Method for evaluating ecological elasticity of microbial community

Abstract

The invention relates to a method for evaluating the ecological elasticity of a microbial community, and belongs to the technical field of microbial ecology, and the method comprises the following steps: S1, obtaining basic data of metagenomics and metatranscriptomics; s2, measuring external disturbance; s3, respectively evaluating resistance in metagenomics and metatranscriptomics, including calculating diversity and connection strength of microbial communities; s4, evaluating functional redundancy in metagenomics and metatranscriptomics respectively; s5, evaluating the ecological elasticity of the global microbial community in metagenomics and metatranscriptomics respectively; and S6, evaluating the ecological elasticity of the local microbial community in metagenomics and metatranscriptomics respectively.

Description

Technical Field

[0001] The present invention belongs to the technical field of microbial ecology and relates to a method for evaluating the ecological resilience of microbial communities. Background Art

[0002] Due to the dynamic changes in their structure and function, microbial communities can rapidly respond to external disturbances. However, the response to external disturbances reshapes the microbial community, disrupting the balance necessary to maintain the material cycle and necessitating the restoration of a new balance. With the intensification of climate change and human impact on ecosystems, the frequency and intensity of external disturbances are changing. Understanding the factors that enable microbial communities to resist or rapidly recover from disturbances is extremely challenging and crucial.

[0003] Ecological resilience reflects the ability of a microbial community to absorb state, driving, and stochastic variables while still maintaining functional persistence. At the same time, ecological resilience is an inherent property of the microbial community, reflecting the non-linear effects of the microbial community structure and function. Therefore, ecological resilience helps to explain the changes in the structure and function of microbial communities under external disturbances. Ecological resilience has been widely considered to be jointly determined by resistance and recovery, where functional redundancy can support the recovery of the microbial community. However, currently, there is no assessable methodology at the microbial community level, which has to some extent hindered the development of the evaluation of the ecological resilience of microbial communities and further restricted the research on the response of microbial community functions to external disturbances. Summary of the Invention

[0004] In view of this, the object of the present invention is to provide a method for evaluating the ecological resilience of microbial communities to address the problem of the difficult evaluation of the ecological resilience of microbial communities, filling the gap in the evaluation of the ecological resilience of microbial communities in various research fields and quantifying the resistance and functional recovery of microbial communities under continuous external disturbances.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for evaluating the ecological resilience of microbial communities, comprising the following steps:

[0007] S1: Obtain basic data of metagenomics and metatranscriptomics;

[0008] S2: Determine external disturbances;

[0009] S3: Evaluate resistance in metagenomics and metatranscriptomics respectively, including calculating the diversity and connection strength of the microbial community;

[0010] S4: Evaluate functional redundancy in metagenomics and metatranscriptomics respectively;

[0011] S5: Evaluate the global microbial community ecological resilience in metagenomics and metatranscriptomics respectively;

[0012] S6: Evaluate the local microbial community ecological resilience in metagenomics and metatranscriptomics respectively.

[0013] Furthermore, the metagenomics contains the complete gene composition of its members, which is used to evaluate the total genomic DNA and its metabolic potential; the data of the metatranscriptomics is related to the subset of genes expressed by microorganisms, provides a sequence-based expression profile, and captures a snapshot of the genes necessary for survival in a specific environment.

[0014] Furthermore, the obtaining of the basic data of metagenomics and metatranscriptomics in step S1 specifically includes:

[0015] Collect microbial samples;

[0016] Under a certain external perturbation, perform DNA and RNA extraction, sequencing, assembly, and species and function annotation on the collected microbial samples, thereby obtaining the basic data of metagenomics and metatranscriptomics.

[0017] Furthermore, the determination of the external perturbation in step S2 specifically includes:

[0018] Determine the specific perturbation and monitor the perturbation;

[0019] The monitored perturbation value and the microbial samples collected in step S1 need to be at the same moment, and the perturbation needs to be normalized. The external perturbation is consistent when evaluating the ecological resilience in metagenomics and metatranscriptomics; the perturbation intensity calculation formula is as follows:

[0020]

[0021] In the formula, DS T is the interference at time T, and D is the interference value.

