Method for monitoring diversity of large benthic invertebrates in river based on eDNA

Abstract

The invention discloses a method for monitoring diversity of large benthic invertebrates in a river based on eDNA. The method comprises the following steps: step 1, selecting a river basin to be measured, and querying basic information of the basin; 2, collection and treatment of eDNA samples: designing sampling points and geographic positions, and collection and pretreatment of water samples; 3, measuring physical and chemical indexes; 4, extraction of eDNA, PCR amplification and high-throughput sequencing are carried out; and 5, performing data analysis on the results, wherein the data analysis comprises bioinformatics analysis, alpha diversity analysis, beta diversity analysis and correlation analysis of large benthic invertebrate communities and environmental factors. The diversity of the large benthic invertebrates is more accurately and efficiently investigated and monitored by analyzing DNA fragments existing in the environment.

Description

Technical Field

[0001] The invention relates to a method for monitoring biodiversity, and in particular to a method for monitoring the diversity of large benthic invertebrates in rivers based on eDNA. Background Art

[0002] Environmental DNA (eDNA) refers to the sum of biological genetic material (DNA) directly extracted from environmental media (such as water, soil, sediment, air, biofilm, ice core, etc.) or mixed biological tissues, including a mixture of DNA from different species such as microorganisms, animals, plants, etc. These DNA fragments come from epidermal cells released into the environment by organisms, such as skin, urine, feces, mucus, etc., and also include DNA fragments released into the environment after cell death and lysis.

[0003] eDNA technology is an emerging technology that uses the specificity within species and the diversity between species to create a biological identification system. This technology includes directly capturing DNA from environmental samples, and performing sample preservation, DNA extraction, PCR amplification, high-throughput sequencing, bioinformatics analysis, biostatistics, quality control and quality assurance processes to determine the species and abundance of target organisms in environmental samples in the survey area, which can be used to detect and monitor biodiversity in the environment.

[0004] Large benthic invertebrates live on the bottom or bottom of water bodies, including oligochaetes, mollusks, aquatic insects and aquatic worms, etc. They play an important role in the decomposition and recycling of benthic environments, play a key role in the nutrient cycle and organic matter degradation of water bodies, and have an important impact on water quality and ecosystem functions. The study of their diversity is one of the important areas of biodiversity conservation and environmental management. In the past few decades, researchers have conducted preliminary surveys and descriptions of large benthic invertebrates in the Luanhe River Basin through traditional field surveys and specimen collection methods. However, these methods have some limitations, such as time-consuming, labor-intensive and difficult species identification. Monitoring and surveys using eDNA technology do not require intervention in the ecological environment or fishing for species. Traditional species survey methods usually require fishing, observation and species identification. eDNA technology is time-saving, labor-saving, low-cost and efficient. The detection rate of eDNA survey results is generally higher than that of traditional capture methods. Summary of the invention

[0005] The present invention aims to provide a method for monitoring the diversity of large benthic invertebrates in rivers based on eDNA, which can more accurately and efficiently investigate and monitor the diversity of large benthic invertebrates by analyzing DNA fragments present in the environment.

[0006] To achieve the above object, the present invention provides the following technical solution: a method for monitoring the diversity of river macrobenthic invertebrates based on eDNA, comprising the following steps:

[0007] Step 1: Select the river basin to be measured and query the basic information of the basin;

[0008] Step 2: eDNA sample collection and processing: including the design and geographical location of sampling sites, water sample collection and pretreatment;

[0009] Step 3: Determination of physical and chemical indicators;

[0010] Step 4: eDNA extraction, PCR amplification and high-throughput sequencing;

[0011] Step 5: Conduct data analysis on the above results: including bioinformatics analysis, α diversity analysis, β diversity analysis, and correlation analysis between large benthic invertebrate communities and environmental factors.

[0012] Preferably, the basic information in step one includes the location, total length, area and climate zone of the river.

[0013] Preferably, after the sampling points in step 2 are designed, each sampling point needs to be sampled separately in four seasons, and water samples are collected from 0.5 meters below the surface water at a distance of about 5 meters from the river bank. Each sampling point is sampled repeatedly and then mixed. The mixed water samples are used for the detection of biological and physicochemical indicators respectively. After the filter membrane is generated at the sampling location, it is sent back to the laboratory and stored at -80°C until subsequent extraction.

[0014] Preferably, the physical and chemical indicators of step three include measuring and recording some physical indicators at the sampling site and detecting chemical indicators at each sampling point, wherein the physical indicators include WT (water temperature), DO (dissolved oxygen), pH, TDS (total dissolved solids), EC (conductivity), ORP (oxidation-reduction potential) and other indicators, and the chemical indicators include TN, TP, NH3-N, NO3 - 、NO2 - , COD, TDN, TDP, Fe, Mn and other indicators.

[0015] Preferably, the extraction of eDNA in step 4 is to take out the filter membrane stored at -80°C in step 2 and thaw it, and use Spin Kit for Soil was used to extract total DNA from the filter membrane obtained by filtration. Each sample was subjected to 3 parallel experiments. After the total DNA extraction was completed, the extracted DNA was detected by 1% agarose gel electrophoresis.

[0016] Preferably, in step 4, PCR amplification is performed using universal primers, and negative controls are set to ensure that there is no DNA contamination during the amplification process and the accuracy and reliability of the experimental results; specific primers with barcodes are synthesized according to the specified sequencing region; low cycle amplification is used as much as possible to ensure that the number of cycles for each sample amplification is consistent to ensure the accuracy and reliability of subsequent data analysis; representative samples are randomly selected for preliminary experiments to ensure that the vast majority of samples can amplify products of appropriate concentrations at the lowest cycle number; all samples are subjected to formal experimental conditions using a PCR instrument, with 3 replicates for each sample, and the PCR products of the same sample are mixed and detected by 2% agarose gel electrophoresis, and the PCR products are purified using an AxyPrepDNA gel recovery kit, eluted with Tris-HCl, and detected by 2% agarose electrophoresis.

[0017] Preferably, the high-throughput sequencing in step 4 uses The library of the amplified DNA was constructed using the Rapid DNA-Seq Kit, and sequencing was performed using the Miseq PE300 platform of Illumina.

[0018] Preferably, the bioinformatics analysis in step five includes data optimization and OTU clustering. Data optimization is to use fastp software to perform quality control on the original sequencing sequence and use FLASH software for splicing; OTU clustering is to use UPARSE software to perform OTU clustering on the sequence according to 97% similarity.

[0019] Preferably, the α diversity analysis in step 5 uses tools such as Mothur to analyze the α diversity index in the sample, such as diversity index, richness index, uniformity index, coverage, etc.

