An environmental DNA and ecological data fusion-based aquatic organism diversity evaluation method

Abstract

The present application relates to the technical field of ecological environment monitoring, in particular to a water-borne organism diversity evaluation method based on environmental DNA and ecological data fusion, comprising the following steps: S1, collecting water samples and completing eDNA sequencing, quality control and species annotation; S2, combining hydrodynamic and spatial data to trace the source and generate a species distribution probability surface; S3, fusing water environment and life history data to calculate the detection probability; S4, correcting amplification, false detection and database bias; S5, calculating diversity and F-IBI and generating an evaluation map; S6, updating parameters based on new data and triggering an early warning. The present application, by fusing environmental DNA, hydrodynamic, environmental physicochemical and species life history data, combining spatial tracing, detection bias correction, diversity evaluation and dynamic early warning, improves the accuracy and continuous monitoring capability of water-borne organism species identification, spatial positioning and ecological health evaluation.

Description

Technical Field

[0001] This invention relates to the field of ecological environment monitoring technology, and in particular to a method for assessing aquatic biodiversity based on the fusion of environmental DNA and ecological data. Background Technology

[0002] With the continuous advancement of ecological environmental protection, aquatic ecosystem restoration, and biodiversity monitoring, aquatic biodiversity has become an important indicator for evaluating the health status of aquatic ecosystems and the stability of ecosystems. Environmental DNA technology, due to its advantages such as non-invasive sampling, high detection sensitivity, and applicability to large-scale monitoring, has been widely applied in aquatic biological resource surveys, monitoring of rare and endangered species, early warning of invasive alien species, and ecological environment assessment. Meanwhile, comprehensive analysis combining hydrodynamics, ecological environmental factors, and species ecological characteristics has become an important development direction for improving the accuracy of aquatic biodiversity assessment.

[0003] However, most existing aquatic biodiversity assessment methods based on environmental DNA rely directly on the detection results of eDNA at sampling points for species distribution analysis, failing to adequately consider the migration, diffusion, and degradation processes of eDNA caused by water flow, which can easily lead to spatial positioning biases of species. Furthermore, they do not adequately utilize ecological information such as the physicochemical conditions of the aquatic environment and the life history characteristics of species, making it difficult to accurately reflect the true distribution of species. In addition, factors such as PCR amplification bias, false positives or false negatives, and incomplete reference databases further affect the accuracy of species identification and abundance estimation. Moreover, existing methods lack the ability to continuously update newly added monitoring data, making it difficult to achieve dynamic optimization and continuous early warning of assessment results, thus limiting the reliability and timeliness of diversity assessment results. Summary of the Invention

[0004] This invention provides a method for assessing aquatic biodiversity based on the fusion of environmental DNA and ecological data. By integrating environmental DNA, hydrodynamics, geospatial data, aquatic environmental physicochemical indicators, and species life history characteristics, and combining eDNA spatial tracing, niche analysis, multi-source bias correction, comprehensive diversity evaluation, and incremental data update mechanisms, it achieves precise location of aquatic species distribution, reliable correction of detection results, and dynamic assessment and early warning of ecological status, thereby improving the accuracy, reliability, and continuous monitoring capability of aquatic biodiversity assessment results.

[0005] A method for assessing aquatic biodiversity based on the fusion of environmental DNA and ecological data includes the following steps:

[0006] S1. Sampling points are set up in the target water area to collect water samples and record sampling information. Environmental DNA is extracted from the water samples, PCR amplification and high-throughput sequencing are performed to obtain raw sequencing data. The raw sequencing data is quality controlled and compared and annotated with the reference database to generate a species detection list and relative sequence abundance for each sampling point. Hydrodynamic parameters and geospatial data of the target water area are collected simultaneously.

[0007] S2, construct an eDNA degradation kinetic model and a hydrodynamic-based eDNA tracing model, combine water flow velocity, flow rate, and flow direction data with geospatial data, perform reverse simulation of the upstream area of ​​the sampling point, calculate the eDNA signal migration probability distribution, and generate a species distribution probability density surface based on the eDNA signal migration probability distribution to achieve species spatial positioning correction;

[0008] S3, based on the species distribution probability density surface, integrates water environment physicochemical index data and species life history characteristic data to construct a species niche model, weighted and corrected the eDNA detection amount, and calculates the fusion detection probability of each species at each sampling point;

[0009] S4 corrects for PCR amplification bias, false positive or false negative detection, and limitations of the reference database. It calculates the species detection confidence probability through positive control, negative control, and genetic distance analysis, and corrects the species sequence abundance and identification results.

[0010] S5 generates a species composition matrix based on the fusion detection probability, corrected species sequence abundance and identification results, calculates the diversity index and fish biological integrity index, and generates an assessment report including a species distribution heat map, a diversity index spatial distribution map and an ecological health status classification map.

[0011] S6 generates an incremental assessment dataset based on newly collected raw sequencing data, hydrodynamic parameters, geospatial data, water environment physicochemical index data, and species life history characteristics data. It also updates the assessment calculation parameters used in the calculation of species distribution probability density surface, fusion detection probability, corrected species sequence abundance, species detection confidence probability, corrected species identification results, species composition matrix, diversity index, and fish biological integrity index. It judges the ecological status based on the preset diversity threshold. When the diversity index is lower than the preset diversity threshold or an invasive alien species is detected, it generates an early warning information and pushes it to the management terminal.

