Method for determining threshold value of functional traits of river benthonic animal community on nutrition stress

Abstract

The invention provides a method for determining a threshold value of functional traits of a river benthonic animal community to nutrition stress, relates to the technical field of benthonic animal community ecology, and comprises the method for determining the threshold value of the functional traits of the river benthonic animal community to the nutrition stress. A method for comprehensively and deeply understanding the relationship between the benthonic animal community and the environmental factors is also provided, and a powerful scientific basis is provided for health management and protection of a river ecosystem; a random forest model is adopted, a plurality of nutrition indexes and environmental factors can be considered at the same time, main driving factors are identified through feature importance scores, and action intensities of different factors are determined; by calculating functional diversity indexes and constructing a site * character abundance matrix, specific functions of the benthonic animals in an ecological system are comprehensively reflected, and traditional species richness and diversity indexes are exceeded.

Description

Technical Field

[0001] The invention relates to the technical field of benthic animal community ecology, and in particular to a method for determining a threshold value of a river benthic animal community functional trait to nutritional stress. Background Art

[0002] Benthic animals, usually large benthic invertebrates, refer to a group of aquatic invertebrates that live at the bottom of the water body for all or most of their life history and have a body length of more than 0.5 mm. Benthic animals in the aquatic environment are widely distributed and diverse, mainly including the Oligochaeta and Hirudinida of the Annelida phylum, the Gastropoda and Bivalvia of the Mollusca phylum, the Insecta and Crustacea of ​​the Arthropoda phylum, etc. Benthic animals are often used as indicator organisms to play an important role in the health assessment of aquatic ecosystems, as they are large in size, easy to collect, fix and identify, have relatively fixed activity sites, have a long lifespan, and are sensitive to environmental changes. For example, a variety of evaluation indicators based on benthic animal communities, such as the ASPT index, the BMWP index, and the Goodnight-Whitley modified index, have been developed to assess the water quality of rivers.

[0003] The study of functional traits has enabled ecologists to re-examine complex ecological processes from a new perspective. The so-called functional traits refer to the morphological, physiological, phenological or behavioral characteristics of organisms that are easy to observe or measure. It reflects the adaptive strategies formed by species to minimize the impact of adverse factors in the environment under the long-term action of natural selection. It is the result of species adapting to different environments during the long-term evolution of species, and therefore can more objectively express the response and adaptability of organisms to the external environment. For example, benthic animals will have their own characteristics at different flow rates. Benthic animals living in torrent environments generally have streamlined bodies (such as Ephemeroptera), which minimize the friction they produce in flowing water; some have flat bodies and can live under stones or in cracks in the bottom; and some benthic animals (such as Simulidae, etc.) have attachment organs such as suckers that can be attached to the surface of stones without being washed away by the water. Different benthic animals have different resistance to pollution. The family of Trichoptera, the family of Gnathophoridae, has no ability to resist pollution at all; the ability of hook shrimps to resist pollution is low; while the families of Lithophoridae and Moth-fly families can adapt to seriously polluted environments. Agriculture has a significant impact on the functional trait composition of benthic animals. Some sensitive groups with swimming and tearing characteristics have disappeared, while groups with polymorphic characteristics have increased significantly; in addition, the functional richness and functional dispersion index of benthic animals have both decreased significantly, which is mainly attributed to the impact of agricultural measures on river habitat stability, habitat heterogeneity, water quality and material circulation. In disturbed habitats, benthic animals usually have a short life history and small individuals; when environmental conditions are good, species will be larger and have strong mobility.

[0004] At present, most studies on benthic animal community ecology are based on traditional species taxonomy, revealing how the structure of benthic animal communities changes with environmental gradients. However, its shortcoming is that it is difficult to directly explain the driving factors behind these changes. For example, the reduction of some benthic animal species may be the result of the combined effect of multiple factors such as pollution, flow velocity changes or habitat destruction, and it is often difficult to determine which specific factor plays a dominant role. The species taxonomy method focuses on the richness and diversity of species, but cannot fully reflect the specific functions of these species in the ecosystem. For example, two species with different functional traits may behave similarly in species diversity, but have significant differences in nutrient cycling, food web support, etc. Traditional methods cannot deeply explore the adaptation strategies formed by species in the long-term evolution process, thereby limiting the understanding of the adaptability and survival strategies of benthic animal communities under different environmental conditions.

