Method and system for identifying key biological class groups influencing lake ecosystem based on sectional structure equation model

Abstract

The invention discloses a method and a system for identifying key biological class groups influencing a lake ecosystem based on a sectional structure equation model, relates to the technical field of environmental ecology, and comprehensively investigates the biocenosis diversity and ecological function characteristics of a lake. A nutrition structure of a lake ecosystem is constructed by analyzing the predation relationship among biological groups; the multifunctionality of the lake is evaluated by integrating clustering analysis and a functional threshold method; the method comprises the following steps: constructing a sectional structure equation model based on a nutrition structure relationship of a food web, fitting each path in the model through a mixed effect model, analyzing a path coefficient between nutrition level richness and multifunctionality, and calculating direct and indirect effects of the path coefficient; and finally, identifying the key biological class group which has the greatest influence on the ecological function of the lake. According to the method, the key biological class groups with decisive influence on the ecological function of the lake can be accurately identified, a clear target is provided for ecological protection and restoration, and the method has practical reference significance in making practical restoration management measures.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental ecology, and more specifically to a method and system for identifying key biological groups affecting lake ecosystems based on a piecewise structural equation model. Background Art

[0002] At present, shallow lakes are an important freshwater resource on the earth, and they undertake multiple ecological functions such as water supply, water purification, biodiversity maintenance and habitat provision. In recent years, with the continuous efforts of researchers in the field of environment, the water quality of my country's shallow lakes has been significantly improved, and lake restoration measures have gradually shifted from simply improving water quality to restoring ecological functions. The loss of biodiversity is one of the important reasons for the decline of lake ecological functions. Therefore, increasing biodiversity is regarded 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 the composition of biological groups, or on biological manipulation of top predator fish. These measures may not meet the functional requirements of the actual lake ecosystem, thus affecting the restoration effect. In addition, there are mutual influences between different biological groups and between biological groups and ecological functions. Inappropriate diversity enhancement 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 urgently needed to analyze the relationship between biodiversity and lake ecological functions, identify key biological groups that affect ecological functions, and formulate more targeted and efficient diversity enhancement and functional restoration measures. The flexibility of the piecewise structural equation model is 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 the food web, especially for those biological groups that are both predators and prey. Based on the piecewise structural equation model, we can deeply analyze the impact of different biological groups on ecological functions while considering the nutritional structure relationship, so as to identify biological groups with key influences, provide scientific guidance and theoretical basis for future restoration and management measures of lake ecological functions, and thus more effectively promote the health and sustainable development of lake ecosystems.

[0004] Therefore, how to identify key biological populations based on the piecewise structural equation model to improve the effect of lake restoration and enhance the targetedness of lake restoration is an urgent problem that technicians in this field need to solve. Summary of the invention

[0005] In view of this, the present invention provides a method and system for identifying key biological groups affecting lake ecosystems based on a piecewise structural equation model to solve the problems existing in the background technology.

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

[0007] A method for identifying key biological groups affecting lake ecosystems based on piecewise structural equation modeling, including:

[0008] Conduct environmental surveys of lake ecosystems, test the physical and chemical properties of water bodies, and record data on aquatic biological groups as functional indicators;

[0009] Aggregate data on biological groups according to predation preferences to determine trophic structural relationships in food webs in lake ecosystems;

[0010] The collected functional indicators were standardized, and the multifunctionality of lake ecosystems was quantitatively evaluated using cluster weighting and single threshold methods to obtain the multifunctionality of lake ecosystems.

[0011] A piecewise structural equation model was constructed based on the relationship of the trophic structure of the food web, in which the relationship between predators and prey was modeled using a mixed effects model to analyze the path coefficients of richness and multifunctionality at each trophic level;

[0012] 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 groups with the strongest total effect were the key biological groups that affected the ecological functions of the lake.

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

[0014] Optionally, the biological group data are summarized according to predation preference, specifically including: aquatic plants, phytoplankton, rotifers, microshells, benthic animals, herbivorous fish, filter-feeding fish, omnivorous fish and carnivorous fish; among which carnivorous fish prey on herbivorous fish, omnivorous fish and filter-feeding fish; herbivorous fish prey on aquatic plants; omnivorous fish prey on aquatic plants, benthic animals, microshells and rotifers; filter-feeding fish prey on phytoplankton and rotifers; microshells prey on phytoplankton and rotifers; rotifers prey on phytoplankton.

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

[0016] For total phosphorus and total nitrogen concentrations, they are standardized according to the benchmark values, and the calculation formula is as follows:

[0017]

[0018] In the formula, x ob is the measured value, x std As the benchmark value, the benchmark values ​​of total phosphorus and total nitrogen are 0.05mg / L and 1.0mg / L respectively;

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

[0020]

[0021] In the formula, x ob is the measured value, Min and Max are the minimum and maximum values ​​respectively.