[0022] Furthermore, the evaluation of the resistance in step S3 in metagenomics and metatranscriptomics respectively includes calculating the diversity and connection strength of the microbial community, specifically including the following steps:

[0023] S31: At the species level, use the species-sample name matrix table of the microbial community to calculate the Shannon diversity of the microbial community in each sample of the metagenomics and metatranscriptomics data; the first column of the species-sample name matrix table is the species name, the first row is the sample name, and the numbers represent the species abundance;

[0024] S32: Based on the species-sample name matrix table, in metagenomics and metatranscriptomics data, first calculate the variance and standard deviation of the abundances of each species in all samples; secondly, calculate the normalization constant of the competition coefficient and the in the denominator. This data is used for the normalization constant and the denominator of all samples under the same external perturbation;

[0025] S33: Based on the species-sample name matrix table, calculate the distance matrix between each species in each sample along the resource spectrum in metagenomics and metatranscriptomics data according to the Euclidean distance;

[0026] S34: According to the calculated normalization constant, denominator, and distance matrix, calculate the competition coefficient of each sample in metagenomics and metatranscriptomics data; the larger the competition coefficient, the stronger the competition and the smaller the connection strength;

[0027] S35: Substitute the Shannon diversity and the competition coefficient into the resistance calculation formula to calculate the resistance of each sample in metagenomics and metatranscriptomics. The resistance calculation formula is as follows:

[0028]

[0029] In the formula, R stru,T is the resistance at time T; S is the number of observed species, p i is the relative abundance of species i; d ij is the distance along the resource spectrum from the average position of the i-th species to the position of the j-th species, calculated by the Euclidean distance; w i and w j are the widths of the function.

[0030] Furthermore, the evaluation of functional redundancy in metagenomics and metatranscriptomics respectively described in step S4 specifically includes:

[0031] S41: Based on the species-sample name matrix table, calculate the Gini-Simpson index of each sample in metagenomics and metatranscriptomics data respectively. The calculation formula is as follows:

[0032]

[0033] In the formula, p i and p j are the microbial classification spectra of taxa i and j, and N is the number of observed species;

[0034] S42: At the species level, a species-functional matrix table for each sample is sorted out from metagenomics and metatranscriptomics data. The first column of the species-functional matrix table is the species name, the first row is the KO number, and the data in the matrix represents the abundance of a certain species participating in a certain function.

[0035] S43: Based on the species-functional matrix table, the Rao's quadratic entropy of each sample in metagenomics and metatranscriptomics data is calculated respectively. The calculation formula is as follows:

[0036]

[0037] In the formula, p i and p j are the microbial classification spectra of taxonomic groups i and j, and f ij represents the functional distance between taxonomic groups i and j, which is calculated by the weighted Jaccard distance between the genomes of pairwise taxonomic groups, and G is the association matrix.

[0038] S44: Subtract the Gini-Simpson index from the Rao's quadratic entropy to obtain the functional redundancy evaluation result of each sample. The calculation formula is as follows:

[0039]

[0040] In the formula, R func,T is the functional redundancy at time T; G represents an S×M association matrix, and M is the number of genes; G ia and G ja represent the abundances of a functional gene in the genomes of taxonomic groups i and j respectively.

[0041] Furthermore, the evaluation of the global microbial community ecological resilience in step S5 in metagenomics and metatranscriptomics specifically includes:

[0042] The global microbial community ecological resilience is jointly determined by resistance and functional redundancy and is affected by external perturbations. The calculation formula is as follows:

[0043]

[0044] In the formula, R es,T is the ecological resilience at time T;

[0045] Substitute the resistance, functional redundancy, and perturbation intensity calculated for each sample in steps S2 - S4 to evaluate the global microbial community ecological resilience in metagenomics and metatranscriptomics respectively.

