Method for monitoring and optimizing fish species based on DNA barcoding and river basin ecological database

Abstract

The application relates to a method for monitoring and optimizing fish species based on DNA barcodes and a river basin ecological database, which comprises the following steps: collecting Cytb gene sequence information original data of fish and water quality parameters, habitat parameters and biological parameter original data through original data collection; constructing a tree structure database, forming a tree structure database with the collected data as nodes, and saving the original data to the corresponding nodes; obtaining Cytb gene sequence information average data and ecological parameter data through sample collection and processing; obtaining Cytb gene sequence information average data of the same fish in different years through database data acquisition and processing; preliminarily determining local fish by comparing the to-be-tested fish data with the fish data in the database; analyzing and predicting ecological parameters, obtaining ecological parameter average data from the database and predicting ecological parameter data of the current year; and finally determining and optimizing measures, determining and optimizing the fish species in the river basin according to the collected ecological parameter data and the predicted ecological parameter data. The application can improve the reliability and accuracy of the monitoring results, and can be applied to various river basins, and has a wide application prospect.

Description

TECHNICAL FIELD

[0001] The present application relates to a method for monitoring and optimizing fish species based on DNA barcoding and watershed ecological database, belonging to the technical field of biological monitoring and ecological optimization. BACKGROUND

[0002] Under the current global ecological environment change and human activity influence, the stability and biodiversity of watershed ecosystem are facing severe challenges. Fish, as a key species in aquatic ecosystem, has important significance for maintaining the balance of aquatic ecology, ensuring water resources safety and promoting the development of fishery economy. However, with the development of technology, the cross-regional spread of species has become more frequent, and the problem of biological invasion is becoming increasingly serious. After the entry of alien invasive fish into the watershed, due to the lack of natural enemies, they can quickly reproduce and spread, competing with native fish for food and living space, posing a serious threat to the survival of native fish. Some alien invasive fish may also carry pathogens, causing the spread of fish diseases, further disrupting the ecological balance of the watershed. Therefore, accurately monitoring the composition and changes of fish species, timely detecting alien invasive species and assessing the impact of fish on the ecological environment are crucial for protecting the balance and stability of the watershed ecosystem.

[0003] Current existing technologies have made some progress in fish monitoring and management, which can use fish DNA sequences to establish a database for fish monitoring and management. For example, the file number CN113234836A "A method for constructing a DNA barcode database of fish in Taihu Lake and its application" and the file number CN119132424A "A fish DNA barcode database and its construction method based on mitochondrial genome full sequence and application" both use fish DNA barcodes to establish a database to monitor fish in the watershed. However, these existing technologies focus more on the collection of static data, lack analysis of time series data, and are difficult to capture dynamic changes in the ecological environment. Moreover, relying solely on a single gene sequence for research cannot comprehensively assess the survival status of fish and their relationship with the environment. In complex ecological environments, it is still a challenge to distinguish between native species and alien invasive species of the same fish in the watershed, especially under the combined action of multiple factors. SUMMARY

[0004] To solve the above-mentioned problems in the prior art, the present application provides a method for monitoring and optimizing fish species based on DNA barcoding and watershed ecological database. This method collects not only Cytb gene sequence information of fish in the watershed, but also water quality parameters, habitat parameters and biological parameters, to jointly construct a DNA barcode and watershed ecological database, monitor fish and ecological environment in the target watershed, and improve the reliability and accuracy of the monitoring results, thereby improving the correctness of the method for optimizing fish species.

[0005] To achieve the above object, the technical scheme provided by the present application is as follows: a method for monitoring and optimizing fish species based on DNA barcoding and a river basin ecological database, comprising the following steps:

[0006] (1) collecting Cytb gene sequence information original data A of fish in different years, different seasons, different regions and different water layers in a target river basin nijk , wherein n represents the year, n is a natural number, i represents the season, i = 1, 2, 3, 4, j represents the fish species, j is a natural number, k represents the sample number, and k is a natural number; collecting water quality parameter original data B nisk , habitat parameter original data C nisk and biological parameter original data D nisk in the target river basin at the corresponding time, corresponding region and corresponding water layer at the same time as the fish sample is collected; s represents the parameter type, and s is a natural number;

[0007] wherein the data structure of the water quality parameter original data B nisk includes 7 parameters: B ni1k , B ni2k , B ni3k , B ni4k , B ni5k , B ni6k and B ni7k , wherein B ni1k is chlorophyll a, B ni2k is dissolved oxygen DO, B ni3k is pH value, B ni4k is total nitrogen TN, B ni5k is ammonia nitrogen NH3-N, B ni6k is total phosphorus TP, and B ni7k is chemical oxygen demand COD Mn ;

[0008] wherein the data structure of the habitat parameter original data C nisk includes 2 parameters: C ni1k and C ni2k , wherein C ni1k is aquatic plant coverage, and C ni2k is water level average value ratio;

[0009] wherein the data structure of the biological parameter original data D nisk includes 3 parameters: D ni1k , D ni2k and D ni3k , wherein D ni1k is the number of indigenous species of aquatic plants, D ni2k is the number of indigenous species of phytoplankton, and D ni3k is the number of indigenous species of zooplankton.

