Microbiome function-based ecological system man-made interference degree evaluation method

Abstract

The invention provides a microbiome function-based ecological system man-made interference degree evaluation method. The method comprises the following steps: acquiring standardized abundance data of microbial functional genes in all sampling points in an evaluation region; based on historical environment monitoring data or field survey data of all the sampling points, taking the sampling point with the minimum man-made interference degree in all the sampling points as a reference point; aiming at each sampling point, determining the response direction of each functional gene to man-made interference; according to the abundance data, determining a 95% quantile P95, a 5% quantile P5 and a maximum value Max of each functional gene; based on the response direction, determining a score of each functional gene by adopting a ratio method, and accumulating the scores of all the functional genes to obtain a microbiome functional integrity index M-IFI value; and determining the man-made interference degree grade of each sampling point according to the size of the M-IFI value relative to an interference evaluation threshold T, thereby objectively and quantitatively evaluating the influence of various human activities on the ecological system.

Description

Technical Field

[0001] This application relates to the field of ecosystem assessment technology, and in particular to a method for assessing the degree of anthropogenic disturbance in an ecosystem based on microbiome function. Background Technology

[0002] Currently, the assessment of the degree of human disturbance to ecosystems mainly relies on two categories of methods: one is based on physicochemical indicators (such as the physicochemical properties of water bodies and the content of heavy metals in soil), and the other is based on macrobiological indicators (such as vegetation, fish, and large benthic animals).

[0003] However, these methods have significant drawbacks. The limitations of physicochemical indicators: they can only reflect instantaneous, single environmental pressures, making it difficult to comprehensively reflect the cumulative and complex impacts of multiple anthropogenic disturbances (such as land use change, pollution input, and water resource regulation) on ecosystem functions, and thus unable to reveal the overall health status of the ecosystem. The lag nature of macrobiological indicators: large organisms have long life cycles, and their responses to environmental pressures exhibit a lag effect, making early warning impossible. Furthermore, these indicators are not sensitive enough to certain types of disturbances (such as micropollution and habitat fragmentation). Summary of the Invention

[0004] This application provides a method for evaluating the degree of human interference in an ecosystem based on microbiome function. This method solves the problems of existing technologies, which are highly subjective in parameter selection and weight determination, and have relatively coarse evaluation level classifications, making it difficult to meet the needs of precise management. It achieves an objective and quantitative evaluation of the impact of various human activities on the ecosystem and has the advantages of wide applicability, high sensitivity, and strong operability.

[0005] To achieve the above objectives, embodiments of this application provide a method for evaluating the degree of anthropogenic disturbance in an ecosystem based on microbiome function, the method comprising: Abundance data of microbial functional genes in all sampling points within the evaluation area are obtained and standardized, with the count in ppm per million reads; the functional genes include one or more of nitrogen cycle functional genes, carbon cycle functional genes, sulfur cycle functional genes, and phosphorus cycle functional genes. Based on historical environmental monitoring data or field survey data of all sampling points, the sampling point with the least degree of human interference among all sampling points is used as the reference point; For each sampling point, the response direction of each functional gene to human interference was determined; and, based on the standardized abundance data, the 95th percentile P95, 5th percentile P5, and maximum value Max of each functional gene were determined. Based on the response direction, the score of each functional gene is determined by the ratio method, and the scores of each functional gene are accumulated to obtain the microbiome functional integrity index M-IFI value. The degree of human interference at each sampling point is determined based on the magnitude of the M-IFI value relative to the interference evaluation threshold T; the degree of human interference is categorized as no interference, minor interference, small interference, significant interference, and large interference; the interference evaluation threshold T is the 95th percentile of the M-IFI value at the reference point.

[0006] In one possible implementation, obtaining the abundance data of microbial functional genes in all sampling points within the evaluation area includes: Environmental samples were obtained within the evaluation area; the environmental samples included water, sediment, and soil samples. Total microbial DNA was extracted from the environmental samples and metagenomic sequencing was performed to obtain sequencing data. The sequencing data is subjected to quality control, gene annotation, and abundance quantification to obtain the abundance data of the functional genes of the microorganism; the abundance quantification is to determine the number of high-throughput sequencing reads per million sequences of the functional gene.

[0007] In one possible implementation, the functional genes include the following types: Nitrogen cycle functional genes include at least one of the following: amoA, amoB, nirK, nirS, norB, nosZ, nifH, nxrA, and nxrB; Carbon cycle functional genes include at least one of the following: mcrA, pmoA, mmoX, acdS, and acdA; Sulfur cycle functional genes include at least one of the following: dsrA, dsrB, soxB, and aprA; Phosphorus cycle functional genes include at least one of the following: phoA, phoD, phonX, ppx, and ppk.

[0008] In one possible implementation, the functional gene is determined by one or more of the following methods: Genes with an average normalized abundance greater than 1 pmm in microbial genes are defined as the functional genes. Principal component analysis was performed on microbial genes, and genes that contributed significantly to the overall variance were identified as the functional genes. Cluster analysis of microbial genes yields multiple categories, and representative genes from each category are designated as functional genes.

