Application of plant-litter-soil continuum in terrestrial ecosystem

Abstract

The invention relates to the technical field of environmental ecology, in particular to application of a plant-litter-soil continuum in a terrestrial ecosystem. The method comprises the following steps: S1, setting a plurality of standard sample plots in a research area; s2, litter samples and soil samples with different depths in the sample plot are collected; s3, calculating an important value, a community diversity index and a stability index of the plant; s4, evaluating a response mechanism of the plant to soil elements through the homeostatic state model; and S5, disclosing a migration path and a driving mechanism of elements in the plant-litter-soil continuum by utilizing statistical analysis and a structural equation model. According to the application of the plant-litter-soil continuum in the terrestrial ecosystem, provided by the invention, the complex relationship among plant-litter-soil and microbial biomass stoichiometric characteristics is disclosed; the plants, the litters and the soil are closely associated and jointly promote nutrient circulation of the system.

Description

Technical Field

[0001] This invention relates to the field of environmental ecology technology, and in particular to the application of the plant-litter-soil continuum in terrestrial ecosystems. Background Technology

[0002] Forest ecosystems are a crucial component of terrestrial ecosystems, covering approximately 30% of the total land area. As a highly biodiverse ecosystem type, forests play an irreplaceable role in maintaining global ecological balance due to their complex community structure. However, against the backdrop of continuously increasing human activity, forest resources are facing increasingly severe depletion. Through long-term natural restoration, secondary forests have gradually become a major component of forest cover in my country, but they still face many problems such as single-species forests and soil degradation. The southwestern mountains are one of the global biodiversity hotspots and possess significant ecological value. Ecological stoichiometry provides a powerful tool for studying material cycling and nutrient flow in forest ecosystems.

[0003] China is a vast country with a relatively low forest coverage rate, but this rate has been increasing in recent years (State Forestry Administration, 2023). However, many areas in my country still have fragile ecological environments.

[0004] Over a long period of evolution, plant organs have developed distinct functional differentiations. Leaves, as photosynthetic organs, have high N and P content directly related to their rich content of photosynthetic enzyme systems such as Rubisco. This elemental configuration ensures the efficiency of photosynthesis, providing nutrients for the plant (Cai et al., 2015). The stem, as the most important part of tree biomass production, is the main body and a crucial transport site for water and nutrients, responsible for supporting, storing, and transporting them (Fortunel et al., 2012). The root system is the main organ for plants to acquire nutrients and water, serving as a bridge connecting soil and plant nutrient transport (Li et al., 2019a). Its growth and renewal play a vital role in plant nutrient cycling (Cao et al., 2020).

[0005] Understanding the interaction mechanisms between plant element metabolism and community stability provides a new perspective, further enriches forest ecology theory, and offers a scientific basis for forest management and ecological restoration practices. Especially against the backdrop of climate change and increasingly severe human activities, it has significant practical implications for enhancing the sustainability and resilience of forest ecosystems.

[0006] Therefore, we designed the application of the plant-litter-soil continuum in terrestrial ecosystems to provide a technical solution to the above-mentioned technical problems. Summary of the Invention

[0007] Therefore, it is necessary to provide an application of the plant-litter-soil continuum in terrestrial ecosystems to address the aforementioned technical problems.

[0008] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0009] The application of the plant-litter-soil continuum in terrestrial ecosystems follows these steps:

[0010] S1: Multiple standard plots were set up within the study area to investigate the plant species composition and element content of the tree layer, shrub layer, and herb layer;

[0011] S2: Collect litter samples and soil samples from different depths within the sample plot, and determine their elemental content and stoichiometry.

[0012] S3: Calculate the importance value of plants, community diversity index, and stability index;

[0013] S4: Evaluate the plant response mechanism to soil elements using an endostatic model;

[0014] S5: Using statistical analysis and structural equation modeling, we reveal the migration pathways and driving mechanisms of elements in the plant-litter-soil continuum.

[0015] As a preferred embodiment of the application of the plant-litter-soil continuum provided by the present invention in terrestrial ecosystems, the study area consists of three typical forest ecosystems: coniferous forest, mixed coniferous and broad-leaved forest, and broad-leaved forest.

[0016] As a preferred embodiment of the application of the plant-litter-soil continuum provided by the present invention in terrestrial ecosystems, nested quadrat design is performed on standard plots, with the following steps:

[0017] A 20m × 20m tree quadrat was set up at the center of each sample plot;

[0018] Set up a 5m×5m shrub quadrat at each of the four corners of the tree quadrat;

[0019] Two 1m×1m herbaceous quadrats were set up around each shrub quadrat.

[0020] As a preferred embodiment of the application of the plant-litter-soil continuum provided by the present invention in a terrestrial ecosystem, the elements include C, N, P, and K, and their contents are determined by an elemental analyzer and an inductively coupled plasma atomic emission spectrometer.

[0021] As a preferred embodiment of the application of the plant-litter-soil continuum provided by the present invention in a terrestrial ecosystem, the homeostasis model is as follows:

[0022] ;

[0023] Where x represents the element content in the soil, y represents the element content in the plant tissue, and 1 / H represents the degree of homeostasis.

[0024] As a preferred embodiment of the application of the plant-litter-soil continuum provided by the present invention in terrestrial ecosystems, the structural equation model uses a partial least squares path model to analyze the migration paths of elements in the plant-litter-soil continuum.

[0025] As a preferred embodiment of the application of the plant-litter-soil continuum provided by the present invention in terrestrial ecosystems, it further includes the step of calculating soil carbon and nitrogen storage, using the following formula:

[0026] ;

[0027]

[0028] Where BD is the soil bulk density and d is the soil layer thickness. For soil carbon storage, This refers to nitrogen reserves.

[0029] As a preferred embodiment of the application of the plant-litter-soil continuum provided by the present invention in terrestrial ecosystems, it also includes revealing the influence of environmental factors on the stoichiometric characteristics of litter and soil elements through hierarchical segmentation regression analysis and RDA analysis.

[0030] It is clear without a doubt that the technical solution described above in this application can solve the technical problem that this application aims to address.

[0031] Meanwhile, through the above technical solutions, the present invention has at least the following beneficial effects:

[0032] 1. The application of the plant-litter-soil continuum provided by this invention in terrestrial ecosystems reveals the complex relationships between plant-litter-soil and microbial biomass stoichiometry. The close interconnections between plants, litter, and soil collectively promote nutrient cycling within this system, providing crucial evidence for in-depth exploration of the nutrient dynamics of the plant-litter-soil continuum, and possessing significant ecological importance.

[0033] 2. This invention reveals the differences in elemental homeostasis and sensitivity among plants in different forest types and functional groups, reflecting the high adaptability of plants in resource management and growth strategies. Strong homeostasis indicates that plants can maintain a stable distribution of elements under changing environmental conditions through effective physiological regulation, thereby reducing the negative impact of external environmental fluctuations on growth. This elemental homeostasis is particularly important in environments with uneven soil nutrient supply or limited resources. On the other hand, elemental sensitivity reflects a plant's responsiveness to environmental changes, especially the P or N / P ratio. More sensitive parts may exhibit a more pronounced response to environmental changes when resources are limited or competition is intense, thus influencing plant growth and niche selection.

