A method and system for identifying key biological groups of a lake ecosystem based on a segmented structural equation model

Abstract

The application discloses a kind of based on segmented structural equation model's influence lake ecosystem key biological group identification method and system, it is related to environmental ecology technical field, comprehensively investigates the biological community diversity and ecological function characteristics of lake;Through the analysis of the predatory relationship between biological groups, the trophic structure of lake ecosystem is constructed;Comprehensive cluster analysis and function threshold method assesses the multifunctionality of lake;Based on the trophic structure relationship of food web, segmented structural equation model is constructed, each path in the model is fitted by mixed effect model, the path coefficient between trophic level richness and multifunctionality is analyzed, and its direct and indirect effects are calculated;Finally, the key biological group that has the greatest influence on lake ecological function is identified.The present application can accurately identify the key biological group that has a decisive influence on lake ecological function, provide a clear target for ecological protection and restoration, and has practical reference significance in actual restoration management measure formulation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of environmental ecology, and more particularly to a method and system for identifying key biological groups affecting lake ecosystems based on a segmented structural equation model. BACKGROUND

[0002] At present, shallow lakes are important freshwater resources on earth, bearing multiple ecological functions such as water resource supply, water quality purification, biodiversity maintenance, and habitat provision. In recent years, with the continuous efforts of researchers in the field of environment, the water quality of shallow lakes in China has been significantly improved, and lake restoration measures have gradually shifted from pure water quality improvement to ecological function restoration. Biodiversity loss is one of the important reasons for the decline of lake ecological functions, therefore, increasing biodiversity is considered as an effective means to promote functional restoration.

[0003] However, most current studies tend to focus on intervening in biological groups with low diversity in group composition, or manipulating top predators such as fish, which may not meet the functional needs of actual lake ecosystems, thus affecting the restoration effect. In addition, there are mutual influences between different biological groups and between biological groups and ecological functions, and inappropriate biodiversity improvement measures may hinder the normal functioning of ecological functions. In order to ensure the effectiveness of ecological function restoration, a simple and efficient method is needed to analyze the relationship between biodiversity and lake ecological functions, identify key biological groups affecting ecological functions, and develop more targeted and efficient biodiversity improvement and functional restoration measures. The flexibility of the segmented structural equation model lies in that it allows a variable to act as both a predictor and a response variable. This feature greatly facilitates the description of the complex trophic status of biological groups in food webs, especially for those biological groups that are both predators and prey. Based on the segmented structural equation model, the influence of different biological groups on ecological functions can be analyzed in depth while considering the trophic structure relationship, thus identifying key biological groups with critical influence, providing scientific guidance and theoretical basis for future lake ecological function restoration and management measures, and thus more effectively promoting the health and sustainable development of lake ecosystems.

[0004] Therefore, how to identify key biological groups based on the segmented structural equation model to improve the effectiveness and targeting of lake restoration is a problem that needs to be solved by those skilled in the art. SUMMARY

[0005] Therefore, the present application provides a method and system for identifying key biological groups affecting lake ecosystems based on a segmented structural equation model to solve the problems in the background art.

[0006] In order to achieve the above object, the present application adopts the following technical solutions:

[0007] A method for identifying key biological groups affecting a lake ecosystem based on a segmented structural equation model, comprising:

[0008] Performing a lake ecosystem environment survey, detecting water physical and chemical properties, and recording aquatic biological group data as a functional indicator;

[0009] Summarizing the biological group data according to predation preferences to determine the trophic structure relationship of the food web in the lake ecosystem;

[0010] Performing standardization processing on the collected functional indicators, and using a clustering weighting and single threshold method to quantitatively evaluate the multifunctionality of the lake ecosystem to obtain the multifunctionality of the lake ecosystem;

[0011] Based on the trophic structure relationship of the food web, a segmented structural equation model is constructed, wherein the relationship between predators and prey is modeled using a mixed effect model to analyze the path coefficients of the richness of each trophic level and the multifunctionality;

[0012] Based on the path coefficients, the direct effect and indirect effect of the richness of each trophic level on the multifunctionality of the lake ecosystem are calculated, and the total effect is calculated by adding up, wherein the biological group with the strongest total effect is the key biological group affecting the lake ecosystem function.

[0013] Optionally, the water physical and chemical properties specifically include total phosphorus concentration, total nitrogen concentration, dissolved oxygen concentration, total organic carbon concentration, water transparency, lake area vegetation coverage, and bacterial abundance; and the investigated aquatic biological groups specifically include macrophytes, phytoplankton, zooplankton, macrobenthos, and fish.