[0012] Optionally, S1 includes:

[0013] S11. Multiple sampling points are set up in the target water area according to a preset sampling grid, and the coordinates, sampling time and water depth information of each sampling point are recorded. A preset volume of water sample is collected at each sampling point as an environmental DNA sample. At the same time, hydrodynamic parameters of the corresponding location in the target water area are collected simultaneously, and geospatial data of the target water area is obtained. The hydrodynamic parameters include flow velocity, flow rate and flow direction. The geospatial data includes river system vector map, digital elevation model and sampling point and upstream catchment area range data. Finally, a sampling information dataset is generated.

[0014] S12, Environmental DNA is extracted from the environmental DNA sample, and PCR amplification is performed using 12S rDNA gene primers or COI gene primers. A sequencing library is constructed and high-throughput sequencing is performed to obtain raw sequencing data. The raw sequencing data from each sampling point were normalized for sequencing depth, the normalized sequence abundance was calculated, and a normalized sequencing dataset was generated.

[0015] S13, perform quality control on the standardized sequencing dataset to remove low-quality sequences, chimeric sequences, and adapter sequences to obtain valid sequence data. Align and annotate the valid sequence data with the reference database to obtain the species detection list for each sampling point and the relative sequence abundance of each species. Combine the sampling information dataset, hydrodynamic parameters, and geospatial data to generate a basic dataset.

[0016] Optionally, S13 includes:

[0017] S131, perform quality control on the standardized sequencing dataset, calculate the average quality value of each sequencing sequence based on the error recognition probability of each base, determine the preset quality threshold based on the mean and standard deviation of the average quality values ​​of all sequencing sequences, determine the preset length range based on the expected length of the target amplification fragment corresponding to the universal primer and the allowable sequence length deviation, and sequentially remove sequencing sequences with an average quality value lower than the preset quality threshold, sequence length exceeding the preset length range, or including adapter sequences or having chimeric structures, and determine the retained sequencing sequences as valid sequences to generate a valid sequence dataset;

[0018] S132, perform sequence alignment and annotation between the effective sequence dataset and the reference database, determine the corresponding species based on the best sequence matching result, count the number of effective sequences corresponding to each species at each sampling point, and calculate the relative sequence abundance of each species.

[0019] S133, based on the species detection list and the relative sequence abundance of each species at each sampling point, establish an association relationship according to the unique identifier of the sampling point, and associate and bind the sampling information dataset, hydrodynamic parameters and geospatial data with the species detection list and relative sequence abundance of the corresponding sampling point, respectively, to form a basic dataset including sampling information, species detection information, hydrodynamic parameters and geospatial data.

[0020] Optionally, S2 includes:

[0021] S21, based on the degradation process of environmental DNA in the target water area over time, the first-order degradation kinetics relationship is used to calculate the first-order degradation rate constant using the eDNA degradation rate constant. The remaining proportion of the eDNA signal of a species after it has migrated from the upstream spatial grid to the sampling point;

[0022] S22, the target water area is divided into multiple upstream spatial grids, the water flow path between each upstream spatial grid and the sampling point is determined based on geospatial data, and the water flow velocity, flow rate and flow direction data are combined to perform reverse simulation to calculate the migration time and hydrodynamic transmission weight from each upstream spatial grid to the sampling point.

[0023] S23, based on the hydrodynamic transport weight, calculate the... The migration probability of the eDNA signal of each species from each upstream spatial grid to the sampling point, and the migration probability of the first eDNA signal at each sampling point. The relative sequence abundance of each species is multiplied by the migration probability and back-mapped to the corresponding upstream spatial grid to obtain the species spatial distribution value. The corresponding species distribution probability density surface is generated based on the spatial distribution probability value of each upstream spatial grid.

[0024] Optionally, S3 includes:

[0025] S31, using the species spatial distribution probability values ​​represented by the species distribution probability density surface as species distribution response data, and taking the water environment physicochemical index data corresponding to each spatial grid as environmental feature input, a species niche model corresponding to each species is constructed using the maximum entropy model, and the first... The species in the first The probability of environmental suitability in a spatial grid;

[0026] S32, obtain the breeding season, egg-laying type, habitat preference, and migratory habits of each species, and calculate the effect of each life history characteristic on the eDNA release rate and intensity. The life history correction weights for each species during the sampling period, and the life history correction weights are used to adjust the life history of the species. The species in the first The eDNA detection levels at each sampling point were weighted and corrected.

[0027] S33. Calculate the fusion detection probability of each species at each sampling point based on the spatial distribution probability of species in the corresponding spatial grid, the environmental suitability probability, and the eDNA detection amount corrected for life history characteristics.

[0028] Optionally, S4 includes:

[0029] S41, in response to PCR amplification bias and false positive and false negative detection issues, establishes the amplification efficiency of each species using positive control samples, and corrects the species sequence abundance based on the amplification efficiency. At the same time, it calculates the species detection confidence probability by combining negative control samples, sequence alignment scores, database record frequency, detection frequency in similar studies, and environmental suitability probability of sampling points.

[0030] S42. To address the uncertainty in species identification caused by the limitations of the reference database, genetic distance analysis is performed on the sequence alignment results. When the genetic distance difference between the best-matched species and the second-best-matched species is less than the preset genetic distance difference threshold and the reference database record is unique, the corresponding species is marked as pending confirmation, and its impact on diversity calculation is reduced in weight to obtain the corrected species identification results.

[0031] Optionally, S41 includes:

[0032] S411 addresses the issue of sequence abundance deviation caused by differences in amplification efficiency among different species during PCR amplification. It sets up positive control samples with different concentration gradients of DNA templates synthesized by common species in the target region, obtains the number of sequencing sequences corresponding to each species at different template concentrations, establishes the relationship between amplification efficiency of each species, and corrects the sequence abundance of each species based on amplification efficiency.