[0010] (2) Form a tree structure database with fish Cytb gene sequence information, water quality parameters, habitat parameters and biological parameters as data nodes; save the original data collected in step (1) to the corresponding nodes of the tree structure database;

[0011] (3) Collect fish samples to be tested in different regions and different water layers in the target river basin, and collect water quality parameter original data B 0isk , habitat parameter original data C 0isk and biological parameter original data D 0isk at the same time; extract genomic DNA from the collected fish samples, use universal primers to amplify the DNA barcode region of Cytb gene, then sequence the amplification product to obtain the DNA barcode sequence of each collected fish sample, and obtain the Cytb gene sequence information original data A 0ijk of the collected fish samples;

[0012] (4) According to the species of the fish to be tested, obtain the Cytb gene sequence information original data A nijk of the same species from the database of step (2), process the original data A nijk , and obtain the Cytb gene sequence information average data A nij of the same species in different years;

[0013] (5) Compare the Cytb gene sequence information original data A 0ijk of the fish samples collected in step (3) with the Cytb gene sequence information average data A nij of different years in step (4), if the difference between A 0ijk and different years A nij is within the threshold range, it is determined that the fish to be tested is the native fish in the target river basin, and the monitoring is completed; otherwise, record the Cytb gene sequence information original data of the fish sample A 0ijk whose difference with different years A nij is not within the threshold range, and enter step (6);

[0014] (6) Obtain the original water quality parameter data B nisk , habitat parameter data C nisk and biological parameter data D nisk of the same species of fish as the fish to be tested in different years in the target river basin from the database of step (2); s represents the parameter type, and s is a natural number; process the obtained original parameter data to obtain the water quality parameter average data B nis , habitat parameter average data C nis and biological parameter average data D nis of different years; compare the water quality parameter average data Bnis , habitat parameter mean data C nis and biological parameter mean data D nis respectively, to obtain the variation B of the corresponding parameter 1is -B 2is , B 2is -B 3is , …, B (n-1)is -B nis , C 1is -C 2is , C 2is -C 3is , …, C (n-1)is -C nis , D 1is -D 2is , D 2is -D 3is , …, D (n-1)is -D nis ; the mean value of the above variation is obtained to obtain the mean variation AB is , AC is and AD is ; the mean variation AB is , AC is and AD is determine the water quality parameter mean data B (n+1)is , habitat parameter mean data C (n+1)is and biological parameter mean data D (n+1)is of the target basin this year; then step (7) is entered;

[0015] (7) process the water quality parameter raw data B 0isk , habitat parameter raw data C 0isk and biological parameter raw data D 0isk collected in step (3) to obtain the water quality parameter mean data B 0is , habitat parameter mean data C 0is and biological parameter mean data D 0is collected; compare the water quality parameter mean data B 0is , habitat parameter mean data C 0is and biological parameter mean data D 0is collected with the water quality parameter mean data B (n+1)is , habitat parameter mean data C (n+1)is and biological parameter mean data D (n+1)is of this year to obtain the difference X is =B 0is -B (n+1)is , Y is =C 0is -C (n+1)is and Z is =D0is -D (n+1)is ;

[0016] Data indicating an improvement in the overall ecological environment of the target watershed is represented by: X is medium chlorophyll a difference X i1 The difference between the negative dissolved oxygen (DO) value and the negative value is X. i2 The positive value is the pH difference X. i3 The absolute value is not greater than 1, and the difference in total nitrogen (TN) is X. i4 The difference between ammonia nitrogen (NH3-N) and negative values ​​is X. i5 The difference between the total phosphorus (TP) and the negative value X i6 Negative and chemical oxygen demand (COD) Mn Difference X i7 If negative, Y is aquatic plant coverage Y i1 The positive and average water level ratio difference Y i2 Not greater than 1; Z is Difference in the number of native species of aquatic plants Z i1 Z represents the number of native phytoplankton species. i2 For positive and zooplankton native species number Z i3 It is positive;

[0017] Data indicating a deterioration in the overall ecological environment of the target watershed is represented as: X is medium chlorophyll a difference X i1 The difference between dissolved oxygen (DO) and positive values ​​is X. i2 The negative pH difference X i3 The absolute value is not less than 1, and the difference in total nitrogen (TN) is X. i4 The difference between positive and negative ammonia nitrogen (NH3-N) is X. i5 The difference between positive and total phosphorus (TP) values ​​is X. i6 For positive and chemical oxygen demand (COD) Mn Difference X i7 If positive, Y is aquatic plant coverage Y i1 The negative value and the difference between the average water level and the average water level Y i2 Not less than 1; Z is Difference in the number of native species of aquatic plants Z i1 The number of native phytoplankton species Z is negative. i2 For negative and the number of native zooplankton species Z i3 Negative;

[0018] If X is Y is and Z isIf all values ​​are within the threshold range, repeat steps (6) and (7) one year later. If the differences between the mean values ​​of water quality parameters, habitat parameters, and biological parameters are all within the threshold range, the fish to be tested will be identified as native fish in the target watershed, and the monitoring will be completed. Then, the original data of the Cytb gene sequence information of the fish samples recorded in step (5) will be added to the database in step (2); otherwise, if X is There are no fewer than 4 data points, Y is There is at least one data point and Z is If at least two data points indicate an improvement in the overall ecological environment of the target watershed, the fish species to be tested are identified as beneficial to the overall ecological environment of the target watershed, and the number of the tested fish species is recorded. Upon completion of this monitoring, if the number of the tested fish species accounts for less than S% of the total fish community, artificial propagation of the tested fish species will be carried out; if X is There are no fewer than 4 data points, Y is There is at least one data point and Z is If at least two data points indicate a deterioration in the overall ecological environment of the target watershed, the fish species to be tested will be identified as harmful to the overall ecological environment of the target watershed. Once this monitoring is completed, the fish species to be tested will be artificially removed.