[0046] Furthermore, the evaluation of the local microbial community ecological resilience in step S5 specifically includes the following steps:

[0047] The external perturbations suffered by the local microbial community are consistent with those of the global microbial community, and the resistance and functional redundancy are re-evaluated through the following steps:

[0048] (1) Determine the functions of the local microbial community. One functional gene reflects one function;

[0049] (2) In all samples of metagenomic and metatranscriptomic data, screen out the species involved in this function;

[0050] (3) Establish a local species-sample name matrix table and repeat S31 - S35;

[0051] (4) Establish a local species-function matrix table for each sample and repeat S41 - S44;

[0052] (5) Substitute the perturbation intensity and the local resistance and functional redundancy into the ecological resilience evaluation formula to calculate the ecological resilience of the local microbial community.

[0053] The beneficial effects of the present invention are as follows: By using metagenomic and metatranscriptomic technologies, a stability domain is shaped based on the resistance, functional redundancy, and external perturbations of the microbial community. It is assumed that the stability domain is a symmetric semi-ellipsoid to evaluate the ecological resilience of the microbial community under continuous perturbations. Moreover, in order to explore the impact of changes in the ecological resilience of the overall microbial community on local functions, the evaluation of the ecological resilience of the microbial community can be refined to the local microbial community, providing important technical support for carrying out environmental microbial research and further promoting the research on the impact of ecological resilience on functions.

[0054] Other advantages, objectives, and features of the present invention will be described to some extent in the subsequent specification, and to some extent, will be obvious to those skilled in the art based on the study of the following text, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the following specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be described in detail preferably with reference to the accompanying drawings, where:

[0056] Figure 1 It is a theoretical concept diagram of the competition coefficient of the present invention, where (a) is the equilibrium probability distribution and (b) is the niche overlap;

[0057] Figure 2 It is a schematic diagram of the ecological resilience of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] The following specific examples illustrate the implementation manners of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the diagrams provided in the following embodiments only schematically illustrate the basic concept of the present invention. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0059] It should be noted that the diagrams provided in the following embodiments only schematically illustrate the basic concept of the present invention. Therefore, only the components related to the present invention are shown in the diagrams, rather than being drawn according to the number, shape, and size of the components in actual implementation. The types, numbers, and proportions of the components in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0060] In the following description, a large number of details are explored to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0061] The present invention first proposes a method for evaluating the ecological resilience of a microbial community. This method includes evaluating the resistance of the microbial community, functional redundancy, and the external perturbations to which it responds. Metagenomics contains the complete gene composition of its members and can be used to evaluate the total genomic DNA and its metabolic potential. Metatranscriptomics data is related to the subset of genes expressed by microorganisms, provides a sequence-based expression profile, and captures a snapshot of the genes necessary for survival in a specific environment. Therefore, all evaluations are carried out separately in metagenomics and metatranscriptomics. Secondly, to deeply explore the impact of ecological resilience on specific functions, ecological resilience includes evaluations in the global microbial community and in the local microbial community, where the local microbial community is the microbial community that performs a specific function (usually considered as the set of all species that express a certain functional gene).

[0062] Resistance (R stru) is quantified based on the theoretical contributions of Robert May in 1972, and it represents the response of microbial community structure to perturbations. Two factors, species diversity and connection strength, play important roles in determining resistance. Here, the selected species diversity is Shannon diversity, which takes into account species richness and evenness. The connection strength is measured by using the competition coefficients of equations (6.11) and (6.12) in the book Stability and Complexity in Model Ecosystems published by Robert May in 1973, and its theory is as Figure 1 shown. For a single species, May first assumes a stable equilibrium point, then the distribution of this single species conforms to the result of the logistic equation of population growth, as shown by the solid line in Figure 1 (a). However, in a stochastic environment, perturbations limit population growth, and thus the approximate equilibrium probability distribution is obtained through the Fokker-Planck equation, as shown by the dashed line in Figure 1 (a). The analysis results show that even when the environmental variability is high (20%), the approximations of the two results are considered reasonable. Therefore, the probability distribution of a single species is represented by the Fokker-Planck equation. In a stochastic environment with multiple species, assuming that the resource K is sufficient, there is competition between species, and the distributions of different species overlap, which is called niche overlap, as shown in Figure 1 (b). The function of multiple species using resources is reasonably assumed to be a bell-shaped Gaussian distribution. Thus, the evaluation of resistance is as follows:

[0063]

[0064] where R stru,T is the resistance at time T. S is the number of observed species, p i is the relative abundance of species i. d ij is defined as the distance along the resource spectrum from the average position of species i to the position of species j, calculated by Euclidean distance. w i and w j are the widths of the utilization function.

