A method and system for evaluating ecosystem stability based on interspecies interaction network

Abstract

The present invention proposes an ecosystem stability assessment method and system based on an interspecies interaction network. The server receives parameter relationship data between species in the ecosystem, the network construction module uses the parameter relationship data to construct a species interaction network, and the interaction network assessment model construction module uses species interaction network related parameters, species functional importance, species contribution to ecosystem stability, and corresponding ecosystem stability rating indicators to construct an interaction network assessment model. The constructed model generates cross-ecosystem stability rating indicators based on the ecosystem-related data received by the server to intelligently display the stability of different ecosystems and strengthen the weak links in the ecosystem. The present invention realizes cross-regional monitoring of ecosystems, realizes integrated ecosystem stability monitoring using interspecies interaction networks, improves the effect of qualitative judgment of ecosystem stability, and provides a scientific basis for ecosystem management and protection.

Description

Technical Field

[0001] The present invention belongs to the technical field of ecosystem assessment, and in particular relates to an ecosystem stability assessment method and system based on an interspecies interaction network. Background Art

[0002] Ecosystems are complex, open, dynamic, non-equilibrium and nonlinear systems. Some studies on ecosystem stability focus only on the biological part of the ecosystem (such as taxonomy, biodiversity, etc.), while some consider both the biological and non-biological parts, but only simply link the two through statistical methods (such as correlation analysis and CCA analysis, etc.). In a complex and changing environment, biological and non-biological elements are coupled together through nonlinear phase parameters, and due to the openness of the ecosystem, the two elements themselves often have complex structures. Therefore, previous methods cannot objectively and accurately evaluate ecosystem stability.

[0003] In order to truly describe the stability of ecosystems across systems, space and time, and to scientifically evaluate and predict ecosystem stability, it is necessary to construct an ecosystem stability assessment model based on machine learning methods. In addition to considering the importance of indicators of each species in the ecosystem, the topological structure and stability indicators of the interspecies interaction network will be considered, and the stability of the ecosystem will be evaluated and predicted. This will be incorporated into the model, greatly reducing the subjectivity of previous assessment methods, overcoming the incomparability of the stability of ecosystems with different differences, and making the assessment results of ecosystem stability more accurate.

[0004] In the existing methods for assessing the stability of ecosystems, increasing the amount of data in the model will increase the difficulty of processing, which has certain limitations. Some ecosystem indicator data variables have a greater impact on the stability of the ecosystem, and certain selection is required to achieve low-cost and high-accuracy ecosystem stability assessment. In addition, for traditional ecosystem assessments, there may only be manual methods for assessment, which will result in excessive costs, and the data used in manual assessments have large errors and low accuracy.

[0005] Therefore, it is necessary to design and implement an intelligent assessment method that can monitor ecosystem stability across systems, space and time, and continuously increase the amount of ecosystem stability assessment data. This can effectively and accurately help understand the structure and function of ecosystems, predict the response of ecosystems to external disturbances, and provide a scientific basis for ecosystem management and protection. Summary of the invention

[0006] In order to solve the above technical problems, the present invention proposes an ecosystem stability assessment method and system based on an interspecies interaction network.

[0007] In a first aspect of the present invention, a method for evaluating ecosystem stability based on an interspecies interaction network comprises:

[0008] Obtaining first parameter relationship data between species in a first ecosystem, first functional importance of species, and first stability contribution of species to the ecosystem, and also obtaining corresponding first rating indicators of ecosystem stability;

[0009] In this embodiment, the ecosystem stability rating index can be divided into three levels: low stability, medium stability, and high stability. It can be adjusted at more levels according to the evaluation accuracy of the training model and the value calculated by the model. It can be set by technical personnel in this field according to actual needs and will not be repeated in this embodiment.