[0019] The further improvement to the above technical solution is as follows:

[0020] The raw data for the Cytb gene sequence information of fish includes the percentage of bases A, T, G, and C in the total sequence.

[0021] The steps for forming the tree-structured database in step (2) are as follows:

[0022] 1) The year is used as the root node, which is the starting point of the tree structure database. All data is organized based on the root node.

[0023] 2) Add specific years as second-level nodes under the root node;

[0024] 3) Under each second-level node, add spring, summer, autumn, and winter as third-level nodes. These nodes represent different seasons in different years.

[0025] 4) Under each third-level node, add fish, water quality parameters, habitat parameters, and biological parameters as fourth-level nodes. These nodes represent the research categories in different years and seasons.

[0026] 5) Under each fourth-level node, further add fish species, water quality parameter categories, habitat parameter categories, and biological parameter categories as fifth-level nodes. These nodes represent different directions of different research categories in different years and seasons.

[0027] 6) Under the fifth layer node, further add the fish Cytb gene sequence information original data, water quality parameters, habitat parameters and biological parameters of various categories of original data as the sixth layer node, and the data type of this layer node is an array. These nodes record the original data information under the corresponding category;

[0028] 7) Store the relevant data in the corresponding nodes.

[0029] The genomic DNA extraction in step (3) is specifically: genomic DNA is extracted from the muscle tissue of the collected fish samples by using a conventional phenol-chloroform method. The extracted DNA is used as a template, and the Cytb gene is amplified by PCR using H15915 (5'-CTCCGATCTCCGGATTACAAGAC-3') and L14724 (5'-GACTTGAAAAACCACCGTTG-3') primers. The amplified product is sequenced to obtain the DNA barcode sequence of each sample. The reaction conditions are as follows: 94℃ pre-denaturation for 5 min; 94℃ denaturation for 30 s, 50℃ annealing for 30 s, 72℃ extension for 1 min, a total of 35 cycles; and finally 72℃ extension for 10 min.

[0030] The threshold range of X, Y and Z in step (7) is H% of B, C and D. nij

[0031] The threshold range of X is , Y is and Z is in step (7) is H% of B (n+1)is , C (n+1)is and D (n+1)is .

[0032] The artificial propagation of the fish to be tested in step (7) includes artificial breeding and release and the construction of a propagation and release station. The artificial reduction of the fish to be tested includes gillnet fishing and trap fishing.

[0033] ​According to the above technical scheme, the method for monitoring and optimizing fish species based on the DNA barcode and the river basin ecological database provided by the application collects Cytb gene sequence information raw data of fish and water quality parameters, habitat parameters and biological parameter raw data through original data collection; constructs a tree structure database, forms a tree structure database with the collected data as nodes, and saves the raw data to the corresponding nodes; collects and processes the test sample to obtain Cytb gene sequence information mean data and ecological parameter data; acquires and processes database data to obtain Cytb gene sequence information mean data of the same fish in different years; preliminarily determines the native fish by comparing the test fish data with the fish data in the database; analyzes and predicts the ecological parameters, obtains the ecological parameter mean data from the database and predicts the ecological parameter data of the current year; finally determines and optimizes the measures, and determines and optimizes the fish species in the river basin according to the collected ecological parameter data and the predicted ecological parameter data. Compared with the prior art, the application has the following advantages:

[0034] (1) Strong data comprehensiveness: This method not only collects Cytb gene sequence information raw data of fish, but also collects water quality parameters, habitat parameters and biological parameters and other raw data corresponding to time, region and water layer, and constructs a comprehensive database containing multiple key factors. Compared with some existing monitoring technologies that only focus on a single factor or a few factors, it can more comprehensively reflect the status of the river basin ecosystem, provide more abundant data support for fish species monitoring and ecological environment assessment, and help more accurately grasp the changes of fish living environment and the influence of fish species on the river basin ecology.

[0035] (2) High monitoring accuracy: DNA barcode technology is used to analyze fish samples, and accurate gene sequence information is obtained through PCR amplification and sequencing of Cytb gene to determine whether the test fish is a native fish. Compared with the traditional method of identifying fish species by relying on morphological characteristics, it can effectively avoid misjudgment caused by similar fish morphology, greatly improving the accuracy of species identification. At the same time, the collected parameter data is compared and analyzed with the historical data in the database, and the threshold range is set for comprehensive judgment, further improving the reliability and accuracy of the monitoring results.

[0036] (3) Pay attention to ecological environment consideration: When judging the influence of the test fish on the river basin ecological environment, the changes of water quality parameters, habitat parameters and biological parameters are considered comprehensively, and the trend and difference range of different parameters are used to determine whether the fish is beneficial or harmful. This evaluation method based on multi-dimensional indicators of ecological system fully reflects the comprehensive attention to ecological environment, and can provide more scientific basis for the protection and optimization of river basin ecosystem. The existing technology may be less concerned with such comprehensive ecological environment for monitoring and evaluating fish species.

[0037] (4) Application prospects are wide: The method clearly and specifically sets forth a series of operation steps from data collection, database construction, sample collection and processing, gene amplification sequencing to final result determination and subsequent processing, including specific steps of forming a tree structure database, a method of extracting genomic DNA, reaction conditions of PCR amplification, setting of threshold range, and specific ways of artificial propagation and reduction, etc., which has strong operability and standardization, facilitating researchers and relevant staff to operate and implement according to unified standards, improving the repeatability and comparability of the research, and being conducive to the popularization and application of the method in different regions and scenarios.