[0034] 3. This invention reveals the stoichiometric characteristics of litter and its influencing factors in different forest types. Broadleaf forests exhibit higher N and P contents and reserves, as well as lower C / N and C / P ratios, indicating that this ecosystem may have a faster litter decomposition rate. In contrast, the higher C / N and C / P ratios in mixed coniferous and broadleaf forests reflect a slower nutrient release in this type of forest. At the same time, the stoichiometric characteristics of litter are closely related to plants and soil, and there may be an important feedback mechanism between litter quality and ecosystem stability. The regulatory effect of different forest types on these relationships may be affected by environmental factors such as soil type and climate change. Meanwhile, C and K in litter are negatively correlated, which may be related to their different physiological roles in plants. Litter, soil, and plants interact to jointly affect the structure and function of the ecosystem. There may be an important feedback relationship between litter quality and community diversity and ecosystem stability. Attached Figure Description

[0035] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a schematic diagram illustrating the family, genus, and species composition of different life forms and the overall species of communities in different forest types according to the present invention. Figure 2 This is a schematic diagram showing the distribution of various life forms and overall dominant families in different forest types according to the present invention; Figure 3 This is a schematic diagram illustrating the α-diversity index of various life forms of different forest types according to the present invention; Figure 4 This is a schematic diagram illustrating the relationship between the coniferous forest community stability index and the community diversity index according to the present invention. Figure 5 This is a schematic diagram illustrating the relationship between the community stability index and the community diversity index of the mixed coniferous and broad-leaved forest of the present invention. Figure 6 This is a schematic diagram illustrating the relationship between the broadleaf forest community stability index and the community diversity index according to the present invention. Figure 7 This is a schematic diagram illustrating the relationship between the community stability index and the community diversity index for all forest types in this invention. Figure 8 This is a schematic diagram showing the elements and stoichiometry of various organs of trees in different forest types according to the present invention; Figure 9 This is a schematic diagram showing the elements and stoichiometry of various organs of shrubs in different forest types according to the present invention; Figure 10 This is a schematic diagram showing the elements and stoichiometry of various organs of different forest types of herbs according to the present invention. Figure 11 This is a schematic diagram showing the elemental content and stoichiometry of litter from different forest types according to the present invention. Figure 12 This is a schematic diagram illustrating the elemental reserves of litter from different forest types according to the present invention. Figure 13 This is a schematic diagram illustrating the correlation between elemental content and stoichiometry of litter under different forest types according to the present invention. Figure 14 This is a schematic diagram illustrating the relationship between environmental variables and the elemental content and stoichiometry of litter in this invention. Figure 15 This is a schematic diagram of soil microbial biomass in different soil layers for different forest types according to the present invention; Figure 16 This is a schematic diagram showing the soil carbon and nitrogen content and reserves of different soil layers in different forest types according to the present invention. Figure 17 This is a schematic diagram illustrating the correlation analysis between environmental factors and carbon and nitrogen reserves in this invention. Figure 18 This is a schematic diagram illustrating the relative effects of various environmental factors on soil carbon and nitrogen storage under different forest types according to the present invention. Figure 19 This is a schematic diagram illustrating the impact of multiple environmental factors on soil carbon and nitrogen storage revealed by the partial least squares path analysis of this invention. Figure 20 This is a schematic diagram illustrating the correlation between plants, litter, and soil according to the present invention; Figure 21 This is a schematic diagram of linear regression of key factors in plant-litter-soil composition according to the present invention. Figure 22 This is a schematic diagram of the partial least squares path analysis of plant-litter-soil elements in this invention. Figure 23 This is a schematic diagram of the C:N:P stoichiometric characteristics of each component in the plant-litter-soil continuum of the study area of ​​this invention. Figure 24 This is a schematic diagram illustrating the relative influence of environmental factors on the C:N:P ratio of plant-litter-soil and microbial biomass according to the present invention. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0038] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0039] Reference Figures 1-24 This study investigated the application of the plant-litter-soil continuum in terrestrial ecosystems, using Dianjiang County, Chongqing, China (30°19′45.8″N, 107°19′46.5″E) as the specific research area. Three typical forest ecosystems (coniferous forest, mixed coniferous and broad-leaved forest, and broad-leaved forest) were selected as research subjects in the hilly region of Southwest China. A systematic sampling method was used to establish 54 standard plots, with 18 replicate plots for each forest type, and the plots were spaced at least 500 m apart. Geographic information of the plots was collected using a high-precision GPS positioning instrument (model: Geoxh6000, Trimble, USA) and a geological compass (model: DQY-1, Harbin, China), recording in detail the geographic coordinates (longitude, latitude), altitude, and topographic features (slope, aspect) of each plot. Nested quadrat design was used within each plot: a 20m × 20m tree quadrat was set up at the center of each plot, with a 5m × 5m shrub quadrat at each of its four corners, and two 1m × 1m herb quadrat plots around each shrub quadrat. A total of 702 quadrat plots were established, including 54 tree quadrat plots, 216 shrub quadrat plots, and 432 herb quadrat plots, comprehensively covering all structural levels of the forest ecosystem. Field surveys were conducted during the growing season (June-July) of 2023, a period of vigorous plant growth, which facilitated the accurate acquisition of physiological and ecological data on vegetation.

[0040] I. Sample Collection

[0041] Plant Collection: The sampling principles followed the guidelines outlined in "Investigation, Observation, and Analysis of Terrestrial Biomes." Roots, stems, and leaves of dominant species from the tree, shrub, and herbaceous layers were collected from each plot. Litter Collection: Topsoil litter was collected from each tree plot using a five-point sampling method for elemental analysis. A total of 54 mixed litter samples were collected. Soil Collection: Soil samples were collected at two soil depths (0-20 cm and 20-40 cm) in three typical forest types. A total of 108 mixed soil samples were collected.

[0042] II. Plant element content and its ecostometric characteristics

[0043] 2.1 Determination Methods: Freshly collected plant samples were placed in an oven at 105℃ for 30 minutes to blanch, and then dried at 65℃ to constant weight. The completely dried samples were then ground into a fine powder (particle size < 0.15 mm) using a ball mill (MM400) and packaged in resealable bags for elemental content determination. The C and N contents were determined using an elemental analyzer (Elementar Vario EL, Germany). The P and K contents were determined using inductively coupled plasma atomic emission spectrometry (ICAP 6000, Thermo Fisher, UK). The specific procedure included: accurately weighing 50.0 mg of the dried sample into a digestion vessel, adding 8 ml of analytical grade nitric acid and 2 ml of hydrogen peroxide (30%), digesting in a microwave digester, and then performing the analysis. The elemental contents of each organ of the dominant plant of different life forms were determined separately, and the final values ​​were calculated using the average value method.

[0044] 2.2 Data Processing

[0045] 2.2.1 Calculation of Importance Value

[0046] The relative abundance of plants reflects their numerical dominance in a community, and plant cover and height are key determinants of community diversity and evenness indices. Therefore, to accurately measure the impact of each plant species on the community and their relative importance, this invention uses relative abundance, relative cover, and relative height to calculate the importance value (IV) of each species in the community, as follows:

[0047] Importance value = (relative abundance + relative coverage + relative height) / 3

[0048] In the formula: relative abundance = number of individuals of a certain species in the community / sum of the number of individuals of all species in the community; relative cover = cover of a certain species in the community / sum of cover of all species in the community; relative height = height of a certain species in the community / sum of height of all species in the community.

[0049] 2.2.2 Calculation of Community Diversity Index

[0050] To calculate the community diversity index, refer to the following:

[0051] Margalef richness index: ;

[0052] Shannon-Wiener Diversity Index: ;

[0053] Simpson diversity index: ;

[0054] Pielou evenness index: ;

[0055] In the formula, S represents the total number of species within the quadrat. Let i be the importance value of species i.

[0056] 2.2.3 Calculation of Community Stability Index

[0057] Community stability index (ICV):

[0058] ICV = μ / σ;

[0059] Where μ is the mean relative abundance of all species within the quadrat, and σ is the standard deviation of the mean relative abundance of all species within the quadrat.

[0060] 2.3 Statistical Analysis

[0061] First, the Shapiro-Wilk normality test was performed on all measured indicators. For data that did not conform to a normal distribution, a natural logarithmic transformation was performed to make them conform to the requirements of a normal distribution. The homogeneity of variance of each group of data was assessed using the Levene test. Two-way ANOVA, combined with the least significant difference (LSD) method, was used to analyze the differences in species diversity indices at different community levels, as well as the differences in element content and stoichiometry of different life forms of plants among different forest types and plant organs. The significance level was set at 0.05.