[0033] S412 addresses the issues of false positives and false negatives in eDNA detection. Based on negative control samples, positive control samples, and species detection-related information, it normalizes the sequence alignment score, the frequency of species records in the local database, the detection frequency in similar studies, and the environmental suitability probability of the sampling point. It also calculates the detection confidence probability of each species at each sampling point by combining the correction results of the control samples.

[0034] Optionally, S42 includes:

[0035] S421, from the... Extracting the sequence alignment results of the nth sampling point For each candidate species, the best-matching reference sequence and the second-best-matching reference sequence are used to calculate the genetic distance between the sequence to be identified and the best-matching reference sequence and the second-best-matching reference sequence, respectively, and the genetic distance difference is calculated.

[0036] S422, compare the genetic distance difference with a preset genetic distance difference threshold, and query the reference database. The number of reference sequence records for the best-matching species, when the genetic distance difference is less than a preset genetic distance difference threshold, and the reference database... When a best-matched species has only one reference sequence record, the identification result of the best-matched species is marked as pending confirmation; otherwise, the original species identification status is retained.

[0037] S423, determine the first state variable to be confirmed based on the unconfirmed state variable. The sampling point of the nth sampling point The identification weights of each species are determined, and the sequence abundance or detection probability of the species participating in diversity calculation is reduced using the identification weights to obtain the corrected species identification results.

[0038] Optionally, S5 includes:

[0039] S51, obtain the fusion detection probability of each species at each sampling point, the species sequence abundance after PCR amplification efficiency correction and species downweighting, and the corrected species identification results. Establish a correspondence between sampling points and species, and calculate the... The sampling point of the nth sampling point The species composition values ​​of each species are obtained, and a species composition matrix is ​​constructed from all species composition values;

[0040] S52, based on the species composition matrix, calculate the species richness, Shannon-Wiener diversity index and Pielou evenness index of each sampling point, and calculate the fish biological integrity index according to the fish species composition, trophic structure and tolerance index.

[0041] S53, associate the species composition values ​​in the species composition matrix with the geospatial locations of the corresponding sampling points, generate a species distribution heat map according to the spatial distribution of each species composition value, map the Shannon-Wiener diversity index, Pielou evenness index and fish biological integrity index of each sampling point to the corresponding spatial location to generate a diversity index spatial distribution map, then compare the fish biological integrity index with the preset ecological health grading threshold to determine the ecological health level of each sampling point and generate an ecological health status grading map. The ecological health level includes excellent, good, medium, poor, and very poor. Finally, an assessment report including the species distribution heat map, the diversity index spatial distribution map and the ecological health status grading map is generated.

[0042] Optionally, S6 includes:

[0043] S61, acquire the periodic acquisition data formed in the current acquisition cycle, the periodic acquisition data includes raw sequencing data, hydrodynamic parameters, geospatial data, water environment physicochemical index data and species life history characteristic data, and obtain the periodic assessment results of the current acquisition cycle, the periodic assessment results include species distribution probability density surface, fusion detection probability, corrected species sequence abundance, species detection confidence probability, corrected species identification results, species composition matrix, diversity index and fish biological integrity index. According to the sampling point identifier and sampling time, associate the periodic assessment results of the current acquisition cycle with the periodic assessment results of historical acquisition cycles to form an incremental assessment dataset;

[0044] S62, based on the incremental assessment dataset, update the assessment calculation parameters corresponding to the calculation of species distribution probability density surface, fusion detection probability, corrected species sequence abundance, species detection confidence probability, corrected species identification results, species composition matrix, diversity index and fish biological integrity index. The assessment calculation parameters include the influence weights corresponding to water environment physicochemical indicators, the influence weights corresponding to life history characteristics, PCR amplification efficiency, the weight coefficients used to calculate the species detection confidence probability, and the fish evaluation index weights used to calculate the fish biological integrity index.

[0045] S63, compare the diversity index of each sampling point in the current assessment period with the preset diversity threshold, and determine whether an invasive alien species is detected based on the corrected species identification results, generating the first... For each sampling point, when the diversity index is lower than the preset diversity threshold or an invasive alien species is detected, an early warning information including the sampling point location, anomaly type, and corresponding assessment results is generated and pushed to the management terminal.

[0046] The beneficial effects of this invention are:

[0047] This invention integrates and analyzes environmental DNA sequencing data, hydrodynamic parameters, geospatial data, water environment physicochemical indicators, and species life history characteristics. By combining an eDNA degradation kinetics model and a hydrodynamic-based eDNA spatial tracing model, it performs spatial localization correction on environmental DNA detection signals. This enables reverse inference from sampling point detection results to the actual distribution area of ​​species, effectively reducing the deviation caused by water transport on species spatial localization, improving the accuracy of aquatic organism spatial distribution identification, and providing a more reliable data foundation for aquatic biodiversity assessment.

[0048] This invention constructs a species niche model by integrating aquatic environmental physicochemical index data with species life history characteristic data. It also incorporates PCR amplification efficiency correction, false positive and false negative detection correction, and reference database limitation correction to perform multi-level correction of species detection results. This reduces the impact of PCR amplification differences, incomplete reference databases, and detection errors on the assessment results, improves the reliability of species detection probability, species sequence abundance, and species identification results, thereby enhancing the authenticity and accuracy of aquatic biodiversity assessment results.