[0038] (5) With the function of optimizing fish species: The method can not only monitor fish species, but also optimize fish species according to the monitoring results. Artificial propagation is performed on fish species determined to be beneficial to the ecological environment of the river basin, and artificial reduction is performed on harmful fish species, which is helpful to maintain the balance and stability of the river basin ecosystem, and promote the rational distribution of fish species and the healthy development of the ecosystem. Many existing technologies may only stay at the monitoring level, lacking the optimization and regulation function of fish species. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 Flow chart of the method for monitoring and optimizing fish species based on DNA barcoding and river basin ecological database. DETAILED DESCRIPTION

[0040] The present application will be described in detail below in combination with the drawings and specific examples, but the scope of protection of the present application is not limited to the following examples.

[0041] From Figure 1 It can be known from the flow chart of the method for monitoring and optimizing fish species based on DNA barcoding and river basin ecological database that the present application completes the inspection and optimization of fish species in the river basin through original data collection, construction of tree structure database, collection and processing of samples to be tested, database data acquisition and processing, preliminary determination of local fish, ecological parameter analysis and prediction, and final determination and optimization measures. In the present embodiment, the target river basin is the Pearl River Basin, and the specific steps are as follows:

[0042] (1) Collecting Cytb gene sequence information original data A of fish in different regions, different water layers of the Pearl River Basin in different years and different seasons nijk , n represents the year, n is a natural number, i represents the season, i = 1, 2, 3, 4, j represents the fish species, j is a natural number, and k represents the sample number, k is a natural number; Collecting water quality parameter original data B nisk , habitat parameter original data C niskand biological parameter raw data D nisk ; s represents the parameter type, s is a natural number;

[0043] wherein the data structure of water quality parameter raw data B nisk includes 7 parameters: B ni1k , B ni2k , B ni3k , B ni4k , B ni5k , B ni6k and B ni7k , wherein B ni1k is chlorophyll a, B ni2k is dissolved oxygen DO, B ni3k is pH value, B ni4k is total nitrogen TN, B ni5k is ammonia nitrogen NH3-N, B ni6k is total phosphorus TP, B ni7k is chemical oxygen demand COD Mn ;

[0044] wherein the data structure of habitat parameter raw data C nisk includes 2 parameters: C ni1k and C ni2k , wherein C ni1k is aquatic plant coverage, and C ni2k is water level average value ratio;

[0045] wherein the data structure of biological parameter raw data D nisk includes 3 parameters: D ni1k , D ni2k , D ni3k , wherein D ni1k is number of indigenous species of aquatic plants, D ni2k is number of indigenous species of phytoplankton, and D ni3k is number of indigenous species of zooplankton;

[0046] wherein the Cytb gene sequence information raw data of fish includes percentage of bases A, T, G and C in total sequence;

[0047] (2) Form a tree structure database with fish Cytb gene sequence information, water quality parameters, habitat parameters and biological parameters as data nodes; save the raw data collected in step (1) to the corresponding nodes of the tree structure database; the specific steps are as follows:

[0048] 1) Take year as root node, which is the starting point of the tree structure database, and organize all data based on the root node;

[0049] 2) Add specific years as second layer nodes under the root node;

[0050] 3) Under each second layer node, add spring, summer, autumn and winter as third layer nodes, which represent different seasons in different years;

[0051] 4) Under each third layer node, add fish, water quality parameters, habitat parameters and biological parameters as fourth layer nodes, which represent research categories in different years and different seasons;

[0052] 5) Under each fourth layer node, further add fish species, water quality parameter categories, habitat parameter categories and biological parameter categories as fifth layer nodes, which represent different directions of different research categories in different years and different seasons;

[0053] 6) Under the fifth layer nodes, further add fish Cytb gene sequence information raw data, water quality parameter, habitat parameter and biological parameter category raw data as sixth layer nodes, which are array type, and record the raw data information of the corresponding categories;

[0054] 7) Store the relevant data in the corresponding nodes.

[0055] (3) Collect fish samples to be tested in different regions and different water layers in the Pearl River Basin, and collect water quality parameter raw data B 0isk , habitat parameter raw data C 0isk and biological parameter raw data D 0isk at the same time; extract genomic DNA from the collected fish samples, use universal primers to amplify the DNA barcode region of Cytb gene by PCR, then sequence the amplification product to obtain the DNA barcode sequence of each collected fish sample, and obtain the Cytb gene sequence information raw data A 0ijk of the collected fish samples; the specific operation is as follows:

[0056] Use the conventional phenol-chloroform method to extract genomic DNA from the muscle tissue of the collected fish samples, use the extracted DNA as a template, use H15915 (5'-CTCCGATCTCCGGATTACAAGAC-3') and L14724 (5'-GACTTGAAAAACCACCGTTG-3') primers to amplify the Cytb gene by PCR, and sequence the amplification product to obtain the DNA barcode sequence of each sample; the reaction conditions are as follows: 94℃ pre-denaturation for 5 min; 94℃ denaturation for 30 s, 50℃ annealing for 30 s, 72℃ extension for 1 min, a total of 35 cycles; finally 72℃ extension for 10 min.