[0022] Optionally, the single threshold method is used to calculate the multifunctionality of lake ecosystems. The specific formula is as follows:

[0023]

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

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

[0026] The piecewise structural equation model was established using the psem function of the “piecewiseSEM” package in the R language. The relationship between predators and prey, and the relationship between biological groups and multifunctionality were fitted with the mixed effect model of the “nlme” package to calculate the path coefficients. The fixed effect was set as the prey, the random effect was the difference in sampling points, and the marginal R 2 is the individual variance explained by the fixed effects.

[0027] A system for identifying key biological groups affecting lake ecosystems based on piecewise structural equation modeling, including:

[0028] The data collection module conducts environmental surveys of lake ecosystems, detects the physical and chemical properties of water bodies, and records data on aquatic biological groups as functional indicators;

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

[0030] The multifunctionality calculation module performs standardization on the collected functional indicators and uses cluster weighting and single threshold method to quantitatively evaluate the multifunctionality of the lake ecosystem to obtain the multifunctionality of the lake ecosystem;

[0031] The model building module builds a piecewise structural equation model based on the nutritional structure of the food web, in which the relationship between predators and prey is modeled using a mixed effects model to analyze the path coefficients of richness and multifunctionality at each trophic level;

[0032] The key biological group output module calculates the direct and indirect effects of the richness of each trophic level on the multifunctionality of the lake ecosystem based on the path coefficient, and adds up the total effect. The biological group with the strongest total effect is the key biological group that affects the ecological function of the lake.

[0033] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a method and system for identifying key biological groups that affect lake ecosystems based on a segmented structural equation model. Starting from the relationship between biodiversity and ecological functions, the diversity of biological communities and ecological function characteristics of lakes are comprehensively investigated; by analyzing the predator-prey relationship between biological groups, the nutritional network of lake ecosystems is constructed; comprehensive cluster analysis and functional threshold method are used to evaluate the multifunctionality of lakes; a segmented structural equation model is further constructed based on the nutritional structure relationship of the food web, and each path in the model is fitted by a mixed effects model to analyze the path coefficient between trophic level richness and multifunctionality, and calculate its direct and indirect effects; finally, the key biological groups that have the greatest impact on the ecological functions of lakes are identified. The technical characteristics and advantages of the present invention are mainly reflected in the following three aspects:

[0034] (1) Comprehensiveness: The present invention comprehensively considers the relationship between biodiversity and multifunctionality of lake ecosystems and the trophic structure network based on predator-prey relationships, providing a comprehensive analytical framework.

[0035] (2) Reliability: The present invention adopts a piecewise structural equation model to establish a complex predator-prey relationship network among biological groups. At the same time, each path in the model is fitted by a mixed effects model, incorporating external factors as random effects, which is more in line with the actual state of the lake ecosystem and the simulation results are reliable.

[0036] (3) Practicality: The present invention can accurately identify key biological groups that have a decisive impact on the ecological functions of lakes, provide clear goals for ecological protection and restoration, and have practical reference significance in the formulation of actual restoration and management measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0038] Figure 1 A flow chart of a method for identifying key biological groups that affect lake ecological functions based on a piecewise structural equation model provided by the present invention;

[0039] Figure 2 The nutritional structure relationship of the food web in the lake ecosystem provided by the present invention;

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

[0041] Figure 4 The present invention provides direct and indirect effects of different biological groups on multifunctionality in lake ecosystems, wherein a represents spring and b represents summer. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0043] The embodiment of the present invention discloses a method for identifying key biological groups affecting lake ecosystems based on a piecewise structural equation model. Figure 1 As shown, including:

[0044] (1) Conduct environmental surveys of lake ecosystems, test the physical and chemical properties of water bodies, and record data on aquatic biological groups as functional indicators;

[0045] A shallow lake is located in the northern inland of my country. It has a temperate continental monsoon climate, with an average water temperature of 14°C and an average water depth of 2.5 meters. The water quality meets the Class III water quality standard and is rich in biological resources. 30 sampling points are evenly arranged in the lake, and samples are collected and analyzed once in spring and summer. Among them, the physical and chemical indicators of the water body tested include: total phosphorus concentration, total nitrogen concentration, dissolved oxygen concentration, total organic carbon concentration, water transparency, vegetation coverage in the lake area, and bacterial abundance; the aquatic biological communities investigated include: large aquatic plants, phytoplankton, zooplankton, large benthic animals and fish, and the detection indicators include species composition, abundance and biomass.