[0062] Finally, linear regression analysis was used to analyze the relationship between species diversity index and community stability index. Simultaneously, based on the homeostasis model (y=cx... 1 / H Calculate the magnitude of plant homeostasis. Before analysis, perform a log10 transformation on the formula to obtain the linear regression model. In the formula: x is the element content in the soil, and y is the element content in the plant tissue.

[0063] If the regression relationship of the model is not significant (p > 0.05), 1 / H is considered 0, indicating strong homeostasis in plant tissues. If the regression relationship of the model is significant (p < 0.05), the homeostasis of elements in plant tissues is classified into five levels: strong homeostasis (1 / H ≤ 0), homeostasis (0 < 1 / H ≤ 0.25), weak homeostasis (0.25 < 1 / H ≤ 0.5), weak sensitivity (0.5 < 1 / H ≤ 0.75), and sensitivity (0.75 < 1 / H). Considering that the atmosphere is the main source of carbon in plants, and the influence of carbon in the soil on plants is relatively small, this invention focuses on exploring the homeostasis of macroelements (N, P, and K) and the N / P ratio to characterize plant nutrient requirements and the dynamic balance of soil nutrients. All charts were created using Origin 2022 software.

[0064] 2.4 Species composition and diversity characteristics of different forest types

[0065] 2.4.1 Species Composition

[0066] A total of 293 plant species were surveyed, belonging to 108 families and 207 genera. Figure 1 (D) Among them, the Rosaceae family has the most species, with 22 species, accounting for 7.5% of the total; followed by the Asteraceae, Poaceae, Lauraceae, Fabaceae, Fagaceae, Moraceae, Urticaceae, Theaceae, and Dryopteridaceae families, such as Figure 2 D. Among them, 71 plant species belonging to 37 families and 58 genera were investigated in the tree quadrats; 150 plant species belonging to 59 families and 101 genera were investigated in the shrub quadrats; and 135 plant species belonging to 57 families and 103 genera were investigated in the herbaceous quadrats. Figure 1 D).

[0067] A total of 184 plant species belonging to 83 families and 141 genera were surveyed in the coniferous forest. Figure 1 A) Among them, the Rosaceae family has the most species, with 16 species, accounting for 8.7% of the total number of species, followed by the Poaceae, Asteraceae, Fabaceae, Moraceae, Lauraceae, Fagaceae, and Dryopteridaceae families, such as Figure 2 A. Among them, 30 plant species belonging to 22 families and 28 genera were investigated in the tree quadrats; 101 plant species belonging to 48 families and 71 genera were investigated in the shrub quadrats; and 77 plant species belonging to 40 families and 65 genera were investigated in the herbaceous quadrats. Figure 1 A).

[0068] A total of 163 plant species belonging to 72 families and 129 genera were surveyed in the mixed coniferous and broad-leaved forest. Figure 1(B) Among them, the Rosaceae, Poaceae, and Asteraceae families have the most species, with 10 species each, accounting for 6.1% of the total number of species. This is followed by the Lauraceae, Fagaceae, Euphorbiaceae, Fabaceae, Theaceae, and Moraceae families, such as... Figure 2 B. Among them, 43 plant species belonging to 22 families and 35 genera were investigated in the tree quadrats; 81 plant species belonging to 39 families and 63 genera were investigated in the shrub quadrats; and 68 plant species belonging to 37 families and 60 genera were investigated in the herbaceous quadrats. Figure 1 B).

[0069] A total of 185 plant species belonging to 80 families and 140 genera were surveyed in the broad-leaved forest. Figure 1 (C) Among them, the Asteraceae family has the most species, with 18, accounting for 9.7% of the total species, followed by the Rosaceae, Poaceae, Lauraceae, Fagaceae, Dryopteridaceae, Primulaceae, Moraceae, and Theaceae families, such as Figure 2 C. Among them, 45 plant species belonging to 28 families and 42 genera were investigated in the tree quadrats; 82 plant species belonging to 40 families and 60 genera were investigated in the shrub quadrats; and 87 plant species belonging to 38 families and 68 genera were investigated in the herbaceous quadrats. Figure 1 C).

[0070] in, Figure 1 and Figure 2 In the parentheses, the number before the parentheses indicates the number of species in that family, and the percentage in the parentheses indicates the proportion of species in that family to the total number of species.

[0071] Table 1: Top six species in terms of importance of trees, shrubs, and herbaceous layer in different forest types

[0072]

[0073] The dominant species composition and species diversity of each life form in different forest types (coniferous forest, mixed coniferous and broadleaf forest, and broadleaf forest) were systematically investigated. Importance value analysis yielded the following results: In broadleaf forest, the dominant species in the tree layer were Eucalyptus > Castanopsis > Pterocarya; in the shrub layer, they were Blanched indica > Loropetalum chinense > Rhizoma Cynodon dactylon; and in the herbaceous layer, they were Cibotium barometz > Miscanthus sinensis > Adiantum capillus-veneris (see Table 1). In coniferous forest, the dominant species in the tree layer were Pinus massoniana > Cypress > Cunninghamia lanceolata; in the shrub layer, they were Viburnum praecox > Viburnum jinfoshanense > Quercus acutissima; and in the herbaceous layer, they were Cibotium barometz > Caragana sinica > Miscanthus sinensis (see Table 1). In mixed coniferous and broadleaf forest, the dominant species in the tree layer were Pinus massoniana > Cunninghamia lanceolata > Pterocarya stenoptera; in the shrub layer, they were Viburnum praecox > Eurya japonica > Loropetalum chinense; and in the herbaceous layer, they were Cibotium barometz > Adiantum capillus-veneris sinensis > Miscanthus sinensis (see Table 1). Overall, the forest composition in the study area is dominated by coniferous species, such as Masson pine, cypress, and fir, which play a key role in all forest types. The dominant species in the shrub layer are Viburnum praecox, Eurya spicata, and Loropetalum chinense, while the dominant species in the herbaceous layer are ferns, especially Cibotium barometz and Acer palmatum sinensis.

[0074] 2.4.2 Species Diversity

[0075] Depend on Figure 3 It was found that the α diversity indices (R, H', D, and E) differed significantly among different forest types and life forms. Among different forest types, the α diversity index of the coniferous forest tree layer was significantly lower than that of broad-leaved forests and mixed coniferous and broad-leaved forests. In the shrub layer, coniferous forests exhibited higher R and H' values, a result that contrasts sharply with the diversity distribution pattern of the tree layer. Figure 3 (A, B). At the overall community level, the α diversity index of coniferous forests was significantly lower than that of broad-leaved forests and mixed coniferous and broad-leaved forests. This distribution pattern is significantly consistent with the diversity characteristics of the tree layer.

[0076] From the perspective of the diversity distribution patterns of different life forms and the community as a whole, in broad-leaved forests, the R and H' values ​​of the herbaceous layer are significantly lower than those of the tree layer, shrub layer, and the community as a whole. Figure 3 A, B), while E was significantly higher than that of the tree layer and the community as a whole (A, B), Figure 3 C). In coniferous forests, H' and D show the following order: shrub layer > herbaceous layer > tree layer ( Figure 3 (B, D) R is significantly higher in the shrub layer than in other layers, and E shows the following order: shrub and grass layer > community as a whole > tree layer. Figure 3 (A, C). In mixed coniferous and broad-leaved forests, the α diversity index of the herbaceous layer (except for E) was significantly lower than that of other layers, while E was significantly higher than that of the tree layer and the community as a whole.

[0077] in, Figure 3Different uppercase letters indicate differences between different forest types within the same life form, while different lowercase letters indicate significant differences between different life forms within the same forest type.

