Method and device for evaluating stability of water ecological environment based on community diversity

Abstract

The invention discloses a community diversity-based water ecological environment stability evaluation method and device, and belongs to the technical field of water ecological environment monitoring and evaluation. The method comprises the steps of collecting environmental parameters and community data of a plurality of sampling points of a target water area; respectively calculating an environment distance matrix and a community dissimilarity matrix between the samples; constructing a prediction model taking the environment distance as an independent variable and the community dissimilarity as a dependent variable; calculating an expected community dissimilarity of a target sample by using the prediction model, and comparing the expected community dissimilarity with a measured value; and quantifying the ecological stability of the water area of the target sample according to the deviation. Based on the theory of replacing time with space, objective correlation between environmental pressure and community response is established through a zero model, and the problems that traditional stability evaluation highly depends on long-time-sequence monitoring data, the evaluation period is long, and cross-point comparison is difficult are solved. The method is suitable for rapid evaluation in a single sampling scene, the evaluation result is objective, and the method can be widely applied to ecological health evaluation and pollution early warning of rivers, lakes, reservoirs and other water areas.

Description

Technical Field

[0001] This invention relates to the field of water ecological environment monitoring and evaluation technology, specifically a method and device for evaluating the stability of water ecological environment based on community diversity, applicable to ecological health assessment and pollution early warning of rivers, lakes, reservoirs and other water bodies. Background Technology

[0002] Aquatic ecosystems are a core component of the Earth's life support systems, directly impacting drinking water safety, biodiversity conservation, and climate regulation. With accelerated industrialization and urbanization, water pollution, eutrophication, and habitat fragmentation are becoming increasingly severe problems. Rapid and accurate assessment of aquatic ecosystem stability has become a critical technological requirement for achieving precise water management and promoting ecological civilization.

[0003] Currently, the methods for quantifying aquatic ecological stability are mainly divided into three categories: environmental parameter indexing, biomarker methods, and community time series analysis. (1) Environmental parameter indexing, taking the WQI water quality index calculation as an example, relies on physicochemical indicators such as pH and dissolved oxygen, but cannot reflect the response of biological communities to the surrounding habitat. (2) Biomarker methods, such as fish diversity surveys, require long-term species surveys, which are costly and have poor timeliness. Moreover, the results obtained can only reflect the diversity changes of the communities of interest, without comprehensively considering the differences in habitat conditions. (3) Community time series analysis can calculate the changes in α-diversity and β-diversity of communities through multiple sampling and detection, thereby quantifying the changes in diversity over time and reflecting the stability of different regions at different time points. However, its focus is on the stability fluctuations of a single point on a continuous time scale, and it cannot directly compare the stability differences between different sampling points. Therefore, it cannot propose targeted environmental protection and ecological restoration strategies based on the stability differences of different spaces and locations.

[0004] To achieve stability evaluation under a single sampling, the core is to: (1) infer the stability of the community through the spatial heterogeneity of the community environment, for example, by adopting the hypothesis of "space replacing time" in ecology; (2) conduct an objective analysis of the relationship between environmental pressure and community structure response in a fixed process, avoiding subjective threshold setting.

[0005] Based on the ecological definition, a community encompasses a population of different species and each individual within it, making it suitable as a subject for responding to environmental stress. When environmental stress exceeds the system's tolerable fluctuation threshold, the community structure will deviate from the structure corresponding to the current environment, i.e., deviate from the expected value.

[0006] Therefore, this invention constructs a null model of community structure corresponding to environmental conditions to simulate the differences in community distance caused by variations in different environmental factors. It then uses the deviation between the actually observed community diversity and the predicted value as a method for quantifying stability. Developing this method for evaluating aquatic ecological stability, which integrates environmental parameters and community synergistic analysis and requires only a single sampling, has significant application value for the rapid analysis of aquatic environmental ecological stability and can also provide a standardized tool for evaluating the effectiveness of ecological restoration projects. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of current technologies for evaluating the stability of aquatic ecological environments. Based on the diversity changes in community composition structure, this invention proposes a method and device for evaluating the ecological stability of aquatic environments based on zero-model comparison. This addresses the dependence on time series in traditional methods and avoids the bias introduced by the subjectivity of threshold setting.