[0057] (4) According to the species of the fish to be tested, obtain the Cytb gene sequence information raw data A nijkFor the original data A nijk The data were processed to obtain the mean Cytb gene sequence information of the same fish species from different years, A. nij ;

[0058] (5) Take the raw data A of the Cytb gene sequence information of the fish samples collected in step (3). 0ijk Compared with the mean data A of Cytb gene sequence information from different years in step (4) nij Compare them separately, if A 0ijk A from different years nij The differences are all within the threshold range, i.e., A nij If the percentage is within J%, the tested fish species are determined to be native to the Pearl River basin, and the monitoring is considered complete; otherwise, record A. 0ijk A from different years nij The original data of Cytb gene sequence information of fish samples whose difference is not within the threshold range are entered into step (6); where the value of J% is determined together based on the ecological environment data of different watersheds and the Cytb gene sequence information data of different fish species.

[0059] (6) Obtain raw water quality parameter data B from the database of step (2) for different years of the same species of fish in the Pearl River Basin as the fish to be tested. nisk Habitat parameter data C nisk and biological parameter data D nisk ; s represents the parameter type, s is a natural number, and the obtained raw parameter data is processed to obtain the average water quality parameter data B for different years. nis Habitat parameter mean data C nis and biological parameter mean data D nis ; The average water quality parameter data of two consecutive years B nis Habitat parameter mean data C nis and biological parameter mean data D nis By comparing the results separately, the change in the corresponding parameter B can be obtained. 1is -B 2is B 2is -B 3is ... B (n-1)is -B nis C 1is -C 2is C 2is -C 3is ... C (n-1)is -C nis D 1is -D 2is D 2is -D 3is ... D (n-1)is -D nisThe mean change ΔB is obtained by taking the average of the above changes. is ΔC is and ΔD is According to the mean change ΔB is ΔC is and ΔD is Determine the average water quality parameters of the Pearl River Basin for this year (B) (n+1)is Habitat parameter mean data C (n+1)is and biological parameter mean data D (n+1)is Then proceed to step (7);

[0060] (7) The raw water quality parameter data B collected in step (3) 0isk Habitat parameter raw data C 0isk and raw data of biological parameters D 0isk The collected water quality parameter average data B were processed to obtain the data. 0is Habitat parameter mean data C 0is and biological parameter mean data D 0is ; Collect the average water quality parameter data B 0is Habitat parameter mean data C 0is and biological parameter mean data D 0is Compared with the average water quality parameters of this year, B (n+1)is Habitat parameter mean data C (n+1)is and biological parameter mean data D (n+1)is By comparison, the difference X is obtained. is =B 0is -B (n+1)is Y is =C 0is -C (n+1)is and Z is =D 0is -D (n+1)is ;

[0061] Data indicating an improvement in the overall ecological environment of the target watershed is represented by: X is medium chlorophyll a difference X i1 The difference between the negative dissolved oxygen (DO) value and the negative value is X. i2 The positive value is the pH difference X. i3 The absolute value is not greater than 1, and the difference in total nitrogen (TN) is X. i4 The difference between ammonia nitrogen (NH3-N) and negative values ​​is X. i5 The difference between the total phosphorus (TP) and the negative value X i6 Negative and chemical oxygen demand (COD) Mn Difference X i7 If negative, Y is aquatic plant coverage Y i1 The positive and average water level difference Y i2 Not greater than 1; Zis The difference value of the number of native species of aquatic plants Z i1 is positive, the number of native species of phytoplankton Z i2 is positive, and the number of native species of zooplankton Z i3 is positive;

[0062] The data indicating the overall ecological environment of the target river basin is getting worse is represented as: X is The difference value of the median chlorophyll a X i1 is positive, the difference value of dissolved oxygen DO X i2 is negative, the difference value of pH X i3 is not less than 1 in absolute value, the difference value of total nitrogen TN X i4 is positive, the difference value of ammonia nitrogen NH3-N X i5 is positive, the difference value of total phosphorus TP X i6 is positive, and the difference value of chemical oxygen demand COD Mn X i7 is positive, Y is The median aquatic plant coverage Y i1 is negative, and the difference value of the average water level ratio Y i2 is not less than 1; Z is The difference value of the number of native species of aquatic plants Z i1 is negative, the number of native species of phytoplankton Z i2 is negative, and the number of native species of zooplankton Z i3 is negative;

[0063] If X is , Y is and Z is are all within the threshold range, that is, B (n+1)is , C (n+1)is and D (n+1)is are within H% of each other, after one year, steps (6) and (7) are repeated, and if the difference values of the water quality parameter mean data, the habitat parameter mean data and the biological parameter mean data are all within the threshold range, the fish to be tested is determined to be an indigenous fish in the target river basin, the monitoring is completed this time, and the original data of the Cytb gene sequence information of the fish sample recorded in step (5) is added to the database in step (2); otherwise, if not less than 4 items of data in X is , not less than 1 item of data in Y is and not less than two items of data in Z is indicate that the overall ecological environment of the target river basin is getting better, the fish to be tested is determined to be a fish beneficial to the overall ecological environment of the target river basin, and the number of the fish to be tested is recorded. The monitoring is completed this time, and if the proportion of the number of the fish to be tested in the entire fish community is less than S%, the fish to be tested is artificially propagated; if not less than 4 items of data in X is , not less than 1 item of data in Y is and not less than two items of data in Zis If at least two data points indicate a deterioration in the overall ecological environment of the target watershed, the fish species to be monitored are identified as harmful to the overall ecological environment of the target watershed. After the monitoring is completed, these fish species will be manually removed. The values ​​of H% and S% are determined jointly based on the ecological environment data of different watersheds and the fish species.