[0078] like Figure 4 As shown, in coniferous forests, the community stability index exhibits a significant linear functional relationship with the Margalef richness index, Shannon-Wiener diversity index, and Simpson dominance index (p < 0.001), increasing with the increase of these indices. Conversely, the community stability index exhibits a significant quadratic functional relationship with the Pielou evenness index (p < 0.01), and generally increases with the increase of the Pielou evenness index.

[0079] like Figure 5 As shown, in mixed coniferous and broad-leaved forests, the community stability index exhibited a significant quadratic functional relationship with the Margalef richness index (p < 0.01), Pielou evenness index (p < 0.001), and Simpson dominance index (p < 0.001), increasing with the Pielou evenness index and Simpson dominance index. However, with the increase of the Margalef richness index, the community stability index initially increased, then stabilized, and subsequently decreased. The community stability index also showed a significant linear functional relationship with the Shannon-Wiener diversity index (p < 0.001), increasing with the Shannon-Wiener diversity index.

[0080] like Figure 6 As shown, in broadleaf forests, the community stability index exhibits a significant linear functional relationship with the Margalef richness index and the Shannon-Wiener diversity index (p < 0.001), increasing with the increase of these indices. Conversely, the community stability index exhibits a significant quadratic functional relationship with the Simpson dominance index and the Pielou evenness index (p < 0.001), and generally increases with the increase of these indices.

[0081] like Figure 7 As shown, among all forest types, the community stability index exhibits a significant linear functional relationship with the Margalef richness index and the Shannon-Wiener diversity index (p < 0.001), increasing with the increase of these indices. Conversely, the community stability index exhibits a significant quadratic functional relationship with the Simpson dominance index and the Pielou evenness index (p < 0.001), and generally increases with the increase of these indices.

[0082] 2.4.3 Element content and proportion of various organs in plants of different forest types

[0083] like Figure 8 As shown, in terms of elemental content of various organs of tree species in different forest types, the carbon content of stems is significantly higher in coniferous forests and mixed coniferous-broadleaf forests than in broadleaf forests, while the carbon content of roots is significantly higher in coniferous forests than in mixed coniferous-broadleaf forests and broadleaf forests. In both coniferous and mixed coniferous-broadleaf forests, the carbon content of leaves is significantly lower than that of stems and roots. Figure 8 A). The nitrogen content in stems was significantly higher in broad-leaved forests than in coniferous forests and mixed coniferous-broad-leaved forests. In all forest types, the nitrogen content in leaves was significantly higher than that in stems and roots. Figure 8 B). The phosphorus (P) content in leaves and stems of broad-leaved forests was significantly higher than that in coniferous forests and mixed coniferous-broad-leaved forests, while the P content in roots of coniferous forests was significantly lower than that in broad-leaved forests and mixed coniferous-broad-leaved forests. Figure 8 C). The potassium (K) content in leaves and stems was significantly higher in broadleaf forests than in coniferous forests and mixed coniferous-broadleaf forests; within broadleaf and coniferous forests, the K content in different vegetative organs was leaf > stem > root, while in mixed coniferous-broadleaf forests, the K content in leaves was significantly higher than that in stems and roots. Figure 8 D). Looking at the stoichiometric ratios of elements in various organs of tree species in different forest types, the C / N ratio of stems is significantly lower in broad-leaved forests than in coniferous forests and mixed coniferous-broad-leaved forests. In all forest types, the C / N ratio of different organs is significantly lower in leaves than in stems and roots. Figure 8 E). The carbon-to-potassium ratio (C / K) of leaves and stems was significantly lower in broadleaf forests than in coniferous forests and mixed coniferous-broadleaf forests. In both coniferous and mixed coniferous-broadleaf forests, the C / K ratio for different organs was leaf < stem < root, while in broadleaf forests, the C / K ratio of roots was significantly higher than that of leaves and stems. Figure 8 G). The N / P ratio was significantly higher in leaves than in stems and roots across different organs in various forest types (G). Figure 8 H). The nitrogen-potassium ratio (N / K) was significantly lower in stems than in leaves and roots in both broadleaf and coniferous forests, while in coniferous forests it was significantly higher in leaves than in stems. Figure 8 I). The phosphorus-potassium ratio (P / K) was significantly higher in roots than in leaves and stems across different vegetative organs in various forest types. Figure 8 J).

[0084] like Figure 9 As shown, in terms of elemental content of various organs of shrub species in different forest types, the nitrogen content is significantly higher in leaves than in stems and roots in all forest types. Figure 9 B). The phosphorus (P) content in leaves was significantly higher in broad-leaved forests than in coniferous forests, and the P content in leaves of broad-leaved forests was significantly higher than that in roots (…). Figure 9 C). K content was significantly higher in leaves than in roots in all forest types. Figure 9D). In terms of stoichiometry among shrub species, the C / N ratio of roots was significantly lower in broad-leaved forests than in mixed coniferous and broad-leaved forests. In all forest types, the C / N ratio of leaves was significantly lower than that of roots and stems. Figure 9 E). The C / P ratio of leaves was significantly higher in coniferous forests than in broadleaf forests; in broadleaf forests, the C / P ratio of roots was significantly higher than that of leaves and stems, while in coniferous forests, the C / P ratio of leaves was significantly higher than that of stems (E). Figure 9 F). In all forest types, the C / K ratio was significantly higher in roots than in leaves and stems (F). Figure 9 G). The N / P ratio of leaves was significantly higher in coniferous forests than in broad-leaved forests and mixed coniferous-broad-leaved forests; in both coniferous and mixed coniferous-broad-leaved forests, the N / P ratio was significantly higher in leaves than in stems and roots. Figure 9 H). The N / K ratio of roots was significantly higher in coniferous forests than in mixed coniferous and broadleaf forests; in broadleaf forests, the N / K ratio of roots was significantly higher than that of stems, while in coniferous forests, the N / K ratio of stems was significantly lower than that of leaves and roots. Figure 9 I). The P / K ratio of leaves was significantly higher in broad-leaved forests than in coniferous forests; in coniferous forests, the P / K ratio of roots was significantly higher than that of leaves and stems ( Figure 9 J).

[0085] in, Figure 8 and Figure 9 In this context, different uppercase letters indicate differences between different forest types of the same organ, while different lowercase letters indicate significant differences between different organs of the same forest type.

[0086] like Figure 10 As shown, in terms of elemental content of various organs of herbaceous layer species in different forest types, the C content of roots is significantly higher in coniferous forests than in mixed coniferous and broad-leaved forests; in coniferous forests, the C content of stems is significantly higher than that of leaves; and in mixed coniferous and broad-leaved forests, the C content of roots is significantly lower than that of leaves and stems. Figure 10 A). The nitrogen (N) content in roots was significantly higher in coniferous forests than in mixed coniferous and broadleaf forests and broadleaf forests. In all forest types, the N content in leaves was significantly higher than that in stems and roots. Figure 10 B). In coniferous forests and mixed coniferous and broad-leaved forests, the phosphorus content in leaves was significantly higher than that in roots. Figure 10 C). The potassium (K) content in stems was significantly higher in coniferous forests than in mixed coniferous and broad-leaved forests. In all forest types, the K content was significantly higher in leaves than in roots. Figure 10 D). In terms of elemental stoichiometry, the C / N ratio of stems is significantly lower in coniferous forests than in mixed coniferous and broad-leaved forests. In all forest types, the C / N ratio of leaves is significantly lower than that of stems and roots. Figure 10 E); In both broadleaf and coniferous forests, the C / K ratio was significantly higher in roots than in leaves and stems ( Figure 10 G); In broad-leaved forests, the N / K ratio of roots was significantly higher than that of stems, while in coniferous forests, the N / K ratio of roots was significantly lower than that of leaves and stems. In mixed coniferous and broad-leaved forests, the N / K ratio of leaves was significantly higher than that of stems. Figure 10I); In both broadleaf and coniferous forests, the P / K ratio was significantly higher in roots than in leaves and stems ( Figure 10 J).