[0020] Preferably, in step 5, the β diversity analysis: the β diversity distance matrix is ​​calculated using Qiime software, and the vegan software package in the R language is used to perform NMDS analysis and Adonis analysis.

[0021] The principle and beneficial effects of this technical solution:

[0022] The eDNA technology was used to detect the biodiversity of large benthic invertebrates in the main stream and first-level tributaries of the river in different seasons. At the same time, the environmental factors in various sections of the Luanhe River Basin were detected and correlation analysis was performed. eDNA monitoring is a potential method for assessing biodiversity and evaluating ecological quality. The application of eDNA to monitor the biodiversity of large benthic invertebrates provides new research ideas and experimental basis. By optimizing sampling methods, primer design and PCR optimization, as well as reasonably interpreting and analyzing data, it can provide important experimental basis for biological protection, ecological environment restoration and rational use of resources. In addition, combining eDNA monitoring data with other ecological data and environmental information can obtain more comprehensive ecological information and promote scientific decision-making and the formulation of management measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flow chart of the present invention;

[0024] Figure 2 This is a sampling map of the Luanhe River Basin in an embodiment of the present invention;

[0025] Figure 3 This is the dilution curve of macrobenthic invertebrates in the Luanhe River Basin in the embodiment of the present invention;

[0026] Figure 4 is the relative abundance ratio of macrobenthic invertebrates in the embodiment of the present invention;

[0027] Figure 5 A pie chart of the macrobenthic invertebrate community at the level of the embodiment of the present invention;

[0028] Figure 6 This is a Heatmap of the macrobenthic invertebrate community at the family level according to an embodiment of the present invention;

[0029] Figure 7 This is a graph showing the inter-group differences in the alpha diversity index of macrobenthic invertebrates in different seasons in an embodiment of the present invention;

[0030] Figure 8 The NMDS analysis diagram of macrobenthic invertebrates in different seasons in the embodiment of the present invention;

[0031] Fig. 9 The Manteltest heat map of macrobenthic invertebrate community structure and environmental factors in different seasons of the embodiment of the present invention;

[0032] Fig.10 A Heatmap diagram of the correlation between macrobenthic invertebrates and environmental factors according to an embodiment of the present invention;

[0033] Fig.11 This is a distance attenuation change diagram of large benthic invertebrates in the Luanhe River Basin according to an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The present invention is further described in detail below with reference to the accompanying drawings and embodiments:

[0035] Example:

[0036] like Figure 1 As shown in the figure, a method for monitoring the diversity of river macrobenthic invertebrates based on eDNA includes the following steps:

[0037] Step 1: Select the river basin to be measured and query the basic information of the basin: This embodiment selects the Luanhe River Basin for research.

[0038] The Luanhe River Basin is a national key ecological functional area and the second largest river system in Hebei Province. It is located in the northeast of the Haihe River Basin, starting from the Mongolian Plateau in the north, facing the Bohai Sea in the south, the Chaobai River and Jiyun River systems in the west, and adjacent to the Liaohe River Basin in the east. It is 888 kilometers long and has a total area of ​​5.55km 2 . The basin belongs to the East Asian monsoon climate zone, with significant monsoon, four distinct seasons, concentrated rainfall, rain and heat in the same season, diverse terrain and complex climate. The upper and middle reaches are the water source conservation area and ecological environment support area of ​​Beijing-Tianjin-Hebei, with 16 nature reserves, including 7 national nature reserves, which are important ecological barriers and ecological corridors of Beijing-Tianjin-Hebei. There are many large-scale water storage projects distributed in the middle and lower reaches. Panjiakou and Daheding reservoirs are the water sources for the Luan River to Tianjin project. The cleaning of the reservoir cages has achieved remarkable results. The fish cleaning work was completed at the end of March 2017. The water quality and ecological environment of the reservoir have continued to improve and reached the surface water Class II standard. The water quality in the middle and lower reaches has declined. Affected by industrial development and urbanization, the discharge of sewage has increased, which has affected the water quality of the Luan River. Changes in the ecological environment of the basin have brought challenges to the environmental security of the Beijing-Tianjin-Hebei region.

[0039] Step 2: Collection and processing of eDNA samples: including the design and geographical location of sampling points, collection and pretreatment of water samples.

[0040] (1) Design and geographical location of sampling points

[0041] This study collected eDNA samples in the Luanhe River Basin in winter (February), spring (June), summer (August), and autumn (October) of 2023. The sampling points were arranged as follows: Figure 2As shown, there are 19 points in the main stream of the Luanhe River Basin and 9 points in the first-level tributaries. Points 1 to 19 are the main stream water bodies of the Luanhe River Basin, of which points 1 to 3 are the source section of the Luanhe River Basin, mainly located in Zhenglan Banner and Duolun County of Xilin Gol League, Inner Mongolia Autonomous Region; points 4 to 6 are the upper reaches of the Luanhe River Basin, mainly located in Longhua County, Chengde City, Hebei Province; points 7 to 16 are the middle reaches of the Luanhe River Basin, mainly located in Luanping County, Chengde County, Xinglong County, Qianxi County, and Qian'an City, Chengde City, Hebei Province; points 17 to Point 19 is the lower reaches of the Luanhe River, mainly located in Luanzhou City, Leting County, Tangshan City, Hebei Province, and Changli County, Qinhuangdao City; points 20 to 28 are first-level tributaries of the Luanhe River, among which point 20 is Xiaoluan River, point 21 is Xingzhou River, point 22 is Yison River, point 23 is Wulie River, point 24 is Laoniu River, point 25 is Liu River, point 26 is Puhe River, point 27 is Sahe River, and point 28 is Qinglong River.

[0042] (2) Water sample collection and pretreatment

[0043] Use a 3L organic glass water sampler to collect surface water samples (about 0.5 meters below the surface of the river) at a distance of about 5 meters from the river bank and put them into a 3L sterile sampling bottle. Repeat 3 times for each sampling point. After the water samples are mixed evenly on site, 3L is taken for biological detection, and 1L is taken for the determination of physical and chemical indicators. The collected water samples are placed in a -20℃ portable refrigerator and filtered within 24 hours. The filter membrane uses a 0.22μm hydrophilic nylon membrane and uses a SHB-III vacuum pump for filtration. After the sample is filtered, the obtained filter membrane is placed in a 2mL sterile centrifuge tube and temporarily stored in a -20℃ portable refrigerator. After the filter membrane is returned to the laboratory, it is stored at -80℃ until extraction.

[0044] During the sampling process, please note that the plexiglass water sampler needs to be repeatedly rinsed with water from the sampling point to avoid biological contamination on the inner wall of the water sampler. Each sampling point needs to be sampled at least 3 times to increase the representativeness of the sample in this area. Before each sampling, the sampler needs to wear sterile disposable gloves and masks to reduce human contamination.

