A method for identifying the influence of a driving factor on soil respiration and components of natural vegetation

Abstract

The application provides a kind of driving factor influence identification method for natural vegetation soil respiration and component, belong to environmental science and technology field.The method includes: through literature research, extract relevant data, obtain response variable, and carry out hierarchical classification to driving factor and climate condition, the data that cannot be obtained from literature is supplemented by field experiment data, quantifies the overall influence size of different levels of driving factor on response variable and soil respiration and component index under different climate vegetation conditions;Calculate the contribution size of climate vegetation condition to soil respiration and component change under the action of driving factor;Test the correlation and importance between each response variable;Analyze the multi-factor influence of typical climate driving factor and vegetation condition on soil respiration and component.The application clarifies the influence mechanism of natural driving factor on soil carbon release process under different climate conditions, and can provide key parameters for further carbon cycle simulation.

Description

Technical Field

[0001] This invention belongs to the field of environmental science and technology, and in particular relates to a method for identifying the influence of driving factors on the respiration and composition of natural vegetation soil. Background Technology

[0002] Since the Industrial Revolution, the global greenhouse effect has continued and expanded, with the global average surface temperature rising by 0.6°C over the past century. The occurrence of events such as sea-level rise, glacial melting, and extreme weather disasters has drawn significant attention from scientists. According to the IPCC Sixth Report, global emissions totaled 59.1 (±5.9) Gt CO2 in 2019. Among these emissions, ecosystem respiration and decomposition processes, represented by soil autotrophic respiration, heterotrophic respiration, and litter decomposition, play a crucial role in regulating atmospheric CO2 concentration and global climate change, as one of the largest fluxes in the global carbon cycle. However, these carbon release pathways are largely influenced by global change drivers such as global warming, nitrogen (N) deposition, increased atmospheric CO2 concentration, and changes in precipitation. Therefore, understanding the mechanisms by which these driving factors affect key carbon release processes is essential for predicting the future global carbon cycle. However, the mechanisms by which different ecosystem types and experimental treatments affect carbon release processes vary significantly. Such research can greatly enhance our understanding of the response to carbon cycle processes under the background of climate change. Although some studies have synthesized existing findings to preliminarily determine the response of soil respiration to these driving factors, few studies have fully quantified the effects of natural forests, grasslands, and wetlands, and have not distinguished the impacts of these driving factors on artificial and natural vegetation. Therefore, it is necessary to conduct in-depth research on the mechanisms by which typical climate and vegetation factors affect the carbon release process of natural ecosystems in order to further explore their impact on the carbon cycle. Summary of the Invention

[0003] To address the aforementioned shortcomings in existing technologies, this invention provides a method for identifying the influence of driving factors on soil respiration and composition in natural vegetation, thereby exploring changes in the natural vegetation-soil carbon cycle process and promoting the carbon sequestration capacity of natural vegetation systems under the background of climate change.

[0004] To achieve the above objectives, the technical solution adopted by this invention is as follows: a method for identifying the influence of driving factors on the respiration and composition of natural vegetation soil, comprising the following steps:

[0005] S1. Determine the driving factors, response variables, and soil respiration and its components among climate-vegetation factors and soil carbon release processes in natural ecosystems, and collect data on ecosystem types, soil physicochemical properties, soil environmental factors, microbial indicators, vegetation parameters, and soil respiration and its components from the literature.

[0006] S2. Integrate and classify the natural vegetation-soil system, and divide it into zones according to climate-vegetation conditions based on the type of natural ecosystem, and determine the typical natural vegetation in each region under the conditions of each climate-vegetation zone.

[0007] S3. Based on the collected index data, the response variables are screened, and the driving factors and climate conditions are set according to the experimental area based on the climate-vegetation zoning conditions.

[0008] S4. Based on the typical natural vegetation in each region, combined with the indicator data and the set levels, conduct experiments on typical natural vegetation lacking data under different zoning conditions.

[0009] S5. Based on literature review data and experimental data, quantify the magnitude of the influence of different levels of driving factors on response variables and soil respiration and its components under different climatic and vegetation conditions.