[0087] 2.4.4 Homeostasis of different life forms of plants in different forest types

[0088] The homeostasis analysis results (Table 2) show that in coniferous forests, N and K in tree roots, stems, and leaves, as well as P and N / P ratio in stems, exhibit strong homeostasis. P in leaves and roots is weakly sensitive (p < 0.05, 1 / H = 0.558, 0.703), while the N / P ratio in leaves and roots is sensitive (p < 0.05, 1 / H = 0.911, 0.918). In mixed coniferous and broadleaf forests, N, P, and K in tree roots, stems, and leaves, as well as the N / P ratio in leaves and stems, exhibit strong homeostasis, while the N / P ratio in roots is sensitive (p < 0.05, 1 / H = 0.918). In broadleaf forests, N, P, and K in tree roots, stems, and leaves, as well as the N / P ratio in leaves and roots, exhibit strong homeostasis, while the N / P ratio in stems is sensitive (p < 0.05, 1 / H = 0.937). Overall, the N and K elements in the roots, stems and leaves of trees, as well as the P in the stems, exhibit strong homeostasis. However, the P in the leaves and the N / P ratio in the stems show weak homeostasis (p < 0.05, 1 / H = 0.425, 0.456), while the P in the roots and the N / P ratio in the leaves and roots show weak sensitivity (p < 0.05, 1 / H = 0.556, 0.634, 0.729).

[0089] In coniferous forests, the N, P, K, and N / P ratios of shrub roots, stems, and leaves all exhibit strong homeostasis. In mixed coniferous and broadleaf forests, the N, P, K, and N / P ratios in shrub roots, stems, and leaves, as well as the N / P ratio in leaves and roots, all exhibit strong homeostasis, while the N / P ratio in stems shows weak sensitivity (p < 0.05, 1 / H = 0.556). In broadleaf forests, the N, P, K, and N / P ratios of shrub roots, stems, and leaves all exhibit strong homeostasis. Overall, nitrogen (N) in shrub roots, stems, and leaves, phosphorus (P) in roots, and potassium (K) in leaves and stems exhibit strong homeostasis. However, phosphorus in stems and the N / P ratio in roots show weak homeostasis (p < 0.05, 1 / H = 0.396, 0.466, respectively), the N / P ratio in stems shows weak sensitivity (p < 0.05, 1 / H = 0.578), and phosphorus and the N / P ratio in leaves show sensitivity (p < 0.05, 1 / H = 0.837, 0.781, respectively). In coniferous forests, the nitrogen (N) in herbaceous roots, stems, and leaves, the phosphorus (P) in leaves, and the potassium (K) in leaves and stems exhibit strong homeostasis, while the N / P ratio in leaves shows weak sensitivity (p < 0.05, 1 / H = 0.625). The phosphorus in stems and roots, the potassium in roots, and the N / P ratio in stems and roots are sensitive (p < 0.05, 1 / H = 0.851, 1.176, 1.060, 1.199, 1.270, respectively). In mixed coniferous and broad-leaved forests, the nitrogen (N) and phosphorus (P) in herbaceous roots, stems, and leaves, as well as the potassium (K) and N / P ratio in leaves, all exhibit strong homeostasis, while the N / P ratio in roots shows weak sensitivity (p < 0.05, 1 / H = 0.670). The potassium (K) in stems and roots, and the N / P ratio in stems are sensitive (p < 0.05, 1 / H = 0.851, 1.176, 1.252, respectively). In broad-leaved forests, the N and P of herbaceous roots, stems and leaves, the K of leaves and stems, and the N / P ratio of roots all exhibit strong homeostasis; the N / P ratio of stems is weakly sensitive (p < 0.05, 1 / H = 0.722); the K of roots and the N / P ratio of leaves are sensitive (p < 0.05, 1 / H = 1.112, 0.864, respectively). Overall, the nitrogen (N) in the roots, stems, and leaves of herbaceous plants, the phosphorus (P) in the leaves, and the potassium (K) in the stems all exhibit strong homeostasis; the potassium in the leaves exhibits weak homeostasis (p < 0.05, 1 / H = 0.462); the phosphorus in the stems and roots, and the N / P ratio in the leaves, are weakly sensitive (p < 0.05, 1 / H = 0.620, 0.648, 0.622), while the potassium in the roots and the N / P ratio in the stems and roots are sensitive (p < 0.05, 1 / H = 0.895, 1.029, 0.884).

[0090] Table 2: Stoichiometric internal stability coefficients of different organs in different life forms of different forest types

[0091]

[0092] 2.5 Homeostasis of different life forms of plants in different forest types

[0093] The N / P ratio in roots is highly sensitive, likely because roots, as organs in direct contact with and exchanging nutrients with the soil, can rapidly sense changes in the soil environment. During metabolism, phosphorus (P) plays a crucial role in cell division, leading to changes or migration of P in the roots. Overall, the stability of elemental content in plants may be related to relatively stable climate and soil conditions, as well as long-term ecological succession. In contrast, individual plant organs (P in leaves or roots and the N / P ratio in stems) exhibit weaker homeostasis or sensitivity, indicating that plants remain relatively sensitive to environmental changes in certain parts, especially in the regulation of scarce elements like P, which is easily affected by local environmental fluctuations.

[0094] III. Elemental content and ecostoichiometry characteristics of litter

[0095] 3.1 Measurement Method

[0096] (1) Determination of basic physical and chemical properties of soil

[0097] Soil moisture content (SWC) was determined by oven drying; soil bulk density (BD) was determined by ring weighing; soil pH was determined by soil-to-water extraction at a ratio of 1:2.5 and analyzed using a pH meter; soil ammonium nitrogen (NH4+) was measured. + - N), nitrate nitrogen (NO3) - - The determination of N and available phosphorus (AP) was performed using a fully automated intermittent chemical analyzer.

[0098] (2) Determination of soil microbial biomass

[0099] Soil microbial biomass, as an important component of the soil nutrient pool, plays a key regulatory role in the cycling and transformation of organic matter. This invention uses the following method to determine microbial biomass:

[0100] ① Soil microbial biomass carbon (MBC)

[0101] This invention employs a direct extraction method using chloroform fumigation, followed by determination using an organic carbon analyzer.

[0102] Soil microbial biomass carbon calculation: Soil microbial biomass carbon: B C = F C / K C ;

[0103] In the formula, F C K represents the difference between fumigated and unfumigated soil. C This is the conversion factor, with a value of 0.45.

[0104] ② Soil microbial biomass nitrogen (MBN)

[0105] This invention employs a direct extraction method using chloroform fumigation, followed by determination using an organic carbon analyzer.

[0106] Soil microbial biomass nitrogen calculation: B N = F N / K N ;

[0107] In the formula F N K represents the difference in mineral nitrogen between fumigated and unfumigated soil. N The conversion factor represents the proportion of soil microbial biomass nitrogen killed by chloroform fumigation that is mineralized into mineral nitrogen during a 10-day incubation period, and is generally taken as 0.45.

[0108] ③ Soil microbial biomass phosphorus (MBP)

[0109] This study used the fumigation culture-NaHCO3 extraction method for determination.

[0110] Soil microbial biomass phosphorus calculation: B P = F P / K P ;

[0111] In the formula: F P K represents the difference between fumigated and unfumigated soil. P This is the conversion factor, with a value of 0.4.

[0112] The biomass of forest litter was determined by drying to constant weight after 24 hours at 80°C. Litter samples were dried in an oven to constant weight, and then ground using a ball mill (MM400). The ground samples were sealed in resealable bags for elemental analysis. The methods for determining C, N, P, and K in litter and soil samples followed the analytical procedure for plant samples described in this invention.

[0113] 3.2 Statistical Analysis

[0114] Nutrient reabsorption rate (NRE, %) is an indicator of a plant's ability to store and utilize nutrients.