[0008] A method for evaluating the stability of aquatic ecological environment based on community diversity includes the following steps:

[0009] S1. Collect environmental parameter data and community data from multiple sampling points in the target water area;

[0010] S2. Calculate the environmental distance matrix between samples based on the environmental parameter data;

[0011] S3. Calculate the community dissimilarity matrix between samples based on the community data;

[0012] S4. Using the environmental distance matrix as the independent variable and the community dissimilarity matrix as the dependent variable, construct a prediction model, and use the prediction model to calculate the expected community dissimilarity of the target sample;

[0013] S5. For the target sample, compare the expected community dissimilarity with the actual observed community dissimilarity, and quantify the ecological stability of the water area where the target sample is located based on the comparison results.

[0014] Furthermore, the environmental parameter data includes: water temperature, pH value, dissolved oxygen, nutrient concentration, pollutant concentration, and comprehensive water quality indicators. The nutrient concentration includes the concentrations of total nitrogen, total phosphorus, nitrate nitrogen, ammonia nitrogen, and nitrite nitrogen. The pollutant concentration includes the concentrations of various heavy metals, organic matter, and new pollutants. The comprehensive water quality indicators include chemical oxygen demand (COD) and biochemical oxygen demand (BOD). The community data includes relative abundance information of species, i.e., different species classifications and population sizes. The community data is obtained according to the different characteristics of organisms. For aquatic planktonic and benthic organisms, it is obtained through microscopic counting and sieving and morphological identification methods. For fish, it is obtained through sonar detection. For microorganisms in soil and sediment, it is obtained through environmental DNA, high-throughput sequencing, or metagenomic analysis. For algae and phytoplankton, it is obtained through underwater in-situ imaging and algae artificial intelligence analysis.

[0015] Furthermore, the calculation method of the environmental distance matrix includes: standardizing the environmental parameter data and calculating the distance between samples using Euclidean distance or Manhattan distance algorithms. The standardization process includes scaling to the [0,1] interval using Min-max normalization and calculating using logarithmic normalization. The calculation method of the community dissimilarity matrix includes: calculating the community dissimilarity between samples based on one of the following algorithms: Bray-Curtis dissimilarity, Jaccard dissimilarity, Sørensen dissimilarity, or Unifrac dissimilarity.

[0016] Furthermore, the method for constructing the prediction model in step S4 includes: establishing a quantitative relationship between environmental distance and community dissimilarity using at least one of linear regression model, generalized linear model, generalized dissimilarity model, random forest or XGBoost.

[0017] Furthermore, step S5 specifically includes:

[0018] Based on the expected community dissimilarity of the target sample and the actual observed community dissimilarity, the deviation between the two is calculated, where the expected community dissimilarity is the expected value and the actual observed community dissimilarity is the measured value.

[0019] The ecological stability quantification value is determined based on the ratio between the deviation and the expected value, and the quantification value ranges from [-1, 1].

[0020] If the measured value is greater than the expected value, the ecological stability of the water body is determined to be weak; if the measured value is less than the expected value, the ecological stability of the water body is determined to be strong.

[0021] The measured value is obtained by calculating the average community dissimilarity between the sample and all other samples.

[0022] A device for evaluating the stability of aquatic ecological environment based on community diversity, applied to the method described above, the device comprising:

[0023] The data acquisition module is used to collect environmental parameter data and community data from multiple sampling points in the target water area;

[0024] The environmental distance calculation module is used to calculate the environmental distance matrix between samples based on environmental parameter data;

[0025] The community dissimilarity calculation module is used to calculate the community dissimilarity matrix between samples based on community data.

[0026] The model building module is used to build a prediction model with the environmental distance matrix as the independent variable and the community dissimilarity matrix as the dependent variable.

[0027] The prediction module is used to calculate the expected community dissimilarity of the target sample using the prediction model.

[0028] The stability quantification module is used to compare the expected community dissimilarity with the actual observed community dissimilarity for a target sample, and quantify the ecological stability of the water area where the target sample is located based on the comparison results.

[0029] Furthermore, the environmental distance calculation module includes a standardization unit and a distance calculation unit, which are used to standardize environmental parameter data and calculate the environmental distance between samples.

[0030] Furthermore, the community dissimilarity calculation module uses at least one of the following algorithms to calculate community dissimilarity: Bray-Curtis dissimilarity, Jaccard dissimilarity, Sørensen dissimilarity, or Unifrac dissimilarity.

