CN117634990A - Method for evaluating stability of freshwater ecosystem - Google Patents

Method for evaluating stability of freshwater ecosystem Download PDF

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CN117634990A
CN117634990A CN202410109915.XA CN202410109915A CN117634990A CN 117634990 A CN117634990 A CN 117634990A CN 202410109915 A CN202410109915 A CN 202410109915A CN 117634990 A CN117634990 A CN 117634990A
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刘心愿
刘聚涛
付莎莎
张立昕
沈丽琴
汤显强
黄佳聪
吴召仕
唐见
黄琪
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Jiangxi Academy Of Water Resources Jiangxi Dam Safety Management Center Jiangxi Water Resources Management Center
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Abstract

The invention discloses a method for evaluating the stability of a freshwater ecosystem, which comprises the following steps: forming an aquatic organism data and water environment factor data index original data set of a research area; acquiring a diversity index of each aquatic organism through the aquatic organism data and performing multiple collinearity diagnosis on the water environment factor data to form an index pretreatment data set; carrying out homogenization treatment on the index pretreatment data set; constructing a freshwater ecological system stability evaluation model based on a maximum flow principle; simulating an assessment model of the stability of the freshwater ecological system by using the self-organizing map network; the stability of the freshwater ecological system is evaluated through analysis of entropy values, and driving factors of the stability of the freshwater ecological system are identified through analysis of index weights. The invention has the beneficial effects that: the method can rapidly, accurately and sensitively reflect the stability condition of the freshwater ecosystem, identify the dominant factors driving the freshwater ecosystem to evolve, and is beneficial to the protection of the freshwater ecosystem.

Description

Method for evaluating stability of freshwater ecosystem
Technical Field
The invention relates to the technical field of freshwater ecological health evaluation, in particular to a method for evaluating the stability of a freshwater ecological system.
Background
The freshwater ecosystem acts as a complex, open, dynamic, unbalanced and nonlinear system. For the study of the stability of the freshwater ecosystem, some are only focused on biological parts (such as taxonomies, biodiversity and the like) in the ecosystem, and some consider biological parts and non-biological parts, but only connect the biological parts and the non-biological parts simply through statistical means (such as correlation analysis, CCA analysis and the like). In complex and diverse environments, organisms and non-organisms are coupled together through nonlinear interactions, and both themselves often have complex structures due to the openness of the ecosystem. Therefore, the prior method can not objectively and accurately evaluate the stability of the freshwater ecological system.
In order to truly describe the dynamic evolution process of the freshwater ecological system in time and space, the stability of the freshwater ecological system is scientifically evaluated and predicted, a freshwater ecological system stability evaluation model is necessarily constructed based on the maximum flow principle, the importance of aquatic organisms and water environment factor indexes is considered, the interaction relation among the indexes is also included in the model, the subjectivity of the traditional evaluation method is greatly reduced, the incomparability of the stability of the freshwater ecological system in time and space is overcome, and the evaluation result of the stability of the freshwater ecological system is more accurate.
Disclosure of Invention
The invention aims to provide a method for evaluating the stability of a freshwater ecological system, which takes the interaction relationship between aquatic organisms and water environment factor indexes in the freshwater ecological system as an entry point and improves the accuracy and objectivity of the stability evaluation result of the freshwater ecological system based on the maximum flow principle.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows: a method for assessing the stability of a freshwater ecosystem, comprising the steps of:
step S1, aiming at a selected research area, aquatic organism data and water environment factor data are collected through field monitoring and indoor experiments, and finally an aquatic organism data and water environment factor data index original data set of the research area is formed;
step S2, calculating Shannon-wiener diversity index of each aquatic organism through the aquatic organism data of the index original data set in the step S1, and performing multiple collinearity diagnosis on the water environment factor data of the index original data set; the processed aquatic organism data and the processed water environment factor data form an index pretreatment data set;
step S3, carrying out homogenization treatment on the index pretreatment data set in the step S2;
s4, constructing a freshwater ecological system stability evaluation model based on a maximum flow principle;
step S5, the index pretreatment data set after the homogenization treatment in the step S3 is simulated by using a self-organizing map network to obtain an evaluation model of the stability of the freshwater ecosystem in the step S4;
and S6, inputting the entropy value and the index weight obtained by simulation in the step S5 into an Excel table, evaluating the stability of the freshwater ecological system through analysis of the entropy value, and identifying the driving factors of the stability of the freshwater ecological system through analysis of the index weight.
