Community structure water source driving cause and effect relationship analysis method, device, equipment and medium

Abstract

The application discloses a kind of community structure water source driving cause-effect relationship analytical method, device, equipment and medium.It includes: obtaining soil water sample, plant water sample, precipitation water sample and groundwater water sample in target sample plot;The layered treatment of soil water sample obtains multilayer soil water sample and soil moisture content;The target sample plot is divided to obtain groundwater depth zoning data;The species number, species proportion and simpson diversity of annual plant and perennial plant are calculated as community structure characterization index;Test to obtain the isotopic characteristic data of each water sample, and input bayesian mixture model to calculate utilization ratio;Key water source factor is identified by redundancy analysis;The cause-effect relationship influence path is analyzed using partial least squares path model, and the complete cause-effect chain of community structure variation and ecological risk early warning basis are output.The present application realizes shallow groundwater grassland plant water source utilization quantification and water source driving cause-effect relationship analysis, and provides technical support for ecological early warning and water resources development.

Description

Technical Field

[0001] This invention relates to the field of water resource management and water ecological protection technology, and in particular to a method, apparatus, equipment and medium for analyzing the causal relationship of water source driving community structure. Background Technology

[0002] In areas with shallow groundwater, groundwater and natural vegetation are closely hydraulically connected, which is crucial for maintaining ecological stability during droughts. However, water level fluctuations caused by high-intensity groundwater development can easily disrupt this hydraulic connection. When the burial depth exceeds the water absorption range of plant roots, it can directly lead to ecological degradation, necessitating evidence to support risk prevention and control regarding plant-based groundwater utilization. Furthermore, precipitation, as a core water source for grasslands, is significantly affected by global warming, with frequent extreme hydrological events driving water competition between annual and perennial plant species, thereby altering community structure. The difference in competitive advantage between these two types of plants is central to assessing ecological stability; therefore, exploring the response relationship between water source and community structure under dual disturbances is crucial for elucidating vegetation adaptation mechanisms and preventing ecological degradation.

[0003] Current research has limited understanding of the water-driven mechanisms in shallow groundwater grassland ecosystems. There is a lack of scientific explanations from the perspective of differences in water use strategies among different plants regarding the driving mechanisms of community succession. This restricts the construction of an ecological early warning system and the rational development and utilization of water resources in this field, and further in-depth research is urgently needed to provide effective technical support. Summary of the Invention

[0004] Based on this, the present invention provides a method, apparatus, equipment and medium for analyzing the causal relationship driven by water source in community structure, in order to solve the problem that the existing technology cannot accurately quantify the use of groundwater by plants, clarify the driving relationship and logic between water source variation and the structure of shallow groundwater grassland communities, and thus cannot provide effective support for ecological early warning and rational development of water resources.

[0005] In a first aspect, embodiments of the present invention provide a method for analyzing the causal relationship driven by water resources in community structure, including: Multiple key water samples were obtained from the target plot in the shallowly buried grassland environment. These key water samples included soil water, plant water, precipitation water, and groundwater. Based on the soil depth data recorded during soil water sampling, the soil water samples were stratified to obtain multi-layer soil water samples, and the soil moisture content corresponding to each layer of soil water samples was measured. Based on the measured burial depth data recorded when collecting groundwater samples, the target sample plot is divided into shallow burial area and deep burial area, and the division results are used as groundwater burial depth zoning data. Based on the community survey data obtained from the vegetation survey conducted in the target plot, the number of species, species ratio, and Simpson diversity of annual and perennial plants were calculated as indicators of community structure. The test obtained isotopic characteristic data of each key water sample, and then input the isotopic characteristic data of each key water sample into a Bayesian mixture model based on Markov chains to calculate the utilization ratio of precipitation, soil water in each layer and groundwater by different dominant plant species. Based on utilization ratio, groundwater depth zoning data, precipitation data matched with precipitation samples, and soil moisture content, key water source factors driving community structure variation were identified through redundancy analysis. Based on key water source factors and community structure characterization indicators, the causal relationship influence path of community structure driven by water source is analyzed using a partial least squares path model. Based on the causal relationship influence path, the complete causal chain of community structure variation and the basis for ecological risk early warning are output.

[0006] Secondly, embodiments of the present invention also provide a device for analyzing the causal relationship of water source-driven community structure, comprising: The water sampling module is used to acquire multiple key water samples from target plots in the shallow buried grassland environment. These key water samples include soil water samples, plant water samples, precipitation water samples, and groundwater samples. The soil water sample stratification module is used to stratify the soil water sample based on the soil depth data recorded when collecting the soil water sample to obtain multiple layers of soil water sample, and to measure the soil moisture content corresponding to each layer of soil water sample. The groundwater zoning module is used to divide the target sample plot into shallow and deep groundwater zones based on the measured burial depth data recorded when collecting groundwater samples, and the zoning results are used as groundwater burial depth zoning data. The community structure characterization index construction module is used to calculate the number of species, species ratio, and Simpson diversity of annual and perennial plants based on the community survey data obtained from the vegetation survey conducted in the target sample plot, as community structure characterization indicators. The proportion calculation module is used to test and obtain isotopic characteristic data of each key water sample. The isotopic characteristic data of each key water sample is then input into a Bayesian mixture model based on Markov chains to calculate the utilization ratio of different dominant plant species on precipitation, soil water in each layer, and groundwater. The key water source factor identification module is used to identify the key water source factors driving community structure variation based on utilization ratio, groundwater depth zoning data, precipitation matched with precipitation samples, and soil moisture content through redundancy analysis. The causal relationship influence path generation module is used to analyze the causal relationship influence path of community structure driven by water source based on key water source factors and community structure characterization indicators using a partial least squares path model. The causal chain and early warning output module is used to output the complete causal chain of community structure variation and the basis for ecological risk early warning based on the causal relationship influence path.

[0007] Thirdly, embodiments of the present invention also provide an electronic device, the electronic device comprising: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to execute a community structure water source-driven causal relationship analysis method according to any embodiment of the present invention.

[0008] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer instructions, which are used to cause a processor to execute and implement the community structure water source-driven causal relationship analysis method described in any embodiment of the present invention.

[0009] Fifthly, embodiments of the present invention also provide a computer program product, the computer program product including a computer program, which, when executed by a processor, implements a community structure water source-driven causal relationship analysis method as described in any embodiment of the present invention.

[0010] This invention standardizes the collection and processing of four key water samples: plants, soil, precipitation, and groundwater. It simultaneously achieves precise acquisition of soil water sample stratification, groundwater zoning, and related quantitative data. By quantifying community structure characterization indicators and accurately calculating the utilization ratio of various water sources by plants, it establishes a practical link between "water source conditions - plant absorption - community response." Through the step-by-step application of redundancy analysis and partial least squares path models, it accurately identifies key water source factors driving community structure variation and clarifies causal relationship influence paths. The entire process is standardized and repeatable, and can be directly applied to ecological monitoring, degradation early warning, and water resource development management of shallowly buried groundwater grasslands, providing concrete and implementable technical support for on-site vegetation protection and rational groundwater extraction.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in 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 only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart of a method for analyzing the causal relationship of water source-driven community structure according to Embodiment 1 of the present invention; Figure 2 This is a flowchart of another method for analyzing the causal relationship of water source driving community structure according to Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of a community structure water source-driven causal relationship analysis device provided in Embodiment 3 of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device that implements a community structure water source-driven causal relationship analysis method according to an embodiment of the present invention. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0015] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0016] Example 1 Figure 1This is a flowchart illustrating a method for analyzing the causal relationship of water source driving in community structure according to Embodiment 1 of the present invention. This embodiment is applicable to the accurate analysis of the causal relationship of water source driving in community structure of shallow groundwater grassland ecosystems to assess ecological risks and understand the rational development of water resources. This method can be executed by a device for analyzing the causal relationship of water source driving in community structure. This device can be implemented in hardware and / or software and can be configured in a shallow groundwater grassland ecological monitoring terminal, a grassland ecological research station, or a regional water resource management server. Figure 1 As shown, the method includes: S110. Obtain multiple key water samples from the target plot in the shallow buried grassland environment, including soil water samples, plant water samples, precipitation water samples and groundwater samples.