[0010] S6. Based on the impact results, analyze the influence of different climate driving factors and their interactions on each response variable under the climate-vegetation zoning, and quantify the contribution of each climate and vegetation factor to the changes in soil respiration and its components through analysis of variance, as well as test the correlation and importance among each response variable, and analyze the multifactorial influence of climate driving factors and vegetation conditions on soil respiration and its components through structural equation modeling.

[0011] The beneficial effects of this invention are as follows: Through literature review, soil parameters, plant parameters, and soil respiration and components (Rs, Rh, Ra) are extracted. Soil and plant parameter indicators are screened by setting conditions to obtain n response variables. Four driving factors are then hierarchically classified. For data that cannot be obtained from the literature, field experiments are used to supplement the data. The LRR effect value is used to quantify the effect of each level of driving factor on response variables K1 to K2. n The overall impact of climate and vegetation factors on soil respiration and its components (Rs, Rh, Ra) was determined. Then, analysis of variance was used to quantify the contribution of each climate-vegetation factor to the changes in soil respiration and its components. Finally, regression analysis was used to test the response variables K1–K. n The correlation and importance of LRR among different vegetation types were investigated. Finally, a structural equation model was used to analyze the multifactorial effects of typical climate drivers and vegetation conditions on soil respiration and its component LRR. This study aims to explore changes in the natural vegetation-soil carbon cycle process under the background of climate change and to promote the carbon sequestration capacity of natural vegetation systems.

[0012] Further, step S1 includes the following steps:

[0013] S101. Temperature, precipitation, atmospheric CO2 concentration and N deposition are typical driving factors of soil carbon release processes in natural ecosystems. Literature search was conducted using soil respiration and its components (Rs, Rh, Ra) as keywords. Rs represents soil respiration, Rh represents soil heterotrophic respiration and Ra represents soil autotrophic respiration.

[0014] S102. Conduct keyword visualization analysis to determine the relevant parameters that affect soil respiration and its components under the influence of driving factors;

[0015] S103. Collect data from literature on ecosystem types, soil physicochemical properties, environmental factors, microbial indicators, vegetation parameters, and soil respiration and its components.

[0016] The beneficial effects of the above-mentioned further solutions are: Based on literature research, this invention determines the soil and plant parameters that respond to changes in temperature, precipitation, atmospheric CO2 concentration and N deposition, and obtains key data such as soil respiration and components (Rs, Rh, Ra), providing data support for subsequent mechanism identification.

[0017] Furthermore, step S2 includes the following steps:

[0018] S201. Integrate and classify the natural vegetation-soil system according to climatic vegetation conditions;

[0019] S202. Based on the integrated classification results and combined with the natural ecosystem type, the study area is divided into zones according to climate-vegetation conditions, and natural vegetation and climate-soil conditions are classified at the same time.

[0020] S203. Based on the processing results of step S202, determine the top j types of natural vegetation whose cumulative area ratio of natural soil-vegetation system exceeds the threshold of b% in different climate-vegetation zones.

[0021] S204. Based on the first j types of natural vegetation, determine the typical natural vegetation in each region under the conditions of each climate-vegetation zone.

[0022] The beneficial effects of the above-mentioned further solutions are: the present invention divides the study area into zones based on the characteristics of climate, altitude and vegetation, and determines the main natural vegetation in each study area, providing a basis for the division of climate and vegetation factors in subsequent mechanism identification.

[0023] Furthermore, step S3 includes the following steps:

[0024] S301. Based on the collected index data, using a% of the literature volume as the cutoff value, the response variables were screened, and indicators with insufficient data were eliminated to obtain the response variables K1 to K2 that affect soil carbon flux under the influence of driving factors. n, where K n Represents the nth reaction variable;

[0025] S302. Based on the zoning conditions in step S2, collect nitrogen deposition data for each region after climate-vegetation zoning from the literature and the variation range of temperature, precipitation and atmospheric CO2 concentration under different emission modes determined by the IPCC.

[0026] S303. Based on the magnitude of change, set the driving factors and climate conditions into levels according to the experimental area.