Further, in step S3, a homogenization process is performed on the index pretreatment dataset in step S2; the method comprises the following steps:
the stability of the freshwater ecological system is improved along with the increase of index values of aquatic organisms and water environment factors, and the pre-normalization treatment is carried out as shown in a formula (1);
（1）；
wherein o represents the o-th index, b o A unification value, x, representing the o index o Represents the measured value, x, of the o index omin Represents the minimum value of the o index, x omax Represents the maximum value of the o index;
the stability of the freshwater ecological system is reduced along with the increase of index values of aquatic organisms and water environment factors, and the pre-normalization treatment is carried out as shown in a formula (2);
（2）。
further, the aquatic life data includes: phytoplankton, zooplankton, benthonic animals and fish; the water environment factor data includes total nitrogen, total phosphorus, chemical oxygen demand, turbidity and pH.
Further, the Shannon-Winner diversity index of the aquatic organism in step S2 is shown in formula (3):
（3）；
wherein,represents Shannon-wiener diversity index, a represents species, S represents the total number of individuals of all species in the community,is the percentage of the a-th species to the total number of individuals.
Further, in the step S2, multiple collinearity diagnosis is performed by adopting a variance expansion factor method, and the variable with the maximum variance expansion factor method is sequentially deleted, namely, the collinearity water environment factor is deleted until all the water environment factors of the variance expansion factor method are smaller than 5.
Further, in step S4, a freshwater ecological system stability evaluation model is constructed based on a maximum flow principle, wherein the maximum flow principle means that an open complex system far from equilibrium always searches for an optimization process, so that the generalized flow obtained by the open complex system under a given constraint condition or cost reaches a maximum value; the specific process is as follows:
step S41, the aquatic organism and the water environment factor index are the driving force of the evolution of the freshwater ecological system, the driving force of the evolution of the freshwater ecological system indicates the capability of the aquatic organism and the water environment factor index to accept generalized information flow, and the form of the written vector is x= (x) 1 , x 2 , ……, x n ) The volume unit of a gamma space formed by n freshwater ecosystem subsystems is dx= (dx) 1 , dx 2 , ……, dx n ) The method comprises the steps of carrying out a first treatment on the surface of the The generalized flux function calculation of the freshwater ecological system is shown in a formula (4);
（4）；
wherein J (x) represents generalized entropy, eta and eta obtained by the volume unit at the moment tAs coupling coefficient, i, j, k, l is four indexes of aquatic organism and water environment factor respectively, and is->And->Is an exponential term, defined as a potential function, byAnd->Reflecting the characteristic of the comprehensive performance evolution of the freshwater ecological system; x is x i , x j , x k And x l Normalized values of the aquatic organism and the water environment factor indexes i, j, k and l are respectively obtained;
the average generalized entropy of all microscopic states at the moment t is shown in a formula (5);
（5）；
wherein,representing general characteristics of information entropy of the freshwater ecosystem, wherein rho (x, t) is a time-varying probability density function of a random sequence set of aquatic organism and environmental factor indexes in the freshwater ecosystem at the moment t, and meets the requirement of；
Step S42, the generalized entropy J (x) obtained by the volume unit at the moment t of the freshwater ecosystem has no boundary, and constraint is needed to define the freshwater ecosystem; the constraint condition is converted into a first-order momentum equation to a fourth-order momentum equation, and the formula (6) is as follows:
（6）；
wherein f 1 For the first order momentum equation, f 2 Is a second order momentum equation, f 3 Is a third order momentum equation, f 4 In the form of a fourth order momentum equation,< >representing a statistical average;wherein more than four interactions between aquatic organisms and water environment factor indexes must exist in the freshwater ecosystem;