[0064] In this embodiment, to verify the above method, the lower reaches of the Xijiang River in the Pearl River Basin were used as the experimental area, and the spotted mandarin fish from the middle and lower reaches of the Xijiang River in spring were used as the experimental fish species for a simulation experiment. A group of spotted mandarin fish (scientific name: Siniperca scherzeri) from the middle reaches of the Xijiang River were tagged and released into the lower reaches of the Xijiang River. Then, the following steps were followed to monitor the experimental area for one year. The thresholds for various parameters differ for different regions and different fish species. In the data representation of this embodiment, i=1 indicates the season is spring, and j=1 indicates the fish species is spotted mandarin fish. Based on the ecological environment data of the lower reaches of the Xijiang River and the Cytb gene sequence information of the spotted mandarin fish species, J%=2%, H%=1%, and S%=1% were determined. The verification was carried out according to the following steps:

[0065] S1. Before releasing the spotted mandarin fish from the middle reaches of the Xijiang River into the lower reaches of the Xijiang River, collect raw data on water quality parameters of different regions and water layers in the lower reaches of the Xijiang River in spring of different years. nisk , Habitat parameter raw data C nisk and raw data of biological parameters D nisk ; n represents the year, where n is a natural number; s represents the type of parameter, where s is a natural number; the collected raw data are processed to obtain the average water quality parameters of the lower reaches of the Xijiang River in spring of different years when spotted mandarin fish from the Yangtze River basin are released into the lower reaches of the Xijiang River. n1s Habitat parameter mean data C n1s and biological parameter mean data D n1s ;

[0066] S2. One year after releasing mandarin fish from the Yangtze River basin into the lower reaches of the Xijiang River, raw water quality parameter data were collected from different areas and water layers in the lower reaches of the Xijiang River. 01sk Habitat parameter data C 01sk and biological parameter data D 01sk ; s represents the parameter type, and s is a natural number. The obtained raw parameter data is processed to obtain the mean water quality parameter data B. 01s Habitat parameter mean data C 01s and biological parameter mean data D 01s ; Take the average water quality parameter data of the lower reaches of the Xijiang River for two consecutive years from step S1 as B n1s Habitat parameter mean data C nis and biological parameter mean data D n1s By comparing the results separately, the change in the corresponding parameter B can be obtained.11s -B 21s B 21s -B 31s ... B (n-1)1s -B n1s C 11s -C 21s C 21s -C 31s ... C (n-1)1s -C n1s D 11s -D 21s D 21s -D 31s ... D (n-1)1s -D n1s The mean change ΔB is obtained by taking the average of the above changes. 1s ΔC 1s and ΔD 1s According to the mean change ΔB 1s ΔC 1s and ΔD 1s Determine the average water quality parameters of the lower reaches of the Xijiang River for this year (B) (n+1)1s Habitat parameter mean data C (n+1)1s and biological parameter mean data D (n+1)1s Table 1 shows the average water quality, habitat, and biological parameters collected in the lower reaches of the Xijiang River; Table 2 shows the average water quality, habitat, and biological parameters collected in the lower reaches of the Xijiang River this year.

[0067] Table 1. Mean values ​​of water quality, habitat, and biological parameters collected in the lower reaches of the Xijiang River.

[0068]

[0069] Table 2. Average values ​​of water quality, habitat, and biological parameters in the lower reaches of the Xijiang River this year.

[0070]

[0071]

[0072] S3, Collect the average water quality parameter data B 01s Habitat parameter mean data C 01s and biological parameter mean data D 01s Compared with the average water quality parameters of this year, B (n+1)1s Habitat parameter mean data C (n+1)1s and biological parameter mean data D (n+1)1s By comparison, the difference X is obtained. 1s =B 01s -B (n+1)1s Y 1s =C 01s -C (n+1)1sand Z 1s =D 01s -D (n+1)1s The difference data is shown in Table 3.

[0073] Table 3 Difference Data

[0074]

[0075] A statistical analysis of the number of spotted mandarin fish originating from the middle reaches of the Xijiang River in the lower reaches of the river revealed that the proportion of spotted mandarin fish from the middle reaches of the Xijiang River in the fish community in the lower reaches of the river is less than 1%.

[0076] According to the results in Table 3, after the spotted mandarin fish from the middle reaches of the Xijiang River enters the lower reaches of the Xijiang River, X 1s Y 1s and Z 1s Not all of them are in B. (n+1)1s C (n+1)1s and D (n+1)1s The *Siniperca chuatsi* (a type of mandarin fish) in the middle reaches of the Xijiang River has a positive impact on the lower reaches of the Xijiang River, benefiting the overall ecological environment. Furthermore, the *Siniperca chuatsi* from the middle reaches of the Xijiang River accounts for less than 1% of the fish population in the lower reaches of the Xijiang River, and through its biological habits, it decreases the content of chlorophyll a and total phosphorus (TP), increases dissolved oxygen (DO), and reduces pH by less than 1%. It also increases the coverage of aquatic plants by 0.5% in the habitat parameters, and increases the number of native aquatic plant species, phytoplankton species, and zooplankton species in the mesophytic parameters. However, since the *Siniperca chuatsi* from the middle reaches of the Xijiang River constitute less than 1% of the fish population in the lower reaches, artificial propagation of the *Siniperca chuatsi* from the middle reaches of the Xijiang River was ultimately carried out, including artificial breeding and release and the construction of propagation and release stations.