[0009] In a possible implementation, based on the historical environmental monitoring data or on-site investigation data of all sampling points, taking the sampling point with the least degree of human interference among all sampling points as the reference point, includes: Obtain the human activity intensity data within the evaluation area; the human activity intensity data includes one or more of land use type, traffic density, pollution source distribution, and water resource development intensity; Perform spatial analysis based on a geographic information system to identify the area with the least human interference within the evaluation area; Combined with the on-site investigation data of the area with the least human interference, determine the reference point from the sampling points within the area with the least human interference.

[0010] In a possible implementation, the response direction is a positive response or a negative response. The positive response indicates that the functional gene abundance decreases as human interference increases, and the negative response indicates that the functional gene abundance increases as human interference increases; the response direction is determined based on the difference between the abundance data of the functional gene at the reference point and the abundance data of the functional gene at the known disturbed point.

[0011] In a possible implementation, based on the response direction, using the ratio method to determine the score of each functional gene, includes: For the functional gene with a positive response direction, the score calculation formula is: min(functional gene abundance / P95, 1); For the functional gene with a negative response direction, the score calculation formula is: min((Max - functional gene abundance) / (Max - P5), 1).

[0012] In a possible implementation, according to the magnitude of the M-IFI value relative to the interference evaluation threshold T, determining the level of human interference for each sampling point, includes: When the M-IFI value ≥ T, the level of human interference is no interference; When 0.8T ≤ M-IFI value < T, the level of human interference is minor interference; When 0.6T ≤ M-IFI value < 0.8T, the level of human interference is slight interference; When 0.4T ≤ M-IFI value < 0.6T, the level of human interference is relatively large interference; When the M-IFI value < 0.4T, the level of human interference is large interference.

[0013] In a possible implementation, the method further includes: The correlation analysis between the M-IFI value and the preset environmental evaluation index is performed to obtain the evaluation results; The rationality of the evaluation results was verified by combining remote sensing data.

[0014] In one possible implementation, the method further includes: Based on the score of each functional gene, a functional integrity sub-index is obtained; the functional integrity sub-index is a carbon cycle functional integrity index, namely, focusing on one or more of the methane cycle, nitrogen cycle, sulfur cycle, and phosphorus cycle functional integrity indices. The step of determining the level of human interference at each sampling point based on the magnitude of the M-IFI value relative to the interference evaluation threshold T includes: The level of human interference at each sampling point is determined based on the magnitude of the functional integrity sub-index relative to the interference evaluation threshold T.

[0015] The technical solutions provided in this application embodiment have at least the following technical effects or advantages: (1) High comprehensiveness, reflecting complex disturbances. This application integrates the functional genes of multiple key biogeochemical cycles such as nitrogen, carbon, sulfur, and phosphorus to construct a multi-dimensional evaluation system that can simultaneously perceive and comprehensively evaluate the complex disturbance effects caused by various human activities such as land use, pollution, and engineering construction, and provide a more comprehensive view of the ecosystem's functional status.

[0016] (2) High sensitivity, enabling early warning. Due to the short generation time of microorganisms, they respond quickly to environmental changes. Changes in the abundance of their functional genes can show the effects of human interference earlier than macroscopic organisms. Thus, as a sensitive early warning tool, it can buy valuable response time for managers.

[0017] (3) Good objectivity and strong comparability of results. The core ratio method and threshold determination process based on statistical quantiles in this application minimize the interference of human subjective judgment. The whole method is established under a unified mathematical statistical framework, which makes the evaluation results at different times and locations highly comparable and conducive to long-term monitoring and cross-regional comparison.

[0018] (4) Wide applicability and high practical value. The method principle of this application is universal and can be widely applied to various ecosystems such as rivers, lakes, wetlands, marine sediments and soils. It is suitable for evaluating various types of anthropogenic disturbances, from point source pollution to large-scale land use change, and has broad application prospects. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments of this application or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart illustrating a method for evaluating the degree of anthropogenic disturbance in an ecosystem based on microbiome function, provided in this application embodiment; Figure 2 A flowchart of another method for evaluating the degree of anthropogenic disturbance in an ecosystem based on microbiome function, provided in this application embodiment; Figure 3 A schematic diagram illustrating the screening of methane cycle indicator genes provided in an embodiment of this application; Figure 4 A schematic diagram illustrating the screening of nitrogen cycle indicator genes provided in an embodiment of this application; Figure 5 A schematic diagram illustrating the screening of sulfur cycle indicator genes provided in an embodiment of this application; Figure 6 A schematic diagram illustrating the screening of phosphorus cycle indicator genes provided in an embodiment of this application; Figure 7 This is a schematic diagram showing the proportional distribution of sampling points at different integrity levels in the embodiments of this application. Detailed Implementation

[0021] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0022] In the description of the embodiments of this application, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the embodiments of this application and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. The terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; a mechanical connection or an electrical connection; a direct connection or an indirect connection through an intermediate medium; or a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in the embodiments of this application according to the specific circumstances.