[0045] Step 3: Determination of physical and chemical indicators.

[0046] At the sampling site, some physical indicators were measured and recorded, including WT (water temperature), DO (dissolved oxygen), pH, TDS (total dissolved solids), EC (conductivity), ORP (oxidation-reduction potential), etc. According to the national standard and the "Water and Wastewater Monitoring Method" (4th edition), the chemical indicators of each sampling point were tested, including TN, TP, NH3-N, NO3 - 、NO2 -, COD, TDN, TDP, Fe, Mn and other indicators. See Table 1 for specific methods and Table 2 for main instrument models.

[0047] Table 1 Water quality index determination method and used drugs

[0048]

[0049]

[0050] Table 2 Main instrument models

[0051]

[0052] Step 4: eDNA extraction, PCR amplification and high-throughput sequencing;

[0053] (1) eDNA extraction process

[0054] Thaw the filter membrane stored at -80℃ and use The total DNA was extracted from the filter membrane using the Spin Kit for Soil (MP Biomedicals, USA). The experimental steps were strictly performed according to the instructions of the kit. Each sample was run in parallel 3 times. After the total DNA was extracted, the extracted DNA was detected by 1% agarose gel electrophoresis.

[0055] The specific steps of total DNA extraction are as follows: 1) Take 0.5g sample, add 978μL sodium phosphate buffer and 122μL MT buffer to Lysing Matrix E tube; 2) Use MP grinder to shake at 6m / s speed for 40s; 3) Centrifuge at 14000rpm for 10min; 4) Transfer the supernatant to a 1.5mL centrifuge tube, add 250μL PPS and mix thoroughly; 5) Centrifuge at 14000rpm for 5min at room temperature; 6) Transfer the supernatant to a 2mL tube with 900μL Binding Matrix added, mix well, and turn upside down for 3min; 7) Centrifuge for 5sec (i.e. stop as soon as the speed rises), and carefully discard the supernatant; 8) Add 500μL 5.5M guanidine thiocyanate solution, mix well, and transfer to SPIN TM Filter; 9) Add 500 μL SEWS-M, centrifuge at 14000 rpm for 1 min, discard the filtrate, and repeat the wash once; 10) Discard the liquid in the collection tube, centrifuge at 14000 rpm for 3 min, remove the residual solution, and dry for 3 min; 11) Add 100 μL 55℃ preheated DES eluent and let stand for 5 min; 12) Centrifuge at room temperature at 14000 rpm for 2 min, discard the SPIN TM Filter to obtain total DNA.

[0056] (2) PCR amplification

[0057] Universal primers were used to perform PCR amplification on large benthic invertebrates in the Luanhe River Basin. The universal primer sequences were referenced to the national group standard information platform, see Table 3. In order to ensure that there was no DNA contamination during the amplification process and the accuracy and reliability of the experimental results, a negative control was set. According to the specified sequencing region, specific primers with barcodes were synthesized. To ensure the accuracy and reliability of subsequent data analysis, two conditions must be met: 1) use low cycle amplification as much as possible; 2) ensure that the number of cycles amplified for each sample is consistent. Representative samples were randomly selected for preliminary experiments to ensure that the vast majority of samples can amplify products of appropriate concentrations at the lowest number of cycles. The PCR instrument uses ABI 9700 (ABI, USA). All samples were subjected to formal experimental conditions, with three replicates for each sample. PCR products of the same sample were mixed and detected by 2% agarose gel electrophoresis. PCR products were purified using the AxyPrep DNA gel recovery kit (AXYGEN, USA), eluted with Tris-HCl, and detected by 2% agarose gel electrophoresis.

[0058] Table 3 DNA macrobarcode amplification primer information

[0059]

[0060]

[0061] (3) High-throughput sequencing

[0062] use The Rapid DNA-Seq Kit is used to construct a library of amplified DNA, and the steps are as follows: 1) connect adapters; 2) use magnetic beads to purify and remove self-ligated adapter fragments; 3) enrich the library templates through PCR amplification; 4) recover the PCR products with magnetic beads to obtain the final library.

[0063] Sequencing was performed using Illumina's Miseq PE300 platform (tested by Shanghai Meiji Biotechnology Co., Ltd.), and the specific operation process is as follows: 1) One end of the DNA fragment is complementary to the primer base and fixed on the chip; 2) The base sequence on the chip is used as a primer to perform PCR amplification on the DNA fragment to form a target DNA fragment; 3) After denaturation and annealing steps, the other end of the DNA fragment is randomly paired with an adjacent primer and fixed to form a "bridge"; 4) PCR amplification is performed to generate a DNA cluster; 5) The linearized amplicon becomes a single-stranded DNA; 6) The modified DNA polymerase and dNTP labeled with four fluorescent molecules are introduced to perform synthesis reactions base by base; 7) The type of nucleotides polymerized in the first round of reaction for each template sequence is read through laser scanning; 8) Chemical cutting removes the "fluorescent group" and "termination group" to expose the 3' end stickiness so that subsequent nucleotide polymerization can continue; 9) The fluorescence signal data of each round of reaction is counted to determine the sequence information of the template DNA fragment.

[0064] Step 5: Conduct data analysis on the above results: including bioinformatics analysis, α diversity analysis, β diversity analysis, and correlation analysis between large benthic invertebrate communities and environmental factors.

[0065] (1) Bioinformatics analysis

[0066] Data optimization: fastp software was used to perform quality control on the original sequencing sequence, and FLASH software was used for splicing: 1) a 50 bp sliding window was used to filter out bases with a tail quality value lower than 20. If the average quality value in the window was less than 20, the back-end bases were cut off from the window, the bases below 50 bp after quality control were filtered, and the bases containing N bases were removed; 2) the overlapping regions between paired end (PE) reads were used to splice them into a continuous sequence, and the minimum overlap length was set to 10 bp; 3) the maximum mismatch rate allowed in the overlapping region of the spliced ​​sequence was 20%, and the non-compliant sequences were screened; 4) the samples were identified according to the barcodes and primers at both ends of the sequence, and the sequence direction was adjusted. The number of mismatches allowed for the barcode was 0, and the maximum number of mismatches for the primers was 2.

[0067] OTU clustering: UPARSE software was used to perform OTU clustering on the sequences based on 97% similarity. The specific process is as follows:

[0068] 1) Extract non-repetitive sequences from the optimized sequences to reduce computational redundancy during the analysis process;

[0069] 2) exclude isolated single sequences;

[0070] 3) Perform OTU clustering at 97% similarity on non-repetitive sequences, exclude potential chimeric sequences, and obtain OTU representative sequences;

[0071] 4) All optimized sequences were mapped to the OTU representative sequences, sequences with a similarity of more than 97% were screened out, and an OTU classification table was generated.

