusus (specimen number: PE 02236514) was collected by Liu Zhengyu, identified by Lin Qi, and preserved in the Herbarium of the Chinese Academy of Sciences (PE). Previous studies have found that in the ecotone of the Caohai wetland, soil nutrient concentrations and stoichiometric ratios exhibit significant differences under different hydrological conditions (flooded, near-water and far-water areas) [31]. However, there is currently a lack of relevant evidence regarding whether plant elemental concentrations and stoichiometric ratios in the wetland’s ecotone follow consistent patterns along hydrological gradients. Therefore, building upon previous research methodologies, plant samples were collected from various hydrological zones to investigate whether variations in plant elements align with changes in soil nutrients. Additionally, to prevent the influence of fertilizer application from agricultural activities on the research results, the sample plots were set up away from agricultural activity areas. In mature stage of vegetative growth of plant leaves (26 July 2025), we established sampling sites across three hydrological gradients (near-water area, middle area, and far-water area) within the ecotone of each dominant plant community, based on their distance from the lakeshore and inundation levels. The sampling sites were spaced approximately 50 m apart. Sampling points for the three hydrological gradients were set up in the eastern, western, and northern directions of Caohai Lake (the southern area, characterized by dense residential settlements and frequent agricultural activities with no Phragmites australis distribution, was excluded from sampling). Within each hydrological gradient sampling site across the three directions, three 1 m × 1 m quadrats were delineated, from which all mature, healthy, and intact canopy leaf samples of three dominant species were collected. The leaf samples from the three 1 m × 1 m quadrat were combined into one mixed leaf sample, resulting in three mixed samples per species for each hydrological gradient. This sampling design yielded a total of 27 mixed leaf samples (3 mixed leaf samples per species per hydrological gradient, totaling 9 mixed leaf samples per species) for subsequent analysis.

2.3. Element Measurements

The leaf samples were placed in envelopes and dried in an oven at 70 °C for 48 h. After drying, the samples were ground into a fine powder using a mill and sieved through a 0.25 mm mesh screen. The concentrations of C and N were determined using a Dumas combustion-type C-N elemental analyzer (VarioMAX CN, Elementar Analysensysteme GmbH, Hanau, Germany). Meanwhile, P, K, and Ca concentrations were measured via inductively coupled plasma atomic emission spectroscopy (iCAP 7400, Thermo Fisher Scientific, Bremen, Germany). The ratios of carbon to nitrogen (C:N) and carbon to phosphorus (C:P) were calculated as indicators of plant growth rate and nutrient use efficiency (N and P) [36,37]. Additionally, the nitrogen-to-phosphorus ratio (N:P) was computed to assess potential nutrient limitations [18].

2.4. Data Analyses

Statistical analyses were performed using R software (version 4.4.0) for all data processing and visualization. The Kruskal–Wallis one-way test was employed to assess significant differences in leaf C, N, P, K, Ca concentrations and their stoichiometric ratios among different species and across different sampling locations within the same species, following verification of data normality. For post hoc pairwise comparisons, Dunn’s test was implemented. Pearson correlation analysis was conducted to examine relationships among leaf nutrient concentrations and their stoichiometric ratios. To quantify the relative contributions of species identity, sampling location, and their interaction to the variation in foliar traits, we performed analysis of covariance (ANCOVA) using the model: y ~ species + location + species × location, where the dependent variables represented leaf nutrient concentrations and their ratios, and the independent variables included species, sampling location, and their interaction term.