[0017] In this embodiment, the shallow groundwater grassland environment refers to a natural or semi-natural grassland area where the measured groundwater depth is relatively shallow, groundwater can easily rise to the absorption range of plant roots through capillary action, groundwater, soil water, and precipitation mutually replenish each other, and the vegetation community is dominated by herbaceous plants, with the community structure easily regulated by water source conditions. The target plot refers to a sampling area in the shallow groundwater grassland environment, established according to the principles of random sampling or representativeness, that can represent the characteristics of the entire shallow groundwater grassland community and the distribution characteristics of water sources. The number of plots can be adjusted according to the overall area and heterogeneity of the plot, and it serves as the basic carrier for all subsequent sampling, investigation, and testing work. The variation in community structure of shallowly buried groundwater grasslands is mainly driven by water source conditions, while plant growth and development directly depend on the absorption and utilization of various water sources. Therefore, it is necessary to first obtain core water samples that can comprehensively reflect the water source characteristics of the sample plot and are directly related to plant absorption: soil water samples, plant water samples, precipitation water samples, and groundwater samples.

[0018] S120. Based on the soil depth data recorded when collecting soil water samples, the soil water samples are processed into multiple layers to obtain multi-layer soil water samples, and the soil moisture content corresponding to each layer of soil water samples is measured.

[0019] Soil depth data refers to the specific depth information of the soil sample within the soil profile, recorded simultaneously during soil water sampling. It is the core basis for soil water sample stratification. Soil moisture content refers to the water content in each soil water sample layer and is a key parameter reflecting soil water abundance. In shallowly buried grasslands, the water recharge sources of soil water differ at different depths, and the proportion of soil water absorbed and utilized by plant roots at different depths also varies. Simultaneously, soil moisture content is a key parameter reflecting the abundance of soil water in each layer, directly affecting plant absorption selection and thus influencing community structure.

[0020] On the one hand, soil water samples are stratified and classified using soil depth data, clearly distinguishing soil water at different depths. This provides a stratified basis for calculating the utilization ratio of soil water at each layer, ensuring that the calculation results reflect the differences in plant absorption of soil water at different depths. On the other hand, soil moisture content is measured and linked to stratified soil water samples, supplementing the quantitative indicators of water source factors. This provides core explanatory variables for subsequent redundant analysis and improves the identification system of key water source factors. At the same time, the stratified soil water samples can better match the actual hydrological characteristics of shallowly buried groundwater grasslands, providing support for the scientific validity of subsequent analyses.

[0021] S130. Based on the measured burial depth data recorded when collecting groundwater samples, the target sample area is divided into shallow burial area and deep burial area, and the division results are used as groundwater burial depth zoning data.

[0022] Measured burial depth data refers to the actual burial depth of the groundwater corresponding to the groundwater sample, which is recorded simultaneously when the groundwater sample is collected. Groundwater burial depth zoning data refers to the division of the target sample area into shallow burial zone and deep burial zone based on the comparison between the measured burial depth data and the preset burial depth threshold. It includes the specific zoning range and corresponding burial depth zoning values ​​of the two types of areas.

[0023] In grasslands with shallowly buried groundwater, the spatial heterogeneity of groundwater depth is significant. There are substantial differences in the intensity of groundwater recharge to soil water and the difficulty of plant absorption and utilization of groundwater between shallow and deep buried areas, leading to different community structures in the two regions. It is crucial to ensure that subsequent calculations of plant water utilization ratios take into account spatial heterogeneity and provide spatial variables for identifying key water source factors.

[0024] S140. Based on the community survey data obtained from the vegetation survey conducted in the target plot, calculate the number of annual and perennial plant species, the species ratio, and Simpson diversity as indicators of community structure.

[0025] The core characteristics of community structure are reflected in species composition and species diversity. Annual and perennial plants exhibit different adaptability to water conditions, and changes in their species number and proportion directly reflect fluctuations in water conditions. The Simpson diversity index quantifies the species richness and dominance of a community, reflecting the stability of its structure. The core idea is to transform the abstract concept of community structure into quantifiable and analyzable indicators. Furthermore, by differentiating relevant indicators between annual and perennial plants, it is possible to accurately reflect the differences in the responses of different life forms to water-driven factors, thereby revealing the intrinsic mechanisms of community structure variation.

[0026] S150. Test and obtain isotopic characteristic data of each key water sample, and input the isotopic characteristic data of each key water sample into a Bayesian mixture model based on Markov chains to calculate the utilization ratio of precipitation, soil water in each layer and groundwater by different dominant plant species.

[0027] Isotopic characteristic data refers to the hydrogen and oxygen isotope composition data of various key water samples obtained through isotope testing. This data is crucial for distinguishing different water sources and calculating the proportion of water utilization by plants. Because the hydrogen and oxygen isotope compositions of different water sources inherently differ, this data can be used to trace the source of water absorption by plants. A Bayesian mixture model based on Markov chains is a statistical model used to quantitatively calculate the contribution ratio of mixtures, where Markov chains are used to achieve iterative convergence of the model. Bayesian inference is used to improve the reliability of calculation results. Its core is to quantify the differences in absorption and utilization of various water sources by different dominant plant species, obtaining the mediating variable (utilization ratio) between "water source conditions and community structure," providing core data support for subsequent identification of key water source factors and causal path analysis. Simultaneously, by distinguishing the utilization ratios of soil water and groundwater at different layers, it can accurately reflect the absorption and selection of water sources by plants at different spatial locations, closely aligning with the actual characteristics of grasslands with shallow groundwater.

[0028] In this embodiment, the utilization ratio can not only serve as core basic data for calculating and determining key water source factors affecting community structure, but also accurately distinguish the differences in the preferences of different life forms and functional groups of plants in terms of precipitation, soil water in different layers and groundwater utilization, clarify the differentiated water source utilization strategies of various plants, and provide important support for further analysis of interspecific competition mechanisms of plants and elucidation of the adaptation characteristics of vegetation to the hydrological environment.

[0029] S160. Based on the utilization ratio, groundwater depth zoning data, precipitation data matched with precipitation samples, and soil moisture content, the key water source factors driving community structure variation were identified through redundancy analysis.

[0030] Redundancy analysis is a multivariate statistical method used to analyze the correlation between two variable matrices and quantify the contribution of each explanatory variable. It is used to identify key water source factors driving community structure variation. Key water source factors refer to water-related parameters that have a significant driving effect on community structure variation in target plots, and are the core targets for analyzing the causal relationship between water source drivers of community structure.

