River ecological evaluation method and device based on screened periphytic diatom multi-parameter indexes

Abstract

The invention provides a river ecological evaluation method and device based on screened periphytic diatom multi-parameter indexes, and relates to the technical field of ecological evaluation. The method comprises the following steps: acquiring periphytic diatom data and response parameters of each diatom to environmental change, wherein the response parameters comprise species parameters and functional parameters; then different river periphytic diatom multi-parameter indexes are constructed based on the response parameters; then, based on the constructed river periphytic diatom multi-parameter index, an MMI score is calculated; and evaluating the ecological condition of the river according to the MMI score. The method further comprises the step of evaluating the advantages and disadvantages of different MMI indexes from the five aspects of accuracy, deviation degree and the like so as to select the most suitable MMI index. According to the method, the environmental change response sensitivity can be improved through the functional characters, meanwhile, multi-dimensional evaluation is achieved in combination with the species parameters and the functional parameters, natural variable interference is reduced, and the scientificity and accuracy of river ecological evaluation are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of ecological environment monitoring and assessment technology, and more specifically, to a method and apparatus for river ecological assessment based on screening multi-parameter indices of attached diatoms. Background Technology

[0002] River ecosystems are among the most important freshwater ecosystems on Earth, and their health directly impacts the sustainable use of water resources and the protection of biodiversity. In river ecological assessments, epiphytic diatoms are widely used as indicator organisms due to their high sensitivity to environmental changes and rapid response capabilities. Traditional multi-parameter indices (MMIs) based on epiphytic diatoms primarily rely on species parameters, such as the relative abundance of specific indicator taxa and genera. While these parameters can reflect certain environmental information, they have significant limitations. For example, species parameters respond relatively weakly to human disturbances, making it difficult to comprehensively capture the complex changes in environmental stress. Furthermore, the lack of unified standards for parameter selection in MMI construction means that different parameter combinations can lead to significant differences in index performance, thus affecting the accuracy and reliability of the assessment results.

[0003] Compared to species parameters, functional traits are more sensitive to environmental changes. As measurable morphological, physiological, or physical characteristics at the individual level, functional traits directly influence a species' growth, survival, and reproductive processes, reflecting its ecological, morphological, and physiological strategies, thus holding greater potential in ecological assessment. However, current technologies have not fully explored the role of functional traits in constructing ecological metrics (MMIs), especially comprehensive assessment methods combining species and functional parameters, which are still in the exploratory stage. Furthermore, the influence of natural variables (such as longitude, latitude, and river width) on ecological parameters is often insufficiently considered, which may increase the bias of MMIs and thus weaken their assessment performance.

[0004] In view of the above, this application is hereby submitted. Summary of the Invention

[0005] This invention aims to provide a method, device, equipment, and medium for river ecological assessment based on screening multi-parameter indices of attached diatoms. By integrating the advantages of species parameters, functional parameters, and all parameters, it comprehensively evaluates the performance of the index from multiple dimensions such as accuracy, bias, responsiveness, sensitivity, and correlation. This addresses the problem that existing methods lack unified parameter selection standards and that traditional methods mainly rely on species parameters while neglecting functional parameters.

[0006] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution: A river ecological assessment method based on screening multi-parameter indices of attached diatoms includes: S1, acquire data on attached diatoms and the response parameters of each diatom to environmental changes; the response parameters include species parameters and functional parameters; S2, Construct a multi-parameter index of river-borne diatoms based on the response parameters; S3, based on the constructed multi-parameter index of river-borne diatoms, calculate the MMI score; S4 evaluates the river's ecological condition based on the MMI score to select the most suitable multi-parameter index for river-borne diatoms.

[0007] Preferably, the species parameters include distinctive taxa, species richness index, Pielou evenness index, and Simpson index; The species richness index The calculation formula is: ; Where S is the number of species in the sample; The Pielou evenness index The calculation formula is: ; in, The proportion of the i-th species in the total; The formula for calculating the Simpson exponent is as follows: ; in, The Simpson index is mentioned.

[0008] The functional parameters include nitrogen autotrophic type, aerobic type, pH index, salinity index, nutrient index, humicity index, nitrogen / phosphorus indicator type, functional traits, and functional diversity index.

[0009] Preferably, the functional diversity index includes functional richness, functional evenness, and functional heterogeneity; Among them, the feature richness The calculation formula is: ; in, The ecological niche occupied by a species in a community; The absolute value of the feature; The functional uniformity The calculation formula is: ; in, Let i be the weighted evenness of species i; The functional differentiation The calculation formula is: ; in, For species and species Differences in traits between them; , Species , The relative abundance of species is the proportion of the number of individuals of a species to the total number of individuals in the community.