[0072] In order to obtain the species classification information corresponding to each OTU, the RDP classifier Bayesian algorithm was used to perform taxonomic analysis on the OTU representative sequences with a similarity level of 97%, and the community species composition of each sample was statistically analyzed at each taxonomic level: Domain, kingdom, phylum, class, order, family, genus, and species.

[0073] (2) α diversity analysis

[0074] The OTUs annotation results provide information about different species or taxa in the sample, which can be used to further study species diversity, community structure, and ecological functions. To study the diversity of microorganisms in the environment, the richness and diversity of the microbial community can be reflected by Alpha (α) diversity analysis, which includes a series of statistical analysis indices to estimate the species abundance and diversity of the environmental community. The commonly used index algorithms are as follows:

[0075] 1) Shannon Index, also known as Shannon Diversity Index, is an index used to measure species diversity in a community. It takes into account species richness (i.e., the number of species) and uniformity (i.e., the relative abundance of each species). It is often used to reflect community Alpha diversity. If the number of individuals of all species is very uniform, the Shannon Index is high. If a species is very dominant, the Shannon Index is low. The calculation formula is as follows:

[0076]

[0077] Where:

[0078] S obs ——The actual number of OTUs observed;

[0079] n i ——The number of sequences contained in the i-th OTU;

[0080] N – the number of all sequences.

[0081] 2) The Simpson Index is also an indicator used to measure community diversity, but it focuses more on describing the dominance of species. The Simpson Index measures the probability that two individuals belong to the same species in a random sample. In ecology, it is often used to quantitatively describe the biodiversity of a region. If the number of individuals of all species in a community is very uniform, the Simpson diversity index is close to 0. If a species is very dominant, the Simpson diversity index is close to 1. The Simpson Index can be used to measure the uniformity of species in a community. The higher the index, the higher the uniformity. The calculation formula is as follows:

[0082]

[0083] Where:

[0084] S obs ——The actual number of OTUs observed;

[0085] n i ——The number of sequences contained in the i-th OTU;

[0086] N – the number of all sequences.

[0087] 3) This paper uses chao1 to estimate the number of OTUs contained in the sample. chao1 is often used to estimate the total number of species in ecology. The larger the chao1 index value, the higher the richness of the community. The calculation formula used in this analysis is as follows:

[0088]

[0089] Where:

[0090] S obs ——The actual number of OTUs observed;

[0091] n1——the number of OTUs containing only one sequence (such as "singletons");

[0092] n2 - the number of OTUs containing only two sequences (e.g. "doubletons").

[0093] 4) Pielou evenness index is a commonly used ecological indicator used to assess the evenness or distribution uniformity of species in a sample. A higher Pielou evenness index value indicates a more even distribution of species, while a lower value indicates an imbalance in species distribution. The index ranges from 0 to 1, where 1 indicates a completely even distribution of species and 0 indicates an extremely uneven distribution of species. By calculating the Pielou evenness index, the species evenness of different samples or treatment groups can be compared, and the species diversity and relative richness of biological samples can be assessed. The calculation formula is as follows:

[0094]

[0095] Where:

[0096] H′——Shannon index;

[0097] S – the number of species in a community.

[0098] 5) Coverage refers to the coverage rate of each sample library. The higher the value, the higher the probability that the sequence in the sample is detected, and the lower the probability that it is not detected. This index reflects whether the sequencing result represents the actual situation of the microorganisms in the sample. The calculation formula is as follows:

[0099]

[0100] Where:

[0101] n1——the number of OTUs containing only one sequence;

[0102] N – the total number of sequences appearing in the sample.

[0103] (3) β diversity analysis

[0104] Beta (β) diversity analysis is used to compare the similarities or differences in community structure between different samples. Similar to the alpha diversity index, they are both designed to assess the heterogeneity of biodiversity within an environment or geographic region. The distribution differences in species abundance between different samples can be quantified using distance metrics in statistics. By calculating the statistical distance between two samples, a distance matrix is ​​obtained, which can be used for further beta diversity analysis and visual statistical interpretation.

[0105] 1) The algorithm of Bray-Curtis distance matrix is:

[0106]

[0107] Where:

[0108] S A,i ——The number of sequences contained in the i-th biological group in sample A;

[0109] S B,i ——The number of sequences contained in the i-th biological group in sample B.

[0110] 2) The algorithm of Euclidean distance matrix is:

[0111] D 12 =sqrt(∑(X i1 -X i2 2 ),i=1,2,3…

[0112] In the formula,

[0113] 1——number of OUTs of the i-th sample 1;

[0114] 2——the number of OUTs of the i-th sample 2;

[0115] sqrt——square root

[0116] 3) Non-metric multidimensional scaling (NMDS) is a method that simplifies the research object in multidimensional space to a low-dimensional space for positioning, aiming to preserve the original relationship between data points for easy positioning, analysis and classification. This method mainly defines and quantifies distance by comparing the rank differences of samples between different groups.

[0117] 4) Adonis analysis, also known as permutation multi-factor ANOVA or non-parametric multi-factor ANOVA, uses the distance matrix to decompose the total variance and analyze the degree of explanation of sample differences by different grouping factors. This method is combined with permutation test to evaluate the statistical significance and significance level of the differences between each group.

[0118] (4) Use of software and tools

[0119] When analyzing eDNA data, a variety of software and tools were used for pre-processing, plotting, and statistical analysis.

[0120] 1) Preprocessing and data organization: The eDNA data obtained by the test were imported into Excel software for preprocessing, data cleaning, format organization and data standardization.

[0121] 2) Chart drawing: Use Origin8.5 software to draw bar charts, and use Office (Microsoft 2021) to draw tables.

[0122] 3) Species composition and community analysis: Use Circos-0.67-7 software (http: / / circos.ca / ) to draw a Circos sample-species relationship diagram; use R language tools to draw community Bar graphs to show the differences in microbial community structure between different seasons. The distribution of dominant species can be displayed by calculating species abundance or relative abundance.

[0123] 4) α diversity analysis: Use tools such as Mothur (version 1.30.2, https: / / www.mothur.org / wiki / Download_mothur) to analyze the α diversity indices in the samples, such as diversity index, richness index, evenness index, coverage, etc.

[0124] 5) β-diversity analysis: Qiime software (version 1.9.1, http: / / qiime.org / install / index.html) was used to calculate the β-diversity distance matrix, and the vegan package (version 2.4.3) in the R language (version 3.3.1) was used for NMDS analysis and Adonis analysis.

[0125] 6) Community-environment correlation analysis: Use the vegan package of R language to analyze the correlation between biological communities and environmental factors.