[0031] In shallowly buried grasslands, numerous water-related factors influence community structure variation, but not all factors have a significant driving effect. Failure to screen key factors can lead to chaotic and inaccurate subsequent causal relationship analysis. Redundancy analysis can quantify the contribution of each factor, identify key water-related factors that significantly affect community structure variation, and focus on core targets for subsequent causal relationship path analysis.

[0032] S170. Based on key water source factors and community structure characterization indicators, the causal relationship influence path of community structure driven by water source is analyzed using a partial least squares path model.

[0033] Partial least squares path model refers to a statistical model used to analyze causal relationship paths among multiple variables and quantify the influence intensity of each path. It is applicable to the causal relationship analysis of "water source factor-community structure" in this embodiment. The causal relationship influence path refers to the specific path through which key water source factors drive changes in community structure, and it is the core foundation for constructing a complete causal chain and outputting ecological risk early warning.

[0034] After identifying key water source factors, it is also necessary to clarify how these factors drive community structure variation. The core is to analyze the intrinsic mechanism by which community structure is driven by water sources, and to clarify the pathways and intensities through which key water source factors affect community structure. This will transform the fuzzy logic of "water source conditions → community structure" into a clear and quantifiable causal relationship path. At the same time, it will provide core path support for the subsequent construction of a complete causal chain and the formulation of ecological risk early warning criteria.

[0035] S180. Based on the causal relationship influence path, output the complete causal chain of community structure variation and the basis for ecological risk early warning.

[0036] A complete causal chain refers to the integration of all effective causal relationship pathways, forming a complete logical chain of "water source conditions → plant water use ratio → key water source factors → community structure variation." This clearly reveals the intrinsic laws governing the water-driven community structure of shallowly buried groundwater grasslands, providing theoretical support for subsequent related research. Ecological risk early warning criteria refer to the risk levels and early warning judgment standards related to water source fluctuations and community degradation, determined based on the intensity of causal relationship pathways and the ecological characteristics of shallowly buried groundwater grasslands. This provides practical technical support for the ecological protection and restoration of shallowly buried groundwater grasslands.

[0037] This invention standardizes the collection and processing of four key water samples: plants, soil, precipitation, and groundwater. It simultaneously achieves precise acquisition of soil water sample stratification, groundwater zoning, and related quantitative data. By quantifying community structure characterization indicators and accurately calculating the utilization ratio of various water sources by plants, it establishes a practical link between "water source conditions - plant absorption - community response." Through the step-by-step application of redundancy analysis and partial least squares path models, it accurately identifies key water source factors driving community structure variation and clarifies causal relationship influence paths. The entire process is standardized and repeatable, and can be directly applied to ecological monitoring, degradation early warning, and water resource development management of shallowly buried groundwater grasslands, providing concrete and implementable technical support for on-site vegetation protection and rational groundwater extraction.

[0038] Optionally, obtaining multiple key water samples from the target plot in the shallowly buried grassland environment may include: A vegetation survey was conducted at the target plot to obtain community survey data. Based on the community survey data, several dominant plant species were selected, and plant samples were obtained by collecting the dominant plant species. In the target plot, multiple layers of soil samples were collected according to a preset depth, and the soil depth data corresponding to each layer of soil sample was recorded. During the sampling period, natural precipitation and accumulated surface water from historical precipitation-sourced catchment depressions within the target sample plots were collected to obtain precipitation samples and to statistically analyze the precipitation amounts that matched the samples in time and space. Groundwater samples were collected from the target sample plot and the measured burial depth data corresponding to the groundwater samples were recorded. The plant and soil samples were subjected to low-temperature vacuum distillation to extract plant water samples and soil water samples, respectively, and a correspondence between the soil water samples and soil depth data was established. The extracted plant water samples, soil water samples, directly obtained precipitation water samples, and groundwater samples are used together as key water samples for the target plot.

[0039] Dominant plant species refer to those plant species with a high proportion, large coverage, and significant impact on community structure and environment in the target plot. They are the core research objects for subsequent plant water sampling and water utilization ratio calculation. Plant samples refer to plant tissue samples collected from dominant plant species in the target plot for extracting plant water samples. They are the direct carriers for obtaining plant water samples, ensuring accurate reflection of the actual water source characteristics absorbed by dominant plants. In the target plot of groundwater-buried grassland, a vegetation survey was conducted according to conventional vegetation survey standards. Information such as the species, number of individuals, and coverage of each plant species in the plot was systematically recorded and compiled into community survey data. Based on the community survey data, several dominant plant species with a high proportion, large coverage, and significant impact on community structure were selected. For each selected dominant plant species, its active tissue was collected as a plant sample.

[0040] Soil samples refer to soil matrix samples collected at predetermined depths in the target plot for the extraction of soil water samples. They are the direct carriers for obtaining soil water samples, and soil depth data is recorded simultaneously during collection. Soil depth data refers to the specific depth information of the soil sample within the soil profile, recorded simultaneously during soil sample collection. In shallowly buried grasslands, the water recharge sources of soil water differ at different depths, and the proportion of soil water absorbed and utilized by plant roots at different depths also varies. If soil samples are not collected at different depths, the depth differences in the subsequently extracted soil water samples will be indistinguishable, thus affecting the accuracy of calculating the proportion of soil water utilization in each layer.

[0041] Precipitation samples refer to water samples that reflect the characteristics of precipitation sources in the target plot, including natural precipitation during the sampling period and historical precipitation accumulated in catchment depressions, ensuring that no precipitation sources are missed. Surface water in catchment depressions refers to surface water naturally formed in depressions within the target plot that accumulates historical precipitation; it is an important supplement to precipitation sources in shallowly buried grasslands. Precipitation volume refers to the total precipitation that corresponds one-to-one with the collected precipitation samples in time and space; that is, the precipitation volume data corresponding to the same time period and location for a precipitation sample at a specific sampling point within a certain time period.

[0042] Groundwater samples refer to water samples collected within the target sampling site that reflect the characteristics of groundwater sources in the area. During collection, measured burial depth data is recorded simultaneously to ensure the correspondence between the water samples and groundwater distribution characteristics. All collected plant and soil samples are processed separately and placed in specialized distillation equipment. Low-temperature vacuum distillation is used to separate and extract water from the samples, yielding plant water samples and soil water samples respectively. After extraction, the plant and soil water samples are numbered and organized. For soil water samples, a strict correspondence between each sample and soil depth data must be established according to the collection records, ensuring that each soil water sample clearly corresponds to its collected soil depth, forming a complete soil water sample-depth data association system.

[0043] Furthermore, based on the soil depth data recorded during soil water sampling, the soil water samples are stratified to obtain multi-layer soil water samples, and the soil moisture content corresponding to each layer is measured. This may include: Based on the preset depth classification rules and the soil depth data recorded when collecting soil water samples, the soil water samples are classified into corresponding soil depth levels to obtain multi-layer soil water samples; wherein, the classification of the soil water levels is adapted to the plant root distribution characteristics of shallow groundwater grassland. Record the relationship between each soil depth layer and the corresponding soil water sample, and measure the water content of each soil water sample to obtain the soil water content corresponding to the soil water sample. Based on the measured burial depth data recorded during groundwater sampling, the target sample plot was divided into shallow and deep groundwater zones, and the division results were used as groundwater burial depth zoning data, including: The preset burial depth threshold is compared with the measured burial depth data recorded when collecting groundwater samples. Areas where the measured burial depth data is less than the preset burial depth threshold are designated as shallow groundwater areas, and areas where the measured burial depth data is greater than or equal to the preset burial depth threshold are designated as deep groundwater areas. Groundwater burial depth zoning data are formed based on the delineation results. The groundwater burial depth zoning data records the groundwater burial depth zoning values ​​for the shallow groundwater zone and the deep groundwater zone, respectively.