[0023] While existing technologies have developed indices based on microbial community structure (such as M-IBI), most are limited to community structure characteristics (such as species richness and diversity) and fail to directly target functional genes driving key ecological processes, resulting in a weak correlation between evaluation results and ecological functions. Furthermore, existing methods are highly subjective in parameter selection and weight determination, and their evaluation level classifications are relatively coarse, making it difficult to meet the needs of precise management.

[0024] Therefore, this application provides a method for evaluating the degree of anthropogenic disturbance in ecosystems based on microbiome function. It uses microbial functional genes (core biogeochemical functions of carbon, nitrogen, sulfur, and phosphorus) as evaluation indicators; calculates the score of each functional gene using a ratio method; sums the scores of each functional gene to obtain the M-IFI value; calculates the disturbance evaluation threshold based on the distribution of M-IFI values ​​at a reference point; and classifies the degree of anthropogenic disturbance into five levels: no disturbance, minor disturbance, small disturbance, significant disturbance, and large disturbance, according to the threshold. This method can objectively and quantitatively evaluate the impact of various human activities (including land use change, transportation infrastructure development, water resource regulation, and pollution input) on the ecosystem, and has the advantages of wide applicability, high sensitivity, and strong operability.

[0025] Figure 1 A flowchart illustrating a method for evaluating the degree of anthropogenic disturbance in an ecosystem based on microbiome function, provided in this application embodiment; as follows: Figure 1 As shown, the method may include the following steps.

[0026] S101. Obtain the abundance data of microbial functional genes in all sampling points within the evaluation area and standardize them, with the count in ppm per million reads; the functional genes include one or more of nitrogen cycle functional genes, carbon cycle functional genes, sulfur cycle functional genes, and phosphorus cycle functional genes.

[0027] S102. Based on the historical environmental monitoring data or field survey data of all sampling points, the sampling point with the least degree of human interference among all sampling points shall be used as the reference point.

[0028] S103. For each sampling point, determine the response direction of each functional gene to human interference; and, based on the standardized abundance data, determine the 95th percentile P95, 5th percentile P5, and maximum value Max for each functional gene.

[0029] S104. Based on this response direction, the score of each functional gene is determined by the ratio method, and the scores of each functional gene are accumulated to obtain the microbiome functional integrity index M-IFI value.

[0030] The M-IFI value stands for Microbiome Functional Integrity Index. For example, the M-IFI value is determined using the following formula: Where M-IFI_i: the microbiome functional integrity index at sampling point i; S _{ g , i}: The score of functional gene g in sampling point i; n: The total number of functional genes involved in the calculation; Σ: The summation symbol.

[0031] For example, for each sampling point, the scores of all functional genes are summed. This M-IFI value comprehensively reflects the functional integrity of the microbiome at that sampling point. The higher the M-IFI value, the better the functional integrity of the microbiome and the less it is affected by human interference.

[0032] S105. Based on the magnitude of the M-IFI value relative to the interference evaluation threshold T, determine the level of human interference at each sampling point.

[0033] The level of human interference is categorized as no interference, minor interference, small interference, significant interference, and large interference; the interference evaluation threshold T is the 95th percentile of the M-IFI value at the reference point.

[0034] For example, the 95th percentile of the reference point M-IFI value can be used as the interference evaluation threshold T. This interference evaluation threshold T can be determined by the following formula: Where, T: interference evaluation threshold, : 95th percentile function, M-IFI_r: M-IFI value of reference point (no interference sample point) r, R: set of reference points, {M-IFI_r | r ∈ R}: set of M-IFI values ​​of all reference points.

[0035] Based on the above technical solution, by integrating the functional genes of multiple key biogeochemical cycles such as nitrogen, carbon, sulfur, and phosphorus, a multi-dimensional evaluation system is constructed. This system can simultaneously perceive and comprehensively evaluate the compound disturbance effects brought about by various human activities such as land use, pollution, and engineering construction, and provide a more comprehensive view of the functional status of the ecosystem.

[0036] In one possible implementation, S101 may include: acquiring environmental samples within the evaluation area; the environmental samples being water, sediment, and soil samples; extracting total microbial DNA from the environmental samples and performing metagenomic sequencing to obtain sequencing data; performing quality control, gene annotation, and abundance quantification on the sequencing data to obtain abundance data of the functional genes of the microorganism; the abundance quantification being the determination of the number of high-throughput sequencing reads per million sequences of the functional gene.

[0037] In one possible implementation, the functional genes include the following types: nitrogen cycle functional genes include at least one of the following: amoA, amoB, nirK, nirS, norB, nosZ, nifH, nxrA, nxrB, etc.; carbon cycle functional genes include at least one of the following: mcrA, pmoA, milloX, acdS, acdA, etc.; sulfur cycle functional genes include at least one of the following: dsrA, dsrB, soxB, aprA, etc.; phosphorus cycle functional genes include at least one of the following: phoA, phoD, phenX, ppx, ppk, etc. The gene types listed here are merely illustrative and are not intended to be limiting. For example, the relevant data and performance of the carbon cycle functional genes can be characterized by methane cycle functional genes.