[0027] The beneficial effects of the above-mentioned further scheme are as follows: Based on the experimental data obtained through literature review in step S1, this invention removes influencing indicators with insufficient data volume through conditional screening, providing a reference for setting observation indicators for subsequent field experiments. Simultaneously, the driving factors are further divided into four levels, providing support for further investigation into the effects of driving factors of different magnitudes on response variables.

[0028] Furthermore, step S4 includes the following steps:

[0029] S401. Based on ecosystem type and vegetation parameters, compare typical natural vegetation in each region to determine the typical natural vegetation types that are lacking in the literature under different driving factors.

[0030] S402. The driving factors are grouped according to the levels set in the experimental region, and the response variables are K1 to K2. n Using soil respiration and its components (Rs, Rh, Ra) as observation indicators, a field control experiment was conducted on typical natural vegetation types that were lacking in the literature to supplement the data.

[0031] The beneficial effects of the above-mentioned further solutions are: Based on the hierarchical settings and classifications in steps S1-S3, the present invention supplements experimental data that cannot be obtained or is lacking in the literature during the process of ecosystem zoning by conducting field experiments to improve data support.

[0032] Furthermore, step S5 includes the following steps:

[0033] S501. Based on the index data collected in step S1 and the experimental data obtained in step S4, use the LRR effect value to quantify the effect of different levels of driving factors on response variables K1 to K under different climatic and vegetation conditions. n The overall impact on soil respiration and its components (Rs, Rh, Ra);

[0034] S502. Combining the driving factors and climate condition classifications from steps S302 and S303, determine the impact of each driving factor on response variables K1 to K2 under climate-vegetation zoning conditions. nThe magnitude of the influence on soil respiration and its components (Rs, Rh, Ra).

[0035] The beneficial effect of the above-mentioned further scheme is that, by quantifying the LRR effect values, the response variables K1 to K1 under both overall and local climate zoning conditions, different driving factors can be used to influence the response variables K1 to K2. n The magnitude of the influence on soil respiration and its components (Rs, Rh, Ra).

[0036] Furthermore, step S6 includes the following steps:

[0037] S601. Quantify the contribution rate of each climate vegetation factor to soil respiration and components (Rs, Rh, Ra) under the influence of driving factors through analysis of variance, and screen out the top c climate vegetation factors with a cumulative contribution of b%.

[0038] S602. Through regression analysis, examine the driving factors and each response variable K1~K n The correlation and importance between soil respiration and its components (Rs, Rh, Ra) were investigated to identify intermediate variables that have a significant impact on soil carbon release under different driving factors.

[0039] S603. Combining the first c climate and vegetation factors and intermediate variables that affect soil respiration and its components under the driving force in steps S601 and S602, construct a structural equation model to analyze the influence of typical climate driving factors and vegetation factors on soil respiration and its components, and complete the identification of the influence mechanism.

[0040] The beneficial effects of the above-mentioned further scheme are: This invention uses analysis of variance to preliminarily quantify the contribution of climate and vegetation factors to soil respiration and its component changes under the influence of typical driving factors, and then uses regression analysis to test the response variables K1 to K n The correlation and importance between soil respiration and its components (Rs, Rh, Ra) were investigated. Finally, the combined effects of typical climate driving factors and vegetation factors on soil respiration and its components (Rs, Rh, Ra) were analyzed using structural equation modeling, which can effectively identify the impacts. Attached Figure Description

[0041] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0042] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0043] Example

[0044] like Figure 1 As shown, this invention provides a method for identifying the influence of driving factors on soil respiration and composition in natural vegetation, the implementation method of which is as follows:

[0045] S1. Determine the driving factors, response variables, and soil respiration and its components among climate-vegetation factors and soil carbon release processes in natural ecosystems. Collect data from the literature on ecosystem type, soil physicochemical properties, soil environmental factors, microbial indicators, vegetation parameters, and soil respiration and its components. The method for achieving this is as follows:

[0046] S101. Using temperature, precipitation, atmospheric CO2 concentration and N deposition as typical driving factors of soil carbon release processes in natural ecosystems, a literature search was conducted using soil respiration and its components (Rs, Rh, Ra) as keywords. Here, Rs represents soil respiration, Rh represents soil heterotrophic respiration, and Ra represents soil autotrophic respiration.