[0115] ;

[0116] In the formula, NRE is the nutrient reabsorption rate; Nleaf and Nlitter are the nutrient contents (g / kg) of leaves and litter, respectively.

[0117] Climate indices, including mean annual precipitation (MAP) and mean annual temperature (MAT), were extracted from 19 climate factors in the WorldClim database using the "Point-by-Point Extraction" tool in ArcGIS 10.7. Experimental data were initially processed using Excel 2019 and statistically analyzed using SPSS 27.0. Differences in litter elemental stoichiometry and nutrient reabsorption rates of tree leaves among different forest types were tested using one-way ANOVA, with a significance level set at 0.05. Furthermore, Spearman rank correlation analysis was used to explore the correlation between litter elemental content and their stoichiometric ratios.

[0118] 3.3 Results

[0119] 3.3.1 Element content and stoichiometry of litter from different forest types

[0120] The N and P contents of litter showed that broadleaf forests had significantly higher contents than mixed coniferous and broadleaf forests across different forest types, while coniferous forests showed no significant difference from the former two. Figure 11 B, C), the C / N and C / P ratios of litter were significantly higher in mixed coniferous and broadleaf forests than in broadleaf forests, while there were no significant differences between coniferous forests and the two types. Figure 11 E, F).

[0121] like Figure 12 As shown, the carbon storage in litter is significantly lower in coniferous forests than in broadleaf forests and mixed coniferous and broadleaf forests. Figure 12 A); N reserves are ranked as follows: broadleaf forest > mixed coniferous and broadleaf forest > coniferous forest (A); Figure 12 B); P and K reserves were significantly higher in broadleaf forests than in coniferous forests, while there was no significant difference between mixed coniferous and broadleaf forests and the former two. Figure 12 (C, D). Overall, the reserves of C, N, P, and K elements in litter generally show the order of broadleaf forests > mixed coniferous and broadleaf forests > coniferous forests.

[0122] 3.3.2 Correlation between elemental content and stoichiometry in litter

[0123] like Figure 13 As shown, the correlations between litter C, N, P, and K contents and stoichiometric ratios vary across different forest types and at the regional level. For example, in broadleaf forests, litter carbon content (LTC) showed no significant correlation with the litter carbon-to-phosphorus ratio (LTC / LTP) and the litter nitrogen-to-phosphorus ratio (LTN / LTP), while other forest stand types and the community as a whole showed significant correlations (p < 0.05). In coniferous forests and all vegetation types, litter phosphorus content (LTP) was significantly correlated with the litter carbon-to-nitrogen ratio (LTC / LTN), carbon-to-potassium ratio (LTC / LTK), and nitrogen-to-potassium ratio (LTN / LTK). Figure 13A, D), but this significant relationship does not exist in mixed coniferous and broad-leaved forests and broad-leaved forests (A, D). Figure 13 (B, C). Similarly, in coniferous forests and all forest types, litter potassium content (LTK) was significantly correlated with LTC / LTP and LTN / LTP, while this significant relationship was not found in mixed coniferous and broadleaf forests and broadleaf forests.

[0124] Figure 13 In the diagram below, LTC represents total carbon in litter; LTN represents total nitrogen in litter; LTP represents total phosphorus in litter; LTK represents total potassium in litter; LTC / LTN ​​represents the carbon-to-nitrogen ratio in litter; LTC / LTP represents the carbon-to-phosphorus ratio in litter; LTC / LTK represents the carbon-to-potassium ratio in litter; LTN / LTP represents the nitrogen-to-phosphorus ratio in litter; LTN / LTK represents the nitrogen-to-potassium ratio in litter; and LTP / LTK represents the phosphorus-to-potassium ratio in litter.

[0125] 3.3.3 Influence of Multiple Environmental Factors on the Stoichiometric Characteristics of Litter

[0126] RDA analysis results show that ( Figure 14 The first axis explains 45.27% of the elemental content and stoichiometry of litter. From the first axis, it can be concluded that LTC, LTK, LTC / LTK, and LTN / LTK are mainly related to plant carbon content (PTC), plant phosphorus content (PTP), plant potassium content (PTK), soil pH, soil BD, soil phosphorus content (STP), and potassium content (STK). The second axis explains 28.2% of the elemental content and stoichiometry of litter. From the second axis, it can be concluded that LTP, LTC / LTP, LTN / LTP, and LTP / LTK are mainly related to the Shannon index, Pielou evenness index, Simpson dominance index, and MAT at the community level. Among them, broadleaf forests are closely related to the diversity index and LTP / LTK at the community level; coniferous forests are closely related to LTC, PTC, LTC / LTK, BD, pH, STK, PTP, and STP; and mixed coniferous and broadleaf forests are closely related to LTC / LTP and LTN / LTP.

[0127] Figure 14In this context, C_R: Margalef richness index at the community level; C_H': Shannon-wiener diversity index at the community level; C_E: Pielou evenness index at the community level; C_D: Simpson dominance index at the community level; PTC: plant carbon; PTN: plant nitrogen; PTP: plant phosphorus; PTK: plant potassium; NO3--N: nitrate nitrogen; NH4⁺-N: ammonia nitrogen; AP: available phosphorus; SWC: soil moisture content; BD: soil bulk density; STC: soil carbon; STN: soil nitrogen; STP: soil phosphorus; STK: soil potassium; MBC: microbial biomass carbon; MBN: microbial biomass nitrogen; MBP: microbial biomass phosphorus; ALT: altitude; ASP: slope aspect; SLO: slope; MAT: annual mean temperature; MAP: annual mean precipitation. Blue arrows represent explanatory variables, and red arrows represent response variables.

[0128] Table 3: Nutrient reabsorption in the leaves of dominant trees in different forest types

[0129]

[0130] 3.3.4 Nutrient Reabsorption

[0131] Studies have shown that forest type has no significant impact on the reabsorption rates of C, N, P, and K (Table 3). Among different forest types, the nutrient reabsorption rate of C ranged from 1.71% to 5.07%, with an average of 3.39%; the reabsorption rate of N ranged from 19.26% to 22.66%, with an average of 20.96%; the reabsorption rate of P fluctuated between 62.03% and 68.03%, with an average of 65.03%; and the reabsorption rate of K ranged from 76.81% to 80.27%, with an average of 78.54%. The nutrient reabsorption efficiency of different elements showed a consistent pattern across different forest types, exhibiting the order K > P > N > C. Specifically, the reabsorption rates of each element within the same forest type were as follows: in coniferous forests, K ≈ P > N ≈ C; in mixed coniferous and broadleaf forests, K ≈ P > N > C; and in broadleaf forests and throughout the entire forest area, K > P > N > C.

[0132] IV. Soil element content and its ecostostoichiometric characteristics

[0133] 4.1 Statistical Analysis

[0134] The values ​​of Scs and Sns at different soil depths are calculated using the following formula:

[0135] ;

[0136] ;

[0137] In the formula, Scs represents soil carbon storage (tons / hectare: t / ha); Sns represents soil nitrogen storage (t / ha); C represents the percentage content of carbon; N represents the percentage content of nitrogen; and BD is the soil bulk density (g / cm³). 3 ); d is the soil layer thickness (cm) collected.

[0138] To quantify the relative contributions of environmental factors from different forest types to SSc and Sns, hierarchical partitioning regression was performed using the "lavaan" package in R. Partial least squares path modeling (PLS-PM) was further employed to reveal the direct and indirect pathways of each driving factor. PLS-PM was chosen over likelihood-based SEM because it is more suitable for small sample sizes (Rigdon, 2016). The Goodness-of-Fit (GoF) index, ranging from 0 to 1 (a value closer to 1 indicates a better fit), was used to evaluate the overall model performance. To avoid the effects of collinearity and the possibility of overfitting, C and all stoichiometric parameters containing C were excluded when analyzing the effect of total nutrients on SSc. Similarly, Sns were processed using the same method. All statistical analyses were performed using R software (version 4.4.1), and images were created using R software and Origin 2022.