[0049] This invention constructs an incremental assessment dataset and dynamically updates the assessment calculation parameters corresponding to the calculation of species distribution probability density surface, fusion detection probability, species sequence abundance, species detection confidence probability, diversity index, and fish biological integrity index. It also combines diversity thresholds and invasive alien species detection results to judge and warn of ecological status, realizing continuous optimization and dynamic updating of assessment results. This enables timely detection of changes in biodiversity and invasive alien risks in target waters, providing continuous and reliable technical support for aquatic ecological monitoring, ecological protection, and resource management. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This is a schematic diagram of the evaluation method flow according to an embodiment of the present invention. Detailed Implementation

[0052] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. Those skilled in the art may employ other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0053] like Figure 1 As shown, a method for assessing aquatic biodiversity based on the fusion of environmental DNA and ecological data includes the following steps:

[0054] S1. Sampling points are set up in the target water area to collect water samples and record sampling information. Environmental DNA is extracted from the water samples, PCR amplification and high-throughput sequencing are performed to obtain raw sequencing data. The raw sequencing data is quality controlled and compared and annotated with the reference database to generate a species detection list and relative sequence abundance for each sampling point. Hydrodynamic parameters and geospatial data of the target water area are collected simultaneously.

[0055] S2. Construct an eDNA degradation kinetic model and a hydrodynamic-based eDNA tracing model. Combine water flow velocity, flow rate, and flow direction data with geospatial data to perform inverse simulation of the upstream area of ​​the sampling point, calculate the eDNA signal migration probability distribution, and generate a species distribution probability density surface based on the eDNA signal migration probability distribution to achieve species spatial positioning correction.

[0056] S3, based on the species distribution probability density surface, integrates water environment physicochemical index data and species life history characteristic data to construct a species niche model, weighted and corrected the eDNA detection amount, and calculates the fusion detection probability of each species at each sampling point;

[0057] S4 corrects for PCR amplification bias, false positive or false negative detection, and limitations of the reference database. It calculates the species detection confidence probability through positive control, negative control, and genetic distance analysis, and corrects the species sequence abundance and identification results.

[0058] S5 generates a species composition matrix based on the fusion detection probability, corrected species sequence abundance and identification results, calculates the diversity index and fish biological integrity index, and generates an assessment report including a species distribution heat map, a diversity index spatial distribution map and an ecological health status classification map.

[0059] S6 generates an incremental assessment dataset based on newly collected raw sequencing data, hydrodynamic parameters, geospatial data, water environment physicochemical index data, and species life history characteristic data. It also updates the assessment calculation parameters used in calculating species distribution probability density surface, fusion detection probability, corrected species sequence abundance, species detection confidence probability, corrected species identification results, species composition matrix, diversity index, and fish biological integrity index. It judges the ecological status based on a preset diversity threshold. When the diversity index is lower than the preset diversity threshold or an invasive alien species is detected, it generates an early warning message and pushes it to the management terminal.

[0060] S1 includes:

[0061] S11. Multiple sampling points are set up in the target water area according to the preset sampling grid, and the coordinates, sampling time and water depth information of each sampling point are recorded. A preset volume of water sample is collected at each sampling point as an environmental DNA sample. At the same time, the hydrodynamic parameters of the corresponding location in the target water area are collected simultaneously, and the geospatial data of the target water area is obtained. The hydrodynamic parameters include flow velocity, flow rate and flow direction. The geospatial data includes river system vector map, digital elevation model and sampling point and upstream catchment area range data. Finally, a sampling information dataset is generated.

[0062] S12, environmental DNA was extracted from the environmental DNA samples. PCR amplification was performed using 12S rDNA gene primers or COI gene primers. A sequencing library was constructed and high-throughput sequencing was performed to obtain raw sequencing data. To ensure the comparability of sequencing data from different sampling points, the raw sequencing data was further analyzed. The raw sequencing data from each sampling point are normalized for sequencing depth, the normalized sequence abundance is calculated, and a normalized sequencing dataset is generated, represented as follows:

[0063] ;

[0064] in, For the first The normalized sequence abundance of the sequence corresponding to each sampling point. For the first The number of raw sequencing samples corresponding to each sampling point. This represents the total number of valid sequences obtained at this sampling point.

[0065] S13. Quality control is performed on the standardized sequencing dataset to remove low-quality sequences, chimeric sequences, and adapter sequences to obtain valid sequence data. The valid sequence data is then compared and annotated with the reference database to obtain the species detection list for each sampling point and the relative sequence abundance of each species. Finally, the basic dataset is generated by combining the sampling information dataset, hydrodynamic parameters, and geospatial data.

[0066] S13 includes:

[0067] S131, quality control is performed on the standardized sequencing dataset. The average quality value of each sequencing sequence is calculated based on the false identification probability of each base. A preset quality threshold is determined based on the mean and standard deviation of the average quality values ​​of all sequencing sequences. A preset length range is determined based on the expected length of the target amplified fragment corresponding to the universal primer and the allowable sequence length deviation. Sequencing sequences with an average quality value lower than the preset quality threshold, sequence lengths exceeding the preset length range, and sequences including adapter sequences or chimeric structures are sequentially removed. The retained sequencing sequences are determined as valid sequences, generating a valid sequence dataset, represented as:

[0068] ;

[0069] ;

[0070] ;

[0071] ;

[0072] ;

[0073] ;

[0074] in, For the first The first sequencing sequence The mass value of each base. For the first The first sequencing sequence The probability that a base is misidentified. For the first The average quality value of the sequencing sequences. For the first The number of bases contained in a sequencing sequence. Numbering the base positions, To preset the quality threshold, , These are the mean and standard deviation of the average quality values ​​of all sequencing sequences, respectively. This is the quality threshold adjustment coefficient. This refers to the expected length of the target amplification fragment corresponding to the 12S rDNA gene primer or the COI gene primer. To allow for sequence length deviation, The minimum allowed sequence length, The maximum allowed sequence length, For the first The actual length of the sequencing sequence;