[0047] S102. Conduct keyword visualization analysis to determine the relevant parameters that affect soil respiration and its components under the influence of driving factors;

[0048] S103. Collect data from literature on ecosystem types, soil physicochemical properties, environmental factors, microbial indicators, vegetation parameters, and soil respiration and its components.

[0049] In this embodiment, temperature, precipitation, atmospheric CO2 concentration, and nitrogen deposition were used as driving factors. Soil respiration and its components (Rs, Rh, Ra) were targeted and relevant data were searched and downloaded from CNKI and WOS websites. Vosviewer and Citespace software were used for keyword visualization analysis to preliminarily identify soil physicochemical properties, environmental factors, microbial indicators, vegetation parameters, and other relevant parameters that have a significant impact on soil carbon flux under the above driving factors. Origin and GetData software were used to collect data on ecosystem types, soil physicochemical properties, environmental factors, microbial indicators, vegetation parameters, and soil carbon flux from literature.

[0050] S2. Integrate and classify the natural vegetation-soil system, and combine it with the natural ecosystem type to divide it into zones according to climate-vegetation conditions, and determine the typical natural vegetation in each region under the conditions of each climate-vegetation zone. The method for achieving this is as follows:

[0051] S201. Integrate and classify the natural vegetation-soil system according to climatic vegetation conditions;

[0052] S202. Based on the integrated classification results and combined with the natural ecosystem type, the study area is divided into zones according to climate-vegetation conditions, and natural vegetation and climate-soil conditions are classified at the same time.

[0053] S203. Based on the processing results of step S202, determine the top j types of natural vegetation whose cumulative area ratio of natural soil-vegetation system exceeds the threshold of b% in different climate-vegetation zones.

[0054] S204. Based on the first j types of natural vegetation, determine the typical natural vegetation in each region under the conditions of each climate-vegetation zone.

[0055] In this embodiment, climate and vegetation conditions include average annual rainfall, average annual temperature, altitude, soil texture, and vegetation type.

[0056] In this embodiment, the natural vegetation-soil system is integrated and classified, and combined with the ecosystem type extracted in step S1, it is divided into i partitions R according to climate and vegetation conditions. i The study also identified the top j types of natural vegetation with a cumulative area ratio exceeding b% in different climate-vegetation zones, and determined the typical natural vegetation in each region under the conditions of each climate-vegetation zone.

[0057] S3. Based on the collected indicator data, the response variables are screened, and the driving factors and climate conditions are set into levels according to the experimental area, taking into account the climate-vegetation zoning conditions. The implementation method is as follows:

[0058] S301. Based on the collected index data, using a% of the literature volume as the cutoff value, the response variables were screened, and indicators with insufficient data were eliminated to obtain the response variables K1 to K2 that affect soil carbon flux under the influence of driving factors. n , where K n Represents the nth reaction variable;

[0059] S302. Based on the zoning conditions in step S2, collect nitrogen deposition data for each region after climate-vegetation zoning from the literature and the variation range of temperature, precipitation and atmospheric CO2 concentration under different emission modes determined by the IPCC.

[0060] S303. Based on the magnitude of change, set the driving factors and climate conditions into levels according to the experimental area.

[0061] In this embodiment, based on the amount of index data obtained, and using a% of the literature volume as the threshold, the initially determined response variables are screened, eliminating influencing indicators with insufficient data, and obtaining response variables K1 to K2 that have a significant impact on soil carbon flux under the influence of driving factors and have sufficient data. n .

[0062] In this embodiment, the experimental group variables were divided into four levels: CK, Low, Medium, and High, based on climate zoning conditions and by collecting nitrogen deposition data for each region and the variation range of temperature, precipitation (drought and rain enhancement) and atmospheric CO2 concentration under different emission modes determined by the IPCC.

[0063] S4. Based on the typical natural vegetation in each region, combined with indicator data and set levels, experiments were conducted on typical natural vegetation lacking data under different zoning conditions. The implementation method is as follows:

[0064] S401. Based on ecosystem type and vegetation parameters, compare typical natural vegetation in each region to determine the typical natural vegetation types that are lacking in the literature under different driving factors.