[0044] The preset depth classification rules refer to the pre-defined soil depth stratification standards based on the root distribution characteristics of shallowly buried grasslands. These rules are the core criteria for soil water sample classification and stratification, ensuring that the stratification results accurately reflect the actual ecological characteristics of the plot. Soil depth stratification refers to the division of the soil profile into different depth ranges (e.g., 0-20cm, 20-40cm, etc.) according to the preset depth classification rules. Each stratum corresponds to a type of soil water sample and is the core carrier for distinguishing soil water at different depths. Multi-layered soil water sampling refers to the collection of soil water samples belonging to different depth ranges after all soil water samples have been classified into their corresponding soil depth strata according to the preset depth classification rules, ensuring that each water sample corresponds to a clearly defined soil depth.

[0045] First, for the multi-layered soil water samples obtained after stratification, the attribution relationship between each soil depth layer and the corresponding soil water sample is recorded one by one. This can be done using methods such as numbering and tabular recording to clearly identify all soil water samples corresponding to a certain soil depth layer, as well as the soil depth layer to which a particular soil water sample belongs, ensuring that the attribution relationship is clear and traceable. Then, conventional soil moisture content testing methods (such as the oven-drying method and the time-domain reflectometry) are used to measure the moisture content of each soil water sample. The measurement process follows experimental specifications to ensure the accuracy of the measurement data. Finally, the measurement results are compiled to obtain the soil moisture content corresponding to each soil water sample, ensuring that the moisture content data corresponds one-to-one with the soil water sample and soil depth layer, forming a complete data system of "depth layer - soil water sample - soil moisture content".

[0046] The preset groundwater depth threshold refers to the critical depth value pre-set to distinguish between shallow and deep groundwater burial zones, taking into account the actual ecological characteristics of shallowly buried grasslands. Shallow groundwater burial zones refer to areas within the target plot where the measured depth is less than the preset threshold. In these areas, groundwater easily rises to the absorption range of plant roots through capillary action, resulting in a high intensity of groundwater recharge to the soil. Deep groundwater burial zones refer to areas within the target plot where the measured depth is greater than or equal to the preset threshold. In these areas, the intensity of groundwater recharge to the soil is low, and the absorption and utilization of groundwater by plants is relatively more difficult. Groundwater burial depth zoning data refers to the zoning records formed after delineating shallow and deep burial zones based on the comparison between measured depth data and the preset threshold. The core data includes the extent of both types of zones and their respective groundwater burial depth zoning values.

[0047] Optionally, based on utilization rates, groundwater depth zoning data, precipitation data matched with precipitation samples, and soil moisture content, redundancy analysis can be used to identify key water source factors driving community structure variation, including: Extract the corresponding groundwater depth zoning values ​​for shallow and deep groundwater zones from the groundwater depth zoning data, and obtain the soil moisture content of each soil sample obtained from the measurement. Using utilization ratio, groundwater depth zoning values, precipitation matched with precipitation samples, and soil moisture content as explanatory variables, and community structure characterization indicators as response variables, redundancy analysis was performed. The explanatory contribution of each explanatory variable to the response variable is calculated through redundancy analysis. By comparing the explanatory contributions of each explanatory variable, one or more explanatory variables with the highest explanatory contributions are identified as key water source factors.

[0048] Explanatory variables refer to the input variable set of the redundancy analysis model, specifically including utilization rate, groundwater depth zoning data, precipitation, and soil moisture content. These are all quantitative data related to water source conditions and are used to explain the driving factors of community structure variation. Response variables refer to the output variables of the redundancy analysis model, specifically community structure characterization indicators, used to reflect the variation characteristics of the target plot's community structure. These are the core variables that are explained and driven. Explanatory contribution refers to the degree to which each explanatory variable explains the variation of the response variable, calculated through the redundancy analysis model. The higher the contribution, the stronger the driving effect of that variable on community structure variation.

[0049] First, the required quantitative data are compiled, including the utilization ratio of different dominant plant species, groundwater depth zoning data of the target plot, precipitation matching the precipitation samples, and soil moisture content corresponding to soil water samples from each layer. These four types of data are then standardized into a format that meets the requirements of redundancy analysis and used as the explanatory variable set for the model. Next, the community structure characterization indicators of the target plot are compiled and also standardized into a suitable format, serving as the response variable for the model. Finally, using conventional redundancy analysis tools, the explanatory variable set and response variable are input to construct a complete redundancy analysis model. The basic parameters of the model are preset to ensure that the model is suitable for the data analysis scenario of shallowly buried groundwater grassland.

[0050] The pre-built redundant analysis model is launched, and the analysis process is run according to the preset model parameters. The model will quantify the degree of explanation of each explanatory variable for the variation of the response variable through statistical calculations, and output the explanatory contribution of each explanatory variable. During the operation, the model fitting effect is monitored to ensure that the calculation results converge and the data is free of anomalies. After the operation is completed, the explanatory contribution of each explanatory variable is sorted by the size of the contribution to form a complete contribution statistical result, ensuring that the data is traceable and verifiable.

[0051] Furthermore, based on key water source factors and community structure characterization indicators, partial least squares path models are used to analyze the causal relationship influence paths of community structure driven by water sources, which may include: Using the key water source factors as causal variables and the community structure characterization indicators as outcome variables, a partial least squares path model is constructed. By running the partial least squares path model, the path coefficients from the causal variable to the outcome variable are calculated; Based on the sign and magnitude of the calculated path coefficients, the direct influence path of the causal variable on the outcome variable is analyzed, which serves as the causal relationship influence path of community structure driven by water source.

[0052] Partial Least Squares (PLS) path models are core statistical models used to analyze the influence paths of causal relationships. Their core function is to analyze multi-level causal relationships among multiple variables and quantify the influence strength (path coefficients) of each path, making them suitable for "cause-effect" variable association analysis scenarios. Causal variables refer to the driving variables in the PLS path model, specifically key water source factors. These are quantitative data related to water sources that can directly or indirectly affect community structure, serving as the starting point of the causal relationship path. Outcome variables refer to the response variables in the PLS path model, specifically community structure characterization indicators. These are the final manifestation of the driving effect of the causal variables, reflecting the variability in community structure, and represent the endpoint of the causal relationship path.

[0053] The variable association logic refers to the pre-defined association between causal and outcome variables when constructing a partial least squares path model. It adheres to the core logic of "causal variable → outcome variable," aligning with the ecological reality of shallowly buried groundwater grasslands. Path coefficients, calculated through the partial least squares path model, quantify the intensity and direction of influence between variables. Positive numbers indicate positive influence, negative numbers indicate negative influence, and larger absolute values ​​indicate stronger influence; these are the core quantitative basis for analyzing causal relationships. The causal influence path refers to the statistically significant association path between causal and outcome variables obtained in the final analysis, clearly reflecting the specific process by which water source conditions (key water source factors) drive community structure variation.