[0075] S132, perform sequence alignment and annotation between the effective sequence dataset and the reference database, determine the corresponding species based on the best sequence match results, count the number of effective sequences corresponding to each species at each sampling point, and calculate the relative sequence abundance of each species, expressed as:

[0076] ;

[0077] in, For the first The sampling point of the nth sampling point The relative sequence abundance of each species, For the first The annotation for the sampling point is the first one. The number of valid sequences for each species For the first The total number of species detected at each sampling point;

[0078] S133, based on the species detection list and the relative sequence abundance of each species at each sampling point, establish the association relationship according to the unique identifier of the sampling point, and associate and bind the sampling information dataset, hydrodynamic parameters and geospatial data with the species detection list and relative sequence abundance of the corresponding sampling point, respectively, to form a basic dataset including sampling information, species detection information, hydrodynamic parameters and geospatial data.

[0079] S2 includes:

[0080] S21, based on the degradation process of environmental DNA in the target water area over time, the first-order degradation kinetics relationship is used to calculate the first-order degradation rate constant using the eDNA degradation rate constant. The remaining proportion of the eDNA signal of a species after migrating from the upstream spatial grid to the sampling point is expressed as:

[0081] ;

[0082] in, For the first The eDNA signal of the species is generated by the first The remaining proportion of each upstream spatial grid after migration to the sampling point For the first Each species corresponds to a certain eDNA degradation rate constant. eDNA signaling is generated by the first The migration time from the upstream spatial grid to the sampling point It is a natural exponential function;

[0083] S22, the target water area is divided into multiple upstream spatial grids. Based on geospatial data, the flow path between each upstream spatial grid and the sampling point is determined. Inverse simulation is then performed using flow velocity, flow rate, and flow direction data to calculate the migration time and hydrodynamic transport weight from each upstream spatial grid to the sampling point, expressed as:

[0084] ;

[0085] ;

[0086] in, For the first The migration time from the upstream spatial grid to the sampling point This represents the number of path segments contained in the corresponding water flow path. For the first The upstream spatial grid corresponds to the first water flow path. The length of each path segment For the first The water flow velocity in each path segment For the first Hydrodynamic transport weights of upstream spatial grids For the first The flow rate corresponding to each upstream spatial grid is [missing information]. The water flow connectivity coefficient, determined based on the flow direction, is set to 1 if the spatial grid can be connected to the sampling point along the flow direction, and 0 otherwise. This represents the total number of spatial grids upstream of the sampling point;

[0087] S23, based on the hydrodynamic transport weight, calculate the... The migration probability of the eDNA signal of each species from each upstream spatial grid to the sampling point, and the migration probability of the first eDNA signal at each sampling point. The relative sequence abundance of each species is multiplied by its migration probability and then back-mapped to the corresponding upstream spatial grid to obtain the species spatial distribution value. Based on the spatial distribution probability value of each upstream spatial grid, a corresponding species distribution probability density surface is generated, represented as:

[0088] ;

[0089] ;

[0090] in, For the first The eDNA signal of the first species originates from the first The migration probability of each upstream spatial grid. For the first The species in the first Spatial distribution probability values ​​in each upstream spatial grid. Sampling points The Middle Relative sequence abundance of each species.

[0091] S3 includes:

[0092] S31 uses the spatial distribution probability values ​​of species, represented by the species distribution probability density surface, as the species distribution response data. The water environment physicochemical index data corresponding to each spatial grid are used as environmental feature inputs. A maximum entropy model is used to construct the species niche model corresponding to each species, and the first... The species in the first The environmental suitability probability in each spatial grid, based on aquatic environmental physicochemical index data including water temperature, dissolved oxygen, pH, total nitrogen, total phosphorus, ammonia nitrogen, conductivity, turbidity, chlorophyll a concentration, and water depth, is expressed as follows:

[0093] ;

[0094] in, For the first The species in the first The probability of environmental suitability in a spatial grid For the first In the _ spatial grid, the _ ... Standardized values ​​of water environment physicochemical indicators For the first The weighting of water environment physicochemical indicators data This represents the total number of physicochemical indicators of the water environment. It is a natural exponential function;

[0095] S32, obtain the breeding season, egg-laying type, habitat preference, and migratory habits of each species, and calculate the effect of each life history characteristic on the eDNA release rate and intensity. The life history correction weights for each species during the sampling period, and the life history correction weights for the first species. The species in the first The eDNA detection levels at each sampling point are weighted and corrected, and expressed as follows:

[0096] ;

[0097] ;

[0098] in, For the first Life history correction weights for each species For the first The species The eigenvalues ​​corresponding to each life history feature For the first The influence weights corresponding to each life history characteristic This refers to the total number of life history characteristics, which include breeding season, egg-laying type, habitat preference, and migratory habits. For the first The sampling point of the nth sampling point eDNA detection levels of each species after life history correction;

[0099] The specific eigenvalues ​​corresponding to life history characteristics include:

[0100] ;

[0101] in, For the first The breeding season characteristics of each species, For the first The number of days that overlap between the breeding season of a species and the current sampling period. For the first The number of days a species' complete breeding season lasts;

[0102] ;

[0103] ;

[0104] in, For the first The spawning type characteristic value of each species, For the first DNA release impact score for each species corresponding to its oviposition type The minimum score for the impact of spawning type on all species. The maximum score for the influence of spawning type on all species. For the first The impact of primordial DNA release on the oviposition type of each species. , These represent the minimum and maximum values ​​of the impact of original DNA release on all species, respectively.