3. Results

The analysis reveals significant differences in leaf nutrient concentrations and stoichiometric ratios among different species, whereas no significant variations were observed for the same species across different sampling sites. Among leaf nutrient contents (Table 1), significant interspecific differences were observed in leaf C, P, K, and Ca concentrations (p < 0.05), while no significant variations were found across sampling locations (Figure 1A,C–E). Leaf N concentration showed significant variations both among species and between sampling sites (Figure 1B). Regarding leaf stoichiometric characteristics (Table 2), the C:P and N:P ratios exhibited significant interspecific differences but remained consistent across sampling locations (Figure 1G,H). In contrast, the N:P ratio varied significantly both among species and between sampling sites (Figure 1F). Specifically in Phragmites australis leaves, N and P concentrations were significantly higher in both near and far-water areas compared to middle areas (Figure 1B,C), whereas Ca concentration, C:N, and C:P ratios showed the opposite trend (Figure 1F,G). For Scirpus validus, leaf P concentration was lower than in near-water and far-water areas (Figure 1C), while the C:P ratio demonstrated an inverse pattern (Figure 1G). In Juncus effusus leaves, P and K concentrations were reduced in near-water and far-water areas relative to middle areas (Figure 1C,D), though Ca concentration was notably higher in far-water areas compared to both middle areas and near-water areas (Figure 1E).
Correlation analysis revealed that leaf C concentration was significantly negatively correlated with N (R2 = −0.58, p < 0.001), P (R2 = −0.58, p < 0.001), K (R2 = −0.93, p < 0.001), and Ca (R2 = −0.45, p < 0.05) concentrations (Figure 2). Significant positive correlations were observed among N (R2 = 0.91, p < 0.001), P (R2 = 0.52, p < 0.01), and K (R2 = 0.54, p < 0.01) concentrations. Potassium (K) concentration showed a significant positive association with Ca concentration (R2 = 0.37, p < 0.05). The C:N and C:P ratios exhibited significant positive correlations with C concentration (R2 = 0.64, p < 0.01; R2 = 0.64, p < 0.01) but were negatively correlated with N (R2 = −1.00, p < 0.001; R2 = −0.91, p < 0.001), P (R2 = −0.92, p < 0.001; R2 = −0.92, p < 0.001), and K (R2 = −1.00, p < 0.001) concentrations. Additionally, a strong positive correlation was found between the C:N and C:P ratios (R2 = 0.92, p < 0.001).

4. Discussion

Contrary to our initial hypothesis, we found that foliar nutrient concentrations did not vary significantly with flooding intensity. The observed interspecific differences in leaf C, P, K, and Ca concentrations (Figure 1A,C–E) align with known variations in nutrient allocation strategies among plant species, driven by differences in physiology, growth rates, and adaptive traits [4,38,39,40]. In both near-water and far-water areas, Phragmites australis and Scirpus validus exhibited higher leaf P concentrations (Figure 1B,C). The lack of spatial variation in leaf C, P, K, and Ca (except N; Figure 1B) suggests that intrinsic species traits override environmental heterogeneity in these nutrients, consistent with studies emphasizing phylogenetic over environmental control of certain leaf elements [4,16,41].
Significant variations in leaf N concentration were observed across different sampling areas (Figure 1B), likely attributable to localized differences in soil nitrogen dynamics, such as mineralization rates or legacy effects of organic matter inputs [42,43]. Previous studies in Caohai Lake revealed that hydrothermal conditions in near-water and far-water areas favored nitrification, where ammonium N is progressively oxidized into nitrate N under microbial activity, leading to reduced soil ammonium levels. Concurrently, plant growth heavily depletes ammonium N to meet N demands, exacerbating soil N deficiency. This N scarcity may further constrain plant growth [30]. Notably, in this study, the leaf N:P ratios of the three dominant plant species consistently remained below 14, reinforcing evidence of N-limited growth [18].
The interspecific differences in C:P and C:P ratios (Figure 1G,H) align with the Growth Rate Hypothesis (GRH), which posits that faster-growing species exhibit lower C:P and C:P ratios due to higher P demand for ribosomal RNA [17,44]. Among the three dominant species in this study, Phragmites australis exhibited lower C:N and C:P ratios compared to Scirpus validus and Juncus effusus (Figure 1F,G), indicating its faster growth rate. This characteristic may also be a key factor contributing to Phragmites australis expansion in the Caohai Lake wetland [45]. The inverse trends in Ca concentration and C:N/P ratios in Phragmites australis (Figure 1E–G) may reflect Ca’s role in cell wall integrity and antagonism with P uptake, as Ca-P precipitation can limit P bioavailability in calcareous wetlands [46,47]. Notably, the calcium concentration in Juncus effusus from distant waters was significantly higher (Figure 1E), which may be attributed to the leaching of calcium-rich soils surrounding Caohai Lake into the wetlands, subsequently absorbed and accumulated by the plants [48].
The correlation analysis revealed significant relationships among leaf C and nutrient concentrations (N, P, K, Ca), as well as stoichiometric ratios (C:N, C:P). The strong negative correlations between leaf C and nutrient concentrations (N, P, K, Ca) suggest a trade-off between carbon accumulation and nutrient acquisition in leaves. This aligns with the growth-rate hypothesis [44], which posits that organisms with higher growth rates (requiring more N, P, and K) tend to have lower C concentrations due to metabolic demands. The pronounced negative correlation observed between C and K likely stems from functions of K in osmotic regulation and enzyme activation [14], wherein enhanced metabolic activity associated with potassium utilization leads to a concomitant reduction in carbon accumulation. The significant positive correlations among N, P, and K concentrations support the concept of coupled nutrient uptake and allocation in leaves [3,49]. This may be attributed to the fact that high N promotes leaf growth, requiring more P to support energy metabolism, while K+ maintains ion balance to facilitate cell expansion [14,50,51]. The C:N and C:P ratios showed positive correlations with C concentration but negative correlations with N, P, and K concentrations. This pattern can be attributed to biomass dilution effect, where increased plant biomass leads to reduced nutrient concentrations in plant tissues [3,15].
The nutrient content of plants in the water–land ecotone of Caohai Lake wetland may be influenced by soil nutrient availability. As this study did not investigate the synergistic relationship between soil and plant nutrients, future research should incorporate soil nutrient analysis to comprehensively examine the dynamics of wetland plant nutrients in transitional zones. Such findings would provide references for selecting appropriate vegetation in constructed wetlands designed for pollutant removal (particularly N and P elimination).

