A method for establishing nitrogen and phosphorus nutrient criteria based on multi-level factor responses

Abstract

The present invention discloses a method for formulating nitrogen and phosphorus nutrient criteria based on multi-level factor responses, including: obtaining big data of biological communities based on high-throughput sequencing technology and conducting big data analysis across biological taxa for the big data of biological communities; using the multi-level factor response factors under nitrogen and phosphorus input as response variables to characterize the response characteristics of the ecosystem and deriving the criterion values of nitrogen and phosphorus nutrients; when obtaining the big data of biological communities, the present invention uses environmental DNA technology, which is environmentally friendly, belongs to non-destructive sampling, and overcomes the problems of high cost, time-consuming and laborious, and inaccurate results in traditional morphological identification, as well as the limitation of only applying single-factor response relationships in the traditional stress-response method. Based on the multi-level factor response relationship including biological community characteristics and water body physical and chemical factors, it is convenient to formulate more scientific and perfect nutrient criteria. The technical process of the present invention is easy to standardize, facilitating comparative analysis and research of different regional studies.

Description

Technical Field

[0001] The present invention belongs to the field of nutrient criteria formulation, and particularly relates to a method for formulating nitrogen and phosphorus nutrient criteria based on multi-level element response. Background Art

[0002] Nutrient criteria refer to the maximum concentration or level of nutrients that do not have an adverse or harmful impact on the function or use of water bodies. Nitrogen and phosphorus are important indicators for characterizing and evaluating water eutrophication.

[0003] The main technical methods for formulating nutrient criteria include statistical analysis methods and stress-response model methods. The former is generally applicable to areas with less human interference. After statistically analyzing the continuously observed data, the criterion value is calculated according to the set frequency intervals. The latter is applicable to areas with severe human activity interference. By studying the response relationship between environmental stress factors and response factors, the criterion value is determined, and it is further divided into non-parametric inflection point method, classification and regression tree method, etc. The stress factors are generally nitrogen, phosphorus, etc., and the response factors are generally single factors such as chlorophyll a, dissolved oxygen, transparency, etc. In the current nutrient criterion formulation methodology, one of the important problems is that the response factors are all primary-level single-factor indicators, which have limited representativeness for the ecosystem and cannot truly reveal the response relationship of the ecosystem to environmental stresses such as nitrogen and phosphorus. Whether the nutrient criterion values formulated based on this can truly control eutrophication is questionable. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides a method for formulating nitrogen and phosphorus nutrient criteria based on multi-level element response, including:

[0005] Obtaining big data of biological communities based on high-throughput sequencing technology, and performing big data analysis across biological taxa on the big data of biological communities;

[0006] Taking the multi-level element response factors under nitrogen and phosphorus input as response variables to characterize the response relationship of the ecosystem after nitrogen and phosphorus input, and deriving the criterion values of nitrogen and phosphorus nutrients.

[0007] Preferably, the process of obtaining big data of biological communities based on high-throughput sequencing technology includes:

[0008] Setting sampling points to collect water samples and obtain environmental DNA filter membrane samples, measuring the physical and chemical indexes of the water samples, and extracting environmental DNA from the filter membrane samples;

[0009] Designing universal amplification primers for the macro-barcode genes of different aquatic organisms, and amplifying the macro-barcode genes of aquatic organisms;

[0010] Electrophoresis was used to detect the amplification of the target barcode gene in the filter membrane sample, and the target band was cut out. The sample was purified using a nucleic acid purification column, and the purified sample was subjected to high-throughput sequencing to obtain big data on biological communities; the big data on biological communities includes, but is not limited to, the types of organisms, operational taxonomic units, and relative abundances.

[0011] Preferably, the process of setting sampling points to collect water samples and obtain environmental DNA filter membrane samples includes:

[0012] Setting sampling points that are conducive to observing the effective responses of biological communities and ecosystems to the concentrations of nitrogen and phosphorus nutrients; among them, the water samples collected at the sampling points include a first water sample for water physical and chemical detection and a second water sample for environmental DNA extraction;

[0013] The second water sample was filtered through a nitrocellulose filter membrane, and the filtered nitrocellulose membrane was stored at -80 °C to obtain the environmental DNA filter membrane sample.

[0014] Preferably, the process of determining the water physical and chemical indexes of the water sample includes:

[0015] Determine the contents of nitrate, nitrite, and chlorophyll a in the first water sample, and detect DO, water temperature, redox potential, turbidity, and conductivity in the first water sample on-site at the sampling site.

[0016] Preferably, the process of extracting environmental DNA from the filter membrane sample includes:

[0017] Genomic DNA was extracted based on a DNA extraction kit to obtain the environmental DNA of aquatic organisms on the nitrocellulose membrane, and 1% agarose gel electrophoresis was used to detect the integrity of the DNA. At the same time, NanoDrop and Qubit were used to detect the concentration and purity of the DNA.