[0010] Preferably, the process of constructing a multi-parameter index of river-borne diatoms based on the response parameters is as follows: Multi-parameter indices of river-adherent diatoms were constructed using species parameters, functional parameters, and all parameters, respectively. All response parameters are preprocessed to filter the data; The filtered parameters are reassigned to normalize their distribution range to the 0-100 interval; for each filtered parameter, a value is assigned using the following formula based on its response to environmental stress: For cases where parameter values ​​increase with intensifying environmental stress, the following formula is used for assignment, and when... hour, ; ; Where V is the numerical value of each parameter after it has been assigned a value; For the i-th column parameter; 95 percentile for the parameter in column i; 5 percentile for the parameter in column i; For cases where the parameter value decreases as environmental stress intensifies, the following formula is used for assignment, and when... hour, ,when hour, : ; The final MMI score is obtained by weighted summing of all assigned parameters, using the following formula: ; in, MMI score; The weight of the k-th parameter; The value after assigning a value to the k-th parameter.

[0011] Preferably, the preprocessing includes: Calculate the distribution range of all parameters, and remove parameters whose distribution variation among sample points is less than the set first threshold and whose missing values ​​or zero values ​​are greater than the set second threshold; Each parameter is fitted with a random forest regression to the natural variables to eliminate the influence of the natural variables on each parameter; Perform correlation analysis on all parameters and remove variables whose correlation with other parameters is higher than the set third threshold; Analyze the inter-group differences of each parameter between the reference point and the damaged point, and remove variables with insignificant inter-group differences; After removing the data, create grouped box plots for each parameter, dividing them into a reference point group and a damaged point group, with the box range being from the 25th percentile to the 75th percentile. If the 50th percentile of one group of box plots falls within the range of another group of box plots, then that parameter is removed.

[0012] Preferably, the river's ecological condition is evaluated based on the calculated MMI score, specifically as follows: If the MMI score is higher than the 25th percentile of the MMI score at the reference point, the ecological condition of the river at the sample point is defined as excellent. If the MMI score is between the 5th and 25th percentiles of the reference point's MMI score, the river's ecological condition at the sample point is defined as good. If the score is less than the 5th percentile of the MMI score at the reference point, the river ecological condition of that sample point is defined as poor.

[0013] Preferably, it also includes evaluating the reliability and practicality of the multi-parameter index (MMI) of attached diatoms from different performance indicators; the different performance indicators include accuracy, bias, responsiveness, sensitivity, and correlation. The accuracy The formula used to measure the stability of MMI among undisturbed reference sites is as follows: ; ; in, The coefficient of variation values ​​for each MMI among the reference sites; The standard deviation of MMI between reference sites. This represents the average MMI across reference sites; The bias is the explanatory power of natural variables for the changes in MMI. Random forest modeling is performed between the MMI of the reference point and the natural variables, and the explanatory power of natural variables for the changes in MMI is characterized by the coefficient of determination of the model. The smaller the explanatory power of natural variables, the smaller the bias of MMI. The responsiveness is the absolute value of the average MMI difference between the reference point and the damaged point, and the formula is: ; in, The average value of the MMI at the reference point; This represents the average MMI at the damaged points; The value represents the responsiveness; a larger value indicates a higher responsiveness. The sensitivity The proportion of the damaged point's MMI score that is less than the 10th percentile value of the reference point's MMI score is given by the following formula: ; in, This represents the number of sample points in the damaged area whose MMI score is less than the 10th percentile of the MMI score of the reference point. This represents the total number of damaged samples. A higher value indicates higher MMI sensitivity; The correlation refers to the degree of correlation between MMI and human activity disturbance. The percentage of impervious surface area in the city is selected as the core human disturbance indicator. The relationship between MMI and the percentage of impervious surface area in the city is fitted by univariate linear regression, and the coefficient of determination of the model is used as the basis for correlation evaluation. The larger the correlation value, the more significant the linear correlation between MMI score and human disturbance.

[0014] Preferably, the distinctive categories include: Category 1: Diatom species with small cell size and slime stalks, whose numbers are decreasing with increasing environmental stress, including species of the genera *Curculigo*, *Curculigo*, *Diatomella*, *Ovalella*, *Cyclophora*, *Bridge-shaped Algae*, and *Rhystophyta*. The second category consists of diatom species that aggregate into communities, and whose numbers increase with the intensification of environmental stress. These include species from the genera *Isophyta*, *Short-slit Algae*, *Fragile Algae*, *Heterocereus*, *Heterocereus*, *Straciformis*, and *Needle Algae*. The third category consists of diatom species capable of mobility, whose numbers increase with the intensification of environmental stress, including the genera *Navicula*, *Nigeria*, *Saddleula*, and *Nigeria*.