step S43, optimizing the freshwater ecological system, obtaining the maximum generalized entropy under the given constraint condition, and maximizing the formula (5) by utilizing Lagrangian multipliers under the given constraint condition described by the formula (6), wherein the formula is specifically expressed as the formula (7):
（7）；
where ρ is the probability density, exp () represents an exponential function based on the natural constant e,obtained through Lagrangian optimization;
the potential function ensures the stability of the freshwater ecosystem mode, and is expressed as:
（8）；
wherein the potential functionRepresents the basic characteristics of the evolution of the freshwater ecosystem, x represents the random sequence of the aquatic organism and environmental factor indexes in the freshwater ecosystem, and +.>And->Obtained by Lagrangian optimization, +.>Is a parameter; by generalized potential function->Theoretical analysis of the formation and evolution of the river ecosystem, the nature of which is defined by the parameter +.>To determine, parameter->Directly by parameters that regulate the micro-kinetic rules of the information interaction;
performing translation transformation on the formula (8) and diagonalizing transformation on the transformed constant term matrix to obtain a formula (9):
（9）；
wherein,is the stability entropy value of the freshwater ecological system, and is->Formed by interaction and connection of various indexes, is a key parameter for evaluating the steady state of the freshwater ecosystem, a i Indicating index x i Corresponding connection weight, x i The normalized value of the index i of the aquatic organism and the water environment factor is obtained;
according to the relation between the potential function equation and the dynamic evolution equation, a random evolution equation of the ordered mode of the freshwater ecological system is derived and expressed as a formula (10):
（10）；
wherein,representation ofThe first derivative with respect to time, lambda is defined by a i The eigenvalues that make up the matrix,is a nonlinear interaction term reflecting the interaction between indexes, F (t) is randomAn item.
Furthermore, the self-organizing map network in step S5 is an unsupervised learning neural network model, which has the capability of processing complex nonlinear problems, and simulates or learns a sample space or an external unknown environment.
Further, in step S5, the self-organizing map network simulation is applied to the index preprocessing data set after the homogenization treatment in step S3, which specifically includes: the data after normalization processing is put into a self-organizing map network of MATLAB 2018 for simulation, the training step length is set to 300, and index weight and entropy value representing the steady level of the freshwater ecosystem are obtained through simulation.
The beneficial effects of the invention are as follows: (1) The stability evaluation of the freshwater ecological system is carried out by depending on the data of the aquatic organisms and the water environment factors, and the importance of the aquatic organisms and the water environment factors is considered, and the interaction relation among the indexes is also related; the method is characterized in that a freshwater ecological system stability evaluation model is built based on the maximum flow principle, and a self-organizing mapping network is adopted for simulation, so that the evolution trend of the freshwater ecological system stability in different time and space can be intuitively compared, and key factors for driving the freshwater ecological system to evolve can be judged through index weights.
(2) According to the method, aquatic organisms and water environment factors are combined, a freshwater ecological system is analyzed as a dynamic whole, a single causal relationship is weakened, and an obtained result is likely to be more similar to the essence. The method provided by the invention has strong operability, can rapidly, sensitively, accurately, comprehensively and objectively reflect the stability condition of the freshwater ecological system, and can be used for ascertaining key driving factors, thereby providing important theoretical basis for the protection and repair measures of the freshwater ecological system.
Drawings
FIG. 1 is a flow chart for evaluating the stability of a freshwater ecosystem;
FIG. 2 is a configuration diagram of ecological systems of different river reach waters of yellow river main stream;
FIG. 3 is a weight graph of each index in the water ecosystem of different river sections of yellow river main stream;
fig. 4 is a graph of the evaluation results of the stability of the water ecosystem of different river sections of yellow river dry stream.