[0065] Functional redundancy (R func ) represents the response of microbial community function to external perturbations. The evaluation method of functional redundancy assumes that the functional redundancy of a community is interpreted as the part in α-taxonomic diversity (TD α ) that cannot be explained by α-functional diversity (FD α ), that is, the functional similarity of two randomly selected species. TD α is the Gini-Simpson index in α-diversity, while FDα is Rao's quadratic entropy. The evaluation of functional redundancy is shown as follows:

[0066]

[0067] where R func,T is the functional redundancy at time T. G represents an S×M association matrix, and M is the number of genes. G ia and G ja represent the abundances of a functional gene in the genomes of taxon i and taxon j, respectively.

[0068] Ecological resilience (R es ) is the ability of the system to absorb state variables, driving variables, and random variables, which is characterized by the dynamic characteristics of multiple stable domains. In the phase plane, all possible trajectories of a stable system spiral towards an equilibrium. Conversely, under a disturbance, the attractor (i.e., the system) spirals upward from the equilibrium point and can surround up to the entire stable domain at most. The shape and size of the current stable domain reflect the current ecological resilience of the system. Therefore, the ecological resilience is quantified by the surface area that the attractor moves within the stable domain. In addition, the greater the disturbance intensity, the easier it is for the system to transition from the original stable domain to a new stable domain, which results in a lower ecological resilience of the original stable domain. We assume that the stable domain is half of a vertically symmetric ellipsoid, and thus the ecological resilience can be approximately evaluated as follows.

[0069]

[0070] where R es,T is the ecological resilience at time T.

[0071] This method only considers the ecological resilience of the microbial community under a certain external disturbance, but sometimes there may be multiple external disturbances. At the same time, when evaluating ecological resilience, all trajectories in the phase plane are merged into one trajectory to represent the trajectory of the microbial community in the stable domain. The methodological steps for evaluating ecological resilience are the same for the global microbial community and the local microbial community, and both require separately evaluating resistance, functional redundancy, and external disturbance. For the local microbial community, according to the needs of the researcher, target functional genes are selected, and then all species involved in the target functional genes are screened out, and a local microbial community is formed by the set of these species. If amplicon sequencing is used to obtain the microbial community composition and specific gene abundances, the accuracy of ecological resilience evaluation will be affected due to insufficient sequencing depth. This constraint applies to the entire content of the present invention.

[0072] Example 2:

[0073] Based on the schematic diagram of ecological resilience under continuous external disturbances as Figure 2 shown, the present invention provides a method for evaluating the ecological resilience of a microbial system, including the following steps:

[0074] S1. Acquisition of multi-omics data

[0075] Collect activated sludge mixed liquor in a sewage treatment plant for continuous culture experiments. Under a certain external perturbation, start collecting samples at regular time intervals. Filter the collected microbial samples through a 0.22 μm mixed cellulose ester membrane and store them in cryotubes for DNA and RNA extraction, sequencing, assembly, and species and function annotation, thereby obtaining basic data on metagenomics and metatranscriptomics.

[0076] S2. Measurement of external perturbation

[0077] The researcher determines the specific perturbation and monitors it. Among them, the monitored perturbation value and the microbial samples collected in S1 need to be at the same moment, and the perturbation needs to be normalized so that the data can be compared. The external perturbation is consistent when evaluating ecological resilience under metagenomics and metatranscriptomics. The perturbation intensity (DS) is evaluated by normalizing the external perturbation for comparison, as shown below.

[0078]

[0079] In the formula, DS T is the interference at time T. D is the interference value.