[0015] This invention also provides a river ecological assessment device based on screening multiple parameter indices of attached diatoms, comprising: The data acquisition unit is used to acquire data on attached diatoms and response parameters of each diatom to environmental changes; the response parameters include species parameters and functional parameters. MMI building unit, used to construct a multi-parameter index of river-borne diatoms based on the response parameters; The MMI score calculation unit is used to calculate the MMI score based on the constructed multi-parameter index of river-borne diatoms; The evaluation unit is used to assess the ecological status of rivers based on MMI scores in order to select the most suitable multi-parameter index for river-borne diatoms.

[0016] The present invention also provides a river ecological assessment device based on screening multiple parameters of attached diatoms, including a processor and a memory. The memory stores a computer program that can be executed by the processor to realize the river ecological assessment method based on screening multiple parameters of attached diatoms as described above.

[0017] The present invention also provides a computer-readable storage medium storing computer-readable instructions, which, when executed by a processor of the device on which the computer-readable storage medium is located, implement the above-described method for river ecological assessment based on screening multi-parameter indices of diatoms.

[0018] In summary, compared with the prior art, the present invention has the following beneficial effects: This invention significantly improves the sensitivity of diatoms to environmental changes by introducing functional parameters (including functional traits and functional diversity indices). Functional traits directly reflect the ecological strategies of individual organisms, enabling them to more quickly capture changes in environmental stress. Simultaneously, through parameter correction and natural variable removal methods, the interference of natural environmental factors on the multi-parameter river index (MMI) is reduced, making the index more regionally adaptable. Furthermore, by combining species and functional parameters, the river's ecological status is comprehensively evaluated from multiple dimensions, avoiding the bias that may result from a single parameter. Through multi-dimensional performance evaluations including accuracy, bias, responsiveness, sensitivity, and correlation, the selected MMIs are ensured to have optimal performance and accurately reflect the river's ecological status. Finally, the river's ecological status is graded based on the MMI scores, facilitating practical application and management decisions. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained from these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating a river ecological assessment method based on screening multi-parameter indices of attached diatoms, as provided in Example 1.

[0021] Figure 2 This is a diagram showing the MMI scores of the reference point and the damaged point provided in Example 1.

[0022] Figure 3 The diagram showing the relationship between the MMI of attached diatoms based on trait parameters and environmental factors is provided in Example 1.

[0023] Figure 4 This is a schematic diagram of a river ecological assessment device based on screening multi-parameter indices of attached diatoms, as provided in Example 2.

[0024] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Detailed Implementation

[0025] 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 a part of the embodiments of the present invention, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0026] Example 1 Embodiment 1 of the present invention provides a river ecological assessment method based on screening multiple parameter indices of attached diatoms, which can be implemented by a river ecological assessment device based on screening multiple parameter indices of attached diatoms (hereinafter referred to as assessment device), specifically, executed by one or more processors within the assessment device.

[0027] In this embodiment, the evaluation device may be an electronic device equipped with a processor, which carries a computer program for the river ecological evaluation method based on screening multi-parameter indices of diatoms and the computer program can be executed, such as a computer, smartphone, smart tablet, workstation, etc., which are not limited here.

[0028] like Figure 1 As shown, a river ecological assessment method based on screening multi-parameter indices of attached diatoms includes steps S1 to S4.

[0029] S1, acquire data on attached diatoms and the response parameters of each diatom to environmental changes; the response parameters include species parameters and functional parameters.

[0030] Multiple sampling points in a watershed were selected for monitoring. Data on diatoms and natural environmental variables were collected and recorded. The corresponding parameters of each diatom in response to environmental changes were searched from existing literature.

[0031] The species parameters include distinctive taxa (obtained from existing literature), species richness index, Pielou evenness index, and Simpson index.

[0032] The featured categories include the following three types: Category 1: Diatom species with small cell size and slime stalks, whose numbers are decreasing with increasing environmental stress, including species of the genera *Curculigo*, *Curculigo*, *Diatomella*, *Ovalella*, *Cyclophora*, *Bridge-shaped Algae*, and *Rhystophyta*. The second category consists of diatom species that aggregate into communities, and whose numbers increase with the intensification of environmental stress. These include species from the genera *Isophyta*, *Short-slit Algae*, *Fragile Algae*, *Heterocereus*, *Heterocereus*, *Straciformis*, and *Needle Algae*. The third category consists of diatom species with mobility, which can escape risks and find resources more effectively. The number of diatoms increases with the intensification of environmental stress, including the genera *Neptune*, *Neptune*, *Saddle-shaped*, and *Neptune*.

[0033] The species richness index The calculation formula is: ; Where S is the number of species in the sample; The Pielou evenness index The calculation formula is: ; in, The proportion of the i-th species in the total; The formula for calculating the Simpson exponent is as follows: ; in, The Simpson index is mentioned.

[0034] The functional parameters include 10 categories. Categories 1-6 of diatom parameters are nitrogen autotrophic type, aerobic type, pH index, salinity index, nutrient index, and humic index, which can be found on the Freshwaterecology website (https: / / www.freshwaterecology.info / ).