Detailed Description
The invention is further described in connection with the following detailed description, in order to make the technical means, the creation characteristics, the achievement of the purpose and the effect of the invention easy to understand. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1, a method for evaluating the stability of a freshwater ecosystem, comprising the steps of:
step S1, aiming at a selected research area, aquatic organism data and water environment factor data are collected through field monitoring and indoor experiments, and finally an aquatic organism data and water environment factor data index original data set of the research area is formed;
step S2, calculating Shannon-wiener diversity index of each aquatic organism through the aquatic organism data of the index original data set in the step S1, and performing multiple collinearity diagnosis on the water environment factor data of the index original data set; the processed aquatic organism data and the processed water environment factor data form an index pretreatment data set;
step S3, carrying out homogenization treatment on the index pretreatment data set in the step S2;
s4, constructing a freshwater ecological system stability evaluation model based on a maximum flow principle;
step S5, the index pretreatment data set after the homogenization treatment in the step S3 is simulated by using a self-organizing map network to obtain an evaluation model of the stability of the freshwater ecosystem in the step S4;
and S6, inputting the entropy value and the index weight obtained by simulation in the step S5 into an Excel table, evaluating the stability of the freshwater ecological system through analysis of the entropy value, and identifying the driving factors of the stability of the freshwater ecological system through analysis of the index weight.
Examples:
according to the invention, aquatic organisms and water environment factors are taken as basic data, a stability evaluation model is constructed based on a maximum flow principle to evaluate the stability of the freshwater ecological system, and the water ecological systems of different river sections of the yellow river main flow are taken as research objects to perform evaluation.
44 sampling sections are distributed from a yellow river dry stream source region to a sea entrance, wherein 26 natural river sections and 6 typical reservoirs (Dragon-sheep gorge, liu-family gorge, bronze gorge, wanjai village, sanjia gorge and Surge-bottom water reservoirs) are distributed from the yellow river dry stream source region to the sea entrance, and each reservoir comprises three sampling sections which are distributed in a reservoir head, a reservoir tail respectively. The water environment factor of the yellow river dry stream is measured and the aquatic organism sample is collected on the 44 sampling sections.
The total of 17 water environmental factors include Water Temperature (WT), conductivity (Cond), dissolved Oxygen (DO), pH, oxidation-reduction potential (ORP), total Dissolved Solids (TDS), flow rate (V), turbidity (Tur), chemical Oxygen Demand (COD), total Phosphorus (TP), total Dissolved Phosphorus (TDP), phosphorus orthophosphate, total Nitrogen (TN), ammonia nitrogen, nitrate nitrogen, nitrite nitrogen, and Total Dissolved Nitrogen (TDN).
Aquatic organism samples total 3 species including phytoplankton (FYZW), zooplankton (FYDW) and benthonic animals (DQDW).
And (3) adopting multiple collinearity diagnosis on the water environment factors, sequentially deleting the largest variables until all variables are smaller than 5, and adding 12 residual water environment factors, wherein the total water environment factors comprise Water Temperature (WT), conductivity (Cond), dissolved Oxygen (DO), pH, oxidation-reduction potential (ORP), flow rate (V), turbidity (Tur), chemical Oxygen Demand (COD), total Phosphorus (TP), total Dissolved Phosphorus (TDP), nitrite nitrogen and Total Dissolved Nitrogen (TDN).
Shannon-wiener diversity index was calculated for 3 aquatic organisms.
The 3 aquatic organisms Shannon-Winner diversity index and 12 water environment factor data are normalized, and the results are shown in Table 1:
TABLE 1 normalization processing results of yellow river dry flow aquatic organisms and water environment factor indexes
The normalized data of each index is manufactured into radar graphs (see fig. 2) according to different river segments, and in each radar graph, the characteristic of a river ecosystem network is represented in a line frame, and the characteristic is generated by aquatic organisms and water environment factor indexes. They are the flow mode of generalized information flux, and more intuitively and vividly express the spatial expansion process of the freshwater ecosystem structure.
Then the normalized data of each index is imported into the self-organizing map network of MATLAB 2018 according to different river segments for simulation, and finally index weight a is obtained through simulation ij (see FIG. 3) and entropy values representing steady levels of the freshwater ecosystem(see FIG. 4).
Wherein the index weight reflects competition and cooperation among the indexes in the freshwater ecosystem. They reflect the influence of the index on the entropy value. The greater the weight of the index, the greater the impact of the index on the entropy value.