[0018] Preferably, the process of amplifying the macro barcode gene of aquatic organisms includes:

[0019] For phytoplankton, the target barcode gene is the V9 region of the mitochondrial gene 18S, and the primers are as follows:

[0020] Forward primer: TCCCTGCCHTTTGTACACAC, SEQ ID NO:1;

[0021] Reverse primer: CCTTCYGCAGGTTCACCTAC, SEQ ID NO:2;

[0022] For zooplankton, the target barcode gene is the V4 region of the mitochondrial gene 18S, and the primers are as follows:

[0023] Forward primer: AGGGCAAKYCTGGTGCCAGC, SEQ ID NO:3;

[0024] Reverse primer: GRCGGTATCTRATCGYCTT, SEQ ID NO:4;

[0025] For fish, the target barcode gene is mitochondrial gene 12S, and the primers are as follows:

[0026] Forward primer: GTCGGTAAAACTCGTGCCAGC, SEQ ID NO:5;

[0027] Reverse primer: CATAGTGGGGTATCTAATCCCAGTTTG, SEQ ID NO:6;

[0028] For benthic invertebrates, the target barcode gene is COI, and the primers are as follows:

[0029] Forward primer: GGDACWGGWTGAACWGTWTAYCCHCC, SEQ ID NO:7;

[0030] Reverse primer: CAAACAAATARDGGTATTCGDTY, SEQ ID NO:8;

[0031] The reaction system composition includes: 15 μL of High-Fidelity PCR MasterMix (New England Biolabs) + 0.2 μM primers + 10 ng template environmental DNA;

[0032] The amplification process includes: denaturation at 98°C for 1 minute + 30 amplification cycles + extension at 72°C for 5 minutes;

[0033] Among them, the reaction parameters for each cycle are: denaturation at 98°C for 10S, annealing at 50°C for 30S, and extension at 72°C for 30S.

[0034] Preferably, the process of electrophoretically detecting the amplification of the target barcode gene in the filter membrane sample, cutting out the target band, purifying the sample using a nucleic acid purification column, and performing high-throughput sequencing on the purified sample includes:

[0035] Cutting and purifying the target band until the DNA accounts for ≥50%, loading the amplicon for sequencing, mixing libraries, adding sequencing adapters, purifying the sequencing library, and performing sequence determination by paired-end or single-end sequencing methods after the library is qualified;

[0036] The original image data file obtained by sequencing is converted into an original sequencing sequence through base recognition analysis, and the original sequencing sequence is stored in the FASTO file format; the original sequencing sequence includes the sequence information of the sequencing sequence and the corresponding sequencing quality information.

[0037] Preferably, the process of performing big data analysis across biological taxa on the big data of the biological community includes:

[0038] After cleaning and quality control of the data offloaded from high-throughput sequencing, an OTU sequence set is obtained through sequence clustering;

[0039] Species annotation is performed on the OTU sequence set through database comparison to obtain plankton taxon information; the plankton taxon information includes, but is not limited to, the types of plankton species and the OTU relative abundances at each sampling point.

[0040] Calculate the ratio of silicoflagellates, the Shannon-Wiener diversity index of phytoplankton, and the Shannon-Wiener diversity index of plankton at each point.

[0041] Preferably, after cleaning and quality control of the data offloaded from high-throughput sequencing, an OTU sequence set is obtained through sequence clustering, and the process of performing species annotation on the OTU sequence set through database comparison includes:

[0042] Clean the data offloaded from high-throughput sequencing, remove adapters, filter out low-quality sequencing sequences, and obtain the cleaned data;

[0043] Perform splicing on the filtered paired-end sequencing sequences, remove primer sequences according to the alignment results at both ends of the primers, and sequences with a sequencing error exceeding 1%;

[0044] After filtering, merge the spliced sequences of all samples, calculate the representative sequences through OTU clustering or amplicon sequence variant denoising methods, and obtain the OTU sequence set;

[0045] Perform BLAST comparison of the OTU sequence set with the reference sequences in the target gene metabarcode database, filter out sequences with a comparison length lower than 90% and a similarity lower than 80%, and obtain annotations at five taxonomic levels of species, genus, family, order, and class in sequence according to the thresholds of similarity of 98%, 95%, 90%, and 85%;

[0046] Based on the species annotation of the OTU table, perform statistical analysis on the diversity, community structure, dominant species, and invasive species of different samples;

[0047] The calculation expression of the Shannon-Wiener diversity index is:

[0048]

[0049]

[0050] Among them, S is the number of taxa, P i is the frequency, N i is the number of individuals in the i-th taxon, and N is the total number of individuals in all taxa.

[0051] Preferably, the process of obtaining the benchmark values of nitrogen and phosphorus nutrients includes:

[0052] Constructing a pressure-response model, and the pressure-response model method includes a classification and regression tree model, non-parametric inflection point analysis, and Bayesian inflection point analysis;

[0053] Taking the nitrate concentration and nitrite concentration at the sampling point as pressure variables, and the chlorophyll concentration, OTU abundance, diatom ratio, phytoplankton Shannon-Wiener diversity index, and zooplankton Shannon-Wiener diversity index as response variables, inputting them into the classification and regression tree model, and through the stopping and pruning processes, selecting the optimal tree, and taking the water body nutrient concentration corresponding to the nodes of the optimal tree as the nutrient benchmark candidate value;

[0054] Based on the nutrient benchmark candidate value, deriving the nutrient benchmark. First, use the root node in the classification and regression tree model to obtain the first pressure index benchmark value, and then obtain the subsequent pressure index benchmark values through the child nodes; when all nodes in the classification and regression tree model are the first pollution pressure indicators, use non-parametric inflection point analysis to obtain the subsequent pressure index benchmark values; when all variable index data conform to the normal distribution, use Bayesian inflection point analysis to obtain the final nutrient benchmark value.