[0038] In one possible implementation, the functional gene is determined by one or more of the following methods: identifying genes with an average normalized abundance greater than 1 pmm in the microbial genome as the functional gene; performing principal component analysis on the microbial genome and identifying genes that contribute significantly to the overall variance as the functional gene; or performing cluster analysis on the microbial genome to obtain multiple categories and identifying representative genes from each category as the functional gene.

[0039] In a possible implementation, S102 may include: obtaining human activity intensity data within the evaluation area; the human activity intensity data includes one or more of land use type, traffic density, pollution source distribution, and water resource development intensity; performing spatial analysis based on a geographic information system to identify the area with the least human interference within the evaluation area; and combining the on-site investigation data of the area with the least human interference to determine the reference point from the sampling points in the area with the least human interference.

[0040] In a possible implementation, the response direction is a positive response or a negative response. The positive response indicates that the functional gene abundance decreases as human interference increases, and the negative response indicates that the functional gene abundance increases as human interference increases; the response direction is determined based on the difference between the abundance data of the functional gene at the reference point and the abundance data of the functional gene at the known disturbed point.

[0041] In a possible implementation, S104 may include: for a functional gene with a positive response direction, the score calculation formula is: min(functional gene abundance / P95, 1); for a functional gene with a negative response direction, the score calculation formula is: min((Max - functional gene abundance) / (Max - P5), 1). ?

[0042] For example, for a functional gene with a positive response, the score is determined by the following formula: S_{g,i} = min(X_{g,i} / P95_g, 1); for a functional gene with a negative response, the score is determined by the following formula: S_{g,i} = min((Max_g - X_{g,i}) / (Max_g - P5_g), 1). Where, S_{g,i}: the score of functional gene g at sampling point i, with a value range of [0, 1], X_{g,i}: the normalized abundance value of functional gene g at sampling point i, P95_g: the 95% quantile of functional gene g, P5_g: the 5% quantile of functional gene g, Max_g: the maximum abundance value of functional gene g, min(·, 1): a truncation function to ensure that the score does not exceed 1, that is, if > 1, it is all recorded as 1.

[0043] In a possible implementation, S105 may include: when the M-IFI value ≥ T, the level of human interference is no interference; when 0.8T ≤ M-IFI value < T, the level of human interference is minor interference; when 0.6T ≤ M-IFI value < 0.8T, the level of human interference is slight interference; when 0.4T ≤ M-IFI value < 0.6T, the level of human interference is relatively large interference; when the M-IFI value < 0.4T, the level of human interference is large interference.

[0044] For example, "no disturbance" indicates that the integrity of microbiome function is in a natural or near-natural state, and the ecosystem function is not significantly affected by human activities, corresponding to the ecological baseline state of the reference point. "Small disturbance" indicates a slight decline in microbiome function integrity, with ecosystem function slightly affected but remaining basically stable, requiring attention to potential environmental pressures. "Small disturbance" indicates a significant decline in microbiome function integrity, with some ecosystem functions affected, requiring appropriate management measures. "Large disturbance" indicates significantly impaired microbiome function integrity, with multiple ecosystem functions severely affected, requiring proactive environmental remediation measures. "Major disturbance" indicates severely impaired microbiome function integrity, with ecosystem function nearing collapse, requiring immediate emergency ecological restoration action.

[0045] In one possible implementation, the method further includes: performing a correlation analysis between the M-IFI value and a preset environmental assessment index to obtain an assessment result; and verifying the rationality of the assessment result by combining remote sensing data.

[0046] In one possible implementation, the method further includes: obtaining a functional integrity sub-index based on the score of each functional gene; the functional integrity sub-index is a methane cycle functional integrity index, namely, focusing on one or more of the methane cycle, nitrogen cycle functional integrity index, sulfur cycle functional integrity index, and phosphorus cycle functional integrity index; the above-mentioned determination of the degree of human interference at each sampling point based on the magnitude of the M-IFI value relative to the interference evaluation threshold T includes: determining the degree of human interference at each sampling point based on the magnitude of the functional integrity sub-index relative to the interference evaluation threshold T.

[0047] Figure 2 A flowchart of another method for evaluating the degree of anthropogenic disturbance in an ecosystem based on microbiome function, provided in this application embodiment, is shown below in conjunction with... Figure 2 The workflow of this application is described below. Unless otherwise specified, the methods used in this embodiment are conventional methods, and the reagents used are commercially available reagents unless otherwise specified.

[0048] S201, Sample collection and metagenomic sequencing.

[0049] A. Sample Collection. Twenty-four soil samples (site1-24) were selected from a pesticide-sprayed area. At the same time, nine soil samples (natural woodlands, etc.) from the surrounding area without human interference were collected as background values ​​(site25-33). Each sample was collected using the five-point sampling method. The collected soil samples were immediately transported back to the laboratory in ice packs.