[0054] The pre-built partial least squares path model is launched, and the analysis process is run according to the preset model parameters. The model will quantify the correlation strength and direction between each group of variables through statistical calculations, and output the corresponding path coefficients between each variable (positive numbers indicate positive influence, negative numbers indicate negative influence, and absolute values ​​indicate influence strength). After the model runs, conventional fitting metrics in this field (such as R-squared) are used. 2 Q 2 ) Verify the model's fit, where R 2 Q is used to evaluate how well the model explains each variable. 2 This is used to evaluate the model's predictive ability. If the fit index reaches the preset standard, the model is considered to have met the standard and can be used for subsequent path analysis. If the fit does not meet the standard, the model variable association logic or parameters are adjusted, and the model is rerun until the fit meets the standard.

[0055] After the model fits the target, firstly, all path coefficients and corresponding statistical significance test results (such as P-values) output by the model are compiled, and statistical significance criteria are set. Based on the model fit (which has been verified to meet the criteria), all paths are comprehensively screened: paths with P-values ​​less than the preset significance criteria and path coefficients consistent with the ecological reality of shallowly buried grasslands are selected. Next, based on the positive or negative nature of the path coefficients, the direction of influence of each effective path is clarified, and based on the absolute value of the path coefficients, the intensity of influence of each effective path is determined. Finally, all selected effective paths are integrated to form a complete list of causal relationship influence paths, clarifying the variable associations, direction of influence, and intensity of influence for each path, ensuring that the paths are traceable and verifiable.

[0056] Example 2 Figure 2 This is a flowchart of another method for analyzing the causal relationship of water source-driven community structure provided in Embodiment 2 of the present invention. This embodiment is a refinement based on Embodiment 1, specifically as follows: Figure 2 As shown, the method includes: S210. Obtain multiple key water samples from the target plot in the shallowly buried grassland environment, including soil water samples, plant water samples, precipitation water samples and groundwater samples.

[0057] S220. Based on the soil depth data recorded when collecting soil water samples, the soil water samples are processed into multiple layers to obtain multi-layer soil water samples, and the soil moisture content corresponding to each layer of soil water samples is measured.

[0058] S230. Based on the measured burial depth data recorded when collecting groundwater samples, the target sample area is divided into shallow burial area and deep burial area, and the division results are used as groundwater burial depth zoning data.

[0059] S240. Based on the community survey data obtained from the vegetation survey in the target plot, the number of annual plant species and the number of perennial plant species in the target plot are counted respectively, and the species ratio between annual plants and perennial plants is calculated.

[0060] Annual plants refer to herbaceous plants that complete their entire life cycle—from seed germination, growth, flowering to fruiting—in grasslands with shallow groundwater, requiring only one growing season. These plants are more dependent on short-term rainfall and shallow soil water. Perennial plants refer to herbaceous plants that, in grasslands with shallow groundwater, have a life cycle spanning two or more growing seasons and whose root systems can penetrate deep into the soil. These plants can utilize deep soil water and groundwater, exhibiting greater adaptability to water sources.

[0061] The core variation in the community structure of shallowly buried groundwater grasslands lies in the differences in the composition of annual and perennial plants. These two types of plants exhibit significant differences in water utilization strategies and environmental adaptability, and simply counting the total number of species cannot reflect the functional composition characteristics of the community. Therefore, using community survey data obtained from the vegetation survey of the target plots as the primary basis, all plant species were identified and classified according to the plant lifeform classification criteria: the number of annual plant species was determined by screening and counting all annual plant species; the number of perennial plant species was also determined by screening and counting all perennial plant species; and the species ratio was calculated using the ratio method, with the formula: Species Ratio = Number of Annual Plant Species ÷ Number of Perennial Plant Species. The species ratio can intuitively quantify the relative proportions of the two types of plants, providing a direct basis for judging whether the community structure is regulated by water resources.

[0062] S250. Obtain the number of individuals of each species in the target plot and the total number of individuals of all species in the community from the community survey data, and calculate the Simpson diversity index of the target plot according to the calculation rules of the Simpson diversity index.

[0063] The Simpson Diversity Index is a classic ecological index used to quantify the species richness and dominance of a community. A higher value indicates greater species diversity and a more stable community structure, making it a core indicator of a community's resilience to disturbance. While species quantity and proportion only represent the species composition of a community, they cannot reflect its species evenness and dominance. The Simpson Diversity Index, however, comprehensively quantifies both the diversity level and stability of a community, making it a key indicator for assessing the risk of community degradation.

[0064] Two core data points were extracted from the community survey data: the number of individuals of a single species within the target plot, and the total number of individuals in the community obtained by summing the numbers of all species. The calculation was performed using the commonly used Simpson diversity index formula in ecology. ; Where D is the Simpson diversity index; S is the total number of species in the community; i is the index of each species in the community, corresponding to consecutive integers from 1 to S, referring to the 1st, 2nd... Sth species in the community in sequence; is the number of individuals of the i-th species, that is, the total number of individuals of the species corresponding to index i in the community; N is the total number of individuals in the community.

[0065] S260. The number of species of annual plants, the number of species of perennial plants, the species ratio, and the Simpson diversity index are used together as indicators to characterize community structure.

[0066] A single indicator cannot fully characterize the features of community structure. It is necessary to integrate indicators from three dimensions—composition size, relative structure, and diversity level—to form a complete characterization system. The number of annual plant species, the number of perennial plant species, the species ratio, and the Simpson diversity index are archived separately. These four types of indicators are defined as standardized community structure characterization indicators, forming standardized variables that can be directly adapted to subsequent statistical analysis.

[0067] S270. Obtain the hydrogen and oxygen isotope composition data of the plant water sample, soil water sample from each layer, precipitation water sample and groundwater sample by isotope testing, and obtain the isotope characteristic data corresponding to each type of water sample.

[0068] Isotope testing refers to a standardized experimental method that uses professional mass spectrometry equipment to detect the stable isotope composition of water samples. It is a core means of obtaining the isotopic fingerprint characteristics of water sources. Hydrogen and oxygen stable isotopes are the "fingerprint identifiers" of natural water sources. The hydrogen and oxygen isotope compositions of precipitation, soil water at different depths, and groundwater have inherent differences. The isotopic characteristics of plant water samples are that they absorb mixed signals from various water sources. Only by testing the hydrogen and oxygen isotope compositions of four key types of water samples separately can we obtain the basic data to distinguish independent water sources and analyze mixed water sources.

[0069] Professional testing equipment such as a liquid water stable isotope ratio mass spectrometer can be used to conduct individual tests on plant water samples, soil water samples from various layers, precipitation water samples, and groundwater samples according to the national standards for water environment isotope testing. The hydrogen isotope ratio and oxygen isotope ratio of each type of water sample can be accurately determined. Outliers are eliminated, and the average value of duplicate samples is taken. The standardized hydrogen isotope ratio and oxygen isotope ratio data are integrated to form isotope characteristic data corresponding to each type of water sample.

[0070] S280. The isotopic characteristic data of plant water samples are used as the mixture data of a Bayesian mixture model based on Markov chains, and the isotopic characteristic data of soil water samples, precipitation water samples and groundwater samples from each layer are used as the source data of the Bayesian mixture model; wherein, the Bayesian mixture model is pre-set with Markov chain parameters and convergence conditions.