[0105] Impact value of original DNA release According to the The spawning type of a species is determined and represented as follows:

[0106] ;

[0107] in, The release value corresponds to the spawning method. The release value corresponds to the degree of spawning aggregation. This represents the release value corresponding to the egg attachment characteristics;

[0108] As shown in Table 1, the release values ​​correspond to the spawning methods. Release value corresponding to the degree of egg aggregation and the corresponding release value of egg attachment characteristics Use a fixed value table;

[0109] Table 1. Values ​​of scoring parameters for the influence of DNA release by oviposition type.

[0110]

[0111] ;

[0112] in, For the first Habitat preference characteristics of each species For the first The typical habitat water depth for each species The water depth is sampled at the current sampling point. , These represent the maximum and minimum depth ranges for biological habitat in the target waters;

[0113] ;

[0114] ;

[0115] ;

[0116] ;

[0117] ;

[0118] in, For the first Migratory habits of a species, For the first Score of species migration intensity , These represent the maximum and minimum values ​​of migration intensity for all species. For the first The impact of species migration distance, For the first The impact value of species migration frequency. For the first Impact value of species migration range , , These are the weights corresponding to migration distance, migration frequency, and migration range, respectively. For the first Average annual migration distance for each species , These represent the maximum and minimum annual average migration distances for all species. For the first Number of migrations per unit time for a species , These represent the maximum and minimum number of migrations for all species, respectively. For the first The migration area or water range of a species , These represent the maximum and minimum migration ranges for all species, respectively.

[0119] S33, based on the spatial distribution probability value of species in the corresponding spatial grid of each sampling point, the environmental suitability probability, and the eDNA detection amount corrected for life history characteristics, the fusion detection probability of each species at each sampling point is calculated, expressed as:

[0120] ;

[0121] in, For the first The sampling point of the nth sampling point The probability of detecting fusions in individual species For the first The species in the first Each sampling point corresponds to a spatial distribution probability value in the spatial grid. For the first The species in the first Each sampling point corresponds to an environmental suitability probability in the spatial grid. For the first The total number of species detected at each sampling point.

[0122] S4 includes:

[0123] S41 addresses the issues of PCR amplification bias and false positive / false negative detection by establishing amplification efficiency for each species using positive control samples and correcting species sequence abundance based on amplification efficiency. Simultaneously, it combines negative control samples, sequence alignment scores, database record frequency, detection frequency in similar studies, and sampling site environmental suitability probability to calculate the species detection confidence probability, thereby correcting the reliability of eDNA detection results.

[0124] S42. To address the uncertainty in species identification caused by the limitations of the reference database, genetic distance analysis is performed on the sequence alignment results. When the genetic distance difference between the best-matched species and the second-best-matched species is less than the preset genetic distance difference threshold and the reference database record is unique, the corresponding species is marked as pending confirmation, and its impact on diversity calculation is reduced in weight to obtain the corrected species identification results.

[0125] S41 includes:

[0126] S411 addresses the sequence abundance bias caused by differences in amplification efficiency among different species during PCR amplification. It sets up positive control samples with varying concentrations of synthetic DNA templates from common species in the target region, obtains the sequencing sequence counts for each species at different template concentrations, establishes the amplification efficiency relationship among species, and corrects the sequence abundance for each species based on the amplification efficiency. Specifically, this includes:

[0127] (1) Set up positive control samples with different concentration gradients containing synthetic DNA templates of common species in the target area. Common species in the target area refer to aquatic organisms that are stably distributed and representative in the target water area, including fish, benthic animals, zooplankton and phytoplankton, such as crucian carp, carp, grass carp, silver carp, bighead carp, snails, river clams, cladocerans and diatoms. Count the number of sequencing sequences corresponding to each species under each concentration gradient, establish the amplification efficiency relationship between species template concentration and the number of sequencing sequences, and target the first... For each species, the amplification efficiency is calculated based on the number of sequencing sequences obtained at different template concentrations, and is expressed as follows:

[0128] ;

[0129] in, For the first PCR amplification efficiency of each species For the first Changes in the number of sequencing sequences for each species at different positive control concentrations For the first Changes in the concentration of the positive control DNA template for each species;

[0130] (2) Based on species amplification efficiency, the abundance of sequences of each species in the actual sampled samples is corrected to reduce the abundance shift caused by differences in amplification capabilities among different species. The sampling point of the nth sampling point The corrected sequence abundance of each species is expressed as follows:

[0131] ;

[0132] in, For the first The sampling point of the nth sampling point Corrected sequence abundance for each species For the first The sampling point of the nth sampling point Original sequence abundance of each species;

[0133] S412 addresses the issues of false positives and false negatives in eDNA detection. Based on negative and positive control samples and species detection-related information, it normalizes sequence alignment scores, species recording frequency in the local database, detection frequency in similar studies, and sampling site environmental suitability probability. It then calculates the detection confidence probability for each species at each sampling site, combining the correction results from control samples. Specifically, this includes:

[0134] (1) Obtain the first The species in the first The sequence alignment score corresponding to each sampling point, the record frequency of the species in the local database, the detection frequency in similar studies, and the environmental suitability probability of the sampling point are all normalized to obtain the standardized value corresponding to each evaluation index, expressed as follows:

[0135] ;

[0136] ;

[0137] ;

[0138] ;

[0139] ;

[0140] ;

[0141] ;

[0142] ;

[0143] ;