[0050] B. DNA Extraction and Metagenomic Sequencing. DNA was extracted from soil samples using a soil extraction kit (MoBio, CA, USA) and dissolved in 50 μL of elution buffer. DNA concentration and purity were then checked using a Nanodrop-2000 (Thermo Fisher Scientific, MA, USA) (260 / 280 ≈ 1.8, 260 / 230 ≥ 1.7), and DNA concentration was accurately determined using Qbit 4.0. Agarose gel electrophoresis was used to verify the integrity and suitability of the DNA. Subsequently, metagenomic sequencing was performed using the HiSeq platform of the Illumina sequencing platform, with a sequencing depth >20 Gb for each sample.

[0051] S202, Bioinformatics analysis of sequencing data.

[0052] A. Sequencing data preprocessing. After obtaining the raw metagenomic sequencing sequences, Trimmomatic software was used for quality control to remove low-quality sequences; then bowtie2 software was used to remove contaminating genes such as human genes to obtain high-quality reads.

[0053] B. Microbial Functional Gene Annotation (Mcycl, Ncycl, Scycl, Pcycl). Download the MCycl-DB, NCycl-DB, Scycl-DB, and Pcycl-DB datasets, and construct local reference databases for methane-cycling genes (MCGs), nitrogen-cycling genes (NCGs), sulfur-cycling genes (SCGs), and phosphorus-cycling genes (PCGs), respectively. Annotate the obtained high-quality reads for MCGs, NCGs, SCGs, and PCGs types using DIAMOND software (e-value ≤ 1 × 10⁻⁶). -8 (Identity value ≥ 80%). Sequences annotated as functional genes were then extracted using a Perl script, and abundance normalization (ppm) was performed using R software. Finally, abundance information tables for functional genes such as MCGs, NCGs, ARGs, and VFs were compiled. Among these, the methane cycle functional gene can characterize the relevant data and performance of carbon cycle functional genes.

[0054] S203. Determine the response direction of each functional gene to human interference.

[0055] A. Data Preparation and Grouping. The abundance data of microbial functional genes obtained from S202 were preprocessed, and functional genes with a normalized abundance greater than 1 ppm were retained.

[0056] Importing Group Information: Import the group information file for the sampling points. This file should clearly indicate whether each sampling point belongs to the "Reference" group (usually denoted as Reference) or the "Disturbed" group (usually denoted as Disturbed). The reference point is the point selected in S1 that is not subject to human interference or has minimal interference, while the disturbed point is a point known to be affected by human activities (such as pesticide application areas).

[0057] B. Statistical tests and significance analysis Nonparametric tests. Since environmental microbial data typically do not follow a normal distribution (if they do, a t-test can be used), the Wilcoxon rank-sum test (also known as the Mann-Whitney U test) is used to compare the abundance differences of each functional gene between the "reference point" group and the "disrupted point" group. The null hypothesis (H0) of this test is that there is no significant difference in gene abundance distribution between the two groups.

[0058] Multiple test correction. Statistical tests on all functional genes will produce multiple p-values. To control the false positive rate, the Bonferroni correction method is used to correct all p-values.

[0059] Genes showing significant responses were screened. Based on the corrected p-values, functional genes showing significant responses to human interference were screened. A significance level α was set at 0.05, and functional genes with corrected p-values ​​< 0.05 were retained for subsequent analysis. These genes were considered indicator genes with significant responses to human interference. Subsequently, Spearman rank-sum correlation analysis was performed on the above indicator genes. If the rank correlation coefficient between the two parameters is high, i.e., (|R|), then the gene is considered a significant indicator gene. rho The value ≥ 0.75 indicates that the information reflected by the two parameters overlaps significantly, so one of the parameters is selected to construct the M-IFI evaluation system.

[0060] C. Calculation of effect size and determination of response direction.

[0061] Calculate the effect size. To quantify the degree of difference and determine its direction, the median difference in gene abundance between the two groups is calculated as the effect size. The formula is: Effect size = Median gene abundance of the reference group - Median gene abundance of the affected group.

[0062] Determine the direction of the response. Based on the sign of the effect size, determine the final response direction of each functional gene.

[0063] Positive response: If the effect size > 0, it indicates that the abundance of the functional gene at the reference point is significantly higher than that at the interference point, that is, its abundance decreases as human interference increases, so it is defined as a positive response gene.

[0064] Negative response: If the effect size is <0, it indicates that the abundance of the functional gene at the reference point is significantly lower than that at the interference point, that is, its abundance increases with the increase of human interference, so it is defined as a negative response gene.

[0065] The above methods can be used to identify functional genes for the carbon cycle, nitrogen cycle, sulfur cycle, and phosphorus cycle. (Refer to...) Figures 3-6 , Figure 3 This diagram illustrates the screening of methane cycle indicator genes provided in this embodiment of the application. Specifically, it focuses on methane cycle genes to characterize the relevant data and performance of carbon cycle functional genes. Figure 4 This is a schematic diagram of nitrogen cycle indicator gene screening provided in an embodiment of this application. Figure 5 This is a schematic diagram of the screening of sulfur cycle indicator genes provided in an embodiment of this application. Figure 6 This is a schematic diagram of phosphorus cycle indicator gene screening provided in an embodiment of this application. Figures 3-6 The p-values ​​in the data are all based on the Mann-Whitney U test and corrected using the Bonferroni method.