5. Conclusions

In summary, this study investigated the leaf nutrient characteristics and stoichiometric traits of three dominant plant species in the water–land ecotone of Caohai Lake wetland in Guizhou Province, China. The variations in leaf nutrient elements were primarily observed among species, with no significant differences across sampling locations (except for N). Correlation analysis revealed significant relationships among leaf elements and stoichiometric ratios, showing synergistic interactions between N, P, and K while demonstrating negative correlations with C. These findings provide valuable references for selecting appropriate vegetation in constructed wetlands.

Author Contributions

Conceptualization, X.B. and X.X.; investigation, X.B., B.H., S.Z. and W.L.; methodology, X.B. and B.H.; formal analysis, X.B. and X.X.; writing—original draft preparation, X.B. and X.X.; writing—review and editing, X.B. and X.X. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Bijie Science and Technology Project (bikehe-ZD-[2021]01), the Project of Guizhou Science and Technology Fund (qiankehejichu-ZD-[2024]yiban124) and the Guizhou Key Laboratory of Plateau Wetland Conservation and Restoration (qiankehepingtai[2025]015).

Data Availability Statement

The original contributions presented in this study are included in this article; further inquiries can be directed to the corresponding author.

Acknowledgments

Thanks to the Caohai Management Committee of Weining County for their help with the sampling.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Elser, J.J.; Fagan, W.F.; Denno, R.F.; Dobberfuhl, D.R.; Folarin, A.; Huberty, A.; Interlandi, S.; Kilham, S.S.; McCauley, E.; Schulz, K.L.; et al. Nutritional constraints in terrestrial and freshwater food webs. Nature 2000, 408, 578–580. [Google Scholar] [CrossRef]
  2. Sardans, J.; Janssens, I.A.; Ciais, P.; Obersteiner, M.; Peñuelas, J. Recent advances and future research in ecological stoichiometry. Perspect. Plant Ecol. Evol. Syst. 2021, 50, 125611. [Google Scholar] [CrossRef]
  3. Sterner, R.W.; Elser, J.J. Ecological Stoichiometry: The Biology of Elements from Molecules to the Biosphere; Princeton University Press: Princeton, NJ, USA, 2003. [Google Scholar]
  4. Sardans, J.; Rivas-Ubach, A.; Peñuelas, J. The C: N: P stoichiometry of organisms and ecosystems in a changing world: A review and perspectives. Perspect. Plant Ecol. Evol. Syst. 2012, 14, 33–47. [Google Scholar] [CrossRef]
  5. Güsewell, S. N: P ratios in terrestrial plants: Variation and functional significance. New Phytol. 2004, 164, 243–266. [Google Scholar] [CrossRef]
  6. Reich, P.B.; Oleksyn, J. Global patterns of plant leaf N and P in relation to temperature and latitude. Proc. Natl. Acad. Sci. USA 2004, 101, 11001–11006. [Google Scholar] [CrossRef]
  7. Clarkson, D.T.; Hanson, J.B. The mineral nutrition of higher plants. Annu. Rev. Plant Physiol. 1980, 31, 239–298. [Google Scholar] [CrossRef]
  8. Evans, J.; Poorter, H.J.P.C. Photosynthetic acclimation of plants to growth irradiance: The relative importance of specific leaf area and nitrogen partitioning in maximizing carbon gain. Plant Cell Environ. 2001, 24, 755–767. [Google Scholar] [CrossRef]