[0144] in, For the first The sampling point of the nth sampling point Normalized sequence alignment scores for each species For the first The sampling point of the nth sampling point The raw alignment scores of each species sequence with its corresponding sequence in the reference database. , These represent the maximum and minimum sequence alignment scores for all species, respectively. To score sequence consistency, Score the sequence coverage. Scoring for sequence mismatch penalties , , These are the corresponding weight coefficients. For the first The sampling point of the nth sampling point The number of bases that match during the alignment of a species sequence with a reference sequence. The total number of valid bases used in the comparison. For the first The sampling point of the nth sampling point The number of bases that the species sequence actually covers in the reference sequence. For reference database, the first The target sequence length for each species For the first The sampling point of the nth sampling point The number of mismatched bases in the sequence of each species compared with the reference sequence. This represents the number of insertion or missing positions generated during the alignment process. For effective length comparison, For the first Normalized frequency values ​​of records in the database for each species For the first The number of times a species is recorded in the local database. , These represent the maximum and minimum number of records recorded in the entire candidate species database, respectively. For the first Normalized values ​​of detection frequency in similar studies of individual species For the first The ratio of the number of times a species is detected in similar studies to the total number of studies. , These represent the maximum and minimum detection frequencies for all species, respectively. For including the first The number of studies that detected each species This represents the total number of similar studies included in the statistics. For the first The sampling point of the nth sampling point Normalized values ​​of the probability of environmental suitability for each species , These represent the maximum and minimum probabilities of environmental suitability for all species, respectively.

[0145] (2) Based on the reliability of species detection results analyzed from positive and negative control samples, the detection results of each species are corrected. Among them, the detection sensitivity is determined according to the actual detection of target species in the positive control samples, and the pollution impact coefficient is determined according to the abnormal detection of non-target species in the negative control samples. The control correction coefficient is calculated and expressed as follows:

[0146] ;

[0147] in, For the first Control correction factor for each species, For the first The detection sensitivity for each species was determined based on positive control samples. For the first Pollution impact coefficients for each species determined based on negative control samples;

[0148] (3) Based on the normalized sequence alignment score, database record frequency, detection frequency of similar studies, environmental suitability probability of sampling points, and control correction coefficient, calculate the first... The species in the first The detection confidence probability of each sampling point is expressed as:

[0149] ;

[0150] in, For the first The species in the first Detection confidence probability of each sampling point , , , These are the corresponding weighting coefficients.

[0151] S42 includes:

[0152] S421, from the... Extracting the sequence alignment results of the nth sampling point For each candidate species, the best-matching reference sequence and the second-best-matching reference sequence are used. The genetic distance between the sequence to be identified and the best-matching reference sequence and the second-best-matching reference sequence are calculated, and the difference in genetic distance is calculated and expressed as follows:

[0153] ;

[0154] ;

[0155] in, This represents the genetic distance between the sequence to be identified and the corresponding reference sequence. This represents the number of positions where the two sequences differ in bases within the effective alignment region. To determine the total number of bases in the effectively compared regions, For the first The sequence to be identified in the sampling point is similar to the first... Genetic distance between the best-matched species reference sequences The genetic distance between the sequence to be identified and the reference sequence of the second-best matching species. The difference in genetic distance between the best-matched and second-best-matched species;

[0156] S422, compare the genetic distance difference with a preset genetic distance difference threshold, and query the reference database. The number of reference sequence records for the best-matched species, when the genetic distance difference is less than a preset genetic distance difference threshold, and the reference database contains the number of reference sequence records for the best-matched species. When a best-matched species has only one reference sequence record, the identification result of the best-matched species is marked as pending confirmation; otherwise, the original species identification status is retained, as shown below:

[0157] ;

[0158] in, For the first The sampling point of the nth sampling point Unconfirmed state variables for each species This indicates that the status is pending confirmation. This indicates that the original identification status will be retained. To preset the genetic distance difference threshold, For reference database, the first Number of reference sequence records for each best-matching species;

[0159] ;

[0160] in, , These represent the mean and standard deviation of the genetic distance differences between the best and second-best matches of all candidate species in the target region, respectively. This is the threshold adjustment coefficient;

[0161] S423, determine the first [state variable to be confirmed] based on the unconfirmed state variable. The sampling point of the nth sampling point The identification weights of each species are determined, and the sequence abundance or detection probability of that species in diversity calculations is weighted down using these identification weights to obtain the corrected species identification results, expressed as follows:

[0162] ;

[0163] ;

[0164] in, For the first The sampling point of the nth sampling point The identification weight of each species, To preset the weighting coefficient, The abundance of species sequences after weighting.

[0165] S5 includes:

[0166] S51, obtain the fusion detection probability of each species at each sampling point, the species sequence abundance after PCR amplification efficiency correction and species downweighting, and the corrected species identification results. Establish a correspondence between sampling points and species, and calculate the... The sampling point of the nth sampling point The species composition values ​​of each species are used to construct a species composition matrix from all species composition values, as shown below:

[0167] ;

[0168] ;

[0169] in, For the first The sampling point of the nth sampling point Species composition value of each species For the first The number of species detected at each sampling point For species composition matrix, The total number of sampling points. This represents the total number of species detected at all sampling points.