[0031] Furthermore, the community dissimilarity calculation module uses at least one of the following algorithms to calculate community dissimilarity: Bray-Curtis dissimilarity, Jaccard dissimilarity, Sørensen dissimilarity, or Unifrac dissimilarity.

[0032] Furthermore, the stability quantization module is specifically used for:

[0033] Based on the expected community dissimilarity of the target sample and the actual observed community dissimilarity, the deviation between the two is calculated, where the expected community dissimilarity is the expected value and the actual observed community dissimilarity is the measured value.

[0034] The ecological stability quantification value is determined based on the ratio between the deviation and the expected value, and the quantification value ranges from [-1, 1].

[0035] If the measured value is greater than the expected value, the ecological stability of the water body is determined to be weak; if the measured value is less than the expected value, the ecological stability of the water body is determined to be strong.

[0036] The measured value is obtained by calculating the average community dissimilarity between the sample and all other samples.

[0037] The method for evaluating the stability of aquatic ecological environment based on community diversity provided by this invention calculates the diversity and dissimilarity of communities based on their response characteristics to environmental changes. It uses the idea of ​​a "zero model" by comparing expected values ​​with measured values, providing an evaluation method for the stability of aquatic ecological environment that does not require historical data reference. It has ecological and statistical rationality and can solve the problem of lack of historical data in actual monitoring. Attached Figure Description

[0038] Figure 1 This is a flowchart illustrating the method for evaluating the stability of aquatic ecological environment based on community diversity according to the present invention. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] Please see Figure 1 This invention provides a method for evaluating the stability of aquatic ecological environment based on community diversity, comprising the following steps:

[0041] The first step is to collect environmental parameter data and community data from multiple sampling points in the target water area;

[0042] This embodiment uses environmental parameter data measured in the field using the `varechem` dataset (provided in the `vegan` package of the R language) and community data measured in the field using `varespec` as examples to demonstrate the practical application of this calculation method. These two sets of datasets are widely used for the calculation and measurement of ecological indicators due to their representativeness and reproducibility. The data they provide can serve as... Figure 1The data sources for the first two steps are as follows: the varespec dataset contains vegetation and environmental conditions (concentrations of N, P, K, Ca, Mg, S, Al, Fe, Mn, Zn, Mo, proportion of bare soil, humus thickness, soil pH, etc.) for 24 soil samples, and the varespec community data contains the number of 44 representative species at 24 sites.

[0043] The second step is to calculate the environmental distance matrix between samples based on the environmental parameter data.

[0044] Specifically, the environmental parameter data is standardized based on its minimum and maximum values ​​(using the `normalize` function in the `BBmisc` package, with the `method` parameter set to "range" to indicate linear scaling of the interval as required, `margin` set to 2 to indicate standardization of each column, and `range` set to default, scaling proportionally to the [0, 1] interval). Finally, the environmental distance is calculated based on the standardized environmental variables. In this example, the `dist` function is used to calculate the Euclidean distance; other distance calculation methods can also be used as needed.

[0045] The third step is to calculate the community dissimilarity matrix between samples based on the community data. Specifically, community dissimilarity is calculated by the difference in the number of species at different locations. In this example, the `vegdist` function is used to calculate the Bray-Curtis dissimilarity. Other diversity calculation methods, such as Manhattan, Euclidean, and Jaccard distances, can also be used as needed.

[0046] The fourth step involves constructing a prediction model using the environmental distance matrix as the independent variable and the community dissimilarity matrix as the dependent variable, and then using this model to calculate the expected community dissimilarity of the target sample. In this embodiment, linear models (LM), multiple linear regression (MLR), generalized linear models (GLM), and generalized dissimilarity models (GDM) are employed. Other machine learning models, such as random forests and XGBoost, can also be used to establish the correlation between environmental distance and community dissimilarity.

[0047] Fifth, for the target sample, the expected community dissimilarity is compared with the actual observed community dissimilarity, and the ecological stability of the water area where the target sample is located is quantified based on the comparison results.

[0048] By comparing the diversity (dissimilarity) of communities predicted by the model with that of actually observed communities, the stability of communities in a unified environment is quantified using a null model approach. The algorithm for quantifying stability should be consistent and reasonable. Consistency means that for the same expected and measured data, the returned stability values ​​are the same; reasonableness means that the trend of stability value changes is consistent with the actual ecological conditions. For example, when the actual dissimilarity continuously deviates from the expected value, the stability index should be able to sensitively reflect its changing trend and degree, and be comparable across similar different water bodies or different spatiotemporal scales.