  9. Han, W.; Fang, J.; Guo, D.; Zhang, Y. Leaf nitrogen and phosphorus stoichiometry across 753 terrestrial plant species in China. New Phytol. 2005, 168, 377–385. [Google Scholar] [CrossRef]
  10. López-Sepulcre, A.; Amaral, J.R.; Gautam, N.; Mohamed, A.; Naik, S. The eco-evolutionary dynamics of stoichiometric homeostasis. Trends Ecol. Evol. 2024, 39, 1111–1118. [Google Scholar] [CrossRef]
  11. Zhang, X.; Zhang, L.; Wang, Z.; Wang, J. Reviews and syntheses: Ecological stoichiometry of carbon, nitrogen, and phosphorus in shrubs and shrublands. EGUsphere 2025, 1–34. [Google Scholar] [CrossRef]
  12. Bhatla, S.C.; Lal, M.A. Plant Physiology, Development and Metabolism; Springer: Singapore, 2023. [Google Scholar]
  13. Alberts, B.; Heald, R.; Johnson, A.; Morgan, D.; Raff, M.; Roberts, K.; Walter, P. Molecular Biology of the Cell: Seventh International Student Edition with Registration Card; W.W. Norton & Company: New York, NY, USA, 2022. [Google Scholar]
  14. Marschner, H. Marschner’s Mineral Nutrition of Higher Plants; Elsevier: Amsterdam, The Netherlands; Academic Press: Cambridge, MA, USA, 2012. [Google Scholar]
  15. Ågren, G.I. Stoichiometry and nutrition of plant growth in natural communities. Annu. Rev. Ecol. Evol. Syst. 2008, 39, 153–170. [Google Scholar] [CrossRef]
  16. Kerkhoff, A.J.; Fagan, W.F.; Elser, J.J.; Enquist, B.J. Phylogenetic and growth form variation in the scaling of nitrogen and phosphorus in the seed plants. Am. Nat. 2006, 168, E103–E122. [Google Scholar] [CrossRef]
  17. Minden, V.; Kleyer, M. Internal and external regulation of plant organ stoichiometry. Plant Biol. 2014, 16, 897–907. [Google Scholar] [CrossRef]
  18. Koerselman, W.; Meuleman, A.F.M. The vegetation N:P ratio: A new tool to detect the nature of nutrient limitation. J. Appl. Ecol. 1996, 33, 1441–1450. [Google Scholar] [CrossRef]
  19. Mitsch, W.J.; Gosselink, J.G. Wetlands; Wiley: Hoboken, NJ, USA, 2015. [Google Scholar]
  20. Bastviken, D.; Tranvik, L.J.; Downing, J.A.; Crill, P.M.; Enrich-Prast, A. Freshwater methane emissions offset the continental carbon sink. Science 2011, 331, 50. [Google Scholar] [CrossRef] [PubMed]
  21. Davidson, N.C. How much wetland has the world lost? Long-term and recent trends in global wetland area. Mar. Freshwater Res. 2014, 65, 934–941. [Google Scholar] [CrossRef]
  22. Cadenasso, M.L.; Pickett, S.T.; Weathers, K.C.; Jones, C.G. A framework for a theory of ecological boundaries. BioScience 2003, 53, 750–758. [Google Scholar] [CrossRef]
  23. Naiman, R.J.; Decamps, H. The ecology of interfaces: Riparian zones. Annu. Rev. Ecol. Syst. 1997, 28, 621–658. [Google Scholar] [CrossRef]
  24. Kayranli, B.; Scholz, M.; Mustafa, A.; Hedmark, Å. Carbon storage and fluxes within freshwater wetlands: A critical review. Wetlands 2010, 30, 111–124. [Google Scholar] [CrossRef]
  25. Fluet-Chouinard, E.; Stocker, B.D.; Zhang, Z.; Malhotra, A.; Melton, J.R.; Poulter, B.; Kaplan, J.O.; Goldewijk, K.K.; Siebert, S.; Minayeva, T.; et al. Extensive global wetland loss over the past three centuries. Nature 2023, 614, 281–286. [Google Scholar] [CrossRef]
  26. Mao, T.; Zhao, Q. Characteristics of climate change in typical karst plateau lake: A case study of Caohai in Guizhou. Chin. Agric. Sci. Bull 2020, 36, 92–98. [Google Scholar]