[0139] 4.2 Basic properties of soils in different forest types

[0140] Forest type and soil depth significantly influenced most soil properties (Tables 4 and 5), including soil BD, SWC, pH, and NO3. - - The values ​​of N, AP, C, N, P, K, C / N, C / P, C / K, N / P, and N / K all showed significant differences across different forest types and soil depths. However, no significant interaction was observed between forest type and soil depth on soil properties. The BD (body weight distribution) in the topsoil (0–20 cm) of coniferous forests was significantly higher than that in mixed coniferous and broad-leaved forests (p < 0.05); the BD in the topsoil of mixed coniferous and broad-leaved forests was significantly lower than that in deeper soils (20–40 cm). In the topsoil, the SWC (soil stress concentration) in broad-leaved forests and mixed coniferous and broad-leaved forests was significantly higher than that in coniferous forests (p < 0.01), and in both broad-leaved forests and mixed coniferous and broad-leaved forests, SWC significantly decreased with increasing depth (p < 0.05). Soil pH was significantly higher in coniferous forests than in mixed coniferous and broad-leaved forests (p < 0.01).

[0141] Table 4: Available Nutrients and Physical Properties of Soil Layers in Different Forest Types

[0142]

[0143] Table 5: Chemical properties of soil layers in different forest types

[0144]

[0145] The AP content in the topsoil of broadleaf forests was higher than that of coniferous forests (p < 0.01). Furthermore, in all three forest types, the AP, C, N, and NO3 content were higher. - - N, C / P, C / K, N / P, and N / K all decreased significantly with increasing soil depth (p < 0.001). In all soil layers, the P content in broadleaf forests was significantly higher than that in coniferous forests and mixed coniferous-broadleaf forests (p < 0.01). In coniferous forests, the C / N ratio in the topsoil was significantly higher than that in broadleaf forests, and the C / P ratio in deeper soil layers was still significantly higher than that in broadleaf forests (p < 0.05).

[0146] 4.3 Vertical distribution characteristics of soil element content and stoichiometry in different forest types

[0147] Soil microbial biomass showed significant variations across different forest types and soil depths. Figure 15 In deeper soil layers, the MBC of broadleaf forests was significantly higher than that of coniferous forests and mixed coniferous and broadleaf forests. Figure 15 A). MBN decreased significantly with increasing soil depth in coniferous and mixed coniferous-broadleaf forests. Figure 15 B). In the topsoil, the MBP of mixed coniferous and broad-leaved forests and broad-leaved forests was significantly higher than that of coniferous forests; in the deeper soil, the MBP of mixed coniferous and broad-leaved forests was significantly higher than that of coniferous forests. Figure 15 C). In the topsoil, the MBC / MBP ratio of coniferous forests was significantly higher than that of mixed coniferous and broad-leaved forests. Figure 15 E), the MBN / MBP ratio was significantly higher in coniferous forests than in broadleaf forests and mixed coniferous and broadleaf forests; while in deeper soil layers, the MBN / MBP ratio in coniferous forests was significantly higher than that in mixed coniferous and broadleaf forests. Figure 15 F).

[0148] 4.4 Soil carbon and nitrogen storage and its influencing factors

[0149] Figure 16 The variations in Scs and Sns at different forest types and soil depths are shown. Scs and Sns in the topsoil were 65.09 t / ha and 4.44 t / ha, respectively; Scs and Sns in the deep soil were 40.81 t / ha and 2.98 t / ha, respectively; and Scs and Sns in the entire area (0-40 cm) were approximately 105.9 t / ha and 7.42 t / ha, respectively.

[0150] At the regional scale, soil CN reserves are significantly correlated with litter stoichiometry, soil, climate, and geographical factors. Figure 17 Specifically, Scs and Sns are related to LTN, LTP, LTC / LTN, LTC / LTP, C, N, P, K, C / N, C / P, C / K, N / P, N / K, and NO3. - - N, AP, SWC, MBC, MBN, MBP, ALT, and MAT were significantly correlated. Except for LTC / LTN, LTC / LTP, and MAT, the other significant factors were positively correlated with soil CN storage.

[0151] The CN stock prediction models established for different forest types (broadleaf forest, coniferous forest, and mixed coniferous and broadleaf forest) showed strong explanatory power, explaining 91%, 79%, and 89% of the SCS variation and 85%, 62%, and 89% of the Sns variation, respectively. Figure 18 Soil factors were the most significant driving force, contributing 73.9%, 11.8%, and 95.26% to the explanation of SCS in broadleaf forests, coniferous forests, and mixed coniferous and broadleaf forests, respectively. Figure 18 AC), contributing 90.41%, 57.18%, and 78.93% respectively to the explanation of Sns. Figure 18 DF). Notably, nitrogen (N) content consistently acts as a positive driver of litter (Scs), while carbon (C) content also shows a positive impact on sns. Litter stoichiometry also plays a significant role, particularly in coniferous and broadleaf forests, accounting for 61.15% and 26.78% of the impact on Scs, respectively. Figure 18 The effects of A and B on Sns, and on broadleaf forests and mixed coniferous and broadleaf forests, accounted for 6.94% and 11.11%, respectively. Figure 18 D, F). Microbial biomass contributed relatively little to Scs in mixed coniferous and broad-leaved forests (4.38%). Figure 18 C), but its contribution to Sns in coniferous forests and mixed coniferous and broad-leaved forests was more significant, at 22.34% and 10% respectively. Figure 18 E, F). Plant diversity is an important predictor of Scs in coniferous forests, accounting for 27.05% (E, F). Figure 18 B), while climate variables explained 18.71% of the Sns variation in coniferous forests (B). Figure 18 E). The structural equation model constructed for CN reserves showed good fit, explaining 64% and 59% of the variation in Scs and Sns, respectively. Figure 19 A, C). Specifically, both Scs and Sns increase with increasing altitude, available nutrients, total nutrients, pH, and SWC, but decrease with increasing soil depth, litter stoichiometry, and MAT. However, their sensitivity to environmental factors differs: Scs is more limited by pH, total nutrients, and litter stoichiometry. Figure 19B), while Sns is more constrained by total nutrients, available nutrients, and soil depth (B). Figure 19 D). In contrast, forest type, plant diversity, and microbial biomass have a relatively small impact on CN reserves.

[0152] V. Ecostoichiometric Characteristics of the Plant-Litter-Soil Continuum

[0153] 5.1 Statistical Analysis: Spearman correlation analysis was used to explore the correlations among elements in the plant-litter-soil continuum. Specific fitting relationships were clarified through linear fitting, and the PLS-PM method was used to explain potential relationship paths within the plant-litter-soil continuum. To differentiate the influence of various environmental conditions on the C:N:P ratio of the plant-litter-soil continuum in different forest types, hierarchical segmented multiple linear regression analysis was performed using the "lavaan" software package. To avoid collinearity, C, N, and P contents and related stoichiometric characteristics were excluded, and the explanatory power of different environmental factors on the C:N:P ratio was evaluated. All data analyses were performed using R-4.4.1, and visualization was achieved using ggplot2 and Origin 2022.

[0154] 5.2 Results

[0155] 5.2.1 Correlation between plants, litter, and soil: C content in plant leaves was significantly positively correlated with soil MBC (p < 0.01); P content in leaves was significantly positively correlated with LTK and STP (p < 0.01); K content in plant leaves was significantly positively correlated with LTK (p < 0.001); C content in plant stems was significantly negatively correlated with LTK (p < 0.05) and STP (p < 0.01); P content in plant stems was significantly positively correlated with STP (p < 0.05); K content in plant stems was significantly positively correlated with LTK (p < 0.05); P content in plant roots was significantly positively correlated with LTK (p < 0.01), STP (p < 0.01), and STK (p < 0.05); LTC was significantly positively correlated with soil nitrogen content (STN); LTN was significantly positively correlated with soil carbon content (STC) (p < 0.05) and STN (p < 0.05). 0.01) Significantly positive correlation; LTK and STK are significantly positively correlated. There is no significant correlation among the other three.