[0065] S402. The driving factors are grouped according to the levels set in the experimental region, and the response variables are K1 to K2. n Using soil respiration and its components (Rs, Rh, Ra) as observation indicators, a field control experiment was conducted on typical natural vegetation types that were lacking in the literature to supplement the data.

[0066] In this embodiment, the obtained ecosystem type and vegetation are compared with the existing typical natural vegetation R. ij This study aimed to identify typical natural vegetation types lacking in literature under different driving forces. Based on the identified experimental area, field experiments were conducted. Driving factors were categorized into four levels according to the experimental area, and specific observation indicators were based on the determined response variables K1 to K2. n Using soil respiration and its components (Rs, Rh, Ra) as observation indicators, data were supplemented for typical natural vegetation types that were lacking in the literature.

[0067] S5. Based on literature review data and experimental data, quantify the influence of different levels of driving factors on response variables and soil respiration and its components under different climatic and vegetation conditions. The method for achieving this is as follows:

[0068] S501. Based on the index data collected in step S1 and the experimental data obtained in step S4, use the LRR effect value to quantify the effect of different levels of driving factors on response variables K1 to K under different climatic and vegetation conditions. n The overall impact on soil respiration and its components (Rs, Rh, Ra);

[0069] S502. Combining the driving factors and climate condition classifications from steps S302 and S303, determine the effect of each driving factor on response variables K1 to K2 under climate-vegetation zoning conditions. n The magnitude of the influence on soil respiration and its components (Rs, Rh, Ra).

[0070] In this embodiment, step S501 yields the overall impact, representing the average impact over a large region. Step S502 divides the region based on the driving factors and climate conditions in steps S302 and S303, thereby determining the impact of different driving factors under different climate conditions, i.e., the average regional impact.

[0071] In this embodiment, combining the literature survey data obtained in step S1 and the experimental data obtained in step S4, the LRR effect value is used to quantify the effect of each driving factor on the response variables K1 to K2. n The overall impact of soil respiration and its components (Rs, Rh, Ra) was determined; based on typical natural vegetation and lacking typical natural vegetation types in each region, the effects of each driving factor on response variables K1 to K2 under climate-vegetation zoning conditions were determined. n The magnitude of the influence on soil respiration and its components (Rs, Rh, Ra).

[0072] S6. Based on the impact results, analyze the influence of different climate driving factors and their interactions on each response variable under climate-vegetation zoning. Quantify the contribution of each climate-vegetation factor to the changes in soil respiration and its components using analysis of variance, and examine the correlation and importance among the response variables. Finally, analyze the multi-factor impact of climate driving factors and vegetation conditions on soil respiration and its components using structural equation modeling. The implementation method is as follows:

[0073] S601. Quantify the contribution rate of each climate vegetation factor to soil respiration and components (Rs, Rh, Ra) under the influence of driving factors through analysis of variance, and screen out the top c climate vegetation factors with a cumulative contribution of b%.

[0074] The expression for the contribution rate is as follows:

[0075]

[0076] Among them, CT i Indicates contribution rate, SS i denoted as the sum of squares of deviations of the sample under the influence of driving factor i, f represents the degrees of freedom of driving factor i, MSE represents the root mean square error of the sample under the influence of driving factor i, and SST represents the total sum of squares of deviations of the sample under the combined influence of each factor and its interaction.

[0077] S602. Through regression analysis, examine the driving factors and each response variable K1~K n The correlation and importance between soil respiration and its components (Rs, Rh, Ra) were investigated to identify the intermediate variables that have a significant impact on soil carbon release under different driving factors.

[0078] S603. Combining the first c climate and vegetation factors and intermediate variables that affect soil respiration and its components under the driving force in steps S601 and S602, construct a structural equation model to analyze the influence of typical climate driving factors and vegetation factors on soil respiration and its components, and complete the identification of the influence mechanism.