[0010] constructing a first species interaction network using the first parameter relationship data, calculating first interaction network related parameters according to the first species interaction network, and jointly constructing an interaction network evaluation model using the first interaction network related parameters, the first functional importance, the first stability contribution and the first rating index;

[0011] Receive second parameter relationship data between species in a second ecosystem, the second functional importance of species, and the second stability contribution of species to the ecosystem, use the second parameter relationship data to construct a second species interaction network, calculate the second interaction network related parameters according to the second species interaction network, and input the second interaction network related parameters, the second functional importance, and the second stability contribution into the interaction network evaluation model to obtain the second rating index of the second ecosystem, so as to realize high-precision integrated stability evaluation of the ecosystem across systems.

[0012] Furthermore, parameter relationship data include: the level of the food chain in which the species is located in the ecosystem, the types and number of relationships between species;

[0013] The parameters related to the interaction network include: the average path length, connectivity and node degree of the interaction network constructed with species as nodes and the degree of association calculated based on the types and number of relationships between species.

[0014] Furthermore, the interaction network evaluation model is constructed using an improved support vector machine model.

[0015] Furthermore, before constructing the improved support vector machine model, the first interaction network related parameters are selected, and the method steps are as follows:

[0016] Step S1, dividing the data set consisting of the first interaction network related parameters into a training set R and a test set E;

[0017] Step S2, using the training set R to establish a Random Forest model;

[0018] Step S3: using the criticality A of the first interaction network related parameters i To select the first interaction network related parameters;

[0019] Step S4: According to the criticality A i To select the relevant parameters of the first interaction network, a specific training set SR is obtained.

[0020] Furthermore, the interaction network evaluation model is constructed using the specific training set SR according to an improved support vector machine model.

[0021] Also provided is an ecosystem stability assessment system based on interspecies interaction network, the system comprising a data receiving module, a network construction module, an interaction network assessment model construction module, an ecosystem stability assessment module, a visualization module, and a data storage module, characterized in that:

[0022] The data receiving module receives first parameter relationship data and second parameter relationship data between species in the ecosystem, and receives a first rating indicator of ecosystem stability;

[0023] The network construction module: constructs a species interaction network using the first parameter relationship data, and calculates the first species interaction network related parameters, the first importance of species functions, and the first stability contribution of species to the ecosystem; constructs a species interaction network using the second parameter relationship data, and calculates the second species interaction network related parameters, the second importance of species functions, and the second stability contribution of species to the ecosystem;

[0024] The interaction network evaluation model construction module is used to jointly construct an interaction network evaluation model according to the first species interaction network related parameters, the first importance of species functions, the first stability contribution of species to the ecosystem and the first stability rating index of the ecosystem;

[0025] The ecosystem stability assessment module is used to receive the second species interaction network related parameters, the second importance of species functions, and the second stability contribution of species to the ecosystem, and generate the second rating index of ecosystem stability using the interaction network assessment model; and is also used to transmit the second species interaction network related parameters, the second importance of species functions, the second stability contribution of species to the ecosystem, and the second rating index of ecosystem stability to the data storage module for storage;

[0026] The visualization display module is connected to the ecosystem stability assessment module and is used to realize 3D visualization display of species interaction networks and ecosystem stability rating indicators through visualization technology.

[0027] The data storage module is used to store parameters related to species interaction networks, species functional importance, species contribution to ecosystem stability, and a second rating indicator of ecosystem stability generated using the interaction network assessment model;

[0028] The interaction network evaluation model constructed by the present invention utilizes objective data that can be actually measured, such as species interaction network-related parameters, species functional importance, and species contribution to ecosystem stability. It utilizes multiple characteristics, and the ecosystem stability grade classification obtained by the constructed model evaluation is more accurate.

[0029] Before constructing the improved support vector machine model, the present invention selects a large number of parameter characteristic variable data sets in the interaction network by the criticality of the parameters. Constructing the improved support vector machine model can reduce the problem of differentiation of the selected interaction network parameter categories that may exist in the present invention, so as to obtain a higher accuracy ecosystem stability assessment.

[0030] The present invention also provides for retraining the model using the ecosystem data obtained through model evaluation, so as to realize continuous training of the model to achieve an ecosystem stability evaluation with higher accuracy, so that the model can continuously evolve, obtain a more accurate ecosystem stability evaluation level, and give corresponding repair links, based on which repair suggestions can be given later. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a flow chart of an ecosystem stability assessment method based on an interspecies interaction network of the present invention;

[0032] Figure 2 It is a schematic diagram of the structure of an ecosystem stability assessment system based on an interspecies interaction network of the present invention;

[0033] Figure 3 This is a schematic diagram showing the stability of an ecosystem. DETAILED DESCRIPTION

[0034] The invention is further described below in conjunction with the accompanying drawings and specific implementation methods.

[0035] The first embodiment of the present invention:

[0036] In a first aspect of the present invention, a method for evaluating ecosystem stability based on an interspecies interaction network comprises:

[0037] Obtaining first parameter relationship data between species in a first ecosystem, first functional importance of species, and first stability contribution of species to the ecosystem, and also obtaining corresponding first rating indicators of ecosystem stability;

[0038] constructing a first species interaction network using the first parameter relationship data, calculating first interaction network related parameters according to the first species interaction network, and jointly constructing an interaction network evaluation model using the first interaction network related parameters, the first functional importance, the first stability contribution and the first rating index;

[0039] Receive second parameter relationship data between species in a second ecosystem, the second functional importance of species, and the second stability contribution of species to the ecosystem, use the second parameter relationship data to construct a second species interaction network, calculate the second interaction network related parameters according to the second species interaction network, and input the second interaction network related parameters, the second functional importance, and the second stability contribution into the interaction network evaluation model to obtain the second rating index of the second ecosystem, so as to realize high-precision integrated stability evaluation of the ecosystem across systems.

[0040] In this embodiment, the first ecosystem or the second ecosystem is a grassland (original) ecosystem (Qinghai-Tibet Plateau alpine ecosystem). The first ecosystem or the second ecosystem can also be the same grassland (original) ecosystem (Qinghai-Tibet Plateau alpine ecosystem) of different trophic levels (cross-trophic levels). The first ecosystem or the second ecosystem can also be understood as different trophic levels (cross-trophic levels) of the same grassland (original) ecosystem (Qinghai-Tibet Plateau alpine ecosystem).

[0041] Furthermore, parameter relationship data include: the level of the food chain in which the species is located in the ecosystem, the types and number of relationships between species;

[0042] In this embodiment, the ecosystem includes not only microorganisms, but also animals, plants, insects, etc. Since the stability of the ecosystem requires the calculation of microorganisms, the data of microorganisms are obtained by estimation. In addition, in this implementation, since the situation of higher-level organisms in the ecosystem can reflect the stability of the ecosystem to a large extent, but the reaction is relatively delayed, parameters are selected as close to producers or higher-level consumers as possible.

[0043] For ease of understanding, in this embodiment, we can imagine that the food chain level of the species can be 4 levels. A simple food chain can be seen as rabbits eat grass, foxes eat rabbits, and eagles eat foxes or rabbits, then rabbits are in the second level and foxes are in the third level. Among the types of inter-species relationships, foxes are counted as having 2 types of inter-species relationships, foxes and rabbits are in a predator-prey relationship, and foxes and eagles are both predator-prey and competitive relationships. The number of inter-species relationships of rabbits is 3, namely grass and rabbits, foxes and rabbits, and rabbits and eagles.

[0044] In this embodiment, those skilled in the art may classify the species as insects, and the food chain may include arthropods (herbivorous insects, pollinating insects) and microorganisms. The relevant data of the food chain may refer to the above and will not be repeated here.

[0045] Those skilled in the art understand that there are differences in the diversity between groups at different trophic levels (for example, in the same ecosystem, the species diversity of microorganisms can be as high as thousands of species, the species diversity of arthropods is often dozens to hundreds, and the species diversity of large mammals is very low. Then there will be orders of magnitude differences in the networks they form. Therefore, in order to avoid this phenomenon, an exponential function is used to reduce the order of magnitude of the corresponding data. This is a conventional method for reducing the order of magnitude used by those skilled in the art. After the order of magnitude change, the inter-species related parameters of the ecosystem will have no order of magnitude difference. The specific method will not be repeated here.

[0046] The parameters related to the interaction network include: the average path length, connectivity and node degree of the interaction network constructed with species as nodes and the degree of association calculated based on the types and number of relationships between species.

[0047] In this embodiment, according to graph theory and network analysis methods, the interaction network includes not only average path length, connectivity and node degree, but also clustering coefficient and the like.

[0048] Furthermore, the calculation formula for species functional importance is as follows:

[0049]

[0050] In the formula, Indicates the functional importance of species, It represents the number of species counted in the ecosystem. represents the density of species in an ecosystem area, It represents the frequency of species occurrence under unit sampling, It indicates the number of species occurring per unit sampling.

[0051] In this embodiment, The selected ecosystem areas are delineated by technicians in the field. It represents the frequency of species occurrence under 10 unit samplings, It indicates the number of species occurring under 10 unit samplings.

[0052] Furthermore, the calculation formula for species' contribution to ecosystem stability is:

[0053]

[0054] In the formula, represents the contribution of species to the stability of the ecosystem, K represents the level of the food chain where the species is located, It represents the degree value of the node of the species in the interaction network.

[0055] Furthermore, the interaction network evaluation model is constructed using an improved support vector machine model.

[0056] Furthermore, before constructing the improved support vector machine model, the first interaction network related parameters are selected, and the method steps are as follows:

[0057] Step S1, dividing the data set consisting of the first interaction network related parameters into a training set R and a test set E;

[0058] Step S2, using the training set R to establish a Random Forest model;

[0059] Step S3: using the criticality A of the first interaction network related parameters i To select the first interaction network related parameters, A i The calculation formula is as follows:

[0060]

[0061] Said Indicates the first interaction network related parameters characteristic variables; is the number of variables; is the error of the test set E; is the test set error after adding noise; is the number of decision trees;

[0062] Step S4: According to the criticality A i To select the relevant parameters of the first interaction network, a specific training set SR is obtained.

[0063] Furthermore, the interaction network evaluation model is constructed using the specific training set SR according to an improved support vector machine model.

[0064] Furthermore, the specific function expression of the improved support vector machine model is:

[0065]

[0066] Where ω, b are the normal vector and intercept of the hyperplane, respectively, X i The variables that connect the combination of SR, species functional importance, and species contribution to ecosystem stability for a specific training set, is the sample size, is the average functional importance of all species in the ecosystem, It is the average contribution of all species in an ecosystem to the stability of the ecosystem.

[0067] In this embodiment, since large organisms are generally easier to count and have a slower response to ecosystem stability, the average species functional importance and species average stability contribution of the entire ecosystem will be considered when evaluating stability, which can be calculated from rough statistical laws. For arthropods (herbivorous insects, pollinating insects) and microorganisms, which are not large organisms, the average species functional importance and species average stability contribution are calculated using statistical laws that are roughly similar to the actual order of magnitude.

[0068] Also provided is an ecosystem stability assessment system based on interspecies interaction network, the system comprising a data receiving module, a network construction module, an interaction network assessment model construction module, an ecosystem stability assessment module, a visualization module, and a data storage module, characterized in that:

[0069] The data receiving module receives first parameter relationship data and second parameter relationship data between species in the ecosystem, and receives a first rating indicator of ecosystem stability;

[0070] The network construction module: constructs a species interaction network using the first parameter relationship data, and calculates the first species interaction network related parameters, the first importance of species functions, and the first stability contribution of species to the ecosystem; constructs a species interaction network using the second parameter relationship data, and calculates the second species interaction network related parameters, the second importance of species functions, and the second stability contribution of species to the ecosystem;

[0071] The interaction network evaluation model construction module is used to jointly construct an interaction network evaluation model according to the first species interaction network related parameters, the first importance of species functions, the first stability contribution of species to the ecosystem and the first stability rating index of the ecosystem;

[0072] The ecosystem stability assessment module is used to receive the second species interaction network related parameters, the second importance of species functions, and the second stability contribution of species to the ecosystem, and generate the second rating index of ecosystem stability using the interaction network assessment model; and is also used to transmit the second species interaction network related parameters, the second importance of species functions, the second stability contribution of species to the ecosystem, and the second rating index of ecosystem stability to the data storage module for storage;

[0073] The visualization display module is connected to the ecosystem stability assessment module and is used to realize 3D visualization display of species interaction networks and ecosystem stability rating indicators through visualization technology.

[0074] In this embodiment, visualization is performed using WebLG technology.

[0075] The data storage module is used to store parameters related to species interaction networks, species functional importance, species contribution to ecosystem stability, and a second rating indicator of ecosystem stability generated using the interaction network assessment model;

[0076] Furthermore, the calculation formula for species functional importance is as follows:

[0077]

[0078] In the formula, Indicates the functional importance of species, It represents the number of species counted in the ecosystem. represents the density of species in an ecosystem area, It represents the frequency of species occurrence under 10 unit samplings, It indicates the number of species appearing under 10 unit samplings;

[0079] The formula for calculating the species' contribution to the stability of the ecosystem is:

[0080]

[0081] In the formula, represents the contribution of species to the stability of the ecosystem, K represents the level of the food chain where the species is located, It represents the degree value of the node of the species in the interaction network.

[0082] The interaction network evaluation model constructed by the present invention starts from constructing a network for interspecific parameters, and uses objective data that can be actually measured, such as parameters related to species interaction networks, the functional importance of species, and the contribution of species to the stability of ecosystems. It uses a variety of characteristics, and the ecosystem stability grade classification obtained by the constructed model evaluation is more accurate.

[0083] Before constructing the improved support vector machine model, the present invention selects numerous parameter characteristic variables in the interaction network by the criticality of the parameters. Constructing the improved support vector machine model can reduce the problem of differentiation of the selected interaction network parameter categories that may exist in the present invention, so as to obtain a higher accuracy ecosystem stability assessment.

[0084] The present invention also uses the ecosystem data obtained by model evaluation to train the model again, so as to achieve continuous training of the model to achieve a higher accuracy ecosystem stability evaluation, so that the model continues to evolve and obtain a more accurate ecosystem stability evaluation level. In this embodiment, the ecosystem stability evaluation level can be divided into three levels: low, medium, and high. According to the needs of technicians in this field, it can also be divided into more levels to evaluate the ecosystem stability state. And the corresponding repair link is given, and repair suggestions can be given later based on this.

[0085] Of course, it can be understood that each embodiment of the present invention can achieve one of the effects alone, and a combination of multiple embodiments of the present invention can achieve all of the above effects, but it is not required that every embodiment of the present invention achieve all of the above advantages and effects, because each embodiment of the present invention can constitute a separate technical solution and make one or more contributions to the prior art.

[0086] The present invention does not particularly specify the structure of some modules, which shall be subject to the contents recorded in the prior art. The prior art mentioned in the above background technology section and the specific embodiment section of the present invention can be used as part of the present invention to understand the meaning of some technical features or parameters. The scope of protection of the present invention shall be subject to the contents actually recorded in the claims.

Claims

1. A method for evaluating ecosystem stability based on interspecies interaction networks, characterized in that: The method comprises: Obtaining first parameter relationship data between species in a first ecosystem, first functional importance of species, and first stability contribution of species to the ecosystem, and also obtaining corresponding first rating indicators of ecosystem stability; constructing a first species interaction network using the first parameter relationship data, calculating first interaction network related parameters according to the first species interaction network, and jointly constructing an interaction network evaluation model using the first interaction network related parameters, the first functional importance, the first stability contribution and the first rating index; The interaction network evaluation model is constructed using an improved support vector machine model; The specific function expression of the improved support vector machine model is: Where ω, b are the normal vector and intercept of the hyperplane, respectively, X i The variables that connect the combination of SR, species functional importance, and species contribution to ecosystem stability for a specific training set, is the sample size, is the average functional importance of all species in the ecosystem, The average contribution of all species in an ecosystem to the stability of the ecosystem; Before constructing the improved support vector machine model, the first interaction network related parameters are selected, and the method steps are as follows: Step S1, dividing the data set consisting of the first interaction network related parameters into a training set R and a test set E; Step S2, using the training set R to establish a Random Forest model; Step S3: using the criticality A of the first interaction network related parameters i To select the first interaction network related parameters; Step S4: According to the criticality A i To select the relevant parameters of the first interaction network, a specific training set SR is obtained; Receive second parameter relationship data between species in a second ecosystem, the second functional importance of species, and the second stability contribution of species to the ecosystem, use the second parameter relationship data to construct a second species interaction network, calculate the second interaction network related parameters according to the second species interaction network, and input the second interaction network related parameters, the second functional importance, and the second stability contribution into the interaction network evaluation model to obtain the second rating index of the second ecosystem, so as to realize high-precision integrated stability evaluation of the ecosystem across systems.

2. The method for evaluating ecosystem stability based on interspecies interaction networks according to claim 1, characterized in that: Parameter relationship data include: the level of the food chain in which the species is located in the ecosystem, the types and number of relationships between species; The parameters related to the interaction network include: the average path length, connectivity and node degree of the interaction network constructed with species as nodes and the degree of association calculated based on the types and number of relationships between species.

3. The method for evaluating ecosystem stability based on interspecies interaction networks according to claim 1, characterized in that: The interaction network evaluation model is constructed using the specific training set SR according to an improved support vector machine model.

4. An ecosystem stability assessment system based on interspecies interaction network, the system implements the method according to claim 3, the system comprises a data receiving module, a network construction module, an interaction network assessment model construction module, an ecosystem stability assessment module, a visualization module, and a data storage module, characterized in that: The data receiving module receives first parameter relationship data and second parameter relationship data between species in the ecosystem, and receives a first rating indicator of ecosystem stability; The network construction module: constructs a species interaction network using the first parameter relationship data, and calculates the first species interaction network related parameters, the first importance of species functions, and the first stability contribution of species to the ecosystem; constructs a species interaction network using the second parameter relationship data, and calculates the second species interaction network related parameters, the second importance of species functions, and the second stability contribution of species to the ecosystem; The interaction network evaluation model construction module is used to jointly construct an interaction network evaluation model according to the first species interaction network related parameters, the first importance of species functions, the first stability contribution of species to the ecosystem and the first stability rating index of the ecosystem; The interaction network evaluation model is constructed using an improved support vector machine model; The specific function expression of the improved support vector machine model is: Where ω, b are the normal vector and intercept of the hyperplane, respectively, X i The variables that connect the combination of SR, species functional importance, and species contribution to ecosystem stability for a specific training set, is the sample size, is the average functional importance of all species in the ecosystem, The average contribution of all species in an ecosystem to the stability of the ecosystem; Before constructing the improved support vector machine model, the first interaction network related parameters are selected, and the method steps are as follows: Step S1, dividing the data set consisting of the first interaction network related parameters into a training set R and a test set E; Step S2, using the training set R to establish a Random Forest model; Step S3: using the criticality A of the first interaction network related parameters i To select the first interaction network related parameters; Step S4: According to the criticality A i To select the relevant parameters of the first interaction network, a specific training set SR is obtained; The ecosystem stability assessment module is used to receive the second species interaction network related parameters, the second importance of species functions, and the second stability contribution of species to the ecosystem, and generate a second rating indicator of ecosystem stability using the interaction network assessment model; It is also used to transmit the second species interaction network related parameters, the second importance of species functions, the second stability contribution of species to the ecosystem, and the second rating index of ecosystem stability to the data storage module for storage; Visualization display module: connected to the ecosystem stability assessment module, used to realize 3D visualization display of species interaction network and ecosystem stability rating indicators through visualization technology; The data storage module is used to store parameters related to the species interaction network, the functional importance of species, the contribution of species to the stability of the ecosystem, and the second rating indicator of ecosystem stability generated by using the interaction network evaluation model.

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