[0126] Test results

[0127] 1. High-throughput sequencing results of large benthic invertebrates based on eDNA

[0128] Based on the eDNA macrobarcode sequencing technology, the large benthic invertebrate populations in the water bodies of the Luanhe River Basin were sequenced and analyzed, and the diversity data analysis of 309 samples was completed. After screening the number of sequences that can match the forward primer and reverse primer at the same time in the original data, and filtering out low-quality sequences, a total of 564,196 valid sequences were obtained, with an average sequence length of 313bp. The rarefaction curve of large benthic invertebrates (OTUs) is shown in Figure 2. Figure 3 As shown in the figure, as the amount of data extracted increases, the Shannon index tends to be flat, indicating that when the similarity threshold of this curve is 97%, the amount of sequencing data can meet the diversity analysis of subsequent samples.

[0129] 2. Analysis of macrobenthic invertebrate community composition

[0130] (1) Taxonomic unit statistics

[0131] The taxonomic unit statistics of the large benthic invertebrate samples collected in the Luanhe River Basin were carried out. The clustering method adopted the USEARCH11-uparse algorithm, the OTU sequence similarity was set to 0.97, and the nt_v20221012 species classification database was used. A total of 1419 large benthic invertebrate OTUs were obtained using the gene COI-1 marker. According to the database comparison results, the OTU level that was not classified was marked as Unclassified and was not included in the species diversity assessment. In this survey of large benthic invertebrates in the Luanhe River Basin, a total of 11 phyla, 30 classes, 83 orders, 319 families, 729 genera and 990 species were obtained. In terms of time, the number of species at each classification level of large benthic invertebrates in each season is shown in Table 4. The order of the number of species at the species level, genus level, family level and order level in each season is: summer>autumn>spring>winter.

[0132] In the autumn, a field survey of large benthic animals was conducted in the Luanhe River Basin. Large benthic animals were collected using Sober nets and Peterson mud samplers. Based on morphological classification, a total of 206 species of large benthic animals were identified, belonging to 8 classes, 22 orders, 70 families and 144 genera. In the mainstream of the Luanhe River Basin and its main tributaries, large benthic invertebrates were collected along the river sections using the kicking net method, and a total of 117 large benthic invertebrate taxa were collected. Based on traditional survey methods, a total of 47 species of benthic animals were collected during 2020, and a total of 45 species were collected during 2021. Compared with the monitoring results of this study using eDNA technology, the above research results based on traditional morphology show that eDNA technology has obvious advantages in detecting the number of species.

[0133] Table 4 Statistics of macrobenthic invertebrate taxa in different seasons

[0134]

[0135]

[0136] The statistics of taxonomic units of large benthic invertebrates at different phylum levels are shown in Table 5. There are a total of 990 benthic animals in 11 phyla, of which Arthropoda accounts for the largest proportion of species, at 78.38%, followed by Mollusca, accounting for 6.57%, Porifera, accounting for 4.34%, Annelida, accounting for 4.04%, Nematoda, accounting for 3.33%, and Platyhelminthes, accounting for 1.11%. The species of the remaining five large benthic invertebrate phyla account for less than 1%.

[0137] Table 5 Statistics of taxonomic units of macrobenthic invertebrates at different phylum levels

[0138]

[0139] (2) Taxonomic composition analysis

[0140] The relative abundance of macrobenthic invertebrates in the Luanhe River Basin was analyzed by species sequence analysis. Figure 4). The relative abundance of macrobenthic invertebrate sequences (phylum level) in different seasons is different. The total number of sequences of the phylum Arthropoda accounts for the highest proportion, and it also accounts for the highest proportion in each season, accounting for 75.24% in autumn, 72.57% in spring, 61.96% in winter, and 50.96% in summer. The total number of sequences of the phylum Mollusca accounts for the second highest proportion. In terms of time, the highest proportion is 45.53% in summer, followed by autumn, accounting for 16.02%, spring accounting for 14.56%, and the lowest proportion is 2.69% in winter. The total number of sequences of the phylum Annelida accounts for the second highest proportion after Arthropoda and Mollusca, accounting for 13.95% in winter, 10.25% in spring, 3.72% in autumn, and less than 1% in summer. See for details. Figure 4 (a).

[0141] The relative abundance of sequences of macrobenthic invertebrates (order level) was analyzed. Diptera was the dominant order in the survey of macrobenthic invertebrates in the Luanhe River Basin, which was consistent with the results of the study. Hymenoptera was the dominant order in autumn, while Neuroptera and Haplosclerida were the dominant orders in winter. Among them, Diptera had the highest proportion of sequences in summer, which was 73.79%, and the lowest proportion in winter, which was 39.34%. Neuroptera and Haplosclerida had the highest proportion of sequences in winter, which were 20.82% and 16.31%, respectively, and the proportions in other seasons were less than 1%. Lepidoptera had the highest proportion of sequences in winter, which was 9.61%, and the lowest proportion in summer, which was 2.24%. Hymenoptera only accounts for a high proportion of the total number of sequences in autumn, accounting for 14.31%, and less than 1% of the total number of sequences in winter. Figure 4 (b).

[0142] The relative abundance of macrobenthic invertebrates in different seasons in the Luanhe River Basin (class level) was analyzed. Figure 5 . Insecta and Gastropoda were the dominant classes in this sampling, which was consistent with the results of the study. Insecta had the highest relative abundance in spring, autumn and winter; Gastropoda had the highest relative abundance in summer and the lowest relative abundance in winter; Polychaeta and Demospongiae had a high relative abundance only in winter.

[0143] The relative abundance of macrobenthic invertebrates in different seasons in the Luanhe River Basin (family level) was analyzed. Figure 6 The cluster tree results showed that the dominant families of benthic animals in spring and summer were more similar. Chironomidae, Insecta, Diptera, and Pachychilidae, Bivalvia, Mollusca were the dominant families in the survey of large benthic invertebrates in the Luanhe River Basin. Chironomidae had the highest relative abundance in spring; Pachychilidae had a significant abundance advantage in summer and the lowest relative abundance in winter; Phloeodictyidae, Capitellidae, Annelida, and Chrysopidae, Insecta, Neuroptera, had higher relative abundance in winter.

[0144] 3. Analysis of α-diversity of macrobenthic invertebrate communities

[0145] (1) Analysis of α-diversity index of macrobenthic invertebrates

[0146] This study analyzed the alpha diversity of large benthic invertebrates in the Luanhe River basin using five diversity indices: Shannon, Simpson, Pielou, Chao1 and Coverage. The results are shown in Table 6.