[0079] In this embodiment, the logic of step S6 is: driving factor → (response variable) → soil respiration and its components. Step S601 describes the direct impact of the driving factor on soil respiration, and step S602 describes the relationship between the response variable and soil respiration. The two are combined to express, through structural equation modeling, the direct impact of the driving factor on soil respiration and its indirect impact on soil respiration through its influence on the response variable. Both steps S601 and S602 are screening processes. Step S601 screens climate and vegetation factors, i.e., through contribution rate calculation; step S602 screens response variables, i.e., the driving factor affects soil respiration by influencing the response variable, i.e., through correlation and significance. The selected factors constitute the components of the structural equation model, providing support for its construction.

[0080] In this embodiment, based on the overall impact, the contribution of climate and vegetation factors to soil respiration and its composition changes under the influence of driving factors is quantified through analysis of variance. Then, correlation analysis is used to test the response variables K1 to K2. n The correlation between soil respiration and its components (Rs, Rh, Ra) was investigated to identify intermediate variables that have a significant impact on soil carbon release under different driving factors.

[0081] Finally, structural equation modeling was used to analyze the combined effects of typical climate driving factors and vegetation factors on soil respiration and components (Rs, Rh, Ra), thus completing a method for identifying the impact of climate and vegetation factors on the carbon release process of natural ecosystems.

[0082] In summary, this invention, through literature review, extracted data on climate vegetation and soil parameters, plant parameters, and soil respiration and its components. By setting conditions, soil and plant parameter indicators were screened to obtain response variables. Driving factors and climate conditions were hierarchically classified. For data unavailable from the literature, field experiments were conducted to supplement the data. The overall impact of different levels of driving factors under different climate and vegetation conditions on response variables and soil respiration and its components was quantified. The influence of different climate driving factors and their interactions on each response variable was analyzed. Analysis of variance was used to calculate the contribution of climate and vegetation conditions to changes in soil respiration and its components under the influence of driving factors. Correlation analysis was then used to examine the correlation and importance between each response variable and soil respiration and its components. Finally, structural equation modeling was used to analyze the multi-factor effects of typical climate driving factors and vegetation conditions on soil respiration and its components. This invention clarifies the influence mechanism of natural driving factors on soil carbon release processes under different climate conditions, providing key parameters for further carbon cycle simulation.

Claims

1. A method for identifying the influence of driving factors on soil respiration and composition in natural vegetation, characterized in that, Includes the following steps: S1. Determine the driving factors, response variables, and soil respiration and its components among climate-vegetation factors and soil carbon release processes in natural ecosystems, and collect data on ecosystem types, soil physicochemical properties, soil environmental factors, microbial indicators, vegetation parameters, and soil respiration and its components from the literature. S2. Integrate and classify the natural vegetation-soil system, and divide it into zones according to climate-vegetation conditions based on the type of natural ecosystem, and determine the typical natural vegetation in each region under the conditions of each climate-vegetation zone. S3. Based on the collected index data, the response variables are screened, and the driving factors and climate conditions are set according to the experimental area based on the climate-vegetation zoning conditions. S4. Based on the typical natural vegetation in each region, combined with the indicator data and the set levels, conduct experiments on typical natural vegetation lacking data under different zoning conditions. S5. Based on literature review data and experimental data, quantify the magnitude of the influence of different levels of driving factors on response variables and soil respiration and its components under different climatic and vegetation conditions. S6. Based on the impact results, analyze the influence of different climate driving factors and their interactions on each response variable under the climate-vegetation zoning, and quantify the contribution of each climate and vegetation factor to the changes in soil respiration and its components through analysis of variance, as well as test the correlation and importance among each response variable, and analyze the multifactorial influence of climate driving factors and vegetation conditions on soil respiration and its components through structural equation modeling.

2. The method for identifying the influence of driving factors on soil respiration and composition in natural vegetation according to claim 1, characterized in that, Step S1 includes the following steps: S101. Using temperature, precipitation, atmospheric CO2 concentration and N deposition as typical driving factors of soil carbon release processes in natural ecosystems, and combining soil respiration and components (Rs, Rh, Ra) as keywords, a literature search was conducted, where Rs represents soil respiration, Rh represents soil heterotrophic respiration, and Ra represents soil autotrophic respiration. S102. Conduct keyword visualization analysis to determine the relevant parameters that affect soil respiration and its components under the influence of driving factors; S103. Collect data from literature on ecosystem types, soil physicochemical properties, environmental factors, microbial indicators, vegetation parameters, and soil respiration and its components.