  27. Wang, R.; An, Y.; Wang, P.; Ma, L. Study on biodiversity conservation hotspots in Guizhou. Res. Soil Water Conserv. 2014, 21, 152–157. [Google Scholar]
  28. Qin, Q.; Chen, Z.; Yao, S. Ecological safety evaluation of Caohai Lake wetland in Weining. J. Hydroecology 2018, 39, 27–33. [Google Scholar]
  29. Peng, Y.; Fu, P.; Yang, R. Assessment of wetland ecosystem health in the Caohai lake of Guizhou province. Earth Environ. 2014, 42, 68–81. [Google Scholar]
  30. Xu, L.; Guo, Y.; Liu, D.; Li, R.; Du, M. Temporal and influencing factors of Caohai water quality from 2013 to 2022. Environ. Prot. Sci. 2024, 50, 127–133. [Google Scholar]
  31. Qiu, M. Study on the Synergistic Evolutionary Pattern of Plant-Soil System in the Aquatic–Terrestrial Ecotones of Lake Caohai. Master’s Thesis, Guizhou Minzu University, Guiyang, China, 2024. [Google Scholar]
  32. Mu, G.; Wen, X.; Zhang, Z. Characteristics and driving mechanism of wetland landscape pattern change in karst region of southwest China over past 35 years: A case study of Caohai wetland in Guizhou. Land Degrad. Dev. 2024, 35, 2813–2823. [Google Scholar] [CrossRef]
  33. Yan, D.; Xia, P.; Song, X.; Lin, T.; Cao, H. Community structure and functional diversity of epiphytic bacteria and planktonic bacteria on submerged macrophytes in Caohai Lake, southwest of China. Ann. Microbiol. 2019, 69, 933–944. [Google Scholar] [CrossRef]
  34. Xia, C.; He, Z.; Wang, K. RS−based vegetation classification of Caohai Wetland in Weining, Guizhou. Guizhou Sci. 2021, 39, 69–75. [Google Scholar]
  35. Kong, M.; Ran, J.; Wu, C. Study on the growth laws of Phragmites australis in Caohai, Weining, Guizhou. Guizhou For. Sci. Technol. 2021, 49, 18–25. [Google Scholar]
  36. McGroddy, M.E.; Daufresne, T.; Hedin, L.O. Scaling of C:N:P stoichiometry in forests worldwide: Implications of terrestrial redfield-type ratios. Ecology 2004, 85, 2390–2401. [Google Scholar] [CrossRef]
  37. Herbert, D.A.; Williams, M.; Rastetter, E.B. A model analysis of N and P limitation on carbon accumulation in Amazonian secondary forest after alternate land-use abandonment. Biogeochemistry 2003, 65, 121–150. [Google Scholar] [CrossRef]
  38. Reich, P.B.; Walters, M.B.; Ellsworth, D.S. From tropics to tundra: Global convergence in plant functioning. Proc. Natl. Acad. Sci. USA 1997, 94, 13730–13734. [Google Scholar] [CrossRef]
  39. Elser, J.J.; Fagan, W.F.; Kerkhoff, A.J.; Swenson, N.G.; Enquist, B.J. Biological stoichiometry of plant production: Metabolism, scaling and ecological response to global change. New Phytol. 2010, 186, 593–608. [Google Scholar] [CrossRef]
  40. Han, W.X.; Fang, J.Y.; Reich, P.B.; Ian Woodward, F.; Wang, Z. Biogeography and variability of eleven mineral elements in plant leaves across gradients of climate, soil and plant functional type in China. Ecol. Lett. 2011, 14, 788–796. [Google Scholar] [CrossRef]
  41. Sardans, J.; Rivas-Ubach, A.; Penuelas, J. The elemental stoichiometry of aquatic and terrestrial ecosystems and its relationships with organismic lifestyle and ecosystem structure and function: A review and perspectives. Biogeochemistry 2012, 111, 1–39. [Google Scholar] [CrossRef]