[0005] Therefore, it is necessary to provide a new method for determining the threshold values ​​of functional traits of river benthic animal communities to nutritional stress to solve the above technical problems. Summary of the invention

[0006] In order to solve the above technical problems, the present invention provides a method for determining the threshold value of the functional traits of river benthic animal communities to nutritional stress.

[0007] The method for determining the threshold value of the functional traits of river benthic animal communities to nutritional stress provided by the present invention comprises the following steps:

[0008] S01. Determination of river nutrient pollutants: Select several areas with different interference levels along the river and set up several monitoring stations to measure the nutrient index of the water body at each monitoring point;

[0009] S02. Divide the nutritional gradient: standardize the nutritional index by z-score, take the average value to obtain the comprehensive nutritional index, and then divide it into gradients;

[0010] S03. Collect benthic animal samples: Collect benthic animal community samples in the water area of ​​each monitoring site;

[0011] S04. Identification of benthic animals: The collected sample mesh is cleaned, and the remaining is placed in a white porcelain plate. The benthic animals are selected and placed in formalin solution for identification and counting;

[0012] S05. Select benthic animal functional traits: Select functional traits of multiple benthic animals that are sensitive to environmental changes;

[0013] S06. Statistical abundance of benthic functional traits: Each functional trait is defined by fuzzy coding to obtain a benthic species × trait coding matrix, and the species × trait coding matrix is ​​normalized and multiplied with the site × species abundance matrix to obtain a site × trait abundance matrix;

[0014] S07. Calculate the functional diversity index of benthic animals: Calculate the functional diversity index of each monitoring site based on the obtained functional trait abundance data;

[0015] S08. Identify the characteristic functional traits of benthic animals: Use the indicator species analysis method to mark the characteristic benthic animal trait groups under different trophic groups;

[0016] S09. Construct and apply random forest model: Use functional diversity index and nutrient indexes in water bodies at each monitoring point to establish random forest model, use the feature importance score of random forest model to determine the impact of nutrient index and environmental factors on benthic animal functional diversity, and visualize the feature importance. According to the output of the model and the analysis results of indicator species, understand the mechanism of action of key nutrient factors;

[0017] S10. Construct the threshold response relationship between benthic animal functions and nutrients: Use the threshold indicator group analysis method to determine the ecological thresholds of nutritional drivers that cause mutations in key functional trait groups and functional diversity of benthic animals.

[0018] Furthermore, in step S01, the nutritional indicators include total nitrogen, ammonia nitrogen, nitrate nitrogen, total phosphorus, total organic carbon, permanganate index and chemical oxygen demand.

[0019] Further, in step S02, the gradient division of the comprehensive nutritional index of each monitoring point is carried out according to the 1 / 3 and 2 / 3 quantiles;

[0020] Those with a comprehensive nutrition index less than or equal to the 1 / 3 quantile were classified as the low nutrition group;

[0021] Those with a comprehensive nutritional index greater than the 1 / 3 quantile and less than or equal to the 2 / 3 quantile were classified as the medium nutritional group;

[0022] Those with a comprehensive nutritional index greater than 2 / 3 quantile were classified as the high nutrition group.

[0023] Furthermore, in step S03, benthic animal specimens in deep water areas are collected using a modified weighted 1 / 16m2 Peterson mud collector;

[0024] For collecting benthic animal specimens in shallow waters, a D-type scoop net was used.

[0025] Furthermore, in step S04, the mesh size is 60 meshes, and the picking tools include tweezers and a straw.

[0026] Furthermore, in step S05, the multiple functional traits include life cycle, individual size, feeding type, reproduction type, chemical properties, diffusion ability, resistance form, breathing mode and movement pattern.

[0027] Furthermore, in step S07, the functional diversity index includes functional richness, functional separation, functional uniformity, quadratic entropy index and functional redundancy index.

[0028] Furthermore, in step S09, a data set is established for the functional diversity index and each nutritional index, and the data set is divided into a training set and a test set. The training set data is used to train the random forest model, and the test set is used to evaluate the performance of the random forest model.

[0029] Furthermore, step S09 also includes selecting the optimal parameter combination of the training set and the test set through cross-validation.