[0077] Therefore, according to the simulation experiment, the method of the present invention can effectively monitor fish and ecological environment in the target watershed. By monitoring the same fish species in different watersheds, the ecological environment of the watershed can be monitored more accurately and effectively. At the same time, the fish species in the watershed can be optimized based on the monitoring results, thereby further optimizing the overall ecological environment of the watershed.

Claims

1. A method for monitoring and optimizing fish species based on DNA barcoding and catchment ecological database, characterized in that The method comprises the following steps: (1) Collecting Cytb gene sequence information raw data A of fish in different years, different seasons, different regions and different water layers in the target basin nijk , where n represents the year, i represents the season, j represents the fish species, and k represents the sample number; while collecting the fish samples, simultaneously collect water quality parameter raw data B nisk , habitat parameter raw data C nisk and biological parameter raw data D nisk corresponding to the time, region and water layer in the target basin; s represents the parameter type (2) Forming a tree structure database with fish Cytb gene sequence information, water quality parameters, habitat parameters and biological parameters as data nodes; saving the original data collected in step (1) to the corresponding nodes of the tree structure database; (3) Collecting fish samples to be tested in different regions and different water layers in the target river basin, and collecting original data B of water quality parameters at the same time 0isk , original data C of habitat parameters 0isk , and original data D of biological parameters 0isk ; extracting genomic DNA from the collected fish samples, using universal primers to amplify the DNA barcode region of the Cytb gene by PCR, then sequencing the amplification products to obtain the DNA barcode sequence of each collected fish sample, and obtaining the original data A of the Cytb gene sequence information of the collected fish samples 0ijk ; (4) According to the fish species to be detected, the Cytb gene sequence information raw data A of the same fish species is obtained from the database of step (2) nijk , and the Cytb gene sequence information raw data A of the same fish species is obtained from the database of step (2) nijk , and the Cytb gene sequence information raw data A of the same fish species is obtained from the database of step (2) nij ; (5) Collecting the fish sample Cytb gene sequence information raw data A in step (3) 0ijk The average data A of Cytb gene sequence information of different years in step (4) nij Respectively compare, if the difference between A 0ijk and different years A nij is within the threshold range, it is determined that the fish to be tested is the native fish in the target river basin, and the monitoring is completed; otherwise, record the fish sample Cytb gene sequence information raw data A 0ijk and different years A nij The difference is not within the threshold range, enter step (6). (6) Obtain the original water quality parameter data B of the same fish species in different years in the target river basin from the database in step (2) nisk , habitat parameter data C nisk and biological parameter data D nisk ; s represents the parameter type, and the obtained original parameter data is processed to obtain the water quality parameter mean data B nis , habitat parameter mean data C nis and biological parameter mean data D nis ; the water quality parameter mean data B nis , habitat parameter mean data C nis and biological parameter mean data D nis of adjacent two years are compared respectively to obtain the change amount B 1is -B 2is , B 2is -B 3is , ……, B (n-1)is -B nis , C 1is -C 2is , C 2is -C 3is , ……, C (n-1)is -C nis , D 1is -D 2is , D 2is -D 3is , ……, D (n-1)is -D nis ; the mean value of the above change amount is taken to obtain the mean change amount ΔB is , ΔC is and ΔD is ; the mean change amount ΔB is , ΔC is and ΔD is are used to determine the water quality parameter mean data B (n+1)is , habitat parameter mean data C (n+1)is and biological parameter mean data D (n+1)is of the target river basin in the current year; then step (7) is entered; (7) The raw water quality parameter data B collected in step (3) 0isk Habitat parameter raw data C 0isk and raw data of biological parameters D 0isk The collected water quality parameter average data B were processed to obtain the data. 0is Habitat parameter mean data C 0is and biological parameter mean data D 0is ; Collect the average water quality parameter data B 0is Habitat parameter mean data C 0is and biological parameter mean data D 0is Compared with the average water quality parameters of this year, B (n+1)is Habitat parameter mean data C (n+1)is and biological parameter mean data D (n+1)is By comparison, the difference X is obtained. is =B 0is -B (n+1)is Y is =C 0is -C (n+1)is and Z is =D 0is -D (n+1)is ; If X is , Y is and Z is are all within the threshold range, after one year, steps (6) and (7) are repeated, if the differences between the mean data of water quality parameters, habitat parameters and biological parameters are all within the threshold range, the fish to be tested is determined as the native fish in the target river basin, the monitoring is completed, and then the original data of the Cytb gene sequence information of the fish sample recorded in step (5) is added to the database in step (2); otherwise, if no less than 4 data in X is , no less than 1 data in Y is and no less than two data in Z is show that the overall ecological environment of the target river basin is getting better, the fish to be tested is determined as the fish beneficial to the overall ecological environment of the target river basin, and the number of the fish to be tested is recorded, the monitoring is completed, if the proportion of the number of the fish to be tested in the entire fish community is less than 1%, the fish to be tested is artificially propagated; if no less than 4 data in X is , no less than 1 data in Y is and no less than two data in Z is show that the overall ecological environment of the target river basin is getting worse, the fish to be tested is determined as the fish harmful to the overall ecological environment of the target river basin, the monitoring is completed, and then the fish to be tested is artificially reduced.

2. The method for monitoring and optimizing fish species based on DNA barcoding and river basin ecological database according to claim 1, characterized in that: The fish Cytb gene sequence information original data includes the percentage of bases A, T, G and C in the total sequence.