[0014] Optionally, the biological group data is summarized according to predation preferences, specifically including aquatic plants, phytoplankton, rotifers, microcrustaceans, benthic animals, herbivorous fish, filter-feeding fish, omnivorous fish, and carnivorous fish; wherein the carnivorous fish preys on herbivorous fish, omnivorous fish, and filter-feeding fish; the herbivorous fish preys on aquatic plants; the omnivorous fish preys on aquatic plants, benthic animals, microcrustaceans, and rotifers; the filter-feeding fish preys on phytoplankton and rotifers; the microcrustaceans prey on phytoplankton and rotifers; and the rotifers prey on phytoplankton.

[0015] Optionally, the standardization processing on the collected functional indicators specifically includes:

[0016] For total phosphorus and total nitrogen concentration, standardization is performed according to a reference value, and the calculation formula is as follows:

[0017]

[0018] wherein x ob is the measured value, x std is the reference value, the reference values of total phosphorus and total nitrogen are 0.05 mg / L and 1.0 mg / L respectively;

[0019] Other functional indicators are standardized by using the extreme value method, and the specific calculation formula is as follows:

[0020]

[0021] wherein x ob is the measured value, Min and Max are the minimum value and the maximum value respectively.

[0022] Optionally, the multifunctionality of the lake ecosystem is calculated by using the single threshold method, and the specific formula is as follows:

[0023]

[0024] wherein Multifunctionality is the multifunctionality, F is the number of functions, x i is the standardized value of the i th function indicator, a is the effectiveness threshold of the function indicator, f i is the discriminant function, when x i >a, the effectiveness of the i th function indicator is equal to the weight of the indicator, otherwise the effectiveness is 0; wherein the weights of the function indicators are obtained by cluster analysis, that is, k-means cluster analysis is performed on all the function indicators, the number of clusters and the function indicators contained in each cluster are determined according to the scatter plot and the classification tree, the weights between the clusters are equal, and the weights of each function indicator within the cluster are also equal.

[0025] Optionally, the construction process of the piecewise structural equation model is as follows:

[0026] The piecewise structural equation model is established by using the psem function of the “piecewiseSEM” package of R language, wherein the relationship between the predator and the prey, and the relationship between the biological group and the multifunctionality are fitted by using the lme function of the “nlme” package to calculate the path coefficients of the mixed effect model, the fixed effect is set as the prey, the random effect is set as the difference of the sampling points, and the marginal R 2 is the individual variance explanation rate of the fixed effect.

[0027] An identification system for key biological groups of lake ecosystems based on a piecewise structural equation model, comprising:

[0028] A data acquisition module for investigating the lake ecosystem environment, detecting the physicochemical properties of the water body, and recording the aquatic biological group data as function indicators;

[0029] The trophic structure relationship determining module aggregates the biological group data according to the predation preference, and determines the trophic structure relationship of the food web in the lake ecosystem;

[0030] The multifunctionality calculating module performs standardization processing on the collected function indicators, and quantitatively evaluates the multifunctionality of the lake ecosystem by using clustering weighting and single threshold value method, to obtain the multifunctionality of the lake ecosystem.

[0031] The model establishing module constructs a segmented structural equation model based on the trophic structure relationship of the food web, wherein the relationship between the predator and the prey is modeled by using a mixed effect model to analyze the path coefficients between the trophic level richness and the multifunctionality.

[0032] The key biological group output module calculates the direct effect and the indirect effect of the trophic level richness on the multifunctionality of the lake ecosystem based on the path coefficients, and adds up to obtain the total effect, wherein the biological group with the strongest total effect is the key biological group affecting the ecological function of the lake.

[0033] According to the technical solution, compared with the prior art, the application provides a key biological group identification method and system for affecting a lake ecosystem based on a segmented structural equation model, which starts from the relationship between biological diversity and ecological function, comprehensively investigates the biological community diversity and ecological function characteristics of the lake, analyzes the predation relationship between the biological groups, constructs a trophic network of the lake ecosystem, comprehensively evaluates the multifunctionality of the lake by using clustering analysis and function threshold value method, further constructs a segmented structural equation model based on the trophic structure relationship of the food web, fits each path in the model by using a mixed effect model, analyzes the path coefficients between the trophic level richness and the multifunctionality, and calculates the direct and indirect effects, and finally identifies the key biological group with the greatest impact on the ecological function of the lake.

[0034] (1) comprehensiveness: the application comprehensively considers the relationship between the biological diversity and the multifunctionality of the lake ecosystem and the trophic structure network based on the predation relationship, and provides a comprehensive analysis framework.