[0035] The 7th and 8th categories of diatom parameters (nitrogen / phosphorus indicator types) are used to identify high / low nitrogen and high / low phosphorus groups by analyzing the indicator species of diatoms in the sample points. The 25th and 75th percentiles of TN and TP concentrations at the sampling points are used as thresholds to determine the low and high nitrogen / phosphorus status.

[0036] The functional traits of the ninth category of parameters can be obtained by searching the literature.

[0037] Category 10 is the functional diversity index, which includes functional richness, functional evenness, and functional heterogeneity.

[0038] Among them, the feature richness The calculation formula is: ; in, The ecological niche occupied by a species in a community; The absolute value of the feature.

[0039] The functional uniformity The calculation formula is: ; in, Let be the weighted evenness of species i.

[0040] The functional differentiation The calculation formula is: ; in, For species and species Differences in traits between them; , Species , The relative abundance of species is the proportion of the number of individuals of a species to the total number of individuals in the community.

[0041] The response of the above 10 parameters to environmental changes is as follows: (1) Nitrogen autotrophic type: With the increase of environmental stress, the number of nitrogen autotrophic diatoms and nitrogen heterotrophic diatoms will increase.

[0042] (2) Types of oxygen demand: With the intensification of environmental stress, the number of high oxygen demand diatoms (habitat oxygen saturation >75%) and low oxygen demand diatoms (habitat oxygen saturation <30%) both increased.

[0043] (3) pH index: With the increase of environmental stress, the number of acidophilic diatoms and alkaliphilic diatoms both increased.

[0044] (4) Salinity index: With the intensification of environmental stress, the number of freshwater diatoms (habitat salinity less than 0.2‰) and saltwater diatoms (habitat salinity greater than 0.9‰) both increased.

[0045] (5) Nutritional indicators: With the intensification of environmental stress, the number of oligotrophic diatoms decreased and the number of eutrophic diatoms increased.

[0046] (6) Humus index: With the increase of environmental stress, the number of oligopolistic diatoms decreases and the number of polypolistic diatoms increases.

[0047] (7) Nitrogen indicator type: With the increase of environmental stress, the number of high nitrogen indicator species diatoms increases and the number of low nitrogen indicator species diatoms decreases.

[0048] (8) Phosphorus indicator type: With the increase of environmental stress, the number of high phosphorus indicator diatoms increases and the number of low phosphorus indicator diatoms decreases.

[0049] (9) Functional traits: The classification of functional traits is shown in Table 1. With the intensification of environmental stress, the number of extremely small and small diatoms decreased, while the number of medium, large and extremely large diatoms increased; the number of mat, stalked, movable and attached diatoms decreased, while the number of other living diatoms increased; the number of tubular colony diatoms decreased, while the number of other growing diatoms increased; the number of high cosite groups, kinetic groups and planktonic groups of diatoms increased, while the number of low cosite groups of diatoms decreased.

[0050] (10) Biodiversity index: With the increase of environmental stress, the functional richness, functional evenness and functional differentiation of attached diatoms will decrease.

[0051] Table 1. Characteristics and classification criteria of epiphytic diatoms

[0052] S2, Construct a multi-parameter index of river-borne diatoms based on the response parameters.

[0053] Multi-parameter indices for river-borne diatoms were constructed using species parameters, functional parameters, and all parameters (including species and functional parameters), as detailed below: All response parameters are preprocessed to filter the data; The filtered parameters are revalued to normalize their distribution range to the 0-100 interval. For each filtered parameter, the following formula is used for value assignment based on its response to environmental stress: For cases where parameter values ​​increase with intensifying environmental stress, the following formula is used for assignment, and when... hour, ; ; Where V is the numerical value of each parameter after it has been assigned a value; For the i-th column parameter; It is the 95th percentile of the parameter in column i; The 5th percentile of the parameter in column i; For cases where the parameter value decreases as environmental stress intensifies, the following formula is used for assignment, and when... hour, ,when hour, : .

[0054] The preprocessing includes: (1) Parameter distribution range screening: Calculate the distribution range of all parameters and remove parameters whose distribution variation among sample points is less than the set first threshold (e.g., 5%) and whose missing values ​​or zero values ​​are greater than the set second threshold (e.g., 25%).

[0055] (2) Parameter correction: Each parameter is fitted with a random forest regression to natural variables (such as longitude, latitude, and river width) to eliminate the influence of natural variables on each parameter. For example, if the change in natural variables explains more than 10% of the dependent variable, the original parameter values ​​are replaced with the residuals of the model.

[0056] (3) Correlation analysis: Perform correlation analysis on all parameters and remove variables whose correlation with other parameters is higher than the set third threshold (e.g., 0.8).