3. The method for monitoring and optimizing fish species based on DNA barcoding and river basin ecological database according to claim 1, characterized in that The operation steps of forming the tree structure database in step (2) are as follows: 1) Take the year as the root node, which is the starting point of the tree structure database, and organize all the data based on the root node; 2) Add specific years as the second layer nodes under the root node; 3) Add spring, summer, autumn and winter as the third layer nodes under each second layer node, which represent different seasons under different years; 4) Add fish, water quality parameters, habitat parameters and biological parameters as the fourth layer nodes under each third layer node, which represent the research categories under different years and different seasons; 5) Further add fish species, water quality parameter categories, habitat parameter categories and biological parameter categories as the fifth layer nodes under each fourth layer node, which represent different directions of different research categories under different years and different seasons; 6) Further add fish Cytb gene sequence information original data, water quality parameter categories, habitat parameter categories and biological parameter categories original data as the sixth layer nodes under the fifth layer nodes, and the data type of this layer node is array, which records the original data information under the corresponding category; 7) Store the related data in the corresponding nodes.

4. The method for monitoring and optimizing fish species based on DNA barcoding and river basin ecological database according to claim 1, characterized in that In step (3), the genomic DNA is extracted from the muscle tissue of the collected fish samples using the conventional phenol-chloroform method. The extracted DNA is used as a template to amplify the Cytb gene using H15915 and L14724 primers, wherein the sequence of H15915 is 5'-CTCCGATCTCCGGATTACAAGAC-3', and the sequence of L14724 is 5'-GACTTGAAAAACCACCGTTG-3'. The amplified product is sequenced to obtain the DNA barcode sequence of each sample. The reaction conditions are as follows: 94℃ pre-denaturation for 5min; 94℃ denaturation for 30s, 50℃ annealing for 30s, 72℃ extension for 1min, a total of 35 cycles; and finally 72℃ extension for 10min.

5. The method for monitoring and optimizing fish species based on DNA barcoding and river basin ecological database according to claim 1, characterized in that: The threshold range in step (5) is A nij 2%.

6. The method for monitoring and optimizing fish species based on DNA barcoding and river basin ecological database according to claim 1, characterized in that: The threshold range for X is , Y is , and Z is in step (7) is 1% of B (n+1)is , C (n+1)is , and D (n+1)is .

7. The method for monitoring and optimizing fish species based on DNA barcoding and river basin ecological database according to claim 1, characterized in that: In step (7), the artificial propagation of the test fish includes artificial breeding and release and the construction of a propagation and release station. The artificial reduction of the test fish includes gillnet fishing and trap fishing.

8. The method for monitoring and optimizing fish species based on DNA barcoding and river basin ecological database according to claim 1, characterized in that, The data structure of the water quality parameter raw data B in step (1) nisk The habitat parameter raw data C nisk and the biological parameter raw data D nisk are as follows: Water quality parameter raw data B nisk The data structure includes 7 parameters, B ni1k , B ni2k , B ni3k , B ni4k , B ni5k , B ni6k and B ni7k , wherein B ni1k is chlorophyll a, B ni2k is dissolved oxygen DO, B ni3k is pH value, B ni4k is total nitrogen TN, B ni5k is ammonia nitrogen NH3-N, B ni6k is total phosphorus TP, B ni7k is chemical oxygen demand COD Mn ; Habitat parameter raw data C nisk The data structure comprises 2 parameters, C ni1k and C ni2k , wherein C ni1k is the water plant coverage, and C ni2k is the water level average value ratio; Among them, the raw data of biological parameters D nisk The data structure includes three parameters, D ni1k D ni2k and D ni3k D ni1k Number of native aquatic plant species, D ni2k For the number of native phytoplankton species, D ni3k This represents the number of native zooplankton species.

9. The method for monitoring and optimizing fish species based on DNA barcoding and river basin ecological database according to claim 1, characterized in that, The data performance of the target river basin overall ecological environment getting better and the data performance of the target river basin overall ecological environment getting worse in step (7) are as follows: Data indicating an improvement in the overall ecological environment of the target watershed is represented by: X is medium chlorophyll a difference X i1 The difference between the negative dissolved oxygen (DO) value and the negative value is X. i2 The positive value is the pH difference X. i3 The absolute value is not greater than 1, and the difference in total nitrogen (TN) is X. i4 The difference between ammonia nitrogen (NH3-N) and negative values ​​is X. i5 The difference between the total phosphorus (TP) and the negative value X i6 Negative and chemical oxygen demand (COD) Mn Difference X i7 If negative, Y is aquatic plant coverage Y i1 The positive and average water level ratio difference Y i2 Not greater than 1; Z is Difference in the number of native species of aquatic plants Z i1 Z represents the number of native phytoplankton species. i2 For positive and zooplankton native species number Z i3 It is positive; Data indicating a deterioration in the overall ecological environment of the target watershed is represented as: X is medium chlorophyll a difference X i1 The difference between dissolved oxygen (DO) and positive values ​​is X. i2 The negative pH difference X i3 The absolute value is not less than 1, and the difference in total nitrogen (TN) is X. i4 The difference between positive and negative ammonia nitrogen (NH3-N) is X. i5 The difference between positive and total phosphorus (TP) values ​​is X. i6 For positive and chemical oxygen demand (COD) Mn Difference X i7 If positive, Y is aquatic plant coverage Y i1 The negative value and the difference between the average water level and the average water level Y i2 Not less than 1; Z is Difference in the number of native species of aquatic plants Z i1 The number of native phytoplankton species Z is negative. i2 For negative and the number of native zooplankton species Z i3 It is negative.

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