As can be seen from FIG. 3, the indexes with weights greater than 0.6 in the water ecosystem of four river segments of yellow river comprise Cond, V, tur and TDP. The average of these four index weights is 0.787,0.706,0.775 and 0.732, respectively.
From this, cond has the greatest effect on the stability of yellow river water ecosystem, followed by Tur, TDP and V.
As can be seen from fig. 4, the yellow river dry running water ecosystem self-organizes from 0 to 130 steps, resulting in an oscillating process under environmental influence. When the step number exceeds 130, the dominant modes of the yellow river main stream water ecological systems of different river segments are kept unchanged along with the increase of the step number, so that the simulation result is accurate and reliable.
Entropy of stability of yellow river source region to downstream water ecosystem6.864, 5.855, 5.282 and 3.939, respectively.
It can be seen that the yellow river source zone water ecological system has the best stability, and the downstream and upstream are the worst.
In conclusion, the freshwater ecological system stability assessment method constructed based on the maximum flow principle can be well applied to the freshwater ecological system stability assessment based on the aquatic organism and water environment factor data.
The foregoing description is only illustrative of the present invention and is not intended to limit the scope of the invention, and all equivalent structures or equivalent processes or direct or indirect application in other related arts are included in the scope of the present invention.

Claims (7)

1. A method for assessing the stability of a freshwater ecosystem, characterized by: the method comprises the following steps:
step S1, aiming at a selected research area, aquatic organism data and water environment factor data are collected through field monitoring and indoor experiments, and finally an aquatic organism data and water environment factor data index original data set of the research area is formed;
step S2, calculating Shannon-wiener diversity index of each aquatic organism through the aquatic organism data of the index original data set in the step S1, and performing multiple collinearity diagnosis on the water environment factor data of the index original data set; the processed aquatic organism data and the processed water environment factor data form an index pretreatment data set;
step S3, carrying out homogenization treatment on the index pretreatment data set in the step S2;
s4, constructing a freshwater ecological system stability evaluation model based on a maximum flow principle;
step S5, the index pretreatment data set after the homogenization treatment in the step S3 is simulated by using a self-organizing map network to obtain an evaluation model of the stability of the freshwater ecosystem in the step S4;
s6, inputting the entropy value and the index weight obtained by simulation in the step S5 into an Excel table, evaluating the stability of the freshwater ecological system through analysis of the entropy value, and identifying driving factors of the stability of the freshwater ecological system through analysis of the index weight;
in the step S3, carrying out homogenization treatment on the index pretreatment data set in the step S2; the method comprises the following steps:
the stability of the freshwater ecological system is improved along with the increase of index values of aquatic organisms and water environment factors, and the pre-normalization treatment is carried out as shown in a formula (1);
（1）；
wherein o represents the o-th index, b o A unification value, x, representing the o index o Represents the measured value, x, of the o index omin Represents the minimum value of the o index, x omax Represents the maximum value of the o index;
the stability of the freshwater ecological system is reduced along with the increase of index values of aquatic organisms and water environment factors, and the pre-normalization treatment is carried out as shown in a formula (2);
（2）。
2. a method for assessing the stability of a freshwater ecosystem according to claim 1, wherein: the aquatic life data includes: phytoplankton, zooplankton, benthonic animals and fish; the water environment factor data includes total nitrogen, total phosphorus, chemical oxygen demand, turbidity and pH.
3. A method for assessing the stability of a freshwater ecosystem according to claim 2, wherein: step S2, shannon-Winner diversity index of aquatic organisms is shown as a formula (3):
（3）；
wherein,represents Shannon-Winner diversity index, aRepresents species, S represents the total number of individuals of all species in the community,/->Is the percentage of the a-th species to the total number of individuals.
4. A method for assessing the stability of a freshwater ecosystem according to claim 3, wherein: and S2, performing multiple collinearity diagnosis by adopting a variance expansion factor method, and deleting the variable with the maximum variance expansion factor method in sequence, namely deleting the collinearity water environment factors until all the water environment factors of the variance expansion factor method are smaller than 5.