[0057] (4) Intergroup difference analysis: Analyze the intergroup differences of each parameter between the reference point and the damaged point, and remove variables with insignificant intergroup differences (e.g., greater than 0.05).

[0058] (5) Discriminant analysis: After removing the parameter data, group box plots are made, which are divided into reference point group and damaged point group. The box range is from 25 percentile to 75 percentile. If the 50 percentile of a group of box plots is located within the range of another group of box plots, then this parameter is removed.

[0059] S3, based on the constructed multi-parameter index of river-borne diatoms, calculates the MMI score.

[0060] The final MMI score is obtained by weighted summing of all assigned parameters, using the following formula: ; in, MMI score; The weight of the k-th parameter; The value after assigning a value to the k-th parameter.

[0061] For example, when constructing a multi-parameter index of river-borne diatoms using species parameters, the weighted sum of all assigned species parameters is used to obtain the S-MMI score; the multi-parameter index of river-borne diatoms is constructed using functional parameters to obtain the F-MMI score; and the multi-parameter index of river-borne diatoms is constructed using all parameters to obtain the T-MMI score.

[0062] The final MMI score can be obtained by weighted summation of the S-MMI score, F-MMI score, and T-MMI score.

[0063] S4 evaluates the river's ecological condition based on the MMI score to select the most suitable multi-parameter index for river-borne diatoms.

[0064] The ecological condition of the river is evaluated based on the calculated MMI score, specifically as follows: If the MMI score is higher than the 25th percentile of the MMI score at the reference point, the ecological condition of the river at the sample point is defined as excellent. If the MMI score is between the 5th and 25th percentiles of the reference point's MMI score, the river's ecological condition at the sample point is defined as good. If the score is less than the 5th percentile of the MMI score at the reference point, the river ecological condition of that sample point is defined as poor.

[0065] In a preferred embodiment, the method further includes evaluating the reliability and practicality of the multi-parameter index (MMI) of attached diatoms from different performance metrics; the different performance metrics include accuracy, bias, responsiveness, sensitivity, and correlation.

[0066] The accuracy The formula used to measure the stability of MMI among undisturbed reference sites is as follows: ; ; in, The coefficient of variation values ​​for each MMI among the reference sites; The standard deviation of MMI between reference sites. This represents the average MMI across reference sites.

[0067] The bias is the explanatory power of natural variables for changes in MMI. Random forest modeling is performed between the MMI of the reference point and the natural variables, and the coefficient of determination of the model characterizes the explanatory power of natural variables for changes in MMI. The smaller the explanatory power of natural variables, the smaller the bias of MMI.

[0068] The responsiveness is the absolute value of the average MMI difference between the reference point and the damaged point, and the formula is: ; in, The average value of the MMI at the reference point; This represents the average MMI at the damaged points; The value represents the responsiveness; a higher value indicates a higher responsiveness.

[0069] The sensitivity The proportion of the damaged point's MMI score that is less than the 10th percentile value of the reference point's MMI score is given by the following formula: ; in, This represents the number of sample points in the damaged area whose MMI score is less than the 10th percentile of the MMI score of the reference point. This represents the total number of damaged samples. A higher value indicates higher MMI sensitivity.

[0070] The correlation refers to the degree of correlation between MMI and human activity disturbance. The percentage of impervious surface area in the city is selected as the core human disturbance indicator. The relationship between MMI and the percentage of impervious surface area in the city is fitted by univariate linear regression, and the coefficient of determination of the model is used as the basis for correlation evaluation. The larger the correlation value, the more significant the linear correlation between MMI score and human disturbance.

[0071] Through the above steps, this invention achieves a scientific evaluation of river ecological conditions. In practical applications, multiple sampling points in a watershed are selected for monitoring, collecting data on diatoms and recording natural environmental variables. After parameter selection, MMI construction, performance comparison, and ecological condition evaluation, the ecological condition level of each sampling point is finally determined. For example, in a monitoring session, the MMI score of sampling point A was found to be 70, while the MMI score of sampling point B was 30. Through comparative analysis with a reference point, the ecological condition of sampling point A is defined as excellent, while the ecological condition of sampling point B is defined as poor. This result provides clear decision-making basis for watershed management departments and helps to formulate targeted ecological protection and restoration measures.

[0072] In a preferred embodiment, the normality and homogeneity of variance of each parameter are tested using the Shapiro-Wilk and Levene tests; for parameters that meet the requirements of normality and homogeneity of variance, Pearson correlation analysis is used for correlation analysis, and the t-test is used to test differences between groups; otherwise, Spearman correlation analysis and Wilcox rank-sum test are used for analysis. Indicator species analysis is performed by the "indicspecies" package; correlation analysis is performed by the "PerformanceAnalytics" package; the calculation of species and functional diversity is performed by the "vegan" and "mFD" packages, and one environmental factor with a correlation greater than 0.8 is removed.