[0066] For example, the summary results of indicative functional genes can be shown in Table 1 below.

[0067] Table 1:

[0068] S204. Determine the statistical measures and calculate the scores of each functional gene.

[0069] A. Based on the functional gene abundance data of all sampling points in the evaluation area, calculate three key statistics for each functional gene.

[0070] 95% quantile (P95): Among them, P95_g: the 95th percentile of the functional gene g. X_g: The 95th percentile of the abundance value of gene g across all sampling points; X_g: The set of abundance values ​​of gene g across all sampling points.

[0071] 5th percentile (P5): Among them, P5_g: the 5th percentile of the functional gene g. The 5th percentile of the abundance of gene g across all sampling points.

[0072] Maximum value (Max): Max_g = max(X_g); where Max_g: the maximum abundance value of functional gene g across all sampling points.

[0073] B. Calculation of scores for each functional gene.

[0074] Based on the response direction of functional genes in S3, different ratio methods were used to calculate the score of each functional gene at each sampling point.

[0075] For positively responding functional genes: S_{g,i} = min(X_{g,i} / P95_g, 1); For negatively responding functional genes: S_{g,i} = min((Max_g - X_{g,i}) / (Max_g - P5_g), 1); Where, S_{g,i}: the score of functional gene g in sampling point i, with a value range of [0,1], X_{g,i}: the normalized abundance value of functional gene g in sampling point i, P95_g: the 95th percentile of functional gene g, P5_g: the 5th percentile of functional gene g, Max_g: the maximum abundance value of functional gene g, min(·,1): a cutoff function to ensure that the score does not exceed 1, that is, if >1, it is recorded as 1.

[0076] S205, M-IFI values, and interference evaluation thresholds were determined.

[0077] A. The Microbiome Functional Integrity Index (M-IFI) is determined using the following formula: Where M-IFI_i: the microbiome functional integrity index at sampling point i; S _{ g , i}: The score of functional gene g in sampling point i, n: The total number of functional genes involved in the calculation, Σ: The summation symbol.

[0078] It should be noted that for each sampling point, the scores of all functional genes are summed up, and the resulting M-IFI value comprehensively reflects the functional integrity status of the microbiome at that sampling point. The higher the M-IFI value, the better the functional integrity of the microbiome and the less it is affected by human interference.

[0079] B. Calculation of sub-indices for each major functional category.

[0080] To further analyze the extent to which different biogeochemical processes are affected, sub-indices for each functional category can be determined: M-IFI_i = Σ_{g∈M} S_{g,i}, Methane cycle functional integrity index; N-IFI_i = Σ_{g∈N} S_{g,i}, Nitrogen cycle functional integrity index; S-IFI_i = Σ_{g∈S} S_{g,i}, Sulfur cycle functional integrity index; P-IFI_i = Σ_{g∈P} S_{g,i}, Phosphorus cycle functional integrity index; where M, N, S, and P represent the functional gene sets of the nitrogen cycle, methane cycle, sulfur cycle, and phosphorus cycle, respectively.

[0081] C. Determination of interference evaluation threshold.

[0082] Take the 95th percentile of the reference point M-IFI value as the interference evaluation threshold T, which is determined by the following formula: ; where T: interference evaluation threshold, : 95th percentile function, M-IFI_r: M-IFI value of reference point (undisturbed sample point) r, R: set of reference points, {M-IFI_r | r ∈ R}: set of M-IFI values of all reference points.

[0083] S206. Classification of the degree of human interference.

[0084] A. Classification criteria. According to the magnitude of the M-IFI value of each sampling point relative to the interference evaluation threshold T, the degree of human interference is divided into five levels. The classification of the degree of human interference is as follows: When M-IFI ≥ T, the degree of human interference is no interference; When 0.8T ≤ M-IFI < T, the degree of human interference is minor interference; When 0.6T ≤ M-IFI < 0.8T, the degree of human interference is slight interference; When 0.4T ≤ M-IFI < 0.6T, the degree of human interference is moderate interference; When M-IFI < 0.4T, the degree of human interference is major interference.

[0085] B. Ecological significance of each level.

[0086] No interference (M-IFI ≥ T): The functional integrity of the microbiome is in a natural state or close to a natural state, and the ecosystem function is not significantly affected by human activities, corresponding to the ecological baseline state of the reference point.

[0087] Minor interference (0.8T ≤ M-IFI < T): The functional integrity of the microbiome decreases slightly, and the ecosystem function is slightly affected but remains basically stable, and potential environmental pressures need to be concerned.

[0088] Small disturbances (0.6T ≤ M-IFI < 0.8T): The integrity of microbiome function is significantly reduced, and some ecosystem functions are affected, requiring appropriate management measures.