5. The method for assessing the stability of a freshwater ecosystem of claim 4, wherein:
in the step S4, a freshwater ecological system stability evaluation model is built based on a maximum flow principle, wherein the maximum flow principle refers to that an open complex system far away from equilibrium always searches for an optimization process, so that generalized flow obtained by the open complex system under given constraint conditions or cost reaches the maximum value; the specific process is as follows:
step S41, the aquatic organism and the water environment factor index are driving forces of the freshwater ecosystem evolution, and the written vector is in the form of x= (x) 1 , x 2 , ……, x n ) The volume unit of a gamma space formed by n freshwater ecosystem subsystems is dx= (dx) 1 , dx 2 , ……, dx n ) The method comprises the steps of carrying out a first treatment on the surface of the The generalized flux function calculation of the freshwater ecological system is shown in a formula (4);
（4）；
wherein J (x) represents generalized entropy, eta and eta obtained by the volume unit at the moment tFor coupling coefficient, i, j, k, l is water respectivelyFour indexes of biological and water environment factors, < ->And->Is an exponential term, defined as a potential function, by +.>Andreflecting the characteristic of the comprehensive performance evolution of the freshwater ecological system; x is x i , x j , x k And x l Normalized values of the aquatic organism and the water environment factor indexes i, j, k and l are respectively obtained;
the average generalized entropy of all microscopic states at the moment t is shown in a formula (5);
（5）；
wherein,representing general characteristics of information entropy of the freshwater ecosystem, wherein rho (x, t) is a time-varying probability density function of a random sequence set of aquatic organism and environmental factor indexes in the freshwater ecosystem at the moment t, and the method satisfies>；
Step S42, the generalized entropy J (x) obtained by the volume unit at the moment t of the freshwater ecosystem has no boundary, and constraint is needed to define the freshwater ecosystem; the constraint condition is converted into a first-order momentum equation to a fourth-order momentum equation, and the formula (6) is as follows:
（6）；
wherein f 1 For the first order momentum equation, f 2 Is a second order momentum equation, f 3 Is a third order momentum equation, f 4 In the form of a fourth order momentum equation,< >representing a statistical average;
step S43, optimizing the freshwater ecological system, obtaining the maximum generalized entropy under the given constraint condition, and maximizing the formula (5) by utilizing Lagrangian multipliers under the given constraint condition described by the formula (6), wherein the formula is specifically expressed as the formula (7):
（7）；
where ρ is the probability density, exp () represents an exponential function based on the natural constant e,obtained through Lagrangian optimization;
the potential function ensures the stability of the freshwater ecosystem mode, and is expressed as:
（8）；
wherein the potential functionRepresents the basic characteristics of the evolution of the freshwater ecosystem, x represents the random sequence of the aquatic organism and environmental factor indexes in the freshwater ecosystem, and +.>And->Obtained by Lagrangian optimization, +.>Is a parameter;
performing translation transformation on the formula (8) and diagonalizing transformation on the transformed constant term matrix to obtain a formula (9):
（9）；
wherein,is the stability entropy value of the fresh water ecological system, a i Indicating index x i Corresponding connection weight, x i The normalized value of the index i of the aquatic organism and the water environment factor is obtained;
according to the relation between the potential function equation and the dynamic evolution equation, a random evolution equation of the ordered mode of the freshwater ecological system is derived and expressed as a formula (10):
（10）；
wherein,representation->The first derivative with respect to time, lambda is defined by a i Eigenvalues of the constituent matrix->Is a nonlinear interaction term reflecting the interaction between indices, and F (t) is a random term.
6. A method for assessing the stability of a freshwater ecosystem according to claim 5, wherein: in step S5, the self-organizing map network is an unsupervised learning neural network model, which has the capability of processing complex nonlinear problems, and simulates or learns a sample space or an external unknown environment.
7. The method for assessing the stability of a freshwater ecosystem of claim 6, wherein: in step S5, the index preprocessing data set after the homogenization treatment in step S3 is simulated by using a self-organizing map network, which specifically includes: the data after normalization processing is put into a self-organizing map network of MATLAB 2018 for simulation, the training step length is set to 300, and index weight and entropy value representing the steady level of the freshwater ecosystem are obtained through simulation.
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