[0071] A Bayesian mixture model based on Markov chains refers to a hybrid model that integrates Markov chain Monte Carlo iterative algorithm with Bayesian statistical inference. It is specifically designed for quantitatively calculating the contribution ratio of each source component in a mixture, making it suitable for quantitative analysis scenarios related to ecological water source tracing. Markov chain parameters are core control parameters pre-set for model iterative operations, including the number of iterations, sampling steps, and combustion period length. These are fundamental configurations to ensure the orderly progress of the model operation. The convergence condition is a pre-defined criterion for terminating the model operation, used to determine whether the model iteration results are stable and reliable. Reaching the convergence condition indicates that the operation result is valid.

[0072] The core logic of Bayesian mixture models is to separate the source components of the mixture. It is essential to clearly distinguish between mixture data and source data; otherwise, the model cannot identify the computational objects. Data classification is performed according to the model's computational rules: isotopic characteristic data of plant water samples reflecting the characteristics of mixed water sources are defined as the model's mixture data; isotopic characteristic data of soil water samples, precipitation samples, and groundwater samples reflecting the characteristics of independent water sources are defined as the model's source data. Before model execution, parameter pre-configuration is completed. Markov chain parameters include setting the total number of iterations, the number of sampling steps during the combustion period, and the effective sampling quantity. The convergence condition adopts the latent scale reduction factor standard. After completing the parameter and condition configuration, the classified data is imported into the model to construct the computational framework.

[0073] S290. Run the Bayesian mixture model to perform Bayesian inference and obtain the utilization ratio of precipitation, soil water in each layer and groundwater by each dominant plant species.

[0074] Isotope characteristic data can only qualitatively distinguish water source types, but cannot quantitatively calculate the contribution ratio of each water source. By performing Bayesian inference through the model, the utilization ratio of each independent water source in the plant-mixed water source can be quantitatively separated based on statistical algorithms. At the same time, it can achieve fine-grained separation of stratified soil water and groundwater, solving the technical problems of the inability to quantify and analyze the plant water source utilization strategy in a stratified manner. The Bayesian mixture model with completed data import and parameter configuration is started, and iterative calculations are performed according to the preset Markov chain parameters. The convergence status is monitored in real time during the calculation. When the model output results reach the preset convergence conditions, the iterative calculation is terminated. Based on the statistical results of Bayesian inference, the absorption and utilization ratios of different dominant plant species for precipitation, soil water in each layer, and groundwater in shallow / deep buried areas are output, and the results are compiled into a quantitative utilization ratio.

[0075] S2100, based on utilization ratio, groundwater depth zoning data, precipitation data matched with precipitation samples, and soil moisture content, identified key water source factors driving community structure variation through redundancy analysis.

[0076] S2110. Based on key water source factors and community structure characterization indicators, the causal relationship influence path of community structure driven by water source is analyzed using a partial least squares path model.

[0077] S2120. Based on the causal relationship influence path, output the complete causal chain of community structure variation and the basis for ecological risk early warning.

[0078] This invention primarily employs a combination of vegetation survey data classification and statistical analysis, along with Simpson's index calculation, to construct multi-dimensional community structure indicators, achieving standardized quantitative characterization of community structure. It utilizes specialized hydrogen and oxygen isotope testing of four types of water samples to obtain differentiated water source isotope fingerprint data, providing original measured data for quantitative water source analysis. Through Bayesian mixture model data classification and assignment, and by pre-setting Markov chain parameters and convergence conditions, Bayesian inference calculations are performed to accurately decompose the utilization ratios of precipitation, soil water at each layer, and groundwater. The synergistic matching of these two techniques forms a closed-loop data chain between the standardized community structure indicators and the stratified and zoned quantitative water source utilization data, providing a traceable measured computational foundation for subsequent factor identification and causal analysis.

[0079] Example 3 Figure 3 This is a schematic diagram of a community structure water source-driven causal relationship analysis device provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes: The water sampling module 310 is used to acquire multiple key water samples from the target plot in the shallow buried grassland environment. The key water samples include soil water samples, plant water samples, precipitation water samples and groundwater samples. The soil water sample stratification module 320 is used to stratify the soil water sample according to the soil depth data recorded when collecting the soil water sample to obtain multi-layer soil water samples, and to measure the soil moisture content corresponding to each layer of soil water sample. The groundwater zoning module 330 is used to divide the target sample plot into shallow and deep groundwater zones based on the measured burial depth data recorded when collecting groundwater samples, and to use the zoning results as groundwater burial depth zoning data. The community structure characterization index construction module 340 is used to calculate the number of species, species ratio and Simpson diversity of annual plants and perennial plants based on the community survey data obtained from the vegetation survey in the target sample plot, as community structure characterization indicators. The proportion calculation module 350 is used to test and obtain isotopic characteristic data of each key water sample, and input the isotopic characteristic data of each key water sample into a Bayesian mixture model based on Markov chains to calculate the utilization ratio of different dominant plant species on precipitation, soil water in each layer and groundwater. The key water source factor identification module 360 ​​is used to identify key water source factors driving community structure variation based on utilization ratio, groundwater depth zoning data, precipitation matched with precipitation samples, and soil moisture content through redundancy analysis. The causal relationship influence path generation module 370 is used to analyze the causal relationship influence path of community structure driven by water source based on key water source factors and community structure characterization indicators using a partial least squares path model. The Causal Chain and Early Warning Output Module 380 is used to output the complete causal chain of community structure variation and the basis for ecological risk early warning based on the causal relationship influence path.

[0080] This invention standardizes the collection and processing of four key water samples: plants, soil, precipitation, and groundwater. It simultaneously achieves precise acquisition of soil water sample stratification, groundwater zoning, and related quantitative data. By quantifying community structure characterization indicators and accurately calculating the utilization ratio of various water sources by plants, it establishes a practical link between "water source conditions - plant absorption - community response." Through the step-by-step application of redundancy analysis and partial least squares path models, it accurately identifies key water source factors driving community structure variation and clarifies causal relationship influence paths. The entire process is standardized and repeatable, and can be directly applied to ecological monitoring, degradation early warning, and water resource development management of shallowly buried groundwater grasslands, providing concrete and implementable technical support for on-site vegetation protection and rational groundwater extraction.

[0081] Optionally, based on the above embodiments, the water sample collection module 310 may include: The dominant plant species screening unit is used to conduct vegetation surveys in the target plot, obtain community survey data, screen out multiple dominant plant species based on the community survey data, and obtain plant samples by collecting the dominant plant species. The soil depth data recording unit is used to collect multiple layers of soil samples at a preset depth in the target sample plot and record the soil depth data corresponding to each layer of soil sample. The precipitation collection unit is used to collect natural precipitation and accumulated surface water from historical precipitation in the target sample plot during the sampling period, obtain precipitation samples, and count the precipitation amount that matches the precipitation samples in time and space. The groundwater sampling unit is used to collect groundwater in the target sample plot as groundwater samples and record the measured burial depth data corresponding to the groundwater samples. The water sample extraction unit is used to perform low-temperature vacuum distillation on the plant samples and soil samples to extract plant water samples and soil water samples respectively, and to establish the correspondence between the soil water samples and soil depth data. The key water sample component unit is used to combine the extracted plant water sample, soil water sample, directly obtained precipitation water sample, and groundwater water sample as key water samples for the target sample area.