[0030] Compared with the related art, the method for determining the threshold value of the functional traits of the river benthic animal community to nutritional stress provided by the present invention has the following beneficial effects:

[0031] The method for determining the threshold value of functional traits of river benthic animal communities to nutritional stress has significantly improved the understanding of the relationship between benthic animal communities and environmental factors by combining traditional species taxonomy and functional trait analysis. Specifically, the technical solution includes determining river nutrient pollutants, dividing nutrient gradients, collecting and identifying benthic animal samples, selecting and counting the abundance of benthic animal functional trait groups, calculating functional diversity indexes, identifying characteristic functional traits, constructing and applying random forest models, and constructing threshold response relationships between benthic animal functions and nutrients. This technical solution not only overcomes the limitations of traditional species taxonomy methods, but also provides a more comprehensive and in-depth understanding of the relationship between benthic animal communities and environmental factors, providing a strong scientific basis for the healthy management and protection of river ecosystems. Through multi-level analysis and comprehensive evaluation, the solution significantly optimizes the dynamic response control of benthic animal communities, embodies a multi-level and high-efficiency ecological monitoring and management strategy, and enhances the adaptability and stability of ecosystems in the face of environmental changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A flowchart of a method for determining a threshold value of a river benthic animal community functional trait to nutritional stress provided by the present invention;

[0033] Figure 2 A flowchart of the method for determining river nutrient pollutants provided by the present invention;

[0034] Figure 3 A flowchart of the process of selecting the functional traits of benthic animals provided by the present invention;

[0035] Figure 4 A flowchart of the statistical analysis of the abundance of benthic animal functional traits provided by the present invention;

[0036] Figure 5 A flowchart of the method for calculating the benthic animal functional diversity index provided by the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] In the specific implementation process, Figures 1 to 5 As shown, the method for determining the threshold value of the functional traits of the river benthic animal community to nutritional stress provided by the present invention comprises the following steps:

[0039] S01. Determination of river nutrient pollutants:

[0040] S011. Select monitoring areas and stations: According to the characteristics of the study area, select several areas with different levels of human interference along the river. For example, you can select the upper reaches (nature reserves), the middle reaches (agricultural areas or light industrial areas) and the lower reaches (urban areas or heavy industrial areas), and set up several monitoring stations in each selected area to ensure that the stations can represent the water quality conditions in the area. Usually, at least 3-5 monitoring points should be set up in each area to obtain sufficient sample size for statistical analysis, and ensure that the selection of monitoring stations takes into account the influence of factors such as water flow velocity, river bank type, and land use;

[0041] S012. Sampling frequency and time: Determine the sampling frequency, which can be monthly, quarterly or annual, depending on the research needs. For rivers with large seasonal changes, sampling should be carried out in different seasons. The sampling time should be fixed to reduce the impact of diurnal changes on the results. For example, sampling should always be carried out between 9 and 11 a.m.

[0042] S013. Sampling method: Use a water sampler or automatic sampler to collect water samples from about 0.5 meters below the water surface. Each time you sample, record detailed on-site information, including date, time, weather conditions, water temperature, pH value, etc. For some indicators, such as dissolved oxygen (DO), it is necessary to measure them immediately on site;

[0043] S014. Sample storage and transportation: The collected water samples should be sent to the laboratory for analysis as soon as possible. If they cannot be analyzed immediately, they should be stored in accordance with relevant standards. For example, samples used to determine nitrogen and phosphorus content should be refrigerated at 4°C and analyzed within 24 hours. For indicators that are volatile or mutable (such as ammonia nitrogen), appropriate preservatives (such as sulfuric acid) can be added to stabilize the samples.

[0044] S015. Determination of nutritional indicators:

[0045] Total nitrogen (TN): refers to the total amount of all forms of nitrogen in water, including organic nitrogen and inorganic nitrogen (such as ammonia nitrogen, nitrate nitrogen, etc.), which can be determined by potassium persulfate oxidation and then by UV spectrophotometer;

[0046] Ammonia nitrogen (NH4-N): refers to the ammonium ions (NH4 + ) and free ammonia (NH3), usually determined by Nessler's reagent colorimetric method or electrode method;

[0047] Nitrate nitrogen (NO3-N): refers to the nitrate ion (NO3 - ), can be determined by cadmium column reduction method or ultraviolet spectrophotometry;

[0048] Total phosphorus (TP): refers to the total amount of all forms of phosphorus in water, including dissolved phosphorus and particulate phosphorus, which is determined by molybdenum antimony colorimetry after potassium persulfate digestion;

[0049] Total organic carbon (TOC): refers to the amount of carbon contained in organic matter in water, which can be determined by combustion oxidation-non-dispersive infrared absorption method;

[0050] Permanganate index (COD Mn ): Also known as oxygen consumption, it indicates the amount of oxygen required by a strong oxidant (such as potassium permanganate) to oxidize reducing substances in water under certain conditions. It reflects the degree of pollution of organic matter and some inorganic matter in the water body;

[0051] Chemical oxygen demand (COD cr ): It indicates the amount of oxygen required for potassium dichromate to oxidize reducing substances in water under certain conditions. It is one of the important indicators to measure the degree of organic pollution in water.