3. The method for identifying the influence of driving factors on soil respiration and composition in natural vegetation according to claim 2, characterized in that, Step S2 includes the following steps: S201. Integrate and classify the natural vegetation-soil system according to climatic vegetation conditions; S202. Based on the integrated classification results and combined with the natural ecosystem type, the study area is divided into zones according to climate-vegetation conditions, and the natural vegetation climate-soil-topography conditions are classified. S203. Based on the processing results of step S202, determine the top j types of natural vegetation whose cumulative area ratio of natural soil-vegetation system exceeds the threshold of b% in different climate-vegetation zones. S204. Based on the first j types of natural vegetation, determine the typical natural vegetation in each region under the conditions of each climate-vegetation zone.

4. The method for identifying the influence of driving factors on soil respiration and composition in natural vegetation according to claim 3, characterized in that, Step S3 includes the following steps: S301. Based on the collected index data, using a% of the literature volume as the cutoff value, the response variables were screened, and indicators with insufficient data were eliminated to obtain the response variables K1 to K2 that affect soil carbon flux under the influence of driving factors. n , where K n Represents the nth reaction variable; S302. Based on the zoning conditions in step S2, collect nitrogen deposition data for each region after climate-vegetation zoning from the literature and the variation range of temperature, precipitation and atmospheric CO2 concentration under different emission modes determined by the IPCC. S303. Based on the magnitude of change, set the driving factors and climate conditions into levels according to the experimental area.

5. The method for identifying the influence of driving factors on soil respiration and composition in natural vegetation according to claim 4, characterized in that, Step S4 includes the following steps: S401. Based on ecosystem type and vegetation parameters, compare typical natural vegetation in each region to determine the typical natural vegetation types that are lacking in the literature under different driving factors. S402. The driving factors are grouped according to the levels set in the experimental region, and the response variables are K1 to K2. n Using soil respiration and its components (Rs, Rh, Ra) as observation indicators, a field control experiment was conducted on typical natural vegetation types that were lacking in the literature to supplement the data.

6. The method for identifying the influence of driving factors on soil respiration and composition in natural vegetation according to claim 5, characterized in that, Step S5 includes the following steps: S501. Based on the index data collected in step S1 and the experimental data obtained in step S4, use the LRR effect value to quantify the effect of different levels of driving factors on response variables K1 to K under different climatic and vegetation conditions. n The overall impact on soil respiration and its components (Rs, Rh, Ra); S502. Combining the driving factors and climate condition classifications from steps S302 and S303, determine the effect of each driving factor on response variables K1 to K2 under climate-vegetation zoning conditions. n The magnitude of the influence on soil respiration and its components (Rs, Rh, Ra).

7. The method for identifying the influence of driving factors on soil respiration and composition in natural vegetation according to claim 6, characterized in that, Step S6 includes the following steps: S601. Quantify the contribution rate of each climate vegetation factor to soil respiration and components (Rs, Rh, Ra) under the influence of driving factors through analysis of variance, and screen out the top c climate vegetation factors with a cumulative contribution of b%. S602. Through regression analysis, examine the driving factors and each response variable K1~K n The correlation and importance between soil respiration and its components (Rs, Rh, Ra) were investigated to identify intermediate variables that have a significant impact on soil carbon release under different driving factors. S603. Combining the first c climate and vegetation factors and intermediate variables that affect soil respiration and its components under the driving force in steps S601 and S602, construct a structural equation model to analyze the influence of typical climate driving factors and vegetation factors on soil respiration and its components, and complete the identification of the influence mechanism.

8. The method for identifying the influence of driving factors on soil respiration and composition in natural vegetation according to claim 7, characterized in that, The expression for the contribution rate is as follows: Among them, CT i Indicates contribution rate, SS i Let f represent the sum of squares of deviations of the sample under the influence of driving factor i, f represent the degrees of freedom of driving factor i, MSE represent the root mean square error of the sample under the influence of driving factor i, and SST represent the total sum of squares of deviations of the sample under the combined influence of all factors and their interactions.

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