[0082] Optionally, based on the above embodiments, the soil stratification module 320 may include: The depth classification unit is used to classify the soil water samples into corresponding soil depth levels according to preset depth classification rules and soil depth data recorded when collecting soil water samples, so as to obtain multi-layer soil water samples; wherein, the classification of the soil water level is adapted to the plant root distribution characteristics of shallow groundwater grassland. The moisture content measurement unit is used to record the relationship between each soil depth layer and the corresponding soil water sample, and to measure the moisture content of each soil water sample to obtain the soil moisture content corresponding to the soil water sample. Optionally, based on the above embodiments, the groundwater zoning module 330 may include: The burial depth zoning unit is used to compare a preset burial depth threshold with the measured burial depth data recorded when collecting groundwater samples. The area where the measured burial depth data is less than the preset burial depth threshold is designated as a shallow burial groundwater area, and the area where the measured burial depth data is greater than or equal to the preset burial depth threshold is designated as a deep burial groundwater area. Based on the zoning results, groundwater burial depth zoning data is generated. The groundwater burial depth zoning data records the groundwater burial depth zoning values ​​for the shallow groundwater zone and the deep groundwater zone, respectively.

[0083] Optionally, based on the above embodiments, the community structure characterization index construction module 340 may include: The species number counting unit is used to count the number of annual plants and the number of perennial plants in the target plot based on the community survey data obtained from the vegetation survey conducted in the target plot, and to calculate the species ratio between annual plants and perennial plants. The Simpson diversity index calculation unit is used to obtain the number of individuals of each species in the target plot and the total number of individuals of all species in the community from the community survey data, and calculate the Simpson diversity index of the target plot according to the calculation rules of the Simpson diversity index. The community structure characterization index construction unit is used to combine the number of annual plant species, the number of perennial plant species, the species ratio, and the Simpson diversity index as community structure characterization indicators.

[0084] Optionally, based on the above embodiments, the proportional calculation module 350 may further include: The isotope feature data extraction unit is used to obtain the hydrogen and oxygen isotope composition data of the plant water sample, soil water sample of each layer, precipitation water sample and groundwater sample through isotope testing, and obtain the isotope feature data corresponding to each type of water sample. The data type matching unit is used to use the isotopic characteristic data of plant water samples as the mixture data of a Bayesian mixture model based on Markov chains, and to use the isotopic characteristic data of soil water samples, precipitation water samples and groundwater samples from each layer as the source data of the Bayesian mixture model; wherein, the Bayesian mixture model is pre-set with Markov chain parameters and convergence conditions. The proportion determination unit is used to run the Bayesian mixture model to perform Bayesian inference and obtain the utilization proportions of precipitation, soil water in each layer and groundwater by each dominant plant species.

[0085] Optionally, based on the above embodiments, the key water source factor identification module 360 ​​may include: The data value extraction unit is used to extract the groundwater depth zoning values ​​corresponding to the shallow and deep groundwater zones from the groundwater depth zoning data, and to obtain the soil moisture content of each soil sample obtained from the measurement. The redundancy analysis unit is used to perform redundancy analysis calculations with utilization ratio, groundwater depth zoning values, precipitation matched with precipitation samples, and soil moisture content as explanatory variables and community structure characterization indicators as response variables. The explanatory contribution calculation unit is used to calculate the explanatory contribution of each explanatory variable to the response variable through redundancy analysis. The Explanation Contribution Comparison Unit is used to compare the explanatory contributions of each explanatory variable and identify one or more explanatory variables with the highest explanatory contributions as key water source factors.

[0086] Optionally, based on the above embodiments, the causal relationship influence path generation module 370 may include: A partial least squares path model construction unit is used to construct a partial least squares path model by taking the key water source factors as causal variables and the community structure characterization index as the outcome variable. The path coefficient calculation unit is used to calculate the path coefficients from the cause variable to the result variable by running the partial least squares path model. The influence path analysis unit is used to analyze the causal relationship influence path of the cause variable on the result variable based on the positive and negative signs and the magnitude of the calculated path coefficients, as the causal relationship influence path of community structure driven by water source.

[0087] The community structure water source-driven causal relationship analysis device provided in the embodiments of the present invention can execute the community structure water source-driven causal relationship analysis method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0088] Example 4 Figure 4A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0089] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0090] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0091] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as a community structure water source-driven causal relationship analysis method.

[0092] That is, to obtain multiple key water samples from the target plot in the shallow buried grassland environment, including soil water samples, plant water samples, precipitation water samples and groundwater samples; Based on the soil depth data recorded during soil water sampling, the soil water samples were stratified to obtain multi-layer soil water samples, and the soil moisture content corresponding to each layer of soil water samples was measured. Based on the measured burial depth data recorded when collecting groundwater samples, the target sample plot is divided into shallow burial area and deep burial area, and the division results are used as groundwater burial depth zoning data. Based on the community survey data obtained from the vegetation survey conducted in the target plot, the number of species, species ratio, and Simpson diversity of annual and perennial plants were calculated as indicators of community structure. The test obtained isotopic characteristic data of each key water sample, and then input the isotopic characteristic data of each key water sample into a Bayesian mixture model based on Markov chains to calculate the utilization ratio of precipitation, soil water in each layer and groundwater by different dominant plant species. Based on utilization ratio, groundwater depth zoning data, precipitation data matched with precipitation samples, and soil moisture content, key water source factors driving community structure variation were identified through redundancy analysis. Based on key water source factors and community structure characterization indicators, the causal relationship influence path of community structure driven by water source is analyzed using a partial least squares path model. Based on the causal relationship influence path, the complete causal chain of community structure variation and the basis for ecological risk early warning are output.

[0093] In some embodiments, a community structure water source-driven causal relationship resolution method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the community structure water source-driven causal relationship resolution method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform a community structure water source-driven causal relationship resolution method by any other suitable means (e.g., by means of firmware).

[0094] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0095] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0096] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0097] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0098] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0099] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0100] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0101] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for analyzing the causal relationship driven by water resources in community structure, characterized in that, include; Multiple key water samples were obtained from the target plot in the shallowly buried grassland environment. These key water samples included soil water, plant water, precipitation water, and groundwater. Based on the soil depth data recorded during soil water sampling, the soil water samples were stratified to obtain multi-layer soil water samples, and the soil moisture content corresponding to each layer of soil water samples was measured. Based on the measured burial depth data recorded when collecting groundwater samples, the target sample area is divided into shallow burial area and deep burial area, and the division results are used as groundwater burial depth zoning data. Based on the community survey data obtained from the vegetation survey conducted in the target plot, the number of species, species ratio, and Simpson diversity of annual and perennial plants were calculated as indicators of community structure. The test obtained isotopic characteristic data of each key water sample, and then input the isotopic characteristic data of each key water sample into a Bayesian mixture model based on Markov chains to calculate the utilization ratio of precipitation, soil water in each layer and groundwater by different dominant plant species. Based on utilization ratio, groundwater depth zoning data, precipitation data matched with precipitation samples, and soil moisture content, key water source factors driving community structure variation were identified through redundancy analysis. Based on key water source factors and community structure characterization indicators, the causal relationship influence path of community structure driven by water source is analyzed using a partial least squares path model. Based on the causal relationship influence path, the complete causal chain of community structure variation and the basis for ecological risk early warning are output.