[0073] Random forest was used to fit the relationship between diatom MMI and environmental factors at each sampling point. Random forest is a powerful tree model analysis method that can flexibly perform regression or classification analysis. This method explores the relationship between variables based on machine learning, does not require the data to meet a specific distribution beforehand, is insensitive to multicollinearity among independent variables, and is robust to missing or imbalanced data. It is considered one of the best algorithms currently available.

[0074] Random forests were used to identify key environmental factors influencing the MMI (Mean Influence of Microalbuminuria) of attached diatoms. Cross-validation was employed to evaluate the error of the random forest model constructed from the MMI index of each combination of environmental factors, identifying the combination of environmental factors with the greatest impact on the MMI of attached diatoms. Partial correlation analysis was then performed on these environmental factors and the MMI index of attached diatoms to study the correlation trend of the MMI of attached diatoms with environmental changes. The random forests were constructed using "RandomForest".

[0075] Based on the literature, 57 candidate parameters for attached diatoms were identified, of which 18 were species parameters and 39 were functional parameters. After screening and assignment, parameters for the MMI of attached algae were constructed. MMIs (S-MMI based on diatom species parameters, F-MMI based on diatom functional parameters, and T-MMI based on all diatom parameters) were constructed using 3 species parameters, 13 functional parameters, and all 16 parameters, respectively. The MMI scores of reference points and damaged points along a major river in a certain city are shown below. Figure 2 As shown, the MMI scores of the reference points were significantly higher than those of the damaged points. The average S-MMI, FMMI, and T-MMI of the reference points were 61.8, 70.0, and 68.5, respectively; while the average S-MMI, F-MMI, and T-MMI of the damaged points were 38.7, 39.2, and 39.1, respectively.

[0076] The performance of S-MMI, FMMI, and T-MMI was evaluated in five aspects: accuracy, bias, responsiveness, sensitivity, and correlation, as shown in Table 2. F-MMI outperformed S-MMI and T-MMI in all four aspects (accuracy, bias, responsiveness, and correlation), while T-MMI outperformed S-MMI and F-MMI in sensitivity. Therefore, overall, F-MMI, constructed based on functional parameters, performed best in river ecological assessment.

[0077] Table 2. Performance Comparison of Three MMIs

[0078] Here, we use the F-MMI to evaluate the ecological status of the city's rivers. Rivers with F-MMI scores higher than the 25th percentile (60.7) of the reference point are considered to have excellent ecological status; those with F-MMI scores between the 5th and 25th percentiles (45.8-60.7) are considered to have good ecological status; and those with scores lower than the 5th percentile (45.8) are considered to have poor ecological status. Based on the F-MMI scores of the sample points, the ecological health of the rivers at each of the nine watersheds was evaluated.

[0079] This experiment investigated F-MMI and eight environmental factors (water temperature WT, dissolved oxygen DO, flow velocity VE, pH, nitrate nitrogen). ammonium nitrogen Phosphate Permanganate index Random forest modeling was performed to select the environmental factors that have the greatest impact on the MMI of attached diatoms.

[0080] This experiment found that the F-MMI score was similar to that of phosphorus phosphate. and nitrate nitrogen Significantly correlated, they explained 32.9% of the variation in F-MMI. Partial correlation analyses were performed on these important environmental factors and the F-MMI of attached diatoms. Figure 3 As shown, overall, with the sample points and With increasing concentration, the FMMI of attached diatoms showed a decreasing trend.

[0081] Furthermore, the F-MMI based on diatom functional parameters was found to perform best in river ecological assessment. This may be because the species parameters of attached diatoms are less sensitive to human disturbance than the diatom parameters themselves. Functional traits are measurable morphological, physiological, or physical characteristics of organisms at the individual level. They can influence processes such as growth, survival, and reproduction of a species, reflecting its ecological, morphological, and physiological strategies. These traits are directly influenced by the environment.

[0082] In summary, compared with the prior art, the present invention has the following beneficial effects: This invention significantly improves the sensitivity to environmental changes by introducing functional parameters. Functional traits directly reflect the ecological strategies of individual organisms, enabling them to capture changes in environmental stress more quickly. Simultaneously, through parameter correction and natural variable removal, the interference of natural environmental factors on the MMI (Multi-parameter Index of River-borne Diatoms) is reduced, making the index more regionally adaptable.

[0083] Furthermore, by combining species and functional parameters, a comprehensive evaluation of river ecological status is conducted from multiple dimensions, avoiding the bias that may result from a single parameter. Through multi-dimensional performance evaluations including accuracy, bias, responsiveness, sensitivity, and correlation, the selected MMIs are ensured to possess optimal performance and accurately reflect the river's ecological status. Finally, the river's ecological status is graded based on the MMI scores, facilitating practical application and management decision-making. This invention provides a scientific, systematic, and efficient method for river ecological assessment, offering crucial technical support for river ecological protection and restoration.