[0052] S016. Data recording and organization: Record the results of each sampling and measurement in detail, including sampling location, time, specific values, etc., and organize them into a spreadsheet or database format to facilitate subsequent data processing and analysis.

[0053] S02. Divide the nutrient gradient:

[0054] S021. Data preparation: Collect all nutrient index data measured in step S01, including total nitrogen (TN), ammonia nitrogen (NH4-N), nitrate nitrogen (NO3-N), total phosphorus (TP), total organic carbon (TOC), permanganate index (COD Mn ) and chemical oxygen demand (COD cr ) to ensure that the data of each monitoring station is complete and outliers have been removed;

[0055] S022. z-score standardization: z-score standardization is a statistical method used to convert data into a standard normal distribution with a mean of 0 and a standard deviation of 1, thereby eliminating the impact of the original data dimension and facilitating comparison. Each nutritional indicator is z-score standardized. The z-score standardization formula is:

[0056]

[0057] Among them, x is the concentration of each nutritional index, μ is the mean of each nutritional index, σ is the standard deviation of each nutritional index, and then the average is taken to obtain the comprehensive nutritional index; the standardized value reflects the degree of deviation of a nutritional index at each monitoring point relative to the overall average level, and the unit is standard deviation;

[0058] S023. Calculate the comprehensive nutrition index: For each monitoring point, add up the z-score values ​​of all nutrition indicators and take the average to obtain a comprehensive nutrition index. The formula is as follows:

[0059]

[0060] Among them: F 综合 It is a comprehensive nutritional index, which is obtained by averaging the z-score values ​​of multiple nutritional indicators and is used to describe the overall nutritional pollution level of the monitoring point;

[0061] S024. Determine quantiles: Calculate the 1 / 3 and 2 / 3 quantiles of the comprehensive nutritional index of all monitoring points. These quantiles will be used as thresholds for dividing low nutritional, medium nutritional and high nutritional levels;

[0062] 1 / 3 quantile (Q1): indicates that the comprehensive nutritional index of 1 / 3 of the monitoring points is lower than this value;

[0063] 2 / 3 quantile (Q2): It means that the comprehensive nutrition index of 2 / 3 of the monitoring points is lower than this value;

[0064] S025. Divide the nutrient gradient: Divide the monitoring points into three nutrient levels according to the 1 / 3 and 2 / 3 quantiles:

[0065] Low nutrition: comprehensive nutrition index is less than 1 / 3 quantile (Q1);

[0066] Medium nutrition: the comprehensive nutrition index is between the 1 / 3 quantile (Q1) and the 2 / 3 quantile (Q2);

[0067] High nutrition: comprehensive nutrition index is greater than 2 / 3 quantile (Q2);

[0068] Quantiles are specific position values ​​in a set of data, usually used to describe the characteristics of data distribution. For example, quartiles (Q1, Q2, Q3) represent the data positions of 25%, 50%, and 75%, respectively. Through the above steps, the nutritional status of each monitoring point is effectively divided into different levels;

[0069] S026. Results collation and visualization: Record the nutritional level of each monitoring point. You can use a spreadsheet or database format to draw a nutritional gradient map using GIS software to visually display the nutritional level distribution of each monitoring point.

[0070] S03. Collect benthic animal samples:

[0071] S031. Prepare sampling tools: improved weighted 1 / 16m2 Peterson mud sampler and D-type scoop net;

[0072] Improved weighted 1 / 16m 2 Peterson mud sampler is an improved mud sampler specially used for sampling benthic animals in deep water. It is designed to have a sampling area of ​​1 / 16 square meter. The weighted device ensures that it can go deep into the water to collect benthic animals in the sediment.

[0073] D-net: A handheld sampling tool shaped like the letter "D" that is suitable for shallow waters and can be easily dragged across the riverbed to collect benthic animals and the sediments attached to them;

[0074] S032. Determine sampling locations: Select appropriate sampling points at each monitoring site to ensure that the sampling points can represent the overall water quality of the site. For deep water areas, select areas with relatively stable water flow and uniform bottom sediments. For shallow water areas, select locations with gentle water flow and easy operation.