[0089] Significant disturbance (0.4T ≤ M-IFI < 0.6T): The integrity of microbiome function is significantly impaired, and the functions of multiple ecosystems are severely affected, requiring proactive environmental remediation measures.

[0090] Large disturbance (M-IFI<0.4T): The integrity of microbiome function is severely damaged, and the ecosystem function is close to collapse, requiring immediate emergency ecological restoration actions.

[0091] For example, the evaluation results of the Microbiome Functional Integrity Index (M-IFI) for various plots can be shown in Table 2 below.

[0092] Table 2:

[0093] Sample M-IFI (Functional Integrity Index) NCG__IFI (Nitrogen Cycle Functional Integrity Index) MCG__IFI (Methane Cycle Functional Integrity Index) SCG__IFI (Sulfur Cycle Functional Integrity Index) PCG__IFI (Phosphorus Cycle Functional Integrity Index) Integrity Class site1 (sampling point 1) 45.00 7.07 11.87 14.43 11.63 Minor interference site10 (sampling point 10) 46.69 7.44 13.31 13.82 12.11 Minor interference site11 (Sampling point 11) 49.61 8.43 13.32 15.21 12.65 Minor interference site12 (sampling point 12) 42.20 7.83 11.70 11.65 11.01 Minor interference site13 (sampling point 13) 44.42 7.61 11.44 13.59 11.79 Minor interference site14 (sampling point 14) 39.36 5.98 10.25 12.63 10.50 Significant interference site15 (sampling point 15) 40.51 5.99 10.21 13.55 10.76 Significant interference site16 (sampling point 16) 41.17 6.29 12.85 10.99 11.03 Significant interference site17 (sampling point 17) 47.68 7.88 12.80 14.35 12.65 Minor interference site18 (Sampling point 18) 46.93 7.09 12.84 14.61 12.39 Minor interference site19 (sampling point 19) 41.35 5.82 10.84 14.30 10.38 Minor interference site2 (sampling point 2) 43.49 6.86 11.79 13.64 11.20 Minor interference site20 (sampling point 20) 46.39 7.33 12.65 14.90 11.52 Minor interference site21 (Sampling point 21) 48.71 7.65 13.41 15.81 11.84 Minor interference site22 (sampling point 22) 42.30 6.64 11.04 13.63 11.00 Minor interference site23 (sampling point 23) 41.30 6.33 11.24 13.37 10.37 Minor interference site24 (sampling point 24) 40.42 6.68 11.58 11.64 10.51 Significant interference site25 (sampling point 25) 60.72 9.38 16.52 19.80 15.01 Less interference site26 (sampling point 26) 63.37 10.01 18.21 19.42 15.73 Less interference site27 (sampling point 27) 65.32 9.91 19.22 20.13 16.05 Less interference site28 (Sampling point 28) 63.11 9.21 17.88 20.08 15.94 Less interference site29 (sampling point 29) 52.44 7.76 14.41 17.52 12.75 Minor interference site3 (sampling point 3) 36.75 5.62 9.40 12.54 9.19 Significant interference site30 (sampling point 30) 66.30 9.59 18.71 21.50 16.49 Less interference site31 (sampling point 31) 64.19 9.57 18.26 20.90 15.46 Less interference site32 (sampling point 32) 68.70 10.25 20.29 22.14 16.02 No interference site33 (sampling point 33) 68.49 10.04 19.48 21.95 17.02 Less interference site4 (sampling point 4) 42.03 7.41 11.55 12.45 10.62 Minor interference site5 (sampling point 5) 58.05 9.60 14.34 19.01 15.11 Less interference site6 (sampling point 6) 41.07 6.17 10.83 13.10 10.98 Significant interference site7 (sampling point 7) 43.88 6.87 11.71 14.01 11.30 Minor interference site8 (sampling point 8) 42.22 6.80 10.61 13.59 11.22 Minor interference site9 (sampling point 9) 46.22 7.16 11.96 15.13 11.98 Minor interference

[0094] In some embodiments, the proportional distribution of each sampling point can be visually displayed through images, making it easier for users to view and improving the user experience. (Refer to...) Figure 7 This is a schematic diagram showing the proportional distribution of sampling points at different integrity levels provided in the embodiments of this application.

[0095] Based on the above technical solution, by integrating the functional genes of multiple key biogeochemical cycles such as nitrogen, carbon, sulfur, and phosphorus, a multi-dimensional evaluation system is constructed. This system can simultaneously perceive and comprehensively evaluate the compound disturbance effects brought about by various human activities such as land use, pollution, and engineering construction, and provide a more comprehensive view of the functional status of the ecosystem.

[0096] The various embodiments in this specification are described in a progressive manner. For the same or similar parts between the various embodiments, please refer to each other. Each embodiment focuses on describing the differences from other embodiments.

[0097] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of this application.