[0147] Table 6 Alpha diversity index of macrobenthic invertebrates at each sampling point

[0148]

[0149]

[0150] As shown in Table 6, the Shannon diversity index is 0.980-2.561, with an average of 1.636. The Simpson diversity index is 0.139-0.542, with an average of 0.357. The range of Shannon and Simpson indexes shows the differences in the distribution of biodiversity in different regions. The diversity of Xingzhou River, main stream 7 and Sa River is higher, and the diversity of main stream 1, Wulie River, Pu River and main stream 13 is lower. The Pielou evenness index is 0.389-0.735, with an average of 0.555. The evenness of Xingzhou River and Sa River is higher, and the evenness of Wulie River, main stream 1, Xiaoluan River, main stream 13 and Pu River is lower. The range of Pielou index shows that there are differences in the uniformity of species distribution in different regions. The Chao1 richness index ranged from 12.250 to 58.222, with an average of 29.281. The richness of Laoniu River and mainstream 7 was high, while that of Wulie River, mainstream 1, mainstream 2, and mainstream 13 was low. The Chao1 index range revealed that species richness varied in different geographical locations. Overall, among the mainstream sites, the α diversity of mainstream 7 was high, while that of mainstream 1 and mainstream 13 was low. Among the tributary sites, the α diversity index of Xingzhou River, Sa River, Yison River, and Qinglong River was high, while that of Wulie River and Pu River was low. The Coverage index showed that the coverage rate of the large benthic invertebrate community in this sample was as high as 1, which indicated that the sequencing results could more accurately reflect the actual status of the large benthic invertebrate community in the sample.

[0151] Comparing and analyzing the regional differences of these indices with different sections of the basin, the Shannon index shows that the middle reaches (average value = 1.7116) > the upper reaches (average value = 1.5693) > the lower reaches (average value = 1.5558) > the source reaches (average value = 1.3536). The Simpson index shows that the middle reaches (average value = 0.3386) > the lower reaches (average value = 0.3661) > the upper reaches (average value = 0.3960) > the source reaches (average value = 0.4047). The Pielou index shows that the middle reaches (average value = 0.5748) > the source reaches (average value = 0.5503) > the lower reaches (average value = 0.5192) > the upper reaches (average value = 0.4942). The Chao1 index shows that the upper reaches (average value = 33.3449) > the middle reaches (average value = 30.2610) > the lower reaches (average value = 27.5106) > the source reaches (average value = 19.7539). Based on multiple indices, the α diversity of the middle reaches is the highest, which may be due to the fact that the habitat diversity and complexity of the middle reaches are generally the largest. The diversity of the source reaches is relatively low. Compared with the middle or lower reaches, the habitat structure of the source is relatively simple and lacks diverse microhabitats, which are particularly important for the diversity of large benthic invertebrates. The above research results provide important data for the study of the biodiversity of large benthic invertebrates in the Luanhe River Basin, and are of great value in understanding the diversity, richness and evenness of large benthic invertebrates in the basin ecosystem.

[0152] (2) Analysis of α-diversity differences of macrobenthic invertebrates in different seasons

[0153] The differences in the α-diversity index of macrobenthic invertebrates in different seasons were explored through inter-group difference tests. The metrics used in this study were Shannon, Simpson, Pielou, and Chao1. The Kruskal-Wallis rank sum test was used to test the significance of the inter-group differences. The p-value was corrected for multiple tests using FDR. The Tukey-Kramer post-hoc test was used. The confidence level was set to 0.95. Asterisks represent significance ( * 0.01 <p≤0.05, ** 0.001 <p≤0.01, *** p≤0.001), the results are as follows Figure 7 shown.

[0154] In terms of time, the Chao1 index showed extremely significant differences between different seasons (p<0.001), while the Shannon, Simpson and Pielou indices showed no significant differences in seasons. The Shannon intergroup difference map showed that the species diversity in summer was significantly higher than that in autumn (p<0.05). The Chao1 intergroup difference map showed that the species richness in summer was significantly higher than that in spring and autumn (p<0.01), and significantly higher than that in winter (p<0.05), which indicated that summer promoted the survival and reproduction of more species, thereby increasing species richness; the species richness in winter was significantly higher than that in autumn (p<0.001), which may be due to the adaptation of some species to the cold season environment, or the biomass accumulation effect in late autumn.

[0155] 4. Analysis of β-diversity of macrobenthic invertebrate communities

[0156] The biodiversity and community structure of macrobenthic invertebrates are strongly affected by seasonal environmental factors, which may be related to the availability of food resources, reproductive cycle, changes in water habitat, etc. The community structure of macrobenthic invertebrates in different seasons in the Luanhe River Basin was analyzed by NMDS, using the Pearson distance algorithm and ANOSIM inter-group difference test, such as Figure 8 shown.

[0157] The stress value in the NMDS graph is 0.146 (stress < 0.2), which proves that the sorting effect is good. The graph can better reflect the actual differences in the community structure of large benthic invertebrates. The R value is 0.0681 (R>0), indicating that the differences between groups (different seasons) are greater than the differences within groups (different geographical locations in the same season). The differences between groups are extremely significant (p < 0.01), indicating that the community structure composition of large benthic invertebrates in different seasons is significantly different, which is consistent with the research results.

[0158] Further Adonis multivariate analysis of variance is shown in Table 7. The p values ​​were all less than 0.05, indicating that there were significant differences in the macrobenthic invertebrate communities between different seasons. 2 The value represents the degree to which the grouping factor explains the sample differences, R 2The larger the value, the higher the degree of explanation of the grouping. When grouping by season, the higher the degree of explanation of the differences in benthic invertebrate communities between summer and winter, followed by autumn and winter, spring and winter, spring and summer, spring and autumn, and summer and autumn. The high degree of explanation of differences between summer and winter reflects the extreme changes in environmental conditions in these two seasons. These changes may directly affect the living environment and survival strategies of benthic invertebrates, thereby significantly changing the community structure. In the study of the community structure of large benthic invertebrates in the lower reaches of the Yalong River, the results showed that the benthic animal communities in spring and autumn were significantly different as a whole, which is consistent with some of the results of this study.

[0159] Table 7 Adonis test of macrobenthic invertebrate communities in different seasons

[0160]

[0161]

[0162] 5. Correlation analysis between macrobenthic invertebrate communities and environmental factors

[0163] (1) Correlation between macrobenthic invertebrate community structure and environmental factors in different seasons

[0164] The correlation analysis between the community structure of macrobenthic invertebrates and environmental factors in different seasons was conducted. The Bray-Curits distance algorithm was used for both the community matrix and the environmental factor matrix. The Spearman correlation coefficient type of the environmental factor was selected to draw the Mantel test network heat map, such as Fig. 9 As shown. The lines in the figure represent the correlation between benthic animal communities and environmental factors, and the heat map represents the correlation between environmental factors; line thickness: the correlation between benthic animal communities and environmental factors, drawn by taking Mantel'r (the absolute value of R); Positive and Negative are the positive and negative correlations between benthic animal communities and environmental factors; red and blue in the heat map represent positive and negative correlations, respectively, and the color depth represents the magnitude of positive and negative correlations. The asterisk in the color block represents significance ( * 0.01 <p≤0.05, ** 0.001 <p≤0.01, *** p≤0.001).