[0075] S033, deep water sampling: increase the weight of the modified 1 / 16m 2 Slowly put the Peterson mud sampler into the water until it touches the riverbed, making sure that the mud sampler is completely in contact with the riverbed, and gently push it into the sediment. When lifting the mud sampler, keep it vertical to avoid sample leakage, and transfer the collected sediment to a pre-prepared container, such as a plastic bucket or a large-capacity plastic bag;

[0076] S034. Shallow water sampling: Use a D-type scoop net to trawl sampling in the selected shallow water area. Move the scoop net slowly along the riverbed to cover as much area as possible to obtain representative samples. Repeat the trawl operation several times to ensure that enough samples are collected. Transfer the collected benthic animals and the sediments attached to them into pre-prepared containers.

[0077] S035. Sample processing: The collected samples should be sent to the laboratory immediately for further processing. If they cannot be processed immediately, the samples should be stored under refrigerated conditions (such as 4°C) and sent to the laboratory as soon as possible;

[0078] S036. Field records: record the specific location, sampling time, sampling method and any environmental factors that may affect the results, such as weather conditions, water flow speed, etc. of each sampling point, and record any problems or special circumstances encountered during the sampling process for reference in subsequent analysis;

[0079] S037. Safety precautions: Sampling personnel should wear appropriate protective equipment, such as waterproof boots, gloves and life jackets. They should pay attention to personal safety during sampling, especially in deep water areas and places with turbulent water flow. They should comply with local environmental protection laws and regulations to avoid unnecessary interference with the ecological environment.

[0080] S04. Identification of benthic animals:

[0081] S041. Sample cleaning: Pour the collected benthic animal samples into a 60-mesh (about 250 micron pore size) sieve, rinse the samples with clean water to remove excess sediment and fine particles. Be careful not to use too fast a flow of water to avoid washing away small benthic animals. During the cleaning process, you can gently shake the sieve to help separate sediment and organisms.

[0082] S042. Sorting and fixation: Transfer the cleaned samples to a clean white porcelain plate or other light-colored container, carefully pick out visible benthic animals using tools such as tweezers and pipettes, and place the picked benthic animals in a vial or test tube filled with 10% formalin solution for fixation. Formalin is a commonly used fixative that can preserve the morphological characteristics of organisms and facilitate subsequent identification and preservation. For larger specimens, they can be placed on filter paper to absorb excess moisture before being placed in formalin solution. Each sample should be individually labeled, recording the sampling point information, date, and other relevant information;

[0083] S043. Laboratory identification: In the laboratory, use a dissecting microscope or compound microscope to identify fixed benthic animals. According to their external morphological characteristics, such as body shape, color, and appendage structure, identify them to genus or species. Refer to relevant taxonomic literature and illustrations to ensure the accuracy of identification. For species that are difficult to identify, seek the help of experts or conduct molecular biological identification, such as DNA barcoding, count each benthic animal and record its number;

[0084] S044. Data recording: Record the species composition and quantity of benthic animals at each monitoring site, and establish a detailed database including species name, quantity, sampling point information, etc. If possible, take clear photos or make specimens for subsequent reference and verification;

[0085] S045. Quality control: Regularly calibrate microscopes and other equipment to ensure the accuracy and consistency of instruments, and conduct internal or external quality control checks, such as repeating the identification of some samples to ensure the consistency and reliability of the identification results;

[0086] S05. Select benthic animal functional traits: Select 9 common benthic animal functional traits that are sensitive to environmental changes, including life cycle, individual size, feeding type, reproduction type, chemical properties, diffusion ability, resistance morphology, breathing mode and movement pattern, as shown in Table 1:

[0087] Table 1 Types of functional traits that are sensitive to environmental changes

[0088]

[0089]

[0090] S06. Statistical abundance of benthic animal functional traits:

[0091] S061, functional trait classification and fuzzy coding: according to the benthic animal functional traits selected in step S05, define different categories of each trait, and assign values ​​to different categories of each trait using fuzzy coding method;

[0092] Fuzzy coding is a semi-quantitative method used to describe the degree of similarity between different categories. The specific values ​​are as follows: a value of 0 indicates that there is no similarity in the morphology of the taxa, a value of 1 indicates that there is a small similarity in the morphology of the taxa, a value of 2 indicates that there is a moderate degree of similarity in the morphology of the taxa, and a value of 3 or greater indicates that there is a great similarity in the morphology of the taxa;

[0093] S062. Construct a species × trait coding matrix: For each species, according to the specific characteristics of its functional traits, correspond it to the corresponding fuzzy coding value, and construct a matrix in which the rows represent different species and the columns represent different functional trait categories. Each element in the matrix is ​​the fuzzy coding value of the species in the functional trait category.

[0094] S063. Normalized species × trait coding matrix: In order to ensure that the weights of different functional traits are consistent, the species × trait coding matrix needs to be normalized. The normalization method can be Min-Max Normalization for each column or other appropriate normalization methods;

[0095] S064. Obtain site × species abundance matrix: Obtain the benthic animal species composition and quantity data of each monitoring site from step S04, and construct a matrix in which rows represent different monitoring sites, columns represent different species, and each element in the matrix is ​​the number or relative abundance of the species at the site;

[0096] S065. Calculate the site × trait abundance matrix: multiply the normalized species × trait coding matrix with the site × species abundance matrix to obtain the site × trait abundance matrix. The multiplication operation can be achieved by matrix multiplication.

[0097] S07. Calculate the functional diversity index of benthic animals: Calculate the functional diversity index of each monitoring site based on the obtained functional trait abundance data, including functional richness, functional separation, functional evenness, secondary entropy index and functional redundancy index.

[0098] Functional richness index:

[0099] FR ic =SF ic / R c

[0100] Where SF ic is the niche space occupied by species in community i; R c is the absolute value range of trait c.

[0101] Functional Separation Index:

[0102]

[0103] In the formula, C i is the value of the ith functional trait; A i is the relative abundance of the ith functional trait; is the weighted average of the natural logarithm of the species characteristic values.

[0104] Functional uniformity index:

[0105]

[0106] Where S is the number of species and PEWi is the local weighted evenness of species i.

[0107] Secondary entropy index:

[0108]

[0109] In the formula, p i and p j is the relative abundance of species, S is the number of species, dij is the dissimilarity between species i and j, and varies between (0, 1). When it is 0, it means that the two species have exactly the same traits, and when it is 1, it means that the two species have completely different traits.

[0110] Functional redundancy index:

[0111] FD=RaoQ / H

[0112] Where H is Shannon diversity.

[0113] S08. Identify the characteristic functional traits of benthic animals:

[0114] S081. Prepare data: From the functional diversity index and site × trait abundance matrix obtained in step S07, determine the trophic gradient group (low trophic, medium trophic, high trophic) to which each monitoring site belongs;

[0115] S082. Select analysis tools: Use the indicspecies package in the R language to perform indicator species analysis. The R language is a common tool for ecological data analysis and provides a wealth of packages and functions to process complex ecological data.

[0116] S083. Perform indicator species analysis: Import the site × trait abundance matrix and nutrient gradient grouping information into R or other analysis tools, and use the indicator species analysis method to calculate the indicator value of each functional trait group in different nutrient gradient groups. The indicator value usually includes specificity and fidelity. Specificity: indicates the relative abundance of a functional trait group in a specific group. Fidelity: indicates the frequency of occurrence of a functional trait group in the group. The statistical significance of the indicator value is evaluated by Monte Carlo permutation test. Usually a significance level is set, such as p<0.05, to determine which functional trait groups are significant indicator species.

[0117] S084. Interpretation of results: Identify the functional trait groups with significant indicator values ​​in different nutrient gradient groups, analyze the characteristics of these indicator trait groups, such as their sensitivity to environmental changes, adaptation mechanisms, etc., and combine the data from the previous steps to further understand the distribution patterns and ecological significance of these functional trait groups under different nutrient conditions;

[0118] S085. Visualization and Reporting: Prepare charts, such as bar charts and heat maps, to display indicator functional trait groups and their indicator values ​​in different trophic gradient groups; write technical reports summarizing the results of indicator species analysis, including significant indicator trait groups, their specificity and fidelity, and possible ecological explanations; and provide suggestions for future research and management, such as how to use these indicator trait groups to monitor river health or develop conservation measures;

[0119] S09. Build and apply the random forest model:

[0120] S091. Data preparation: The functional diversity index obtained from step S07, the nutrient index and comprehensive nutrient index of the water bodies at each monitoring point obtained from step S01, and other environmental variables such as water temperature, pH value, flow rate, etc. are considered to increase the explanatory power of the model;

[0121] S092. Data preprocessing: For any missing values, interpolation methods such as K nearest neighbor interpolation, mean / median interpolation, or deletion of data points with missing values ​​can be used. Since different nutritional indicators and environmental variables have different units and scales, they should be z-score standardized or min-max normalized to ensure that all features are at the same order of magnitude;

[0122] S093. Dataset partitioning: The dataset is divided into a training set and a test set, with a split ratio of 70% for training and 30% for testing. Stratified sampling is used to maintain the consistency of the distribution of each category in the training set and the test set.