[0080] S3. Evaluation of resistance

[0081] The evaluation of resistance includes calculating the diversity and connection strength of the microbial community.

[0082] (1) At the species level, use a matrix table with the species name in the first column, the sample name in the first row, and the numbers representing species abundance in the microbial community to calculate the Shannon diversity of the microbial community for each sample. This step needs to calculate the values separately in both metagenomics and metatranscriptomics.

[0083] (2) Based on the species-sample name matrix table, first calculate the variance and standard deviation of the abundance of each species in all samples; secondly, calculate the standardized constant of the competition coefficient and the in the denominator. Under the same external perturbation, the standardized constant and denominator of all samples use this data. This step needs to calculate the values separately in both metagenomics and metatranscriptomics.

[0084] (3) Also based on the species-sample name matrix table, calculate the distance matrix between each species in each sample along the resource spectrum according to the Euclidean distance. This step needs to calculate the values separately in both metagenomics and metatranscriptomics.

[0085] (4) Calculate the competition coefficient for each sample based on the normalization constant, denominator, and distance matrix obtained from the previous two steps. The larger the competition coefficient, the stronger the competition and the smaller the connection strength; conversely, the larger the connection strength. This step needs to calculate the values separately in metagenomics and metatranscriptomics.

[0086] (5) Substitute the Shannon diversity and competition coefficient into the resistance calculation formula to calculate the resistance of each sample in metagenomics and metatranscriptomics.

[0087] S4. Evaluate functional redundancy

[0088] (1) Based on the species-sample name matrix table in S3, calculate the Gini-Simpson index for each sample according to the formula. This step needs to calculate the values separately in metagenomics and metatranscriptomics.

[0089] (2) At the species level, separately organize the species-function matrix table for each sample in metagenomics and metatranscriptomics data. The first column of this table is the species name, the first row is the KO number, and the data in the matrix represents the abundance of a certain species participating in a certain function.

[0090] (3) Based on the species-function matrix table, calculate the Rao's quadratic entropy for each sample according to the formula. This step needs to calculate the values separately in metagenomics and metatranscriptomics.

[0091] (4) Subtract the Gini-Simpson index and Rao's quadratic entropy calculated for each sample to obtain the evaluation result of functional redundancy for each sample. This step needs to calculate the values separately in metagenomics and metatranscriptomics.

[0092] S5. Evaluate the ecological resilience of the global microbial community

[0093] Ecological resilience is jointly determined by resistance and functional redundancy and is affected by external disturbances. The evaluation of ecological resilience substitutes into the calculation formula the resistance, functional redundancy, and disturbance intensity obtained from steps S2, S3, and S4 for each sample to evaluate the ecological resilience of the global microbial community in metagenomics and metatranscriptomics respectively.

[0094] S6. Evaluate the ecological resilience of the local microbial community

[0095] The external disturbance received by the local microbial community is the same as that of the global microbial community. Therefore, the ecological resilience of the local microbial community needs to re-evaluate resistance and functional redundancy.

[0096] (1) Determine the function of the local microbial community, that is, which functional gene the local microbial community participates in. Here, the present invention focuses on that one functional gene reflects one function.

[0097] (2) Screen out the species involved in this function from all samples. This step needs to be carried out in both metagenomics and metatranscriptomics.

[0098] (3) Establish a local species-sample name matrix table and repeat all steps in S3.

[0099] (4) Establish a local species-function matrix table for each sample and repeat all steps in S4.

[0100] (5) Substitute the perturbation intensity and local resistance and functional redundancy into the ecological resilience evaluation formula to calculate the ecological resilience of the local microbial community.

[0101] The method of the present invention realizes the evaluation of the ecological resilience of the global and local microbial communities. By establishing the methodologies of resistance, functional redundancy, and ecological resilience, it reveals how the microbial community maintains its function under external perturbations, providing important technical support for environmental microbial research.

[0102] In the above embodiments, the reference in the specification to "this embodiment" means that the specific features, structures, or characteristics described in conjunction with the embodiment are included in at least some embodiments, but not necessarily all embodiments. Multiple occurrences of "this embodiment" do not necessarily all refer to the same embodiment.