[0049] Specifically, based on the expected community dissimilarity of the target sample and the actual observed community dissimilarity, the deviation between the two is calculated, where the expected community dissimilarity is the expected value and the actual observed community dissimilarity is the measured value.

[0050] The ecological stability quantification value is determined based on the ratio between the deviation and the expected value, and the quantification value ranges from [-1, 1].

[0051] If the measured value is greater than the expected value, the ecological stability of the water body is determined to be weak; if the measured value is less than the expected value, the ecological stability of the water body is determined to be strong.

[0052] The measured value was obtained by calculating the average community dissimilarity between the sample and all other samples.

[0053] The specific calculation method for the ecological stability quantification value is as follows:

[0054] If the measured value is greater than the expected value, then the stability quantification value = (expected value - measured value) / (1 - expected value);

[0055] If the measured value is less than or equal to the expected value, then the stability quantification value = (expected value - measured value) / expected value.

[0056] Table 1 quantifies the stability assessment of the 24 input soil samples using different models in this embodiment. The stability values ​​are standardized to the range of [-1, 1]. The lower the stability, the weaker the resistance to disturbance; the higher the stability, the smaller the deviation of the community from the target area after disturbance.

[0057] In the embodiment, except for a few points, the output results of different models all show similar trends (for example, point 28 has lower stability and point 11 has higher stability). Furthermore, by performing Spearman rank-sum correlation tests on the quantization results, the test results in Table 2 can be obtained. Multiple models mutually verify the reliability of this quantization method.

[0058] Table 1. Quantification results of different models on community stability

[0059]

[0060] Table 2 Spearman correlation coefficients ρ and p-values ​​among different models

[0061]

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] 1. Breaking the dependence on time-series data: Based on the theory of "space replacing time", this invention transforms spatial heterogeneity into a stability evaluation basis by constructing a predictive model between environmental distance and community dissimilarity. This completely solves the problem of traditional methods relying on long-term time-series monitoring data and having long evaluation cycles, and enables the completion of aquatic ecological stability assessment with a single sampling.

[0064] 2. Objective quantification to avoid subjective bias: The zero-model comparison approach is adopted, and the prediction benchmark is established by using the objective correlation between environmental parameters and community diversity. The deviation between the measured value and the expected value is used as a stability indicator, which avoids the subjectivity brought about by traditional threshold setting or expert scoring. The evaluation results are more statistically reasonable and repeatable.

[0065] 3. Supports cross-site comparison: This invention does not rely on the temporal changes of a single site, but is based on a unified environment-community relationship model, which can make horizontal comparisons of the stability of different sampling points, providing a scientific basis for ecological health classification, pollution hotspot identification and remediation priority zone delineation at the watershed scale.

[0066] 4. Strong comprehensive ecological response: With community diversity (β diversity) as the core indicator, it comprehensively reflects the response of multiple species to environmental pressure. Compared with single physicochemical indicators or biomarkers, it can better reflect the overall stability changes of the ecosystem.

[0067] 5. Wide range of applications and high practical value: The standardized methodology can be flexibly adapted to various modeling methods such as linear regression and machine learning. It is applicable to ecological health assessment, pollution early warning, and ecological restoration effect tracking of various water bodies such as rivers, lakes, and reservoirs, providing a fast and reliable technical tool for water environment management.

[0068] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for evaluating the stability of aquatic ecological environment based on community diversity, characterized in that, Includes the following steps: S1. Collect environmental parameter data and community data from multiple sampling points in the target water area; S2. Calculate the environmental distance matrix between samples based on the environmental parameter data; S3. Calculate the community dissimilarity matrix between samples based on the community data; S4. Using the environmental distance matrix as the independent variable and the community dissimilarity matrix as the dependent variable, construct a prediction model, and use the prediction model to calculate the expected community dissimilarity of the target sample; S5. For the target sample, compare the expected community dissimilarity with the actual observed community dissimilarity, and quantify the ecological stability of the water area where the target sample is located based on the comparison results.