[0055] Compared with the prior art, the present invention has the following advantages and technical effects:

[0056] (1) The present invention overcomes the limitation of the traditional pressure-response method in establishing a response relationship based on a single factor, establishes a multi-level element response relationship including biological community characteristics and water body physical and chemical information, etc., realizes the orientation of ecosystem health, and is convenient for formulating more scientific and perfect nutrient benchmarks.

[0057] (2) The present invention uses environmental DNA technology, has environmental friendliness, belongs to non-destructive sampling, and overcomes the problems of high cost, time-consuming, laborious, and inaccurate results of traditional morphological identification, is convenient for obtaining big data of biological communities, and is more conducive to obtaining nutrient benchmark values with high certainty;

[0058] (3) The technical process of the present invention is easy to standardize, which is convenient for comparative analysis and research of different regional studies. Description of the Drawings

[0059] The accompanying drawings, which form a part of this application, are used to provide a further understanding of this application. The schematic embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation of this application. In the drawings:

[0060] Figure 1 Schematic diagram for deriving the reference values of nitrate and nitrite with chlorophyll a concentration as the stress variable in the embodiment of the present invention;

[0061] Figure 2 Schematic diagram for deriving the reference values of nitrate and nitrite with OTU abundance as the stress variable in the embodiment of the present invention;

[0062] Figure 3 Schematic diagram for deriving the reference values of nitrate and nitrite with the ratio of silicoflagellates as the stress variable in the embodiment of the present invention;

[0063] Figure 4 Schematic diagram for deriving the reference values of nitrate and nitrite with phytoplankton diversity as the stress variable in the embodiment of the present invention;

[0064] Figure 5 Schematic diagram for deriving the reference values of nitrate and nitrite with plankton diversity as the stress variable in the embodiment of the present invention. Detailed implementation manners

[0065] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The following will refer to the accompanying drawings and combine with the embodiments to detail this application.

[0066] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0067] To solve the problem that in the current nutrient criteria development methodology, only single-factor response indicators such as chlorophyll a and transparency are used. Although it is simple and direct, the ecosystem is a complex interaction system, and only using single-factor characterization is imperfect for characterizing the health of the ecosystem, which may lead to inaccurate eutrophication control. In this embodiment, a method for developing nitrogen and phosphorus nutrient criteria based on multi-level element responses is provided, including:

[0068] Obtaining big data of biological communities based on high-throughput sequencing technology and performing big data analysis across biological taxa on the big data of biological communities;

[0069] Using the multi-level element response factors under nitrogen and phosphorus input as response variables to characterize the response relationship of the ecosystem after nitrogen and phosphorus input, and deriving the reference values of nitrogen and phosphorus nutrients.

[0070] Furthermore, the process of obtaining big data of biological communities based on high-throughput sequencing technology includes:

[0071] Set sampling points to collect water samples and obtain environmental DNA filter membrane samples, measure the physical and chemical indexes of the water samples, and extract the environmental DNA in the filter membrane samples;

[0072] Design universal amplification primers for the metabarcoding genes of different groups of aquatic organisms, and amplify the metabarcoding genes of aquatic organisms;

[0073] Electrophoretically detect the amplification of the target barcode genes in the filter membrane samples, cut out the target bands, purify the samples using a nucleic acid purification column, and perform high-throughput sequencing on the purified samples to obtain big data of biological communities; the big data of biological communities includes but is not limited to the types of organisms, operational taxonomic units, and relative abundances.

[0074] Furthermore, the process of setting sampling points to collect water samples and obtain environmental DNA filter membrane samples includes:

[0075] Set sampling points that are conducive to observing the effective responses of biological communities and ecosystems to the concentrations of nitrogen and phosphorus nutrients; among them, the water samples collected at the sampling points include a first water sample for water physical and chemical detection and a second water sample for environmental DNA extraction;

[0076] Filter the second water sample through a nitrocellulose filter membrane, store the filtered nitrocellulose membrane at -80 °C, and obtain the environmental DNA filter membrane sample.

[0077] Furthermore, the process of measuring the physical and chemical indexes of the water samples includes:

[0078] Measure the contents of nitrate, nitrite, and chlorophyll a in the first water sample, and detect DO, water temperature, redox potential, turbidity, conductivity, etc. in the first water sample on-site at the sampling site.

[0079] Furthermore, the process of extracting environmental DNA from the filter membrane samples includes:

[0080] Extract genomic DNA based on a DNA extraction kit to obtain the environmental DNA of aquatic organisms on the nitrocellulose membrane, detect the integrity of the DNA by 1% agarose gel electrophoresis, and simultaneously detect the concentration and purity of the DNA using NanoDrop and Qubit.

[0081] Furthermore, the process of amplifying the metabarcoding genes of aquatic organisms includes:

[0082] For phytoplankton, the target barcode gene is the V9 region of the mitochondrial gene 18S, and the primers are as follows:

[0083] Forward primer: TCCCTGCCHTTTGTACACAC, SEQ ID NO:1;

[0084] Reverse primer: CCTTCYGCAGGTTCACCTAC, SEQ ID NO:2;

[0085] For zooplankton, the target barcode gene is the V4 region of mitochondrial gene 18S, and the primers are as follows:

[0086] Forward primer: AGGGCAAKYCTGGTGCCAGC, SEQ ID NO:3;

[0087] Reverse primer: GRCGGTATCTRATCGYCTT, SEQ ID NO:4;

[0088] For fish, the target barcode gene is mitochondrial gene 12S, and the primers are as follows:

[0089] Forward primer: GTCGGTAAAACTCGTGCCAGC, SEQ ID NO:5;

[0090] Reverse primer: CATAGTGGGGTATCTAATCCCAGTTTG, SEQ ID NO:6;

[0091] For benthic invertebrates, the target barcode gene is COI, and the primers are as follows:

[0092] Forward primer: GGDACWGGWTGAACWGTWTAYCCHCC, SEQ ID NO:7;

[0093] Reverse primer: CAAACAAATARDGGTATTCGDTY, SEQ ID NO:8;

[0094] The reaction system composition includes: 15 μL of High-Fidelity PCR Master Mix (New England Biolabs) + 0.2 μM primers + 10 ng template environmental DNA;

[0095] The amplification process includes: denaturation at 98 °C for 1 minute + 30 amplification cycles + extension at 72 °C for 5 minutes;

[0096] Among them, the reaction parameters for each cycle are: denaturation at 98 °C for 10 S, annealing at 50 °C for 30 S, and extension at 72 °C for 30 S.

[0097] Further, the process of electrophoretically detecting the amplification of the target barcode gene in the filter membrane sample, cutting out the target band, purifying the sample using a nucleic acid purification column, and sending the purified sample to a company for high-throughput sequencing includes:

[0098] Cut the target band by gel purification until the DNA accounts for ≥50%, load the amplicons for sequencing, mix the libraries, add sequencing adapters, and purify the sequencing libraries. After the libraries pass the inspection, perform sequence determination by paired-end or single-end sequencing methods;

[0099] Convert the original image data file obtained by sequencing into the original sequencing sequence through base recognition analysis, and store the original sequencing sequence in the FASTO file format; the original sequencing sequence includes the sequence information of the sequencing sequence and the corresponding sequencing quality information.

[0100] Further, the process of performing big data analysis across biological taxa on the big data of biological communities includes:

[0101] After cleaning and quality controlling the off-machine data of high-throughput sequencing, obtain the OTU sequence set through sequence clustering;

[0102] Perform species annotation on the OTU sequence set through database comparison to obtain plankton taxon information; the plankton taxon information includes, but is not limited to, the number of plankton species at each sampling point;

[0103] Calculate the ratio of silicoflagellates, the Shannon-Wiener diversity index of phytoplankton and the Shannon-Wiener diversity index of plankton, primary productivity, evenness index, etc. at each point.

[0104] Further, after cleaning and quality controlling the off-machine data of high-throughput sequencing, the process of obtaining the OTU sequence set through sequence clustering and performing species annotation on the OTU sequence set through database comparison includes:

[0105] Clean the off-machine data of high-throughput sequencing, remove the adapters, filter out low-quality sequencing sequences, and obtain the cleaned data;

[0106] Assemble the filtered paired-end sequencing sequences, and remove the primer sequences and sequences with sequencing errors exceeding 1% according to the alignment results of the primers at both ends;

[0107] After filtering, merge the assembled sequences of all samples, calculate the representative sequences by OTU clustering or amplicon sequence variant denoising methods, and obtain the OTU sequence set;

[0108] The OTU sequence set was subjected to BLAST alignment with the reference sequences in the target gene metabarcoding database, and sequences with an alignment length less than 90% and a similarity less than 80% were filtered out. Annotations at five taxonomic levels of species, genus, family, order, and class were obtained in sequence according to the similarity thresholds of 98%, 95%, 90%, and 85%.

[0109] Based on the species annotations of the OTU table, statistical analyses were performed on the diversity, community structure, dominant species, and invasive species of different samples.

[0110] The calculation formula for the Shannon-Wiener diversity index is as follows:

[0111]

[0112]

[0113] Among them, S is the number of taxa, P i is the frequency, N i is the number of individuals in the i-th taxon, and N is the total number of individuals in all taxa.

[0114] Furthermore, the process of obtaining the benchmark values of nitrogen and phosphorus nutrients includes:

[0115] Construct a pressure-response model. The pressure-response model method includes classification and regression tree models, non-parametric inflection point analysis, and Bayesian inflection point analysis.

[0116] Taking the nitrate concentration and nitrite concentration at the sampling points as pressure variables, and the chlorophyll concentration, OTU abundance, diatom ratio, phytoplankton Shannon-Wiener diversity index, and zooplankton Shannon-Wiener diversity index, etc. as response variables, input them into the classification and regression tree model. Through the processes of stopping and pruning, the optimal tree is selected, and the water body nutrient concentration corresponding to the nodes of the optimal tree is used as the candidate value for nutrient benchmarks.

[0117] Based on the candidate values of nutrient benchmarks, nutrient benchmark derivation is carried out. First, the root node in the classification and regression tree model is used to obtain the first pressure index benchmark value, and then the subsequent pressure index benchmark values are obtained through the child nodes; when all nodes in the classification and regression tree model are the first pollution pressure index, non-parametric inflection point analysis is used to obtain the subsequent pressure index benchmark values; when all variable index data conform to the normal distribution, Bayesian inflection point analysis is used to obtain the final nutrient benchmark value.

[0118] Example 1

[0119] The method for formulating nitrogen and phosphorus nutrient benchmarks based on multi-level factor responses provided in this example includes:

[0120] Obtain big data of biological communities based on high-throughput sequencing technology, and conduct big data analysis across biological taxa on the big data of biological communities;

[0121] Taking the multi-level factor response factors under nitrogen and phosphorus input as response variables, characterizing the response relationship of the ecosystem after nitrogen and phosphorus input, and deriving the benchmark values of nitrogen and phosphorus nutrients.

[0122] Further, (1) Sample collection and environmental DNA acquisition.

[0123] Set ≥30 sampling points, which is conducive to observing the effective responses of biological communities and ecosystems to nutrient concentrations. The volume of water samples collected at each sampling point is ≥3L, of which ≥1L of water samples are used for water body physical and chemical detection to meet the measurement requirements of various water body physical and chemical indicators; ≥2L of water samples are used for environmental DNA extraction to meet the requirements of aquatic biological environmental DNA extraction and identification. For the water samples used for environmental DNA extraction, directly filter them with a nitrocellulose membrane with a pore size of 0.45μm, and store the filter membrane at -80°C until DNA extraction. Capturing the environmental DNA of all other aquatic organisms except prokaryotic microorganisms through a nitrocellulose filter membrane with a pore size of 0.45μm is conducive to more systematically obtaining the community information of aquatic organisms such as plankton, fish, and benthic invertebrates, analyzing the ecological system status of the sampling points, and improving the reliability of the benchmark.

[0124] A further optimized plan is to collect 51 water samples in the Pearl River Estuary waters in January 2021. The volume of each water sample is 3L in total, of which 2L is used for environmental DNA extraction and the other 1L is used for water body physical and chemical parameter detection. The 2L water samples used for environmental DNA extraction are filtered through a nitrocellulose membrane with a pore size of 0.45μm to obtain 51 filter membrane samples.

[0125] (2) Determination of water body physical and chemical indicators.

[0126] According to the requirements for benchmark formulation, the physical and chemical indicators to be measured can be different forms of nitrogen and phosphorus, chlorophyll a, dissolved oxygen, etc.

[0127] A further optimized plan is to use 1L of water sample for water body physical and chemical parameter detection, and measure the contents of nitrate, nitrite, and chlorophyll a in it; in addition, use a portable water quality detector on-site to detect DO, water temperature, redox potential, turbidity, conductivity, etc.

[0128] (3) Filter membrane environmental DNA extraction.

[0129] According to the instructions of the DNA extraction kit, genomic DNA was extracted, and the integrity of the DNA was detected by 1% agarose gel electrophoresis. At the same time, the concentration and purity of the DNA were detected using NanoDrop and Qubit. Amplification primers were designed for the metabarcoding genes of different groups of aquatic organisms, and the metabarcoding genes of various groups of aquatic organisms were amplified.

[0130] For a further optimized protocol, the QIAamp® DNA Stool Mini Kit (Qiagen) was used to extract environmental DNA samples on the filter membrane (nitrocellulose membrane) according to the instructions. After detection by the Nanodrop instrument, the nucleic acid concentration ranged from 11 to 45 ng / μL, and the absorbance detection results were as follows: the ratio of OD280 / OD260 ranged from 1.74 to 1.86, meeting the quality control requirements (sample concentration > 10 ng / μL, OD280 / OD260 ratio around 1.8).

[0131] (4) Gene amplification.

[0132] Plankton are divided into phytoplankton and zooplankton. In this example, the method is demonstrated only for the plankton group.

[0133] For phytoplankton, the target barcode gene is the V9 region of the mitochondrial gene 18S, and the primers are as follows:

[0134] Forward primer: TCCCTGCCHTTTGTACACAC, SEQ ID NO:1;

[0135] Reverse primer: CCTTCYGCAGGTTCACCTAC, SEQ ID NO:2;

[0136] For zooplankton, the target barcode gene is the V4 region of the mitochondrial gene 18S, and the primers are as follows:

[0137] Forward primer: AGGGCAAKYCTGGTGCCAGC, SEQ ID NO:3;

[0138] Reverse primer: GRCGGTATCTRATCGYCTT, SEQ ID NO:4;

[0139] For the selected target barcode genes, their gene sequences have sufficient conservativeness to ensure the accuracy of different species identification and appropriate variability to ensure the discrimination of different species.

[0140] Composition of the reaction system: 15 μL of High-Fidelity PCR Master Mix (New England Biolabs) + 0.2 μM primers + 10 ng template DNA (extracted environmental nucleic acid);

[0141] Amplification process: denaturation at 98°C for 1 minute + 30 amplification cycles (parameters for each cycle: denaturation at 98°C for 10S, annealing at 50°C for 30S, extension at 72°C for 30S) + extension at 72°C for 5 minutes.

[0142] (5) Sample purification and sequencing.

[0143] Electrophoresis was used to detect the amplification of the target barcode gene in 51 samples. The target band was excised and the samples were purified using a nucleic acid purification column. The purified samples were sent to the company for high-throughput sequencing.

[0144] Specifically, the target band was purified by gel cutting to make the DNA proportion ≥ 50%. The amplicons were sequenced on the machine, and steps such as pooling, adding sequencing adapters, and purifying the sequencing library were carried out. After the library was qualified by inspection, sequence determination was performed using the 300bp paired-end or single-end sequencing method on the Illumina MiSeq platform. The original image data file obtained by sequencing was analyzed by base calling and converted into raw sequencing sequences (raw reads). The results were stored in the FASTO (abbreviated as fg) file format, which included the sequence information of the sequencing sequences (reads) and the corresponding sequencing quality information.

[0145] The raw data was cleaned using the USERCH software to remove adapters, filter low-quality reads, and obtain cleandata. The filtered paired-end reads were assembled, and primer sequences were removed according to the alignment results at both ends of the primers, as well as sequences with sequencing errors exceeding 1%. After filtering, the assembled sequences of all samples were merged, and representative sequences (collectively referred to as OTU sequences) were calculated by OTU (operational taxonomic unit) clustering (default) or amplicon sequence variants (ASV) noise reduction method to obtain the OTU sequence set.

[0146] (6) Bioinformatics analysis

[0147] After cleaning and quality control of the sequencing data from the machine, the types and quantities of OTUs were obtained by sequence clustering, and the OTUs were annotated with species through database alignment to obtain plankton community information, including the species types of plankton and the relative abundances of OTUs at each site. In addition, based on the OTU data, the ratio of diatoms, the Shannon-Wiener diversity index of phytoplankton, and the Shannon-Wiener diversity index of plankton (including phytoplankton and zooplankton) were calculated at each site. The formula for the Shannon-Wiener diversity index is as follows:

[0148]

[0149]

[0150] Among them, S is the number of taxa, P i is the frequency, N i is the number of individuals in the i-th taxon, and N is the total number of individuals in all taxa.

[0151] The obtained OTU sequences are subjected to BLAST alignment with the reference sequences in the target gene metabarcoding database, and the sequences with an alignment length lower than 90% and a similarity lower than 80% are filtered out. According to the similarity thresholds of 98%, 95%, 90%, and 85%, annotations at the five taxonomic levels of species, genus, family, order, and class are obtained in sequence.

[0152] Based on the species annotations of the OTU table, statistical analyses are performed on the diversity, community structure, dominant species, and invasive species of different samples. The biological community and ecosystem indicators selected in this example include: OTU relative abundance, phytoplankton diversity (Shannon-Wiener diversity index), zooplankton diversity (Shannon-Wiener diversity index), and diatom ratio. The biological information at each site is shown in Table 1.

[0153] Table 1

[0154]

[0155]

[0156] (7) Construct a pressure-response model to obtain candidate values for nutrient criteria;

[0157] The pressure-response model method in this example includes a classification and regression tree model, non-parametric inflection point analysis, and Bayesian inflection point analysis. Taking the nitrate concentration and nitrite concentration at the site as pressure variables, and the chlorophyll concentration, OTU relative abundance, diatom ratio, phytoplankton Shannon-Wiener diversity index, and zooplankton Shannon-Wiener diversity index as response variables, input them into the classification and regression tree model. Through processes such as stopping and pruning, the optimal tree is selected, and the water body nutrient concentration corresponding to the node of the optimal tree is the candidate value for the nutrient criterion. Taking the nutrients nitrate and nitrite as examples, the benchmark values of the two derived according to the multi-level elements as pressure variables are shown in Table 2. As Figure 1 shown, the left side is a schematic diagram of deriving the nitrate benchmark value with the chlorophyll a concentration as the pressure variable; the right side is a schematic diagram of deriving the nitrite benchmark value with the chlorophyll a concentration as the pressure variable. As Figure 2 shown, the left side is a schematic diagram of deriving the nitrate benchmark value with the OTU abundance as the pressure variable; the right side is a schematic diagram of deriving the nitrite benchmark value with the OTU abundance as the pressure variable. As Figure 3 shown, the left side is a schematic diagram of deriving the nitrate benchmark value with the diatom ratio as the pressure variable; the right side is a schematic diagram of deriving the nitrite benchmark value with the diatom ratio as the pressure variable. AsFigure 4 As shown, the left side is a schematic diagram for deriving the baseline value of nitrate with phytoplankton diversity as the stress variable; the right side is a schematic diagram for deriving the baseline value of nitrite with phytoplankton diversity as the stress variable. As Figure 5 shown, the left side is a schematic diagram for deriving the baseline value of nitrate with plankton diversity as the stress variable; the right side is a schematic diagram for deriving the baseline value of nitrite with plankton diversity as the stress variable.

[0158] Table 2

[0159]

[0160] For the derivation of nutrient criteria, the root node in the classification and regression tree model is preferably used to obtain the baseline value of the first stress index, and then the subsequent stress index baseline values are obtained through the child nodes; when all nodes in the classification and regression tree model are the first pollution stress index, the subsequent stress index baseline values can be obtained by non-parametric inflection point analysis; when all variable index data conforms to the normal distribution, the baseline value can be obtained by Bayesian inflection point analysis.

[0161] (8) Analyze the habitat conditions in different study areas, and select the nutrient baseline value on the premise of ensuring the health of the ecosystem. The chlorophyll a concentration is a parameter indicating the phytoplankton biomass, while the diversity index, the ratio of diatoms to dinoflagellates, etc. are parameters indicating the plankton community structure and the health status of the ecosystem. Their changes are not completely coordinated, so there are differences in the derived nutrient baseline values. The hydrodynamic conditions in the estuary area are complex, and the growth of phytoplankton is greatly affected by light limitation and runoff dilution. Even if the nutrient concentration in the estuary is at a relatively high level, harmful algal blooms rarely occur. Therefore, on the premise of ensuring the health of the ecosystem, it is recommended that the determination of the nutrient baseline values of nitrate and nitrite in the Pearl River Estuary be based on the relatively loose baseline values derived according to the multi-level element response relationship in this embodiment, that is, the baseline of nitrate is 19.03 mg / L, and the baseline of nitrite is 41.75 mg / L.

[0162] The above is only a preferred specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present application should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for formulating nitrogen and phosphorus nutrient criteria based on multi-level element responses, characterized in that, Including: Obtaining big data of biological communities based on high-throughput sequencing technology, and performing big data analysis across biological taxa on the big data of biological communities; Taking multi-level factor response factors under nitrogen and phosphorus inputs as response variables to characterize the response relationship of the ecosystem after nitrogen and phosphorus inputs, and deriving the benchmark values of nitrogen and phosphorus nutrients; The process of obtaining big data of biological communities based on high-throughput sequencing technology includes: Setting sampling points to collect water samples and obtain environmental DNA filter membrane samples, measuring the physical and chemical indexes of the water samples, and extracting environmental DNA from the filter membrane samples; Designing universal amplification primers for the macro-barcode genes of different taxa of aquatic organisms and amplifying the macro-barcode genes of aquatic organisms; Electrophoretically detecting the amplification of the target barcode genes of the filter membrane samples, cutting out the target bands, purifying the samples using a nucleic acid purification column, and performing high-throughput sequencing on the purified samples to obtain big data of biological communities; the big data of biological communities includes but is not limited to the types of organisms, operational taxonomic units, and relative abundances; The process of performing big data analysis across biological taxa on the big data of biological communities includes: After cleaning and quality controlling the off-machine data of high-throughput sequencing, obtaining an OTU sequence set through sequence clustering; Performing species annotation on the OTU sequence set through database comparison to obtain plankton taxon information; the plankton taxon information includes but is not limited to the types of plankton species and the OTU relative abundances at each sampling point; Calculating the ratio of silicoflagellates, the Shannon-Wiener diversity index of phytoplankton, and the Shannon-Wiener diversity index of plankton at each point; The process of obtaining the benchmark values of nitrogen and phosphorus nutrients includes: Constructing a pressure-response model, which includes a classification and regression tree model, non-parametric inflection point analysis, and Bayesian inflection point analysis; Taking the nitrate concentration and nitrite concentration at the sampling points as pressure variables, and the chlorophyll concentration, OTU abundance, ratio of silicoflagellates, Shannon-Wiener diversity index of phytoplankton, and Shannon-Wiener diversity index of plankton as response variables, inputting them into the classification and regression tree model, and through the processes of stopping and pruning, selecting the optimal tree, and taking the water nutrient concentration corresponding to the nodes of the optimal tree as the candidate values of nutrient benchmarks; Based on the candidate values of nutrient benchmarks, performing nutrient benchmark derivation. First, using the root node in the classification and regression tree model to obtain the first pressure index benchmark value, and then obtaining subsequent pressure index benchmark values through child nodes; when all nodes in the classification and regression tree model are the first pollution pressure indicators, non-parametric inflection point analysis is used to obtain subsequent pressure index benchmark values; when all variable index data conforms to a normal distribution, Bayesian inflection point analysis is used to obtain the final nutrient benchmark value.

2. The method for formulating nitrogen and phosphorus nutrient benchmarks based on multi-level factor responses according to claim 1, characterized in that The process of setting sampling points to collect water samples and obtain environmental DNA filter membrane samples includes: Set sampling points that are conducive to observing the effective responses of biological communities and ecosystems to the concentrations of nitrogen and phosphorus nutrients; among them, the water samples collected at the sampling points include a first water sample for water body physical and chemical detection and a second water sample for environmental DNA extraction. Filter the second water sample through a nitrocellulose filter membrane, and store the filtered nitrocellulose membrane at -80 °C to obtain the environmental DNA filter membrane sample.

3. The method for formulating nitrogen and phosphorus nutrient criteria based on multi-level element responses according to claim 2, characterized in that, The process of measuring the physical and chemical indicators of the water body sample includes: Measure the contents of nitrate, nitrite, and chlorophyll a in the first water sample, and detect DO, water temperature, redox potential, turbidity, and conductivity in the first water sample on-site at the sampling site.

4. The method for formulating nitrogen and phosphorus nutrient criteria based on multi-level element response according to claim 1, wherein The process of extracting environmental DNA from the filter membrane sample includes: Extract genomic DNA based on a DNA extraction kit to obtain the environmental DNA of aquatic organisms on the nitrocellulose membrane, and detect the integrity of the DNA by 1% agarose gel electrophoresis. At the same time, use NanoDrop and Qubit to detect the concentration and purity of the DNA.

5. The method for formulating nitrogen and phosphorus nutrient criteria based on multi-level element responses according to claim 1, characterized in that The process of amplifying the macrobarcode gene of aquatic organisms includes: For phytoplankton, the target barcode gene is the V9 region of the mitochondrial gene 18S, and the primers are as follows: Forward primer: TCCCTGCCHTTTGTACACAC, SEQ ID NO:1; Reverse primer: CCTTCYGCAGGTTCACCTAC, SEQ ID NO:2; For zooplankton, the target barcode gene is the V4 region of the mitochondrial gene 18S, and the primers are as follows: Forward primer: AGGGCAAKYCTGGTGCCAGC, SEQ ID NO:3; Reverse primer: GRCGGTATCTRATCGYCTT, SEQ ID NO:4; For fish, the target barcode gene is the mitochondrial gene 12S, and the primers are as follows: Forward primer: GTCGGTAAAACTCGTGCCAGC, SEQ ID NO:5; Reverse primer: CATAGTGGGGTATCTAATCCCAGTTTG, SEQ ID NO:6; For benthic invertebrates, the target barcode gene is COI, and the primers are as follows: Forward primer: GGDACWGGWTGAACWGTWTAYCCHCC, SEQ ID NO:7; Reverse primer: CAAACAAATARDGGTATTCGDTY, SEQ ID NO:8; The reaction system composition includes: 15 μL of High-Fidelity PCR Master Mix New England Biolabs + 0.2 μM primers + 10 ng of template environmental DNA; The amplification process includes: denaturation at 98 °C for 1 minute + 30 amplification cycles + extension at 72 °C for 5 minutes; Among them, the reaction parameters for each cycle are: denaturation at 98 °C for 10S, annealing at 50 °C for 30S, and extension at 72 °C for 30S.

6. The method for formulating nitrogen and phosphorus nutrient criteria based on multi-level factor responses according to claim 1, wherein The process of electrophoretically detecting the amplification of the target barcode gene of the filter membrane sample, cutting out the target band, purifying the sample using a nucleic acid purification column, and performing high-throughput sequencing on the purified sample includes: Cut the purified target band so that the DNA accounts for ≥ 50%, load the amplicons onto the sequencer for sequencing, mix the libraries, add sequencing adapters, and purify the sequencing libraries. After the libraries pass the inspection, sequence determination is performed by paired-end or single-end sequencing methods; Convert the original image data file obtained by sequencing into the original sequencing sequence through base recognition analysis, and store the original sequencing sequence in the FASTO file format; the original sequencing sequence includes the sequence information of the sequencing sequence and the corresponding sequencing quality information.

7. The method for formulating nitrogen and phosphorus nutrient criteria based on multi-level element responses according to claim 6, characterized in that After cleaning and quality control of the off-machine data of high-throughput sequencing, obtaining an OTU sequence set through sequence clustering, the process of species annotation of the OTU sequence set through database comparison includes: Clean the off-machine data of high-throughput sequencing, remove the adapters, filter out low-quality sequencing sequences, and obtain the cleaned data; Perform splicing on the filtered paired-end sequencing sequences, and remove the primer sequences according to the alignment results of the primers at both ends, as well as sequences with a sequencing error exceeding 1%; After filtering, merge the spliced sequences of all samples, and calculate the representative sequences by OTU clustering or amplicon sequence variant denoising methods to obtain an OTU sequence set; Perform BLAST alignment of the OTU sequence set with the reference sequences in the target gene metabarcoding database, filter out sequences with an alignment length lower than 90% and a similarity lower than 80%, and obtain annotations at the five taxonomic levels of species, genus, family, order, and class in turn according to the similarity thresholds of 98%, 95%, 90%, and 85%; Based on the species annotation of the OTU table, perform statistical analysis on the diversity, community structure, dominant species, and invasive species of different samples; The calculation expression of the Shannon-Wiener diversity index is: Among them, S is the number of taxa, P i is the frequency, N i is the number of individuals in the i-th taxon, and N is the total number of individuals in all taxa.

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