2. The method according to claim 1, characterized in that, Multiple key water samples were obtained from the target plot in the shallowly buried grassland environment, including: A vegetation survey was conducted at the target plot to obtain community survey data. Based on the community survey data, several dominant plant species were selected, and plant samples were obtained by collecting the dominant plant species. In the target plot, multiple layers of soil samples were collected according to a preset depth, and the soil depth data corresponding to each layer of soil sample was recorded. During the sampling period, natural precipitation and accumulated surface water from historical precipitation-sourced catchment depressions within the target sample plots were collected to obtain precipitation samples and to calculate the precipitation amount that matched the precipitation samples in time and space. Groundwater samples were collected from the target sample plot and the measured burial depth data corresponding to the groundwater samples were recorded. The plant and soil samples were subjected to low-temperature vacuum distillation to extract plant water samples and soil water samples, respectively, and a correspondence between the soil water samples and soil depth data was established. The extracted plant water samples, soil water samples, directly obtained precipitation water samples, and groundwater samples are used together as key water samples for the target plot.

3. The method according to claim 1, characterized in that, Based on the soil depth data recorded during soil water sampling, the soil water samples were stratified to obtain multi-layer soil water samples, and the soil moisture content corresponding to each layer was measured, including: Based on the preset depth classification rules and the soil depth data recorded when collecting soil water samples, the soil water samples are classified into corresponding soil depth levels to obtain multi-layer soil water samples; wherein, the classification of the soil water levels is adapted to the plant root distribution characteristics of shallow groundwater grassland. Record the relationship between each soil depth layer and the corresponding soil water sample, and measure the water content of each soil water sample to obtain the soil water content corresponding to the soil water sample. Based on the measured burial depth data recorded during groundwater sampling, the target sample area was divided into shallow and deep groundwater zones, and the division results were used as groundwater burial depth zoning data, including: The preset burial depth threshold is compared with the measured burial depth data recorded when collecting groundwater samples. Areas where the measured burial depth data is less than the preset burial depth threshold are designated as shallow groundwater areas, and areas where the measured burial depth data is greater than or equal to the preset burial depth threshold are designated as deep groundwater areas. Groundwater burial depth zoning data are formed based on the delineation results. The groundwater burial depth zoning data records the groundwater burial depth zoning values ​​for the shallow groundwater zone and the deep groundwater zone, respectively.

4. The method according to claim 1, characterized in that, Based on community survey data obtained from vegetation surveys conducted in the target plots, the number of annual and perennial plant species, species ratios, and Simpson diversity were calculated as indicators of community structure, including: Based on the community survey data obtained from the vegetation survey in the target plot, the number of annual plant species and the number of perennial plant species in the target plot were counted respectively, and the species ratio between annual plants and perennial plants was calculated. The number of individuals of each species and the total number of individuals of all species in the target plot are obtained from the community survey data. The Simpson diversity index of the target plot is calculated according to the calculation rules of the Simpson diversity index. The number of annual plant species, the number of perennial plant species, the species ratio, and the Simpson diversity index are used together as indicators of community structure.

5. The method according to claim 1, characterized in that, The test obtained isotopic characteristic data of key water samples, and then input this data into a Bayesian mixture model based on Markov chains to calculate the utilization ratios of precipitation, soil water in each layer, and groundwater by different dominant plant species, including: Hydrogen and oxygen isotope composition data of plant water samples, soil water samples from each layer, precipitation water samples and groundwater samples were obtained by isotope testing, and isotope characteristic data corresponding to each type of water sample were obtained. Isotopic characteristic data of plant water samples are used as mixture data in a Bayesian mixture model based on Markov chains. Isotopic characteristic data of soil water samples, precipitation water samples, and groundwater samples from each layer are used as source data in the Bayesian mixture model. The Bayesian mixture model is pre-set with Markov chain parameters and convergence conditions. The Bayesian mixture model was run to perform Bayesian inference, and the utilization ratios of precipitation, soil water in each layer, and groundwater by each dominant plant species were obtained.

6. The method according to claim 3, characterized in that, Based on utilization rates, groundwater depth zoning data, precipitation data matched with precipitation samples, and soil moisture content, redundancy analysis was used to identify key water source factors driving community structure variation, including: Extract the corresponding groundwater depth zoning values ​​for shallow and deep groundwater zones from the groundwater depth zoning data, and obtain the soil moisture content of each soil sample obtained from the measurement. Using utilization ratio, groundwater depth zoning values, precipitation matched with precipitation samples, and soil moisture content as explanatory variables, and community structure characterization indicators as response variables, redundancy analysis was performed. The explanatory contribution of each explanatory variable to the response variable is calculated through redundancy analysis. By comparing the explanatory contributions of each explanatory variable, one or more explanatory variables with the highest explanatory contributions are identified as key water source factors.

7. The method according to claim 1, characterized in that, Based on key water source factors and community structure characterization indicators, a partial least squares path model is used to analyze the causal relationship influence of water source on community structure, including: Using the key water source factors as causal variables and the community structure characterization indicators as outcome variables, a partial least squares path model is constructed. By running the partial least squares path model, the path coefficients from the causal variable to the outcome variable are calculated; Based on the positive and negative signs and numerical values ​​of the calculated path coefficients, the causal relationship influence path of the cause variable on the result variable is analyzed, which is the causal relationship influence path of community structure driven by water source.

8. A device for analyzing the causal relationship of water source-driven community structure, characterized in that, The device includes: The water sampling module is used to acquire multiple key water samples from target plots in the shallow buried grassland environment. These key water samples include soil water samples, plant water samples, precipitation water samples, and groundwater samples. The soil water sample stratification module is used to stratify the soil water sample based on the soil depth data recorded when collecting the soil water sample to obtain multiple layers of soil water sample, and to measure the soil moisture content corresponding to each layer of soil water sample. The groundwater zoning module is used to divide the target sample plot into shallow and deep groundwater zones based on the measured burial depth data recorded when collecting groundwater samples, and the zoning results are used as groundwater burial depth zoning data. The community structure characterization index construction module is used to calculate the number of species, species ratio, and Simpson diversity of annual and perennial plants based on the community survey data obtained from the vegetation survey conducted in the target sample plot, as community structure characterization indicators. The proportion calculation module is used to test and obtain isotopic characteristic data of each key water sample. The isotopic characteristic data of each key water sample is then input into a Bayesian mixture model based on Markov chains to calculate the utilization ratio of different dominant plant species on precipitation, soil water in each layer, and groundwater. The key water source factor identification module is used to identify the key water source factors driving community structure variation based on utilization ratio, groundwater depth zoning data, precipitation matched with precipitation samples, and soil moisture content through redundancy analysis. The causal relationship influence path generation module is used to analyze the causal relationship influence path of community structure driven by water source based on key water source factors and community structure characterization indicators using a partial least squares path model. The causal chain and early warning output module is used to output the complete causal chain of community structure variation and the basis for ecological risk early warning based on the causal relationship influence path.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform a community structure water source-driven causal relationship analysis method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the method for analyzing the causal relationship of water source-driven community structure according to any one of claims 1-7.

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