  42. Vitousek, P.M.; Cassman, K.E.N.; Cleveland, C.; Crews, T.; Field, C.B.; Grimm, N.B.; Howarth, R.W.; Marino, R.; Martinelli, L.; Rastetter, E.B.; et al. Towards an ecological understanding of biological nitrogen fixation. Biogeochemistry 2002, 57, 1–45. [Google Scholar] [CrossRef]
  43. Liu, P.; Wang, Q.; Bai, J.; Gao, H.; Huang, L.; Xiao, R. Decomposition and return of C and N of plant litters of Phragmites australis and Suaeda salsa in typical wetlands of the Yellow River Delta, China. Procedia Environ. Sci. 2010, 2, 1717–1726. [Google Scholar] [CrossRef]
  44. Ågren, G.I. The C: N: P stoichiometry of autotrophs–theory and observations. Ecol. Lett. 2004, 7, 185–191. [Google Scholar] [CrossRef]
  45. Ran, J.; Meng, B.; Zhang, X.; Huang, X.; Tu, S. Effect of reed expansion on habitat adaptation and behavioral activity characteristics of Grus nigricollis in Guizhou Caohai National Nature Reserve. Chin. J. Wildlife 2025, 46, 107–117. [Google Scholar]
  46. White, P.J.; Broadley, M.R. Calcium in plants. Ann. Bot. 2003, 92, 487–511. [Google Scholar] [CrossRef] [PubMed]
  47. Reddy, K.R.; DeLaune, R.D.; Inglett, P.W. Biogeochemistry of Wetlands: Science and Applications; CRC Press: Boca Raton, FL, USA, 2022. [Google Scholar]
  48. Cao, X.X.; Wu, P.; Yang, S.D.; Liu, S.; Liao, J.H. Hydrochemistry characteristics and estimation of the dissolved inorganic carbon flux in the Caohai lake wetland catchment of Guizhou Province. Huan Jing Ke Xue 2021, 42, 1761–1771. [Google Scholar] [PubMed]
  49. Reich, P.B.; Oleksyn, J.; Wright, I.J.; Niklas, K.J.; Hedin, L.; Elser, J.J. Evidence of a general 2/3-power law of scaling leaf nitrogen to phosphorus among major plant groups and biomes. Proc. R. Soc. Ser. B 2010, 277, 877–883. [Google Scholar] [CrossRef] [PubMed]
  50. Leigh, R.A.; Wyn Jones, R.G. A hypothesis relating critical potassium concentrations for growth to the distribution and functions of this ion in the plant cell. New Phytol. 1984, 97, 1–13. [Google Scholar] [CrossRef]
  51. Shabala, S.; Pottosin, I. Regulation of potassium transport in plants under hostile conditions: Implications for abiotic and biotic stress tolerance. Physiol. Plant. 2014, 151, 257–279. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Analysis of covariance (ANCOVA) of leaf nutrient concentrations and their stoichiometric ratios of dominant plant species across sampling sites in the water-land ecotone of Caohai Lake wetland Guizhou province. Different lower cases indicate the significant (p < 0.05) difference among different sampling locations. Trait abbreviations are provided in the text. (AH) indicates the serial number of figures. n.s., p > 0.05; *, p < 0.05; **, p < 0.01; ***, p < 0.001.
Figure 1. Analysis of covariance (ANCOVA) of leaf nutrient concentrations and their stoichiometric ratios of dominant plant species across sampling sites in the water-land ecotone of Caohai Lake wetland Guizhou province. Different lower cases indicate the significant (p < 0.05) difference among different sampling locations. Trait abbreviations are provided in the text. (AH) indicates the serial number of figures. n.s., p > 0.05; *, p < 0.05; **, p < 0.01; ***, p < 0.001.
Diversity 17 00697 g001