[0046] (2) summarize the data of biological groups according to predation preferences and determine the trophic structure relationships of the food web in the lake ecosystem;

[0047] Based on literature review and empirical data, the predation preferences of different species were determined, and the biological community data were classified and summarized according to the predation preferences: aquatic plants, phytoplankton, rotifers, microshells, benthic animals, herbivorous fish, filter-feeding fish, omnivorous fish and carnivorous fish. The nutritional structure relationship between lake biological groups was further determined, such as Figure 2 shown.

[0048] (3) The collected functional indicators were standardized, and the multifunctionality of lake ecosystems was quantitatively evaluated using cluster weighting and single threshold methods to obtain the multifunctionality of lake ecosystems;

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

[0050] First, each indicator is standardized. For the total phosphorus and total nitrogen concentrations, they are standardized according to the benchmark values. The calculation formula is as follows:

[0051]

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

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

[0054]

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

[0056] Then, objective weighting is performed based on the k-means cluster analysis results of the indicators, setting the weight of each cluster equal, and the weight of each functional indicator within the cluster is also equal. In this case, the number of clusters is determined to be 3, among which cluster 1 is assigned a weight of 0.98 because it cannot be divided evenly, and the final total weight is 2.98. The specific weight of each indicator is shown in Table 1.

[0057] Table 1 Weights of various functional indicators determined based on k-means cluster analysis

[0058]

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

[0060]

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

[0062] (4) A piecewise structural equation model was constructed based on the trophic structure of the food web, in which the relationship between predators and prey was modeled using a mixed effects model to analyze the path coefficients of richness and multifunctionality at each trophic level;

[0063] According to the trophic structure relationship among lake biological groups, the piecewise structural equation model was established using the psem function of the “piecewiseSEM” package of the R language. The relationship between predators and prey, and the relationship between biological groups and multifunctionality were fitted with the mixed effect model of the “nlme” package to calculate the path coefficients. The fixed effect was set as prey, the random effect was set as sampling point difference, and the marginal R 2 (marginalR 2 ) is the individual variance explanation rate of the fixed effect. The piecewise structural equation model of the case lake in spring and summer is shown in Figure 3The models were all statistically significant (p>0.05), and the overall explained rates of variance of multifunctionality by different biological groups reached 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 them up. The biological groups with the strongest total effect were the key biological groups that affected the ecological functions of the lake.

[0065] Based on the path coefficient, the direct and indirect effects (direct effects and indirect effects) of different biological groups on multifunctionality are extracted. The direct effect is the standardized coefficient (path coefficient) corresponding to the direct path of the biological group pointing to multifunctionality, and the indirect effect is the sum of the standardized coefficients of each indirect path of the biological group pointing to multifunctionality. Finally, the total effect value is calculated by adding the direct and indirect effects. The biological group with the highest total effect value is the key biological group that affects the ecological function of the lake. The direct and indirect effects of different biological groups on multifunctionality in the case lake are shown in Figure 4 The key biological groups in spring are aquatic plants (total effect = 0.545), while in summer they are planktonic fish (total effect = 0.908). Therefore, the richness of aquatic plants should be increased in spring, and measures such as the proliferation and release of planktonic fish should be taken in summer to promote the recovery of the ecological functions of the lake.

[0066] A system for identifying key biological groups affecting lake ecosystems based on piecewise structural equation modeling, including:

[0067] The data collection module conducts environmental surveys of lake ecosystems, detects the physical and chemical properties of water bodies, and records data on aquatic biological groups as functional indicators;

[0068] The trophic structure relationship determination module aggregates the biological group data according to predation preferences and determines the trophic structure relationship of the food web in the lake ecosystem;

[0069] The multifunctionality calculation module performs standardization on the collected functional indicators and uses cluster weighting and single threshold method to quantitatively evaluate the multifunctionality of the lake ecosystem to obtain the multifunctionality of the lake ecosystem;

[0070] The model building module builds a piecewise structural equation model based on the nutritional structure of the food web, in which the relationship between predators and prey is modeled using a mixed effects model to analyze the path coefficients of richness and multifunctionality at each trophic level;

[0071] The key biological group output module calculates the direct and indirect effects of the richness of each trophic level on the multifunctionality of the lake ecosystem based on the path coefficient, and adds up the total effect. The biological group with the strongest total effect is the key biological group that affects the ecological function of the lake.