[0035] (2) reliability: the application uses a segmented structural equation model to establish a complex predation relationship network between the biological groups, and fits each path in the model by using a mixed effect model, and takes external factors as random effects, which is more consistent with the real state of the lake ecosystem, and the simulation result is reliable.

[0036] (3) practicality: the application can accurately identify the key biological group with a decisive impact on the ecological function of the lake, provides a clear target for ecological protection and restoration, and has practical reference significance in the actual restoration management measure formulation. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the accompanying drawings in the following description only represent some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative effort based on the provided drawings.

[0038] Figure 1 The flow chart of the method for identifying key biological groups affecting the ecological function of a lake based on a segmented structural equation model provided by the present application;

[0039] Figure 2 The trophic structure relationship of a food web in a lake ecosystem provided by the present application;

[0040] Figure 3 The segmented structural equation model based on the trophic structure relationship of a case lake provided by the present application, wherein a represents spring and b represents summer.

[0041] Figure 4 The direct and indirect effects of different biological groups on multifunctionality in a lake ecosystem provided by the present application, wherein a represents spring and b represents summer. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present application will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments only represent some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort fall within the scope of the present application.

[0043] The embodiments of the present application disclose a method for identifying key biological groups affecting a lake ecosystem based on a segmented structural equation model, as shown in Figure 1 The method comprises the following steps:

[0044] (1) Conduct an environmental investigation of the lake ecosystem, detect the physicochemical properties of the water body, and record the data of aquatic biological groups as functional indicators;

[0045] A shallow lake is located in the inland of northern China, which belongs to temperate continental monsoon climate. The average water temperature is 14℃, the average water depth is 2.5m, the water quality reaches the III class water quality standard, and the biological resources in the lake are abundant. 30 sampling points are uniformly arranged in the lake, and sample collection and analysis are carried out in spring and summer. The detected water physical and chemical indexes include total phosphorus concentration, total nitrogen concentration, dissolved oxygen concentration, total organic carbon concentration, water transparency, lake area vegetation coverage and bacterial abundance. The investigated aquatic biological community includes macrophytes, phytoplankton, zooplankton, macrobenthos and fish, and the detection indexes include species composition, abundance and biomass.

[0046] (2) According to the predation preference, the biological group data is summarized to determine the trophic structure relationship of the food web in the lake ecosystem;

[0047] According to the literature and empirical data, the predation preference of different species is determined, and the biological community data is classified and summarized according to the predation preference: aquatic plants, phytoplankton, rotifers, microcrustaceans, benthic animals, herbivorous fish, filter-feeding fish, omnivorous fish and carnivorous fish. Further determine the trophic structure relationship between biological groups in the lake, such as shown in Figure 2 .

[0048] (3) The collected functional indicators are standardized, and the multi-functionality of the lake ecosystem is quantitatively evaluated by using clustering weighting and single threshold method, and the multi-functionality of the lake ecosystem is obtained;

[0049] The multi-functionality is calculated by comprehensively considering the seven main functions of the lake ecosystem, a total of 16 functional indicators, including total phosphorus concentration, total nitrogen concentration, total organic carbon concentration, dissolved oxygen concentration, transparency, aquatic plant coverage, bacterial abundance, aquatic plant biomass, phytoplankton density, rotifer biomass, microcrustacean biomass, benthic animal biomass, carnivorous fish biomass, omnivorous fish biomass, filter-feeding fish biomass, and herbivorous fish biomass.

[0050] Firstly, each index is standardized, and for total phosphorus and total nitrogen concentration, the standardization is carried out according to the reference value, and the calculation formula is as follows:

[0051]

[0052] In the formula, x ob is the measured value, x std is the reference value, and the reference values of total phosphorus and total nitrogen are 0.05mg / L and 1.0mg / L respectively;

[0053] Other functional indicators are standardized by using the extreme value method, and the specific calculation formula is as follows:

[0054]

[0055] where x ob is the measured value, Min and Max are the minimum and maximum values, respectively.

[0056] Then, the k-means clustering analysis results of the indicators were objectively weighted, and the weight of each cluster was set to be equal, and the weight of each functional indicator within the cluster was also equal. In this case, the number of clusters in the lake was determined to be 3, among which cluster 1 was assigned a weight of 0.98 because it could not be evenly divided, and the final total weight was 2.98. The specific weight of each indicator is shown in Table 1.

[0057] Table 1 Weight of each functional indicator determined based on k-means clustering analysis

[0058]

[0059] Finally, the single threshold method was used to calculate the multifunctionality of the lake ecosystem, and the specific formula is as follows:

[0060]

[0061] where Multifunctionality is the multifunctionality, F is the number of functions, x i is the standardized value of the i th functional indicator, a is the effectiveness threshold of the functional indicator, and f i is the discriminant function. When x i >a, the effectiveness of the i th functional indicator is equal to the weight of the indicator, otherwise the effectiveness is 0; the multifunctionality of the case lake in spring and summer is 0.66±0.08 and 0.67±0.09, respectively.

[0062] (4) Based on the nutritional structure relationship of food web, a segmented structural equation model was constructed, in which the relationship between predators and prey was modeled using a mixed effect model to analyze the path coefficients of each trophic level richness and multifunctionality.

[0063] According to the nutritional structure relationship between biological groups in the lake, the psem function of the “piecewiseSEM” package in R language was used to establish a segmented structural equation model, in which the relationship between predators and prey, and the relationship between biological groups and multifunctionality were fitted using the lme function of the “nlme” package to calculate the path coefficients. The fixed effect was set as the prey, and the random effect was set as the difference between sampling points. The marginal R 2 (marginal R 2 ) is the individual variance explained by the fixed effect. The segmented structural equation models of the case lake in spring and summer are shown in Figure 3, the models were statistically significant (p>0.05), and the total variance explained by different biological groups to the multifunctionality was 75% (spring) and 91% (summer), respectively.

[0064] (5) Based on the path coefficient, the direct and indirect effects of the richness of each trophic level on the multifunctionality of the lake ecosystem were calculated, and the total effect was calculated by adding up. The biological group with the strongest total effect was the key biological group affecting the ecological function of the lake.

[0065] Based on the path coefficient, the direct and indirect effects of different biological groups on multifunctionality (direct and indirect effects) were extracted. The direct effect is the standardized coefficient (path coefficient) of the direct path of the biological group to multifunctionality, and the indirect effect is the sum of the product of the standardized coefficient of each indirect path of the biological group to multifunctionality. The total effect value is calculated by adding the final direct and indirect effects, and the key biological group affecting the ecological function of the lake is the one with the highest total effect value. The direct and indirect effects of different biological groups on multifunctionality in the case lake are shown in Figure 4 The key biological group in spring is aquatic plants (total effect = 0.545), while in summer it is planktivorous fish (total effect = 0.908). Therefore, in spring, the richness of aquatic plants should be increased, and in summer, measures such as releasing planktivorous fish should be taken to promote the restoration of the ecological function of the lake.

[0066] An identification system for key biological groups in a lake ecosystem based on a segmented structural equation model, comprising:

[0067] A data collection module for investigating the lake ecosystem environment, detecting water physical and chemical properties, and recording aquatic biological group data as functional indicators;

[0068] A trophic structure relationship determination module for summarizing biological group data according to predation preferences and determining the trophic structure relationship of the food web in the lake ecosystem;

[0069] A multifunctionality calculation module for performing standardization processing on the collected functional indicators and using clustering weighting and single threshold method to quantitatively evaluate the multifunctionality of the lake ecosystem, obtaining the multifunctionality of the lake ecosystem;

[0070] A model establishment module for constructing a segmented structural equation model based on the trophic structure relationship of the food web, wherein the relationship between predators and prey is modeled using a mixed effect model to analyze the path coefficient of the richness of each trophic level and the multifunctionality;

[0071] The key biological group output module calculates the direct and indirect effects of the richness of each trophic level on the multi-functionality of the lake ecosystem based on the path coefficient, and adds up to calculate the total effect, wherein the biological group with the strongest total effect is the key biological group affecting the ecological function of the lake.

[0072] The various embodiments are described in the specification by way of progression, each building on the last to facilitate ease of understanding. Likewise, the same reference numerals are used throughout the drawings and specification to refer to same or like parts. Not all of the features to which the embodiments are directed are recited in the detailed description because describing every combination would be wearisome and obfuscate the disclosure and the inventive concept. Those of ordinary skill in the art will recognize that elements from the embodiments can be combined with elements from other embodiments without losing the scope of the application.

[0073] The foregoing description of the disclosed embodiments enables a person skilled in the art to carry out or use the application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein can be applied to other embodiments without departing from the spirit or scope of the application. Therefore, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An impact key biological group identification method for a lake ecosystem based on a segmented structural equation model, characterized in that, The method comprises the following steps: carrying out an environmental survey of a lake ecosystem, detecting physicochemical properties of the water body, and recording aquatic biological group data as a functional index; according to predation preferences, the biological group data is summarized to determine the trophic structure relationship of the food web in the lake ecosystem; standardized processing is performed on the collected functional index, and a clustering weighting and single threshold method is used to quantitatively evaluate the multifunctionality of the lake ecosystem, so as to obtain the multifunctionality of the lake ecosystem; based on the trophic structure relationship of the food web, a segmented structural equation model is constructed, wherein the relationship between the predator and the prey is modeled by using a mixed effect model, and path coefficients of the richness of each trophic level and the multifunctionality are analyzed; based on the path coefficients, the direct effect and the indirect effect of the richness of each trophic level on the multifunctionality of the lake ecosystem are calculated, and the total effect is calculated by summation, wherein the biological group with the strongest total effect is the key biological group affecting the ecological function of the lake; the standardized processing of the collected functional index specifically comprises the following steps: for total phosphorus and total nitrogen concentrations, standardization is performed according to a reference value, and the calculation formula is as follows: wherein x ob is the measured value, and x std is the reference value. other functional indexes are standardized by using an extreme value method, and the specific calculation formula is as follows: wherein x ob are the measured values, and Min and Max are the minimum and maximum values, respectively; the multifunctionality of the lake ecosystem is calculated by using a single threshold method, and the specific formula is as follows: wherein Multifunctionality is the multifunctionality, F is the number of functions, x i is the standardized value of the i th function index, a is the effectiveness threshold of the function index, f i is the discriminant function, when x i >a, the effectiveness of the i th function index is equal to the weight of the index, otherwise the effectiveness is 0; wherein the weight of each function index is obtained by cluster analysis, that is, k-means cluster analysis is performed on all function indexes, the number of clusters and the function indexes contained in each cluster are determined according to the scatter plot and the classification tree, the weights between clusters are equal, and the weight of each function index within the cluster is also equal; the construction process of the segmented structural equation model is as follows: The piecewise structural equation model was established using the psem function of the "piecewiseSEM" package in R language. The relationship between predators and prey, and the relationship between biological groups and multifunctionality were fitted with mixed effect models using the lme function of the "nlme" package to calculate the path coefficients. The fixed effect was set as the prey, and the random effect was set as the difference between sampling points. The marginal R 2 was the individual variance explained for the fixed effect.

2. The method for identifying key biological groups in a lake ecosystem based on a segmented structural equation model according to claim 1, characterized in that, the physicochemical properties of the water body specifically include total phosphorus concentration, total nitrogen concentration, dissolved oxygen concentration, total organic carbon concentration, water transparency, lake area vegetation coverage, and bacterial abundance; and the investigated aquatic biological groups specifically include macrophytes, phytoplankton, zooplankton, macrobenthos, and fish.

3. The method for identifying key biological groups in a lake ecosystem based on a segmented structural equation model according to claim 1, characterized in that, according to predation preferences, the biological group data is summarized, and specifically includes aquatic plants, phytoplankton, rotifers, microcrustaceans, benthic animals, herbivorous fish, filter-feeding fish, omnivorous fish, and carnivorous fish; wherein the carnivorous fish preys on the herbivorous fish, the omnivorous fish, and the filter-feeding fish; the herbivorous fish preys on the aquatic plants; the omnivorous fish preys on the aquatic plants, the benthic animals, the microcrustaceans, and the rotifers; the filter-feeding fish preys on the phytoplankton and the rotifers; the microcrustaceans prey on the phytoplankton and the rotifers; and the rotifers prey on the phytoplankton.

4. An impact lake ecosystem key biological group identification system based on a segmented structural equation model, characterized in that, The application of the method for identifying the key biological group of the lake ecosystem based on the segmented structural equation model according to any one of claims 1-3 comprises the following steps: a data acquisition module is configured to carry out an environmental survey of a lake ecosystem, detect physicochemical properties of the water body, and record aquatic biological group data as a functional index; a trophic structure relationship determination module is configured to summarize the biological group data according to predation preferences to determine the trophic structure relationship of the food web in the lake ecosystem; a multifunctionality calculation module is configured to perform standardized processing on the collected functional index, and use a clustering weighting and single threshold method to quantitatively evaluate the multifunctionality of the lake ecosystem, so as to obtain the multifunctionality of the lake ecosystem; a model establishment module is configured to construct a segmented structural equation model based on the trophic structure relationship of the food web, wherein the relationship between the predator and the prey is modeled by using a mixed effect model, and path coefficients of the richness of each trophic level and the multifunctionality are analyzed; and The key biological group output module calculates the direct effect and indirect effect of the richness of each trophic level on the multi-functionality of the lake ecosystem based on path coefficients, and adds up to calculate the total effect, wherein the biological group with the strongest total effect is the key biological group affecting the ecological function of the lake.

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