Figure 2. Pearson correlation analysis of leaf element concentrations and their stoichiometric ratios. Trait abbreviations are provided in the text. *, p < 0.05; **, p < 0.01; ***, p < 0.001.
Figure 2. Pearson correlation analysis of leaf element concentrations and their stoichiometric ratios. Trait abbreviations are provided in the text. *, p < 0.05; **, p < 0.01; ***, p < 0.001.
Diversity 17 00697 g002
Table 1. Leaf nutrient concentrations of three dominant plant species in the water-land ecotone of Caohai Lake wetland, Guizhou (mean ± SD). Trait abbreviations are provided in the text.
Table 1. Leaf nutrient concentrations of three dominant plant species in the water-land ecotone of Caohai Lake wetland, Guizhou (mean ± SD). Trait abbreviations are provided in the text.
SpeciesNutrientsNear-Water Area (mg g−1)Middle-Area (mg g−1)Far-Water Area (mg g−1)
Phragmites australisC437.77 ± 5.67435.74 ± 5.15431.66 ± 5.48
N30.44 ± 0.4226.00 ± 2.2134.4 ± 2.66
P3.25 ± 0.063.08 ± 0.063.22 ± 0.04
K25.06 ± 1.7623.24 ± 1.825.52 ± 3.43
Ca1.42 ± 0.141.79 ± 0.161.37 ± 0.06
Scirpus validusC429.22 ± 8.99427.69 ± 8.45429.84 ± 8.06
N26.56 ± 1.6823.55 ± 6.4428.16 ± 1.61
P2.74 ± 0.502.37 ± 0.283.23 ± 0.01
K33.96 ± 7.7929.1 ± 9.0830.37 ± 5.65
Ca3.03 ± 0.424.14 ± 1.874.58 ± 1.48
Juncus effususC456.63 ± 3.84453.44 ± 3.64457.23 ± 4.07
N16.86 ± 0.9814.75 ± 0.3015.75 ± 1.73
P1.63 ± 0.051.68 ± 0.051.58 ± 0.04
K15.73 ± 0.1017.32 ± 0.6713.93 ± 1.43
Ca1.51 ± 0.081.46 ± 0.492.38 ± 0.23
Table 2. Leaf C, N, and P stoichiometric ratios of three dominant plant species in the water–land ecotone of Caohai Lake wetland, Guizhou (Mean ± SD). Trait abbreviations are provided in the text.
Table 2. Leaf C, N, and P stoichiometric ratios of three dominant plant species in the water–land ecotone of Caohai Lake wetland, Guizhou (Mean ± SD). Trait abbreviations are provided in the text.
SpeciesStoichiometric RatioNear-Water AreaMiddle-AreaFar-Water Area
Phragmites australisC:N14.39 ± 0.3816.85 ± 1.6612.59 ± 0.86
C:P134.54 ± 3.73141.73 ± 4.63134.19 ± 1.53
N:P9.35 ± 0.158.45 ± 0.5810.69 ± 0.79
Scirpus validusC:N16.21 ± 0.9519.18 ± 4.5215.28 ± 0.59
C:P160.49 ± 26.85182.14 ± 18.59133.2 ± 2.01
N:P9.91 ± 1.659.8 ± 1.598.72 ± 0.47
Juncus effususC:N27.16 ± 1.7830.74 ± 0.8329.24 ± 2.82
C:P280.68 ± 9.38269.34 ± 6.27289.48 ± 4.22
N:P10.36 ± 0.688.77 ± 0.439.96 ± 0.89
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Bai, X.; Li, W.; Zou, S.; He, B.; Xue, X. Leaf Stoichiometric Characteristics of Three Dominant Plant Species in the Water–Land Ecotone. Diversity 2025, 17, 697. https://doi.org/10.3390/d17100697

AMA Style

Bai X, Li W, Zou S, He B, Xue X. Leaf Stoichiometric Characteristics of Three Dominant Plant Species in the Water–Land Ecotone. Diversity. 2025; 17(10):697. https://doi.org/10.3390/d17100697

Chicago/Turabian Style

Bai, Xiaolong, Wangjun Li, Shun Zou, Bin He, and Xiaohui Xue. 2025. "Leaf Stoichiometric Characteristics of Three Dominant Plant Species in the Water–Land Ecotone" Diversity 17, no. 10: 697. https://doi.org/10.3390/d17100697

APA Style

Bai, X., Li, W., Zou, S., He, B., & Xue, X. (2025). Leaf Stoichiometric Characteristics of Three Dominant Plant Species in the Water–Land Ecotone. Diversity, 17(10), 697. https://doi.org/10.3390/d17100697

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop