Method for determining threshold of functional traits of river benthic animal community under nutrient stress

Abstract

The present invention provides a method for determining the threshold of the functional traits of river benthic animal communities under nutrient stress, which relates to the ecological technology field of benthic animal communities. The method for determining the threshold of the functional traits of the river benthic animal communities under nutrient stress not only overcomes the limitations of traditional species taxonomy methods, but also provides a more comprehensive and in-depth way to understand the relationship between benthic animal communities and environmental factors, providing a strong scientific basis for the health management and protection of river ecosystems; by using a random forest model, multiple nutrient indicators and environmental factors can be considered simultaneously, and the main driving factors can be identified through feature importance scoring to clarify the action intensity of different factors; by calculating the functional diversity index and constructing a site × trait abundance matrix, the specific functions of benthic animals in the ecosystem can be comprehensively reflected, exceeding the traditional species richness and diversity indicators.

Description

Technical Field

[0001] The present invention relates to the technical field of benthic animal community ecology, and particularly to a method for determining the threshold of the functional traits of river benthic animal communities under nutrient stress. Background Art

[0002] Benthic animals, usually macroinvertebrates, refer to aquatic invertebrate groups that live at the bottom of water bodies for all or most of their life history and have a body length exceeding 0.5 mm. Benthic animals in the water environment are widely distributed and diverse in species, mainly including Oligochaeta and Hirudinea of Annelida, Gastropoda and Bivalvia of Mollusca, Insecta and Crustacea of Arthropoda, etc. Due to the advantages of benthic animals such as relatively large size, easy collection, fixation and identification, relatively fixed activity sites, long lifespan, and sensitive response to environmental changes, they are often used as indicator organisms and play an important role in the health assessment of water environment ecosystems. For example, at present, various evaluation indexes based on benthic animal communities, such as the ASPT index, the BMWP index, and the Goodnight-Whitley modified index, have been developed to evaluate the water quality of rivers.

[0003] The study of functional traits enables 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 adaptation strategies formed by species under the long-term action of natural selection to minimize the impact of adverse factors in the environment. It is the result of the long-term evolution of species adapting to different environments, and thus can more objectively express the response and adaptability of organisms to the external environment. For example, benthic animals have their own characteristics under different flow velocities. Benthic animals living in rapids environments generally have streamlined body shapes (such as groups of Ephemeroptera), which minimize the frictional force generated in flowing water; some bodies are flat, enabling them to inhabit under stones or in substrate crevices; there are also some benthic animals (such as Simuliidae, etc.) with attachment organs such as suckers, which can adsorb on the surface of stones and are not washed away by the water flow. Different benthic animals have different resistance abilities to pollution. Philopotamidae of Trichoptera completely lack the ability to resist pollution; Gammaridae have a low ability to resist pollution; while groups such as Erpobdellidae and Psychodidae can adapt to severely polluted environments. Agriculture has a significant impact on the composition of benthic animal functional traits. Some sensitive groups with traits such as swimming and tearing-feeding disappear, while groups with traits of polyvoltinism increase significantly; in addition, both the functional richness and functional dispersion indices of benthic animals decrease significantly, which is mainly attributed to the impact of agricultural measures on the stability, heterogeneity, water quality, and material cycle of river habitats. In disturbed habitats, the life history of benthic animals is usually short and their bodies are small; while in good environmental conditions, the body size of species is larger and their mobility is stronger.

[0004] At present, the research on benthic animal community ecology mostly focuses on traditional species taxonomy, revealing how the benthic animal community structure changes with environmental gradients. However, its shortcoming is that it is difficult to directly clarify the driving factors behind these changes. For example, the reduction of certain benthic animal species may be the result of the combined effects of multiple factors such as pollution, flow velocity changes, or habitat destruction, and it is often difficult to determine which factor plays a dominant role. The species taxonomy method focuses on species richness and diversity, but cannot comprehensively reflect the specific functions of these species in the ecosystem. For example, two species with different functional traits may show similar species diversity, but there are significant differences in aspects such as nutrient cycling and food web support. The traditional method cannot deeply explore the adaptation strategies formed by species during long-term evolution, thus limiting the understanding of the adaptation ability 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 of the functional traits of river benthic animal communities against nutrient stress to solve the above technical problems. Summary of the Invention

[0006] To solve the above technical problems, the present invention provides a method for determining the threshold of the functional traits of river benthic animal communities against nutrient stress.

[0007] The method for determining the threshold of the functional traits of river benthic animal communities provided by the present invention includes the following steps:

[0008] S01. Measuring river nutrient pollutants: Select several regions with different degrees of disturbance along the river and set several monitoring stations, and measure the nutrient indicators of the water body at each monitoring point;

[0009] S02. Dividing nutrient gradients: Standardize the nutrient indicators by z-score, take the mean to obtain the comprehensive nutrient index, and then conduct gradient division;

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

[0011] S04. Identifying benthic animals: Wash the collected sample net sieve, place the remaining matter in a white porcelain plate, pick out the benthic animals, put them into formalin solution for identification and counting;

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

[0013] S06. Statistically analyze the abundance of benthic functional trait groups: Each functional trait is defined using a fuzzy coding method to obtain a benthic species × trait coding matrix. The species × trait coding matrix is normalized and multiplied by the site × species abundance matrix to obtain a site × trait abundance matrix;

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

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

[0016] S09. Construct and apply a random forest model: Establish a random forest model using the functional diversity index and the nutrient indicators in the water bodies of each monitoring point. Use the feature importance scores of the random forest model to determine the impacts of nutrient indicators and environmental factors on the functional diversity of benthos, visualize the feature importance, and understand the action mechanism of key nutrient factors based on the results output by the model in combination with the results of the indicator species analysis;

[0017] S10. Construct the threshold response relationship between benthic functions and nutrients: Use the threshold indicator taxa analysis method to determine the ecological thresholds of nutrient driving factors that cause mutations in the key functional trait groups and functional diversity of benthos.

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

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

[0020] The comprehensive nutrition index less than or equal to the 1 / 3 quantile is classified as the low-nutrient group;

[0021] The comprehensive nutrition index greater than the 1 / 3 quantile and less than or equal to the 2 / 3 quantile is classified as the medium-nutrient group;

[0022] The comprehensive nutrition index greater than the 2 / 3 quantile is classified as the high-nutrient group.

[0023] Furthermore, in step S03, for the benthic specimens in the deep water area, a modified weighted 1 / 16 m2 Petersen grab sampler is used for collection;

[0024] For the collection of benthic specimens in the shallow water area, a D-type dip net is used.

[0025] Furthermore, in step S04, the sieve is 60 mesh, and the picking tools include tweezers and pipettes.

[0026] Further, in step S05, the multiple types of functional traits include life cycle, individual size, feeding type, reproductive type, voltinism, dispersal ability, resistance form, respiratory mode, and locomotion pattern.

[0027] Further, in step S07, the functional diversity indices include functional richness, functional distinctness, functional evenness, quadratic entropy index, and functional redundancy index.

[0028] Further, in step S09, a data set is established for the functional diversity indices and each nutritional index, the data set is divided into a training set and a test set, the random forest model is trained using the training set data, and the performance of the random forest model is evaluated with the test set.

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

[0030] Compared with the related technologies, the method for determining the threshold of the functional traits of river benthic animal communities provided by the present invention has the following beneficial effects:

[0031] The method for determining the threshold of the functional traits of river benthic animal communities significantly improves 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 measuring river nutrient pollutants, dividing nutrient gradients, collecting and identifying benthic animal samples, selecting and counting the abundances of benthic animal functional trait groups, calculating functional diversity indices, identifying characteristic functional traits, constructing and applying a random forest model, and constructing the threshold response relationship 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 method for understanding the relationship between benthic animal communities and environmental factors, providing a strong scientific basis for the health management and protection of river ecosystems. Through multi-level analysis and comprehensive evaluation, this solution significantly optimizes the dynamic response control of benthic animal communities, reflecting a multi-level and high-efficiency ecological monitoring and management strategy, and enhancing the adaptability and stability of the ecosystem in the face of environmental changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a flowchart of the method for determining the threshold of the functional traits of river benthic animal communities provided by the present invention;

[0033] Figure 2 It is a flowchart of measuring river nutrient pollutants provided by the present invention;

[0034] Figure 3 It is a flowchart of selecting benthic animal functional traits provided by the present invention;

[0035] Figure 4 Flow chart for statistically analyzing the abundances of benthic functional trait groups provided by the present invention;

[0036] Figure 5 Flow chart for calculating the functional diversity index of benthic animals provided by the present invention. Specific implementation manners

[0037] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0038] In the specific implementation process, as Figures 1 to 5 shown, the method for determining the thresholds of the functional traits of river benthic animal communities provided by the present invention includes the following steps:

[0039] S01. Measuring river nutrient pollutants:

[0040] S011. Selecting monitoring areas and sites: According to the characteristics of the research area, several areas with different degrees of human disturbance are selected along the river. For example, the upstream (nature reserve), the middle stream (agricultural area or light industrial area), and the downstream (urban area or heavy industrial area) can be selected. Several monitoring sites are arranged in each selected area to ensure that the sites can represent the water quality conditions of the area. Generally, at least 3-5 monitoring points should be set in each area to obtain a sufficient sample size for statistical analysis and ensure that the selection of monitoring sites takes into account the influence of factors such as water flow velocity, riverbank type, and land use;

[0041] S012. Sampling frequency and time: Determine the sampling frequency, which can be once a month, once a quarter, or once a year, depending on the research needs. For rivers with large seasonal variations, sampling is carried out in different seasons, and the sampling time is fixed to reduce the influence of diurnal variations on the results. For example, sampling is always carried out between 9 am and 11 am.

[0042] S013. Sampling method: Use a water sampler or an automatic sampler to collect water samples from about 0.5 meters below the water surface. Each time sampling is carried out, detailed on-site information, including date, time, weather conditions, water temperature, pH value, etc., is recorded. For some indicators, such as dissolved oxygen (DO), it needs to be measured immediately on-site;

[0043] S014. Sample Preservation and Transportation: The water samples collected should be sent to the laboratory for analysis as soon as possible. If they cannot be analyzed immediately, they need to be preserved according to relevant standards. For example, the samples for measuring nitrogen and phosphorus contents should be refrigerated at 4°C and analyzed within 24 hours. For volatile or easily variable indicators (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): It refers to the total amount of nitrogen in all forms in water, including organic nitrogen and inorganic nitrogen (such as ammonia nitrogen, nitrate nitrogen, etc.), and can be determined by ultraviolet spectrophotometry after potassium persulfate oxidation.

[0046] Ammonia Nitrogen (NH4-N): It refers to the sum of ammonium ions (NH4 + ) and free ammonia (NH3) in water, and is commonly determined by Nessler's reagent colorimetry or electrode method.

[0047] Nitrate Nitrogen (NO3-N): It refers to the nitrate ions (NO3 - ) in water, and can be determined by cadmium column reduction method or ultraviolet spectrophotometry.

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

[0049] Total Organic Carbon (TOC): It refers to the carbon content in organic matter in water, and can be determined by combustion oxidation - non-dispersive infrared absorption method.

[0050] Permanganate Index (COD Mn ): Also known as oxygen consumption, it represents the amount of oxygen required for strong oxidants (such as potassium permanganate) to oxidize reducing substances in water under certain conditions, and it reflects the pollution degree of organic matter and some inorganic substances in water bodies.

[0051] Chemical Oxygen Demand (COD cr ): It represents the amount of oxygen required for potassium dichromate to oxidize reducing substances in water under certain conditions, and it is one of the important indicators for measuring the degree of organic matter pollution in water bodies.

[0052] S016. Data Recording and Arrangement: Record the results of each sampling and determination in detail, including sampling location, time, specific values, etc., and organize them into electronic spreadsheet or database format for subsequent data processing and analysis.

[0053] S02. Division of Nutritional Gradients:

[0054] S021. Data Preparation: Collect all the data of nutrient indicators 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 ), ensure that the data of each monitoring site is complete and outliers have been removed;

[0055] S022. z-score Standardization: z-score standardization is a statistical method used to transform data into a standard normal distribution with a mean of 0 and a standard deviation of 1, thus eliminating the influence of the original data dimension for easy comparison. Perform z-score standardization on each nutrient indicator. The z-score standardization formula is:

[0056]

[0057] where x is the concentration of each nutrient indicator, μ is the mean of each nutrient indicator, σ is the standard deviation of each nutrient indicator, and then take the mean to obtain the comprehensive nutrition index; the standardized value reflects the deviation degree of a certain nutrient indicator at each monitoring point relative to the overall average level, with the unit of standard deviation;

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

[0059]

[0060] where: F 综合 is the comprehensive nutrition index, a comprehensive index obtained by taking the average of the z-score values of multiple nutrient indicators, used to describe the overall nutrient pollution level of the monitoring point;

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

[0062] 1 / 3 Quantile (Q1): It means that the comprehensive nutrition 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: According to the 1 / 3 and 2 / 3 quantiles, divide the monitoring points into three nutrition levels:

[0065] Low nutrition: The comprehensive nutrition index is less than the 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: The comprehensive nutrition index is greater than the 2 / 3 quantile (Q2);

[0068] Quantiles are specific position values in a set of data and are 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 grades;

[0069] S026. Result sorting and visualization: Record the nutritional grade of each monitoring point, which can be in the format of a spreadsheet or database, and draw a nutritional gradient map using GIS software to intuitively display the distribution of nutritional grades of each monitoring point;

[0070] S03. Collect benthic animal samples:

[0071] S031. Prepare sampling tools: Modified weighted 1 / 16 m2 Petersen grab sampler and D-type dip net;

[0072] Modified weighted 1 / 16 m 2 The Petersen grab sampler is an improved grab sampler specifically used for sampling benthic animals in deep waters. Its design makes the sampling area 1 / 16 square meters, and through a weighting device, it ensures that it can penetrate the bottom to collect benthic animals in the sediment;

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

[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 status of the site. For deep waters, select areas with relatively stable water flow and uniform bottom sediment. For shallow waters, select positions with gentle water flow and easy operation;

[0075] S033. Sampling in deep waters: Slowly lower the modified weighted 1 / 16 m 2 Petersen grab sampler into the water until it touches the riverbed. Ensure that the grab sampler is in full contact with the riverbed and gently push it into the sediment. Keep the grab sampler vertical when lifting it to avoid sample leakage. Transfer the collected sediment to a pre-prepared container, such as a plastic bucket or a large-capacity plastic bag;

[0076] S034, Sampling in shallow water areas: Use a D-shaped dip net to conduct trawl sampling in the selected shallow water areas. Slowly move the dip net along the riverbed to cover as much area as possible to obtain a representative sample. Repeat the trawl operation several times to ensure an adequate sample size. Transfer the benthic animals collected along with the attached sediment to a pre-prepared container;

[0077] S035, Sample processing: Immediately send the collected samples to the laboratory 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, On-site recording: Record the specific location, sampling time, sampling method of each sampling point, and any environmental factors that may affect the results, such as weather conditions, water flow velocity, etc. Record any problems or special situations encountered during the sampling process for reference during subsequent analysis;

[0079] S037, Safety precautions: Sampling personnel should wear appropriate protective equipment, such as waterproof boots, gloves, and life jackets. Pay attention to personal safety during the sampling process, especially in deep water areas and where the water flow is rapid. Comply with local environmental protection regulations and avoid causing unnecessary interference to the ecological environment;

[0080] S04, Identifying 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. Note that the water flow should not be too strong to avoid washing away small benthic animals. During the cleaning process, gently shake the sieve to help separate the sediment and organisms;

[0082] S042, Sorting and fixing: Transfer the cleaned samples to a clean white porcelain plate or other light-colored containers. Use tools such as tweezers and pipettes to carefully pick out visible individual benthic animals. Place the picked benthic animals into small bottles or test tubes containing 10% formalin solution for fixation. Formalin is a commonly used fixative that can preserve the morphological characteristics of organisms for subsequent identification and preservation. For larger specimens, first place them on filter paper to absorb excess moisture and then put them into the 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 a compound microscope to identify the fixed benthic animals. According to the external morphological characteristics of the benthic animals, such as body shape, color, appendage structure, etc., identify them to the genus or species level. Refer to relevant taxonomic literature and atlases to ensure the accuracy of the identification. For species that are difficult to identify, seek the help of experts or conduct molecular biological identification, such as DNA barcoding. Count each type of benthic animal and record its quantity.

[0084] S044. Data recording: Record the species composition and quantity of benthic animals at each monitoring site, and establish a detailed database, including species names, quantities, 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 the instruments. Conduct internal or external quality control checks, such as re-identifying some samples, to ensure the consistency and reliability of the identification results.

[0086] S05. Select functional traits of benthic animals: Select 9 common functional traits of benthic animals that are sensitive to environmental changes, including life cycle, individual size, feeding type, reproductive type, diapause type, dispersal ability, defensive morphology, respiratory mode, and locomotion pattern, as shown in Table 1:

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

[0088]

[0089]

[0090] S06. Statistically analyze the abundance of functional trait groups of benthic animals:

[0091] S061. Functional trait classification and fuzzy coding: According to the functional traits of benthic animals selected in step S05, define different categories for each trait, and use the fuzzy coding method to assign values to different categories of each trait.

[0092] Fuzzy coding is a semi - quantitative method used to describe the degree of similarity between different categories. The specific value assignment is as follows: The value 0 indicates that there is no similarity in the morphology of taxonomic units; the value 1 indicates that there is a small similarity in the morphology of taxonomic units; the value 2 indicates that there is a moderate similarity in the morphology of taxonomic units; the value 3 or greater than 3 indicates that there is a large similarity in the morphology of taxonomic units.

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

[0094] S063. Normalize the species × trait coding matrix: To ensure the consistency of the weights of different functional traits, it is necessary to normalize the species × trait coding matrix. The normalization method can be maximum - minimum normalization (Min - Max Normalization) for each column or other appropriate normalization methods;

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

[0096] S065. Calculate the site × trait abundance matrix: Multiply the normalized species × trait coding matrix by the site × species abundance matrix to obtain the site × trait abundance matrix. The multiplication operation can be achieved through 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 divergence, functional evenness, quadratic entropy index, and functional redundancy index.

[0098] Functional richness index:

[0099] FR ic = SF ic / R c

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

[0101] Functional divergence index:

[0102]

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

[0104] Functional evenness index:

[0105]

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

[0107] Quadratic entropy index:

[0108]

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

[0110] Functional redundancy index:

[0111] FD = RaoQ / H

[0112] Where H is the Shannon diversity.

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

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

[0115] S082. Selecting analysis tools: Use the indicspecies package in the R language to perform indicator species analysis. The R language is a commonly used tool for ecological data analysis, providing a rich set of packages and functions to handle complex ecological data;

[0116] S083. Performing indicator species analysis: Import the site × trait abundance matrix and the trophic gradient grouping information into R or other analysis tools, and use the indicator species analysis method to calculate the indicator values of each functional trait group in different trophic gradient groups. The indicator values usually include specificity and fidelity. Specificity: Represents the relative abundance of a functional trait group in a specific group. Fidelity: Represents the occurrence frequency of a functional trait group in that group. Evaluate the statistical significance of the indicator values through Monte Carlo permutation tests, usually setting a significance level, such as p < 0.05, to determine which functional trait groups are significant indicator species;

[0117] S084. Result Interpretation: 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: Create charts, such as bar charts, heatmaps, etc., to display the indicator functional trait groups and their indicator values in different nutrient gradient groups, write a technical report summarizing the results of the 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. Construct and Apply a Random Forest Model:

[0120] S091. Data Preparation: Obtain the functional diversity indices from step S07, the nutrient indices and comprehensive nutrient indices in the water bodies of each monitoring point from step S01, and consider adding other environmental variables, such as water temperature, pH value, flow velocity, etc., to increase the explanatory power of the model;

[0121] S092. Data Preprocessing: For any missing values, imputation methods can be used, such as K-nearest neighbor imputation, mean / median imputation, or deleting the data points containing missing values. Since different nutrient indices and environmental variables have different units and scales, they should be standardized by z-score or normalized by min-max to ensure that all features are on the same order of magnitude;

[0122] S093. Dataset Partitioning: Partition the dataset into a training set and a test set, with a splitting ratio of 70% for training and 30% for testing, and use stratified sampling to maintain the consistency of the distribution of each category in the training set and the test set;

[0123] S094. Establish 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: Train the random forest model using the training set data;

[0125] S096. Model Evaluation: Evaluate the model performance on the test set, and the following metrics can be used:

[0126] For regression problems: Coefficient of determination R2, Mean Squared Error MSE, Mean Absolute Error MAE;

[0127] S097, Cross-validation and Hyperparameter Tuning: Create a parameter grid listing the hyperparameters to be searched and their possible value ranges, define the scoring criteria for evaluating model performance using negative mean squared error, find the objective function to be maximized, use GridSearchCV or RandomizedSearchCV for hyperparameter tuning, perform cross-validation using the training set data, 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 nutrient indicators and environmental factors have the greatest impact on the functional diversity of benthic animals;

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

[0130] S10, Construct the Threshold Response Relationship between Benthic Animal Function and Nutrients:

[0131] S11, Data Preparation: The functional diversity index obtained from S07 step, the nutrient indicators (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 ) in the water bodies of each monitoring point obtained from S01 step, and the results of indicator species analysis obtained from S08 step, especially the characteristic benthic animal trait groups under different nutrient gradients;

[0132] S12, Selection of Analysis Tools: Use the TITAN2 package in R language for threshold indicator taxa analysis. The TITAN2 package is a tool specifically used to identify the thresholds of the impact of environmental variable changes on biological communities;

[0133] S13, Perform Threshold Indicator Taxa Analysis: Import the functional diversity index and nutrient indicator data into the R environment and use the functions in the TITAN2 package for analysis;

[0134] S14. Result interpretation: The TITAN2 analysis will output the thresholds for each nutrient index. 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 through permutation tests. Usually, a significance level, such as p < 0.05, is set to determine which thresholds are significant. The TITAN2 package provides various graphical outputs, such as cumulative distribution function plots (CDF), probability density function plots (PDF), etc., to help visually display the threshold positions and their significance.

[0135] S15. Combining 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 key nutrient factors. For example, if the TITAN2 analysis shows a sudden change in functional diversity when TN is 1.5 mg / L, and the indicator species analysis shows that certain specific functional trait groups increase or decrease significantly under this condition, this can help explain the reason for this mutation.

[0136] S16. Proposing management suggestions: Based on the results of the TITAN2 analysis, determine the key nutrient driving factors and their corresponding ecological thresholds, and propose specific management suggestions. For example, control the concentrations of TN and TP below the thresholds to protect the health of the river ecosystem. For example, it can be recommended that the management department set water quality standards to ensure that the total nitrogen (TN) does not exceed 1.5 mg / L and the total phosphorus (TP) does not exceed 0.5 mg / L, so as to maintain the health status of the benthic animal community.

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

[0138] Through the description of the above implementation manners, those skilled in the art can clearly understand that each implementation manner can be realized by means of software plus a general - purpose hardware platform, and of course, it can also be realized by hardware. Based on such an understanding, the essence of the above - mentioned technical solution, or the part that contributes to the related technology, can be embodied in the form of a software product. This computer software product can be stored in a computer - readable storage medium, such as ROM / RAM, magnetic disk, optical disc, etc., and includes several instructions to enable 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 foregoing has shown and described the basic principles, main features and advantages of the present invention. For a person skilled in the art, it is obvious that the present invention is not limited to the details of the above-mentioned exemplary embodiments, and without departing from the spirit or basic features of the present invention, the present invention can be implemented in other specific forms. Therefore, in whatever aspect, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to embrace all changes that fall within the meaning and scope of the equivalent elements of the claims in the present invention, and any reference signs in the claims should not be regarded as limiting the claims involved.

[0140] In addition, it should be understood that although this specification is described according to embodiments, not every embodiment only contains an independent technical solution. This narrative way of the specification is only for clarity. A person skilled in the art should regard the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by a person skilled in the art.