[0165] Fig. 9 The results showed that WT, DO, NO3 -, Mn and ORP are the key environmental factors affecting the community structure of macrobenthic invertebrates. Higher water temperature is conducive to the growth and reproduction of benthic animals, which is consistent with the research results; the distribution of benthic animals is closely related to the content of DO, which is consistent with the research results. The community of macrobenthic invertebrates in spring is related to WT, TDS, pH, TDP, EC, Mn, DO, NH3-N, COD, NO3 - It has a very significant positive correlation with ORP, a significant positive correlation with TDN and TP, a positive but not significant correlation with TN and Fe, and a positive but not significant correlation with NO2 - There is a negative correlation but it is not significant. Summer macrobenthic invertebrate community and WT, Mn, NO3 - , pH, DO, NO2 - , TDN, TDP, ORP, TN, EC, TDS and NH3-N were all significantly positively correlated, and positively correlated with Fe, COD and TP but not significantly. - 、NO2 - , Mn, DO and WT have extremely significant positive correlations, have significant positive correlations with EC, TDS, TN, TDN and COD, have positive correlations with TDP and NH3-N but not significant, and have negative correlations with Fe and TP but not significant. - , DO, NH3-N, TDN, TN, EC, TDS, Fe, ORP, Mn, NO2 - , WT and TP all have extremely significant positive correlations, and have positive correlations with pH, ​​COD and TDP but are not significant. The above environmental factors are arranged in the order of high to low correlation. In the study of the seasonal dynamics of the community structure of large benthic invertebrates in Xiangxi River, it was shown that environmental factors in different seasons have a significant effect on the community structure of large benthic animals, and the correlation between some environmental factors and different seasons is consistent with the results of this study.

[0166] The heat map can also reflect the correlation between environmental factors, and its correlation is consistent with the analysis results of 2.2.5.

[0167] (2) Correlation between dominant macrobenthic invertebrates and environmental factors

[0168] The top 20 dominant benthic invertebrates in terms of total abundance at the family classification level were selected to explore the correlation between benthic invertebrates and environmental factors in the Luanhe River Basin during the one-year monitoring period. The Spearman environmental factor correlation coefficient was used to plot the results. Fig.10 As shown. In the heat map, red and blue represent positive and negative correlations, respectively. The depth of the color represents the magnitude of the positive and negative correlations. The asterisk in the color block represents the significance ( * 0.01 <p≤0.05,** 0.001 <p≤0.01, *** p≤0.001).

[0169] Depend on Fig.10 It can be seen that different benthic invertebrate families have significant differences in their preferences and tolerance to environmental factors. Among the top 20 benthic invertebrate families in terms of total abundance, WT, TN, TDN, NO3 - , TP, TDP and COD are the main factors affecting the distribution and abundance of these benthic invertebrate families. Among them, WT is the most critical environmental factor, which is consistent with the research results. COD is an important indicator to measure the concentration of pollution in river water, indicating that these benthic invertebrates are more sensitive to water pollution and are more strongly affected by pollutants. The relationship between dominant species and environmental factors: Pachychilidae has a very significant positive correlation with WT, reflecting its preference for warm waters; Phloeodictyidae has a very significant positive correlation with NO2 - ,TP,TDP,NO3 - , TDN, TN, DO, ORP, TDS and EC were all significantly positively correlated, and were significantly negatively correlated with WT and COD, revealing that this family of species is very sensitive to water quality conditions and prefers clean, oxygenated, and nutrient-rich water environments; Capitellidae and NO2 - ,TP,TDP,NO3 - , TDN, TN, DO, ORP and NH3-N were all significantly positively correlated, and were significantly negatively correlated with WT, also showing a preference for clean and nutrient-rich waters, but the negative correlation with water temperature may indicate adaptation to cooler waters; Chrysopidae was significantly positively correlated with TDP and NO3 - There is a very significant positive correlation with pH, ​​and a very significant negative correlation with pH, ​​which may indicate that these species can better utilize nutrients in the water under acidic conditions. Relationship between specific species and environmental factors: Aphelenchoididae and TP, TDP and NO3 - The results showed that these species were more common in cool and nutrient-rich waters. The Lecithoceridae was significantly positively correlated with TP, TDP, and NO3 - , TDN, DO, TN and ORP were all significantly positively correlated, and were significantly negatively correlated with WT and COD; Chrysomelidae was significantly correlated with NO2 - ,TP,TDP,NO3 -, TDN, DO, TN and ORP were all significantly positively correlated, and COD was significantly negatively correlated; Agromyzidae was significantly correlated with TP, TDP, NO3 - , TDN, TN, DO, ORP and NH3-N were all significantly positively correlated, and were extremely significantly negatively correlated with WT and COD; Nereididae was extremely significantly positively correlated with Mn and TDS; Cecidomyiidae was extremely significantly negatively correlated with Mn and COD, which may reflect the preference of this family for less polluted water environments.

[0170] (3) Changes in distance attenuation of large benthic invertebrates

[0171] The macrobenthic invertebrate community showed the same distance decay relationship (DDR) trend in different seasons, see Fig.11 . Geographic distance explained the highest degree of similarity of benthic animal communities in winter, followed by spring, autumn and summer. All four seasons showed a very significant trend of biological similarity decreasing with increasing distance (p<0.001), but the degree of attenuation of biological community similarity in each season was different. Especially in winter, the effect of geographical distance on the similarity of benthic animal communities was the most significant, followed by autumn, summer and spring. In the winter sampling of the Luanhe River, the water in the basin was partially or completely frozen. This low temperature condition restricted the activity and metabolic rate of aquatic organisms, reduced the communication and diffusion capacity between species, and caused the community similarity of different locations to decline rapidly.

[0172] Summary of results

[0173] In summary, taking the large benthic invertebrates in the Luanhe River Basin as the research object, the community composition, α diversity and β diversity of large benthic invertebrates in different seasons were comprehensively analyzed to analyze the feasibility of eDNA technology to evaluate the diversity of large benthic invertebrates, and the key environmental factors driving the large benthic invertebrate communities in the Luanhe River Basin in different seasons were revealed.

[0174] 1) The taxonomic units of the macrobenthic invertebrate samples collected in the Luanhe River Basin were counted, and a total of 1419 OTUs were obtained using the gene COI-1 marker, belonging to 11 phyla, 30 classes, 83 orders, 319 families, 729 genera and 990 species. The Arthropoda phylum had the largest proportion of species, with 776 species, accounting for 78.38%.

[0175] 2) In the relative abundance analysis of the species sequences of large benthic invertebrates in the Luanhe River Basin, the total number of sequences of the phylum Arthropoda accounted for the highest proportion, and also accounted for the highest proportion in each season, accounting for 75.24% in autumn, 72.57% in spring, 61.96% in winter, and 50.96% in summer. Diptera was the dominant order in the survey of large benthic invertebrates in the Luanhe River Basin, Insecta and Gastropoda were the dominant classes, and Chironomidae and Pachychilidae were the dominant families.

[0176] 3) Five indices, Shannon, Simpson, Pielou, Chao1 and Coverage, were selected to analyze the α diversity of large benthic invertebrates in the Luanhe River Basin. Among the main stream sites, the α diversity of main stream 7 was higher, while the α diversity of main stream 1 and main stream 13 was lower; among the tributary sites, the α diversity index of Xingzhou River, Sa River, Yison River and Qinglong River was at a high level, while the α diversity index of Wulie River and Pu River was lower. From the perspective of different river sections, the α diversity of the middle reaches was the highest, and the diversity of the source river section was relatively low. The Chao1 index between different seasons was extremely significantly different (p<0.001), among which the species richness was the highest in summer. Shannon, Simpson and Pielou indices showed no significant seasonal differences in large benthic invertebrates.

[0177] 4) In the β-diversity analysis of macrobenthic invertebrate communities, the community structure and composition of macrobenthic invertebrates in different seasons were significantly different (p<0.01), with the highest difference between summer and winter communities.

[0178] 5) In the correlation analysis between macrobenthic invertebrate communities and environmental factors, WT, DO, NO3 - , Mn and ORP are the key environmental factors affecting the community structure of macrobenthic invertebrates. Among the top 20 macrobenthic invertebrate families in terms of total abundance, WT, TN, TDN, NO3 - , TP, TDP and COD are the main factors affecting their distribution and abundance, among which WT is the most critical environmental factor. In the distance decay of large benthic invertebrates, the four seasons showed a very significant trend of biological similarity decaying with increasing distance (p<0.001), especially in winter, when the geographical distance had the most significant effect on the similarity of benthic animal communities.

[0179] The above is only an embodiment of the present invention, and the common knowledge such as the known specific technical solutions or characteristics in the solution is not described in detail here. For those skilled in the art, without departing from the technical solution of the present invention, several modifications and improvements can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A method for monitoring the diversity of river macrobenthic invertebrates based on eDNA, characterized by: The following steps are involved: Step 1: Select the river basin to be measured and query the basic information of the basin; Step 2: eDNA sample collection and processing: including the design and geographical location of sampling sites, water sample collection and pretreatment; Step 3: Determination of physical and chemical indicators; Step 4: eDNA extraction, PCR amplification and high-throughput sequencing; Step 5: Data analysis of the above results: including bioinformatics analysis of large benthic invertebrate communities, α diversity analysis, β diversity analysis, and correlation analysis between large benthic invertebrate communities and environmental factors.

2. The method for monitoring the diversity of river macrobenthic invertebrates based on eDNA according to claim 1, characterized in that: The basic information in step one includes the river's location, total length, area, and climate zone.

3. The method for monitoring the diversity of river macrobenthic invertebrates based on eDNA according to claim 1, characterized in that: After the sampling points are designed in step 2, each sampling point needs to be sampled separately in four seasons. Water samples are collected from 0.5 meters below the surface water at a distance of about 5 meters from the river bank. Each sampling point is sampled repeatedly and then mixed. The mixed water samples are used to detect biological and physical and chemical indicators respectively. After the filter membrane is generated at the sampling location, it is sent back to the laboratory and stored at -80℃ until subsequent extraction.

4. The method for monitoring the diversity of river macrobenthic invertebrates based on eDNA according to claim 1, characterized in that: The physical and chemical indicators in step three include measuring and recording some physical indicators at the sampling site and testing the chemical indicators of each sampling point. The physical indicators include WT (water temperature), DO (dissolved oxygen), pH, TDS (total dissolved solids), EC (conductivity), ORP (oxidation-reduction potential) and other indicators. The chemical indicators include TN, TP, NH3-N, NO3 - 、NO2 - , COD, TDN, TDP, Fe, Mn and other indicators.

5. The method for monitoring the diversity of river macrobenthic invertebrates based on eDNA according to claim 3, characterized in that: In step 4, the extraction of eDNA is to take out the filter membrane stored at -80℃ in step 2 and thaw it. Spin Kit for Soil was used to extract total DNA from the filter membrane obtained by filtration. Each sample was subjected to 3 parallel experiments. After the total DNA extraction was completed, the extracted DNA was detected by 1% agarose gel electrophoresis.

6. The method for monitoring the diversity of river macrobenthic invertebrates based on eDNA according to claim 1, characterized in that: In step 4, PCR amplification is performed using universal primers, and negative controls are set to ensure that there is no DNA contamination during the amplification process and the accuracy and reliability of the experimental results; specific primers with barcodes are synthesized according to the specified sequencing region; Use low cycle amplification as much as possible and ensure that the cycle number of each sample amplification is consistent to ensure the accuracy and reliability of subsequent data analysis; randomly select representative samples for preliminary experiments to ensure that the vast majority of samples can amplify products of appropriate concentrations at the lowest cycle number; use a PCR instrument to perform all samples according to formal experimental conditions, with 3 replicates for each sample, and mix the PCR products of the same sample and detect them by 2% agarose gel electrophoresis. Use the AxyPrepDNA gel recovery kit to purify the PCR product, elute with Tris-HCl, and detect it by 2% agarose electrophoresis.

7. The method for monitoring the diversity of river macrobenthic invertebrates based on eDNA according to claim 1, characterized in that: Step 4: High-throughput sequencing The library of the amplified DNA was constructed using the Rapid DNA-Seq Kit, and sequencing was performed using the Miseq PE300 platform of Illumina.

8. The method for monitoring the diversity of river macrobenthic invertebrates based on eDNA according to claim 1, characterized in that: The bioinformatics analysis in step five includes data optimization and OTU clustering. Data optimization is to use fastp software to perform quality control on the original sequencing sequence and use FLASH software for splicing; OTU clustering is to use UPARSE software to perform OTU clustering on the sequence based on 97% similarity.

9. The method for monitoring the diversity of river macrobenthic invertebrates based on eDNA according to claim 1, characterized in that: The alpha diversity analysis in step five uses tools such as Mothur to analyze the alpha diversity index in the sample, such as diversity index, richness index, uniformity index, coverage, etc.

10. The method for monitoring the diversity of river macrobenthic invertebrates based on eDNA according to claim 1, characterized in that: β-diversity analysis in step 5: Qiime software was used to calculate the β-diversity distance matrix, and the vegan package in R language was used for NMDS analysis and Adonis analysis.

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