[0084] Example 2 like Figure 4 As shown, the second embodiment of the present invention also provides a river ecological assessment device based on screening multiple parameter indices of attached diatoms, comprising: The data acquisition unit is used to acquire data on attached diatoms and response parameters of each diatom to environmental changes; the response parameters include species parameters and functional parameters. MMI building unit, used to construct a multi-parameter index of river-borne diatoms based on the response parameters; The MMI score calculation unit is used to calculate the MMI score based on the constructed multi-parameter index of river-borne diatoms; The evaluation unit is used to assess the ecological status of rivers based on MMI scores in order to select the most suitable multi-parameter index for river-borne diatoms.

[0085] Example 3 The third embodiment of the present invention also provides a river ecological assessment device based on screening multiple parameter indices of attached diatoms, which includes a memory and a processor. The memory stores a computer program, which can be executed by the processor to realize the river ecological assessment method based on screening multiple parameter indices of attached diatoms as described above.

[0086] Example 4 The fourth embodiment of the present invention also provides a computer-readable storage medium storing computer-readable instructions. When the computer-readable instructions are executed by the processor of the device where the computer-readable storage medium is located, the river ecological evaluation method based on screening diatom multi-parameter indices as described above is implemented.

[0087] In the several embodiments provided in this invention, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus and method embodiments described above are merely illustrative. For example, the flowcharts in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the invention. In this regard, each block in the flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0088] In addition, the functional modules in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0089] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, electronic device, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks. It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. In the absence of further restrictions, an element defined by the phrase "comprising a..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0090] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0091] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0092] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0093] The use of "first" and "second" in the embodiments is merely to distinguish similar objects and does not represent a specific ordering of objects. It is understood that "first" and "second" can be interchanged in a specific order or sequence where permitted. It should be understood that the objects distinguished by "first" and "second" can be interchanged where appropriate so that the embodiments described herein can be implemented in an order other than those illustrated or described herein.

[0094] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A river ecological assessment method based on screening multi-parameter indices of attached diatoms, characterized in that, include: S1, acquire data on attached diatoms and the response parameters of each diatom to environmental changes; the response parameters include species parameters and functional parameters; S2, Construct a multi-parameter index of river-borne diatoms based on the response parameters; S3, based on the constructed multi-parameter index of river-borne diatoms, calculate the MMI score; S4 evaluates the river's ecological condition based on the MMI score to select the most suitable multi-parameter index for river-borne diatoms.

2. The river ecological assessment method based on screening multiple parameter indices of attached diatoms according to claim 1, characterized in that... The species parameters include distinctive taxa, species richness index, Pielou evenness index, and Simpson index. The species richness index The calculation formula is: ； Where S is the number of species in the sample; The Pielou evenness index The calculation formula is: ； in, The proportion of the i-th species in the total; The formula for calculating the Simpson exponent is as follows: ； in, The Simpson index is mentioned.

3. The river ecological assessment method based on screening multiple parameter indices of attached diatoms according to claim 1, characterized in that... The functional parameters include nitrogen autotrophic type, aerobic type, pH index, salinity index, nutrient index, humicity index, nitrogen / phosphorus indicator type, functional traits, and functional diversity index.

4. The river ecological assessment method based on screening multi-parameter indices of attached diatoms according to claim 3, characterized in that... The functional diversity index includes functional richness, functional evenness, and functional heterogeneity. Among them, the feature richness The calculation formula is: ； in, The ecological niche occupied by a species in a community; The absolute value of the feature; The functional uniformity The calculation formula is: ； in, Let i be the weighted evenness of species i; The functional differentiation The calculation formula is: ； in, For species and species Differences in traits between them; , Species , The relative abundance of species is the proportion of the number of individuals of a species to the total number of individuals in the community.

5. A river ecological assessment method based on screening multi-parameter indices of attached diatoms according to claim 4, characterized in that... The process of constructing a multi-parameter index of river-borne diatoms based on the aforementioned response parameters is as follows: Multi-parameter indices of river-adherent diatoms were constructed using species parameters, functional parameters, and all parameters, respectively. All parameters are preprocessed to filter the data; The filtered parameters are re-assigned values ​​to normalize their distribution range to the 0-100 interval. For each filtered parameter, the following formula is used for assignment based on its response to environmental stress: For cases where parameter values ​​increase with intensifying environmental stress, the following formula is used for assignment, and when... hour, ; ； Where V is the numerical value of each parameter after it has been assigned a value; For the i-th column parameter; It is the 95th percentile of the parameter in column i; The 5th percentile of the parameter in column i; For cases where the parameter value decreases as environmental stress intensifies, the following formula is used for assignment, and when... hour, ,when hour, : ； The final MMI score is obtained by weighted summing of all assigned parameters, using the following formula: ； in, MMI score; The weight of the k-th parameter; The value after assigning a value to the k-th parameter.