[0156] Based on the above correlation analysis results, linear regression analysis was performed on the significant correlation factors among the plant-litter-soil continuum, and the results are as follows: Figure 21 As shown, MBC increases significantly with increasing C content in plant leaves ( Figure 21 A, p <0.01); LTK and STP increased with increasing P content in plant leaves ( Figure 21 B, C, p < 0.01); LTK increases with increasing K content in plant leaves ( Figure 21 D, p < 0.001); LTK and STP decreased with increasing C content in plant stems ( Figure 21 E,p < 0.05; STP increases with increasing P content in plant stems ( Figure 21 F, p < 0.05); LTK increases with increasing K content in plant stems and P content in plant roots. Figure 21 G, H, p < 0.05); STP and STK increase with increasing P content in plant roots ( Figure 21 H, p < 0.05; STN increases with increasing LTC and LTN ( Figure 21 I, K, p < 0.05); STC increases with increasing LTN ( Figure 21 J, p < 0.05; STK increases with increasing LTK ( Figure 21 L, p < 0.01).

[0157] PLS-SEM was used to further reveal the relationship between C, N, P, and K elements in forest soil, vegetation, and litter in this region. Figure 22 It can be predicted that, in the process of nutrient elements from soil to plants, the path coefficients between soil and roots, and between soil and leaves are 0.29 and 0.34, respectively, indicating a significant positive correlation between the nutrient content of soil and roots / leaves. The path coefficient between soil and stem is 0.25. This result indicates that soil C, N, P, and K elements greatly influence roots and leaves, but do not directly affect stems. The path coefficients from root to stem and from stem to leaf are 0.70 and 0.74, respectively, showing a highly significant positive correlation between the nutrient content of roots and stems, and stems and leaves. This indicates that the C, N, P, and K elements in roots significantly contribute to the C, N, P, and K elements in stems, and significantly affect the C, N, P, and K elements in leaves. The path coefficients from roots, stems, and leaves to litter are 0.22, 0.13, and 0.66, respectively, indicating that the C, N, P, and K elements in litter are mainly input through leaves, with lower contributions from stems and roots. The path coefficient from litter to soil was 0.30, indicating a significant positive correlation between litter nutrients and soil nutrients. This suggests that the C, N, P, and K elements in litter significantly contribute to root nutrient uptake and have a significant direct impact on soil nutrients. Furthermore, the path coefficient from soil microbial biomass to soil was 0.45, also showing a significant positive correlation, indicating that microbial biomass can greatly promote an increase in soil nutrient content.

[0158] 5.2.2 Stoichiometric characteristics of C:N:P in the plant-litter-soil continuum: The C:N:P stoichiometric characteristics of the plant-litter-soil continuum exhibit a clear hierarchical variation pattern. Figure 23 Overall, the stoichiometric ratios of the components were as follows: plants > litter > soil microbial biomass > soil. In terms of life forms, herbaceous plants and shrubs had relatively high C:N:P ratios, while trees had lower ratios. The differences in organ levels among different life forms were as follows: for trees, roots > stems > leaves; for shrubs, roots > stems > leaves; and for herbaceous plants, stems > roots > leaves. Furthermore, the C:N:P ratio in the topsoil was higher than in the deeper soil layers, while the C:N:P ratio in soil microbial biomass was lower than in the deeper soil layers.

[0159] 5.2.3 Effects of Multiple Environmental Factors on C:N:P Stoichiometry in the Plant-Litter-Soil Continuum: The driving factors and relative importance of the C:N:P stoichiometry in the plant-litter-soil continuum were investigated. Figure 24 The results showed that the C:N:P ratio of each component was synergistically regulated by multiple environmental factors, and the relative contribution rates of each driving factor differed significantly (p < 0.05).

[0160] For the plant C:N:P ratio, STK and litter C:N:P ratio are the main positive driving factors, while microbial biomass C:N:P ratio, pH and PTK show negative effects. Figure 24 A). Notably, plant-related factors explained the highest percentage (73.49%) of variation in the plant C:N:P ratio, significantly higher than litter factors (17.45%) and soil factors (7.91%), indicating that plant physiological regulation plays a dominant role in determining the C:N:P ratio. The litter C:N:P ratio is mainly positively regulated by the plant C:N:P ratio and the community Pielou index, while factors such as AP and pH show a negative influence. Figure 24 B). The explanatory power of environmental factors for the variation in litter C:N:P ratio was as follows: plant factors (33.39%) > soil factors (29.51%) > geographical factors (19.24%) > climatic factors (17.83%). The soil C:N:P ratio was mainly regulated by soil factors (54.58%) and climatic factors (23.04%), with soil pH, NH4+-N, and plant C:N:P ratio being the main positive driving factors, while STK and MAP showed negative effects. Figure 24 C). The soil microbial biomass C:N:P ratio was mainly positively regulated by the community Simpson index, Shannon index, and LTK, among which plant factors (40.79%) and litter factors (29.07%) had higher explanatory rates, indicating that the stoichiometric characteristics of microbial biomass were closely related to aboveground vegetation characteristics. Figure 24 D).

[0161] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. The application of the plant-litter-soil continuum in terrestrial ecosystems, characterized in that, The steps are as follows: S1: Multiple standard plots were set up within the study area to investigate the plant species composition and element content of the tree layer, shrub layer, and herb layer; S2: Collect litter samples and soil samples from different depths within the sample plot, and determine their elemental content and stoichiometry. S3: Calculate the importance value of plants, community diversity index, and stability index; S4: Evaluate the plant response mechanism to soil elements using a homeostasis model; S5: Using statistical analysis and structural equation modeling, we reveal the migration pathways and driving mechanisms of elements in the plant-litter-soil continuum.

2. The application of the plant-litter-soil continuum according to claim 1 in terrestrial ecosystems, characterized in that, The study area comprises three typical forest ecosystems: coniferous forest, mixed coniferous and broad-leaved forest, and broad-leaved forest.

3. The application of the plant-litter-soil continuum according to claim 1 in terrestrial ecosystems, characterized in that, For standard sample plots, nested quadrat design is performed, following these steps: A 20m × 20m tree quadrat was set up at the center of each sample plot; Set up a 5m×5m shrub quadrat at each of the four corners of the tree quadrat; Two 1m×1m herbaceous quadrats were set up around each shrub quadrat.

4. The application of the plant-litter-soil continuum according to claim 1 in terrestrial ecosystems, characterized in that, The elements include C, N, P, and K, and their contents are determined using an elemental analyzer and an inductively coupled plasma atomic emission spectrometer.

5. The application of the plant-litter-soil continuum according to claim 1 in terrestrial ecosystems, characterized in that, The internal steady-state model is as follows: ； Where x represents the element content in the soil, y represents the element content in the plant tissue, and 1 / H represents the degree of homeostasis.

6. The application of the plant-litter-soil continuum according to claim 1 in terrestrial ecosystems, characterized in that, The structural equation model uses a partial least squares path model to analyze the migration paths of elements in the plant-litter-soil continuum.

7. The application of the plant-litter-soil continuum according to claim 1 in terrestrial ecosystems, characterized in that, It also includes steps for calculating soil carbon and nitrogen reserves, using the following formulas: ； ； Where BD is the soil bulk density and d is the soil layer thickness. For soil carbon storage, This refers to nitrogen reserves.

8. The application of the plant-litter-soil continuum according to claim 1 in terrestrial ecosystems, characterized in that, It also includes revealing the impact of environmental factors on litter and soil elemental stoichiometry through hierarchical segmentation regression analysis and RDA analysis.

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