[0103] In the above embodiments, although the present invention has been described in conjunction with specific embodiments of the present invention, many substitutions, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art based on the previous description. For example, other storage structures (e.g., dynamic RAM (DRAM)) can be used in the embodiments discussed. The embodiments of the present invention are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims.

[0104] This embodiment also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements any one of the methods in this embodiment.

[0105] This embodiment also provides an electronic terminal, including: a processor and a memory;

[0106] The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory so that the terminal executes any one of the methods in this embodiment.

[0107] For the computer-readable storage medium in this embodiment, those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to a computer program. The foregoing computer program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps including those of the above method embodiments; and the foregoing storage medium includes: various media such as ROM, RAM, magnetic disk, or optical disc that can store program codes.

[0108] The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver, and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication therebetween. The memory is used to store a computer program, the communication interface is used for communication, and the processor and the transceiver are used to run the computer program so that the electronic terminal executes each step of the above method.

[0109] In this embodiment, the memory may include a random access memory (Random Access Memory, abbreviated as RAM), and may also include a non-volatile memory, such as at least one disk memory.

[0110] The above-mentioned processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, abbreviated as CPU), a network processor (Network Processor, abbreviated as NP), etc.; it may also be a digital signal processor (Digital Signal Processing, abbreviated as DSP), an application specific integrated circuit (Application Specific Integrated Circuit, abbreviated as ASIC), a field-programmable gate array (Field-Programmable Gate Array, abbreviated as FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0111] The present invention can be used in many general or special computing system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, small computers, large computers, distributed computing environments including any of the above systems or devices, and so on.

[0112] The present invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The present invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including storage devices.

[0113] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention may be modified or equivalently replaced without departing from the spirit and scope of the present technical solution, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A method for assessing the ecological resilience of a microbial community, characterized in that: The following steps are involved: S1: Acquisition of metagenomics and metatranscriptomics basic data; S2: Determination of external disturbances; S3: Assessment of resistance in metagenomics and metatranscriptomics, including calculation of diversity and connectivity strength of microbial communities; S4: Assessing functional redundancy in metagenomics and metatranscriptomics, respectively; S5: Assessing global microbial community ecological resilience in metagenomics and metatranscriptomics, respectively; S6: Assessing local microbial community ecological resilience in metagenomics and metatranscriptomics, respectively.

2. The method for evaluating the ecological resilience of a microbial community according to claim 1, characterized in that: The metagenomics contains the complete genetic makeup of its members, which is used to evaluate the total genomic DNA and its metabolic potential; the metatranscriptomics data is related to the subset of genes expressed by the microorganism, providing a sequence-based expression profile and capturing a snapshot of the genes necessary for survival in a specific environment.

3. The method for evaluating the ecological resilience of a microbial community according to claim 1, characterized in that: Step S1 of obtaining metagenomics and metatranscriptomics basic data specifically includes: Collect microbiological samples; Under a certain external disturbance, the collected microbial samples are subjected to DNA and RNA extraction, sequencing, assembly, and species and functional annotation to obtain basic metagenomics and metatranscriptomics data.

4. The method for evaluating the ecological resilience of a microbial community according to claim 1, characterized in that: The external disturbance determination in step S2 specifically includes: Identify specific disturbances and monitor them; The disturbance value monitored must be at the same time as the microbial sample collected in step S1, and the disturbance must be normalized. The external disturbance is consistent when evaluating ecological resilience under metagenomics and metatranscriptomics; the disturbance intensity calculation formula is as follows: Where, DS T is the interference at time T, and D is the interference value.