Claims

1. A method for evaluating the degree of anthropogenic disturbance in an ecosystem based on microbiome function, characterized in that, The method includes: Abundance data of microbial functional genes in all sampling points within the evaluation area are obtained and standardized, with the count in ppm per million reads; the functional genes include one or more of nitrogen cycle functional genes, carbon cycle functional genes, sulfur cycle functional genes, and phosphorus cycle functional genes. Based on historical environmental monitoring data or field survey data of all sampling points, the sampling point with the least degree of human interference among all sampling points is used as the reference point; For each sampling point, the response direction of each functional gene to human interference was determined; and, based on the standardized abundance data, the 95th percentile P95, 5th percentile P5, and maximum value Max of each functional gene were determined. Based on the response direction, the score of each functional gene is determined by the ratio method, and the scores of each functional gene are accumulated to obtain the microbiome functional integrity index M-IFI value. The degree of human interference at each sampling point is determined based on the magnitude of the M-IFI value relative to the interference evaluation threshold T; the degree of human interference is categorized as no interference, minor interference, small interference, significant interference, and large interference; the interference evaluation threshold T is the 95th percentile of the M-IFI value at the reference point.

2. The method according to claim 1, characterized in that, The acquisition of abundance data of microbial functional genes in all sampling points within the evaluation area includes: Environmental samples were obtained within the evaluation area; the environmental samples included water, sediment, and soil samples. Total microbial DNA was extracted from the environmental samples and metagenomic sequencing was performed to obtain sequencing data. The sequencing data is subjected to quality control, gene annotation, and abundance quantification to obtain the abundance data of the functional genes of the microorganism; the abundance quantification is to determine the number of high-throughput sequencing reads per million sequences of the functional gene.

3. The method according to claim 1, characterized in that, The functional genes include the following types: Nitrogen cycle functional genes include at least one of the following: amoA, amoB, nirK, nirS, norB, nosZ, nifH, nxrA, and nxrB; Carbon cycle functional genes include at least one of the following: mcrA, pmoA, mmoX, acdS, and acdA; Sulfur cycle functional genes include at least one of the following: dsrA, dsrB, soxB, and aprA; Phosphorus cycle functional genes include at least one of the following: phoA, phoD, phonX, ppx, and ppk.

4. The method according to claim 1, characterized in that, The functional gene is determined by one or more of the following methods: Genes with an average normalized abundance greater than 1 pmm in microbial genes are defined as the functional genes. Principal component analysis was performed on microbial genes, and genes that contributed significantly to the overall variance were identified as the functional genes. Cluster analysis of microbial genes yields multiple categories, and representative genes from each category are designated as functional genes.

5. The method according to claim 1, characterized in that, The reference point, based on historical environmental monitoring data or field survey data from all sampling points, is the sampling point least affected by human interference among all sampling points, including: Obtain data on the intensity of human activities within the evaluation area; the data on the intensity of human activities includes one or more of the following: land use type, traffic density, distribution of pollution sources, and intensity of water resource development. Spatial analysis based on geographic information systems is used to identify the areas with the least human interference from the evaluation area. The reference point is determined from the sampling points in the area with the least human interference by combining the field survey data of the area with the least human interference.

6. The method according to claim 1, characterized in that, The response direction is either positive or negative. A positive response indicates that the abundance of functional genes decreases with increasing human interference, while a negative response indicates that the abundance of functional genes increases with increasing human interference. The response direction is determined based on the difference between the abundance data of functional genes at the reference point and the abundance data of functional genes at known disturbed points.

7. The method according to claim 6, characterized in that, The determination of the score for each functional gene based on the response direction using a ratio method includes: For functional genes that respond positively, the score is calculated as: min(functional gene abundance / P95, 1); For functional genes with a negative response, the score is calculated as: min((Max - functional gene abundance) / (Max - P5), 1).

8. The method according to claim 1, characterized in that, The step of determining the level of human interference at each sampling point based on the magnitude of the M-IFI value relative to the interference evaluation threshold T includes: When the M-IFI value is ≥ T, the level of human interference is no interference; When 0.8T ≤ M-IFI value < T, the level of human interference is considered minor interference. When 0.6T ≤ M-IFI value < 0.8T, the level of human interference is classified as minor interference. When 0.4T ≤ M-IFI value < 0.6T, the level of human interference is considered relatively large. When the M-IFI value is < 0.4T, the level of the artificial interference is classified as large interference.

9. The method according to claim 1, characterized in that, The method further includes: The correlation analysis between the M-IFI value and the preset environmental evaluation index is performed to obtain the evaluation results; The rationality of the evaluation results was verified by combining remote sensing data.

10. The method according to claim 1, characterized in that, The method further includes: Based on the score of each functional gene, a functional integrity sub-index is obtained; the functional integrity sub-index is a carbon cycle functional integrity index, namely, focusing on one or more of the methane cycle, nitrogen cycle, sulfur cycle, and phosphorus cycle functional integrity indices. The step of determining the level of human interference at each sampling point based on the magnitude of the M-IFI value relative to the interference evaluation threshold T includes: The level of human interference at each sampling point is determined based on the magnitude of the functional integrity sub-index relative to the interference evaluation threshold T.

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