[0072] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0073] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for identifying key biological groups affecting lake ecosystems based on a piecewise structural equation model, characterized in that: include: Conduct environmental surveys of lake ecosystems, test the physical and chemical properties of water bodies, and record data on aquatic biological groups as functional indicators; Aggregate data on biological groups according to predation preferences to determine trophic structural relationships in food webs in lake ecosystems; The collected functional indicators were standardized, and the multifunctionality of lake ecosystems was quantitatively evaluated using cluster weighting and single threshold methods to obtain the multifunctionality of lake ecosystems. A piecewise structural equation model was constructed based on the relationship of the trophic structure of the food web, in which the relationship between predators and prey was modeled using a mixed effects model to analyze the path coefficients of richness and multifunctionality at each trophic level; 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 groups with the strongest total effect were the key biological groups that affected the ecological functions of the lake.

2. The method for identifying key biological groups affecting lake ecosystems based on a piecewise structural equation model according to claim 1 is characterized in that: The physical and chemical properties of the water body specifically include: total phosphorus concentration, total nitrogen concentration, dissolved oxygen concentration, total organic carbon concentration, water body transparency, vegetation coverage in the lake area and bacterial abundance; the aquatic biological groups investigated specifically include: large aquatic plants, phytoplankton, zooplankton, large benthic animals and fish.

3. The method for identifying key biological groups affecting lake ecosystems based on a piecewise structural equation model according to claim 1 is characterized in that: The data of biological groups are summarized according to predation preferences, including: aquatic plants, phytoplankton, rotifers, microshells, benthic animals, herbivorous fish, filter-feeding fish, omnivorous fish and carnivorous fish; among them, carnivorous fish prey on herbivorous fish, omnivorous fish and filter-feeding fish; herbivorous fish prey on aquatic plants; omnivorous fish prey on aquatic plants, benthic animals, microshells and rotifers; filter-feeding fish prey on phytoplankton and rotifers; microshells prey on phytoplankton and rotifers; rotifers prey on phytoplankton.

4. The method for identifying key biological groups affecting lake ecosystems based on a piecewise structural equation model according to claim 1 is characterized in that: The standardization process of the collected functional indicators specifically includes: For total phosphorus and total nitrogen concentrations, they are standardized according to the benchmark values, and the calculation formula is as follows: In the formula, x ob is the measured value, x std is the base value; Other functional indicators are standardized using the extreme value method. The specific calculation formula is as follows: In the formula, x ob is the measured value, Min and Max are the minimum and maximum values ​​respectively.

5. The method for identifying key biological groups affecting lake ecosystems based on a piecewise structural equation model according to claim 1 is characterized in that: The single threshold method is used to calculate the multifunctionality of lake ecosystems. The specific formula is as follows: In the formula, Multifunctionality is multifunctionality, F is the number of functions, x i is the standardized value of the ith functional index, a is the validity threshold of the functional index, and f i is the discriminant function, when x i >a, the validity of the i-th functional indicator is equal to the weight of the indicator, otherwise the validity is 0; the weight of each functional indicator is obtained through cluster analysis, that is, k-means cluster analysis is performed on all functional indicators, and the number of clusters and the functional indicators contained in each cluster are determined according to the scree plot and classification tree. The weights between clusters are equal, and the weight of each functional indicator within a cluster is also equal.

6. The method for identifying key biological groups affecting lake ecosystems based on a piecewise structural equation model according to claim 1 is characterized in that: The construction process of the piecewise structural equation model is as follows: The piecewise structural equation model was established using the psem function of the "piecewiseSEM" package of the R language. The relationship between predators and prey, and the relationship between biological groups and multifunctionality were fitted with the mixed effect model of the lme function of the "nlme" package to calculate the path coefficients. The fixed effect was set as the prey, the random effect was the difference in sampling points, and the marginal R 2 is the individual variance explained by the fixed effects.

7. A system for identifying key biological groups affecting lake ecosystems based on a piecewise structural equation model, characterized in that: A method for identifying key biological groups affecting lake ecosystems based on a piecewise structural equation model as described in any one of claims 1 to 6 comprises: The data collection module conducts environmental surveys of lake ecosystems, detects the physical and chemical properties of water bodies, and records data on aquatic biological groups as functional indicators; The trophic structure relationship determination module aggregates the biological group data according to predation preferences and determines the trophic structure relationship of the food web in the lake ecosystem; The multifunctionality calculation module performs standardization on the collected functional indicators and uses cluster weighting and single threshold method to quantitatively evaluate the multifunctionality of the lake ecosystem to obtain the multifunctionality of the lake ecosystem; The model building module builds a piecewise structural equation model based on the nutritional structure of the food web, in which the relationship between predators and prey is modeled using a mixed effects model to analyze the path coefficients of richness and multifunctionality at each trophic level; The key biological group output module calculates the direct and indirect effects of the richness of each trophic level on the multifunctionality of the lake ecosystem based on the path coefficient, and adds up the total effect. The biological group with the strongest total effect is the key biological group that affects the ecological function of the lake.

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