6. The river ecological assessment method based on screening multiple parameter indices of attached diatoms according to claim 5, characterized in that... The preprocessing includes: Calculate the distribution range of all parameters, and remove parameters whose distribution variation among sample points is less than the set first threshold and whose missing values ​​or zero values ​​are greater than the set second threshold; Each parameter is fitted with a random forest regression to the natural variables to eliminate the influence of the natural variables on each parameter; Perform correlation analysis on all parameters and remove variables whose correlation with other parameters is higher than the set third threshold; Analyze the inter-group differences of each parameter between the reference point and the damaged point, and remove variables with insignificant inter-group differences; After removing the data, create grouped box plots for each parameter, dividing them into a reference point group and a damaged point group, with the box range being from the 25th percentile to the 75th percentile. If the 50th percentile of one group of box plots falls within the range of another group of box plots, then that parameter is removed.

7. A river ecological assessment method based on screening multi-parameter indices of attached diatoms according to claim 5, characterized in that... The ecological condition of the river is evaluated based on the calculated MMI score, specifically as follows: If the MMI score is higher than the 25th percentile of the MMI score at the reference point, the ecological condition of the river at the sample point is defined as excellent. If the MMI score is between the 5th and 25th percentiles of the reference point's MMI score, the river's ecological condition at the sample point is defined as good. If the score is less than the 5th percentile of the MMI score at the reference point, the river ecological condition of that sample point is defined as poor.

8. A river ecological assessment method based on screening multiple parameter indices of attached diatoms according to claim 5, characterized in that... It also includes evaluating the reliability and practicality of the multi-parameter index (MMI) of attached diatoms from different performance indicators; the different performance indicators include accuracy, bias, responsiveness, sensitivity and correlation. The accuracy The formula used to measure the stability of MMI among undisturbed reference sites is as follows: ； ； in, The coefficient of variation values ​​for each MMI among the reference sites; The standard deviation of MMI between reference sites. This represents the average MMI across reference sites; The bias is the explanatory power of natural variables for the changes in MMI. Random forest modeling is performed between the MMI of the reference point and the natural variables, and the explanatory power of natural variables for the changes in MMI is characterized by the coefficient of determination of the model. The smaller the explanatory power of natural variables, the smaller the bias of MMI. The responsiveness is the absolute value of the average MMI difference between the reference point and the damaged point, and the formula is: ； in, The average value of the MMI at the reference point; This represents the average MMI at the damaged points; The value represents the responsiveness; a larger value indicates a higher responsiveness. The sensitivity The proportion of the damaged point's MMI score that is less than the 10th percentile value of the reference point's MMI score is given by the following formula: ； in, This represents the number of sample points in the damaged area whose MMI score is less than the 10th percentile of the MMI score of the reference point. This represents the total number of damaged samples. A higher value indicates higher MMI sensitivity; The correlation refers to the degree of correlation between MMI and human activity disturbance. The percentage of impervious surface area in the city is selected as the core human disturbance indicator. The relationship between MMI and the percentage of impervious surface area in the city is fitted by univariate linear regression, and the coefficient of determination of the model is used as the basis for correlation evaluation. The larger the correlation value, the more significant the linear correlation between MMI score and human disturbance.

9. A river ecological assessment method based on screening multiple parameter indices of attached diatoms according to claim 2, characterized in that... The featured categories include: Category 1: Diatom species with small cell size and slime stalks, whose numbers are decreasing with increasing environmental stress, including species of the genera *Curculigo*, *Curculigo*, *Diatomella*, *Ovalella*, *Cyclophora*, *Bridge-shaped Algae*, and *Rhystophyta*. The second category consists of diatom species that aggregate into communities, and whose numbers increase with the intensification of environmental stress. These include species from the genera *Isophyta*, *Short-slit Algae*, *Fragile Algae*, *Heterocereus*, *Heterocereus*, *Straciformis*, and *Needle Algae*. The third category consists of diatom species capable of mobility, whose numbers increase with the intensification of environmental stress, including the genera *Navicula*, *Nigeria*, *Saddleula*, and *Nigeria*.

10. A river ecological assessment device based on screening multi-parameter indices of attached diatoms, characterized in that, include: The data acquisition unit is used to acquire data on attached diatoms and response parameters of each diatom to environmental changes; the response parameters include species parameters and functional parameters. MMI building unit, used to construct a multi-parameter index of river-borne diatoms based on the response parameters; The MMI score calculation unit is used to calculate the MMI score based on the constructed multi-parameter index of river-borne diatoms; The evaluation unit is used to assess the ecological status of rivers based on MMI scores in order to select the most suitable multi-parameter index for river-borne diatoms.

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