[0123] S094. Build a random forest model: Use the scikit-learn library in Python or other suitable machine learning toolkits to implement a random forest regressor or classifier, depending on whether the response variable is continuous or discrete, initialize the random forest model, and set the initial parameters.

[0124] S095. Model training: Use the training set data to train the random forest model;

[0125] S096. Model evaluation: To evaluate the model performance on the test set, the following indicators can be used:

[0126] For regression problems: coefficient of determination R2, mean square error MSE, mean absolute error MAE;

[0127] S097, Cross-validation and hyperparameter tuning: Create a parameter grid, list the hyperparameters to be searched and their possible value ranges, define the scoring criteria for evaluating model performance using negative mean square error, find the maximized objective function, use GridSearchCV or RandomizedSearchCV to perform hyperparameter tuning, use the training set data for cross-validation, find the best parameter combination, obtain and print the best parameter combination, retrain the model using the best parameter combination, and evaluate its performance on the test set;

[0128] S098. Feature importance analysis: Use the feature importance scores provided by the random forest model to determine which nutritional indicators and environmental factors have the greatest impact on the functional diversity of benthic animals;

[0129] S099. Interpretation of results: Based on the results of the model output and combined with the indicator species analysis in step S08, further understand the mechanism of action of key nutrient factors, provide suggestions on which nutrients need special attention, and the specific thresholds for controlling the concentration of these substances, and provide a scientific basis for the control of river nutrient pollutants;

[0130] S10. Constructing the threshold response relationship between benthic animal functions and nutrients:

[0131] S11. Prepare data: the functional diversity index obtained in step S07, the nutrient indicators of the water bodies at each monitoring point obtained in step S01 (total nitrogen TN, ammonia nitrogen NH4-N, nitrate nitrogen NO3-N, total phosphorus TP, total organic carbon TOC, permanganate index COD Mn , Chemical oxygen demand COD cr ), the indicator species analysis results obtained from step S08, especially the characteristic benthic animal trait groups under different trophic gradients;

[0132] S12. Select analysis tools: Use the TITAN2 package in R language to perform threshold indicator group analysis. The TITAN2 package is a tool specifically used to identify the threshold of the impact of environmental variable changes on biological communities;

[0133] S13. Perform threshold indicator cluster analysis: import the functional diversity index and nutritional index data into the R environment and use the functions in the TITAN2 package for analysis;

[0134] S14. Interpretation of results: TITAN2 analysis will output the thresholds of each trophic indicator. These thresholds are the key points that lead to significant changes in the functional diversity of benthic animal communities. The statistical significance of the thresholds is evaluated by permutation test. Usually a significance level is set, such as p<0.05, to determine which thresholds are significant. The TITAN2 package provides a variety of graphical outputs, such as cumulative distribution function (CDF) and probability density function (PDF), to help intuitively display the threshold position and its significance.

[0135] S15. Combine the results of indicator species analysis: Combine the thresholds obtained from the TITAN2 analysis with the results of the indicator species analysis in step S08 to further understand the mechanism of action of the key nutritional factors. For example, if the TITAN2 analysis shows that the functional diversity mutates at TN of 1.5 mg / L, and the indicator species analysis shows that certain specific functional trait groups significantly increase or decrease under this condition, this can help explain the cause of this mutation.

[0136] S16. Make management recommendations: Based on the results of the TITAN2 analysis, identify key nutrient drivers and their corresponding ecological thresholds, and make specific management recommendations, such as controlling the concentrations of TN and TP below the thresholds to protect the health of the river ecosystem. For example, it can be recommended that management departments set water quality standards to ensure that total nitrogen (TN) does not exceed 1.5 mg / L and total phosphorus (TP) does not exceed 0.5 mg / L, thereby maintaining the health of benthic animal communities;

[0137] S17. Report Writing: Write a technical report summarizing the process, main findings and conclusions of the TITAN2 analysis, including graphical outputs of the model, tabular data and recommendations for future research, and provide detailed management recommendations, including specific nutrient concentration control targets and implementation strategies.

[0138] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a general hardware platform, and of course, by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the relevant technology can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0139] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the attached claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention, and any figure mark in the claims should not be regarded as limiting the claims involved.