5. The method for evaluating the ecological resilience of a microbial community according to claim 4, characterized in that: Step S3 describes evaluating resistance in metagenomics and metatranscriptomics, including calculating the diversity and connection strength of the microbial community, and specifically includes the following steps: S31: At the species level, the Shannon diversity of the microbial community of each sample in the metagenomics and metatranscriptomics data was calculated using a species-sample name matrix table of the microbial community; the first column of the species-sample name matrix table is the species name, the first row is the sample name, and the numbers represent the species abundance; S32: Based on the species-sample name matrix, in metagenomics and metatranscriptomics data, first calculate the variance and standard deviation of the abundance of each species in all samples; second, calculate the normalization constant of the competition coefficient and the denominator Under the same external disturbance, the normalization constant and denominator of all samples adopt this data; S33: Based on the species-sample name matrix table, calculate the distance matrix between species along the resource spectrum in each sample in the metagenomics and metatranscriptomics data according to the Euclidean distance; S34: Calculate the competition coefficient of each sample in the metagenomics and metatranscriptomics data based on the calculated normalization constant, denominator, and distance matrix; the larger the competition coefficient, the stronger the competition and the smaller the connection strength; S35: Substitute Shannon diversity and competition coefficient into the resistance calculation formula to calculate the resistance of each sample in metagenomics and metatranscriptomics. The resistance calculation formula is as follows: In the formula, R stru,T is the resistance at time T; S is the number of species observed, p i is the relative abundance of species i; d ij is the distance from the average position of the i-th species to the position of the j-th species along the resource spectrum, calculated by the Euclidean distance; w i and w j is the width of the utilization function.

6. The method for evaluating the ecological resilience of a microbial community according to claim 5, characterized in that: Step S4, respectively, evaluates functional redundancy in metagenomics and metatranscriptomics, specifically including: S41: Based on the species-sample name matrix table, the Gini-Simpson index of each sample in the metagenomics and metatranscriptomics data is calculated separately. The calculation formula is as follows: In the formula, p i and p j is the microbial taxonomic spectrum of taxonomic groups i and j, and N is the number of observed species; S42: At the species level, a species-function matrix table of each sample is compiled in the metagenomics and metatranscriptomics data, wherein the first column of the species-function matrix table is the species name, the first row is the KO number, and the data in the matrix represents the abundance of a species involved in a certain function; S43: Based on the species-function matrix table, Rao's quadratic entropy of each sample in the metagenomics and metatranscriptomics data was calculated separately. The calculation formula is as follows: In the formula, p i and p j is the microbial taxonomic spectrum of taxonomic groups i and j, f ij represents the functional distance between taxa i and j, calculated using the weighted Jaccard distance between the genomes of each taxa, and G is the association matrix; S44: Subtract the Gini-Simpson index from Rao's quadratic entropy to obtain the functional redundancy evaluation result of each sample. The calculation formula is as follows: In the formula, R func,T is the functional redundancy at time T; G ​​represents an S×M association matrix, M is the number of genes; G ia and G ja Represents the abundance of functional gene a in the genomes of taxonomic group i and taxonomic group j, respectively.

7. The method for evaluating the ecological resilience of a microbial community according to claim 6, characterized in that: Step S5 evaluates the global microbial community ecological resilience in metagenomics and metatranscriptomics, respectively, including: The global microbial community ecological resilience is determined by resistance and functional redundancy, and is affected by external disturbances. The calculation formula is as follows: In the formula, R es,T is the ecological elasticity at time T; Steps S2-S4 are used to calculate the resistance, functional redundancy and disturbance intensity of each sample, thereby evaluating the ecological resilience of the global microbial community in metagenomics and metatranscriptomics, respectively.

8. The method for evaluating the ecological resilience of a microbial community according to claim 7, characterized in that: The step S5 of evaluating the ecological resilience of the local microbial community specifically includes the following steps: External perturbations to the local microbiome are consistent with the global microbiome, and resistance and functional redundancy are reassessed through the following steps: (1) Determine the function of the local microbial community, with one functional gene reflecting one function; (2) Screening out species involved in this function from all samples in metagenomics and metatranscriptomics data; (3) Establish a local species-sample name matrix and repeat S31-S35; (4) Establish a local species-function matrix for each sample and repeat S41-S44; (5) Substitute the disturbance intensity, local resistance, and functional redundancy into the ecological resilience assessment formula to calculate the ecological resilience of the local microbial community.

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