Claims

1. Method for determining threshold of functional traits of river benthic animal community to nutrient stress, characterized in that It includes the following steps: S01. Measuring river nutrient pollutants: Select several areas with different interference degrees along the river and set several monitoring stations, and measure the nutrient indexes of the water body at each monitoring point; S02. Dividing nutrient gradients: Standardize the nutrient indexes by z-score, take the mean to obtain the comprehensive nutrition index, and then conduct gradient division; S03. Collecting benthic animal samples: Collect benthic animal community samples in the water area of each monitoring station; S04. Identifying benthic animals: Wash the collected sample sieve, place the residue in a white porcelain plate, pick out the benthic animals, put them into formalin solution for identification and counting; S05. Selecting benthic animal functional traits: Select multiple functional traits of benthic animals that are sensitive to environmental changes; S06. Counting the abundance of benthic animal functional trait groups: Each functional trait is defined by the method of fuzzy coding to obtain the benthic animal species × trait coding matrix, and the species × trait coding matrix is normalized, and multiplied by the site × species abundance matrix to obtain the site × trait abundance matrix; S07. Calculating the functional diversity index of benthic animals: Calculate the functional diversity index of each monitoring station according to the obtained functional trait abundance data; S08. Identifying characteristic functional traits of benthic animals: Use the indicator species analysis method to mark the characteristic benthic animal trait groups under different nutrient groups; S09. Constructing and applying a random forest model: Establish a random forest model with the functional diversity index and the nutrient indexes in the water body of each monitoring point, use the feature importance score of the random forest model to determine the influence of nutrient indexes and environmental factors on the functional diversity of benthic animals, visualize the feature importance, and understand the action mechanism of key nutrient factors according to the results output by the model combined with the results of indicator species analysis; S10. Constructing the threshold response relationship between benthic animal functions and nutrients: Use the threshold indicator taxa analysis method to determine the ecological thresholds of nutrient driving factors that cause mutations in key functional trait groups and functional diversity of benthic animals.

2. The method for determining the threshold of the functional traits of river benthic animal communities against nutrient stress according to claim 1, characterized in that In step S01, the nutrient indexes 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 of the functional traits of river benthic animal communities against nutrient stress according to claim 1, characterized in that In step S02, the gradient division of the comprehensive nutrition 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 are classified into the low nutrition group; Those with a comprehensive nutrition index greater than the 1 / 3 quantile and less than or equal to the 2 / 3 quantile are classified into the medium nutrition group; Those with a comprehensive nutrition index greater than the 2 / 3 quantile are classified into the high nutrition group.

4. The method for determining the threshold of the functional traits of river benthic animal communities under nutrient stress according to claim 1, characterized in that In step S03, for benthic animal specimens in deep water areas, a modified weighted 1 / 16 m2 Petersen grab sampler is used for collection; For the collection of benthic animal specimens in shallow water areas, a D-type dip net is used.

5. The method for determining the threshold of the functional traits of river benthic animal communities under nutrient stress according to claim 1, wherein In step S04, the sieve is 60 mesh, and the picking tools include tweezers and pipettes.

6. The method for determining the threshold of the functional traits of river benthic animal communities under nutrient stress according to claim 1, characterized in that, In step S05, multiple functional traits include life cycle, individual size, feeding type, reproductive type, diapause type, dispersal ability, defensive morphology, respiratory mode, and movement pattern.

7. The method for determining the threshold of the functional traits of river benthic animal communities under nutrient stress according to claim 1, characterized in that In step S07, the functional diversity index includes functional richness, functional distinctness, functional evenness, the quadratic entropy index, and the functional redundancy index.

8. The method for determining the threshold of the functional traits of river benthic animal communities under nutrient stress according to claim 1, characterized in that, In step S09, a data set is established based on the functional diversity index and each nutritional index, the data set is divided into a training set and a test set, the random forest model is trained using the training set data, and the performance of the random forest model is evaluated using the test set.

9. The method for determining the threshold of the functional traits of river benthic animal communities against nutrient stress according to claim 1, characterized in that In step S09, it also includes selecting the optimal parameter combination of the training set and the test set through cross-validation.

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