2. The method according to claim 1, characterized in that, The environmental parameter data includes: water temperature, pH value, dissolved oxygen, nutrient concentration, pollutant concentration, and comprehensive water quality indicators. The nutrient concentration includes the concentrations of total nitrogen, total phosphorus, nitrate nitrogen, ammonia nitrogen, and nitrite nitrogen. The pollutant concentration includes the concentrations of various heavy metals, organic matter, and new pollutants. The comprehensive water quality indicators include chemical oxygen demand (COD) and biochemical oxygen demand (BOD). The community data includes relative abundance information of species, i.e., different species classifications and population sizes. The community data is obtained according to the different characteristics of organisms. For aquatic planktonic and benthic organisms, it is obtained through microscopic counting and sieving and morphological identification methods. For fish, it is obtained through sonar detection. For microorganisms in soil and sediment, it is obtained through environmental DNA, high-throughput sequencing, or metagenomic analysis. For algae and phytoplankton, it is obtained through underwater in-situ imaging and algae artificial intelligence analysis.

3. The method according to claim 1, characterized in that, The calculation method of the environmental distance matrix includes: standardizing the environmental parameter data and calculating the distance between samples using Euclidean distance or Manhattan distance algorithms. The standardization process includes scaling to the [0,1] interval using Min-max normalization and calculating using logarithmic normalization. The calculation method of the community dissimilarity matrix includes: calculating the community dissimilarity between samples based on one of the following algorithms: Bray-Curtis dissimilarity, Jaccard dissimilarity, Sørensen dissimilarity, or Unifrac dissimilarity.

4. The method according to claim 1, characterized in that, The method for constructing the prediction model in step S4 includes: using at least one of the following: linear regression model, generalized linear model, generalized dissimilarity model, random forest or XGBoost to establish a quantitative relationship between environmental distance and community dissimilarity.

5. The method according to claim 1, characterized in that, Step S5 specifically includes: Based on the expected community dissimilarity of the target sample and the actual observed community dissimilarity, the deviation between the two is calculated, where the expected community dissimilarity is the expected value and the actual observed community dissimilarity is the measured value. The ecological stability quantification value is determined based on the ratio between the deviation and the expected value, and the quantification value ranges from [-1, 1]. If the measured value is greater than the expected value, the ecological stability of the water body is determined to be weak; if the measured value is less than the expected value, the ecological stability of the water body is determined to be strong. The measured value was obtained by calculating the average community dissimilarity between the sample and all other samples.

6. A device for evaluating the stability of aquatic ecological environment based on community diversity, characterized in that, The apparatus, used in the method of any one of claims 1-5, comprises: The data acquisition module is used to collect environmental parameter data and community data from multiple sampling points in the target water area; The environmental distance calculation module is used to calculate the environmental distance matrix between samples based on environmental parameter data; The community dissimilarity calculation module is used to calculate the community dissimilarity matrix between samples based on community data. The model building module is used to build a prediction model with the environmental distance matrix as the independent variable and the community dissimilarity matrix as the dependent variable. The prediction module is used to calculate the expected community dissimilarity of the target sample using the prediction model. The stability quantification module is used to compare the expected community dissimilarity with the actual observed community dissimilarity for a target sample, and quantify the ecological stability of the water area where the target sample is located based on the comparison results.

7. The apparatus according to claim 6, characterized in that, The environmental distance calculation module includes a standardization unit and a distance calculation unit, which are used to standardize environmental parameter data and calculate the environmental distance between samples.

8. The apparatus according to claim 6, characterized in that, The community dissimilarity calculation module uses at least one of the following algorithms to calculate community dissimilarity: Bray-Curtis dissimilarity, Jaccard dissimilarity, Sørensen dissimilarity, or Unifrac dissimilarity.

9. The apparatus according to claim 6, characterized in that, The model building module uses at least one of the following to establish a predictive model between environmental distance and community dissimilarity: linear regression model, generalized linear model, generalized dissimilarity model, random forest, or XGBoost.

10. The apparatus according to claim 6, characterized in that, The stability quantization module is specifically used for: Based on the expected community dissimilarity of the target sample and the actual observed community dissimilarity, the deviation between the two is calculated, where the expected community dissimilarity is the expected value and the actual observed community dissimilarity is the measured value. The ecological stability quantification value is determined based on the ratio between the deviation and the expected value, and the quantification value ranges from [-1, 1]. If the measured value is greater than the expected value, the ecological stability of the water body is determined to be weak; if the measured value is less than the expected value, the ecological stability of the water body is determined to be strong. The measured value was obtained by calculating the average community dissimilarity between the sample and all other samples.

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