[0140] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A method for determining the threshold value of the functional traits of river benthic animal communities to nutritional stress, characterized in that: The steps include: S01. Determination of river nutrient pollutants: Select several areas with different interference levels along the river and set up several monitoring stations to measure the nutrient index of the water body at each monitoring point; S02. Divide the nutritional gradient: standardize the nutritional index by z-score, take the average value to obtain the comprehensive nutritional index, and then divide it into gradients; S03. Collect benthic animal samples: Collect benthic animal community samples in the water area of ​​each monitoring site; S04. Identification of benthic animals: The collected sample mesh is cleaned, and the remaining is placed in a white porcelain plate. The benthic animals are selected and placed in formalin solution for identification and counting; S05. Select benthic animal functional traits: Select functional traits of multiple benthic animals that are sensitive to environmental changes; S06. Statistical abundance of benthic functional traits: Each functional trait is defined by fuzzy coding to obtain a benthic species × trait coding matrix, and the species × trait coding matrix is ​​normalized and multiplied with the site × species abundance matrix to obtain a site × trait abundance matrix; S07. Calculate the functional diversity index of benthic animals: Calculate the functional diversity index of each monitoring site based on the obtained functional trait abundance data; S08. Identify the characteristic functional traits of benthic animals: Use the indicator species analysis method to mark the characteristic benthic animal trait groups under different trophic groups; S09. Construct and apply random forest model: Use functional diversity index and nutrient indexes in water bodies at each monitoring point to establish random forest model, use the feature importance score of random forest model to determine the impact of nutrient index and environmental factors on benthic animal functional diversity, and visualize the feature importance. According to the output of the model and the analysis results of indicator species, understand the mechanism of action of key nutrient factors; S10. Construct the threshold response relationship between benthic animal functions and nutrients: Use the threshold indicator group analysis method to determine the ecological thresholds of nutritional drivers that cause mutations in key functional trait groups and functional diversity of benthic animals.

2. The method for determining the threshold value of the functional traits of the river benthic animal community to nutritional stress according to claim 1, characterized in that: In step S01, the nutritional indicators include total nitrogen, ammonia nitrogen, nitrate nitrogen, total phosphorus, total organic carbon, permanganate index and chemical oxygen demand.

3. The method for determining the threshold value of the functional traits of the river benthic animal community to nutritional stress according to claim 1, characterized in that: In step S02, the gradient division of the comprehensive nutritional index of each monitoring point is carried out according to the 1 / 3 and 2 / 3 quantiles; Those with a comprehensive nutrition index less than or equal to the 1 / 3 quantile were classified as the low nutrition group; Those with a comprehensive nutritional index greater than the 1 / 3 quantile and less than or equal to the 2 / 3 quantile were classified as the medium nutritional group; Those with a comprehensive nutritional index greater than 2 / 3 quantile were classified as the high nutrition group.

4. The method for determining the threshold value of the functional traits of the river benthic animal community to nutritional stress according to claim 1, characterized in that: In step S03, benthic animal specimens in deep water areas are collected using a modified weighted 1 / 16m2 Peterson mud collector; For collecting benthic animal specimens in shallow waters, a D-type scoop net was used.

5. The method for determining the threshold value of the functional traits of the river benthic animal community to nutritional stress according to claim 1, characterized in that: In step S04, the mesh size is 60 meshes, and the picking tools include tweezers and a straw.

6. The method for determining the threshold value of the functional traits of the river benthic animal community to nutritional stress according to claim 1, characterized in that: In step S05, the multiple functional traits include life cycle, individual size, feeding type, reproduction type, chemical properties, diffusion ability, resistance form, breathing mode and movement pattern.

7. The method for determining the threshold value of the functional traits of the river benthic animal community to nutritional stress according to claim 1, characterized in that: In step S07, the functional diversity index includes functional richness, functional separation, functional uniformity, secondary entropy index and functional redundancy index.

8. The method for determining the threshold value of the functional traits of river benthic animal communities to nutritional stress according to claim 1, characterized in that: In step S09, a data set is established for the functional diversity index and various nutritional indicators, and the data set is divided into a training set and a test set. The training set data is used to train the random forest model, and the test set is used to evaluate the performance of the random forest model.

9. The method for determining the threshold value of the functional traits of the river benthic animal community to nutritional stress according to claim 1, characterized in that: Step S09 also includes selecting the optimal parameter combination of the training set and the test set through cross-validation.

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