[0170] S52, based on the species composition matrix, calculates the species richness, Shannon-Wiener diversity index, and Pielou evenness index for each sampling point, and calculates the fish biological integrity index based on fish species composition, trophic structure, and tolerance indicators, expressed as:

[0171] ;

[0172] ;

[0173] ;

[0174] ;

[0175] in, For the first The sampling point of the nth sampling point The composition ratio of each species For the first Shannon-Wiener diversity index for each sampling point For the first Pielou uniformity index for each sampling point For the first The number of species with a species composition value greater than zero in each sampling point, i.e., species richness. For the first Fish biological integrity index at each sampling point For the first The sampling point Standardized values ​​for fish evaluation indicators, including species composition indicators, trophic structure indicators, and tolerance indicators. For the first The weights of the fish evaluation indicators The total number of fish evaluation indicators;

[0176] No. The sampling point Standardized values ​​of fish evaluation indicators Represented as:

[0177] ;

[0178] in, For the first The sampling point The original values ​​of the fish evaluation indicators, when hour, Indicates the first The original values ​​of species composition indicators at each sampling point, when hour, Indicates the first The original values ​​of trophic structure indicators at each sampling point, when hour, Indicates the first The original values ​​of the tolerance index at each sampling point. , The first The maximum and minimum values ​​of the fish evaluation indexes at all sampling points;

[0179] ;

[0180] ;

[0181] ;

[0182] in, For the first The number of fish species detected at each sampling point This represents the total number of fish species detected at all sampling points in the target area. For the first The sampling point The proportion of fish species of different nutritional types in the total fish population. For the target area Reference proportions for fish of similar nutritional types. This refers to the total number of nutrient types, which include carnivorous fish, omnivorous fish, herbivorous fish, and filter-feeding fish. For the first Number of pollution-resistant fish species detected at each sampling point;

[0183] S53, associate the species composition values ​​in the species composition matrix with the corresponding geographic locations of the sampling points, generate a species distribution heatmap according to the spatial distribution of each species composition value, map the Shannon-Wiener diversity index, Pielou evenness index, and fish biodiversity integrity index of each sampling point to their corresponding spatial locations, generate a diversity index spatial distribution map, then compare the fish biodiversity integrity index with a preset ecological health grading threshold to determine the ecological health level of each sampling point and generate an ecological health status grading map. The ecological health levels include excellent, good, moderate, poor, and very poor. Finally, an assessment report including the species distribution heatmap, diversity index spatial distribution map, and ecological health status grading map is generated, represented as:

[0184] ;

[0185] in, For the first The ecological health level of each sampling point This indicates that the ecological health status is excellent. This indicates that the ecological health status is good. This indicates that the ecological health status is moderate. This indicates that the ecological health condition is poor. This indicates that the ecological health status is poor. The first ecological health classification threshold, This is the second ecological health grading threshold. This is the threshold for the third ecological health classification. This is the fourth ecological health classification threshold. ;

[0186] ;

[0187] ;

[0188] ;

[0189] ;

[0190] in, , , , These are the 20th, 40th, 60th, and 80th percentiles of the fish biological integrity index sample sequence, respectively.

[0191] S6 includes:

[0192] S61. Obtain the periodic collection data generated in the current collection cycle. The periodic collection data includes raw sequencing data, hydrodynamic parameters, geospatial data, water environment physicochemical index data, and species life history characteristic data. Obtain the periodic assessment results of the current collection cycle. The periodic assessment results include species distribution probability density surface, fusion detection probability, corrected species sequence abundance, species detection confidence probability, corrected species identification results, species composition matrix, diversity index, and fish biological integrity index. According to the sampling point identifier and sampling time, associate the periodic assessment results of the current collection cycle with the periodic assessment results of the historical collection cycles to form an incremental assessment dataset.

[0193] S62, based on the incremental assessment dataset, updates the assessment calculation parameters used in calculating the species distribution probability density surface, fusion detection probability, corrected species sequence abundance, species detection confidence probability, corrected species identification results, species composition matrix, diversity index, and fish biological integrity index. These assessment calculation parameters include the influence weights corresponding to aquatic environmental physicochemical indicators, the influence weights corresponding to life history characteristics, PCR amplification efficiency, the weight coefficients used to calculate the species detection confidence probability, and the weights of fish evaluation indicators used to calculate the fish biological integrity index, expressed as follows:

[0194] ;

[0195] in, For the updated number Evaluation calculation parameters, The first of the previous collection cycle Evaluation calculation parameters, The first one, recalculated based on the incremental evaluation dataset. Evaluation calculation parameters, To update the coefficients, the evaluation calculation parameters include: , , , , , , as well as ;

[0196] S63, compare the diversity index of each sampling point in the current assessment period with the preset diversity threshold, and determine whether an invasive alien species is detected based on the corrected species identification results, generating the first... For each sampling point, when the diversity index falls below a preset diversity threshold or an invasive alien species is detected, an early warning status variable generates an early warning message including the sampling point location, anomaly type, and corresponding assessment result, and pushes it to the management terminal, as shown below:

[0197] ;

[0198] in, For the first The warning status variable for each sampling point This indicates that an alert has been triggered. This indicates that no warning will be triggered. To preset the diversity threshold, For the first The variable for detecting invasive alien species at each sampling point is set to 1 if an invasive alien species is detected, and 0 otherwise.

[0199] ;

[0200] in, , These represent the mean and standard deviation of the diversity index for each sampling point within the reference period, respectively. This is the preset threshold adjustment coefficient.

[0201] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0202] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for assessing aquatic biodiversity based on the fusion of environmental DNA and ecological data, characterized in that, Includes the following steps: S1. Sampling points are set up in the target water area to collect water samples and record sampling information. Environmental DNA is extracted from the water samples, PCR amplification is performed, and high-throughput sequencing is conducted to obtain raw sequencing data. The raw sequencing data is subjected to quality control and reference database comparison and annotation to generate a species detection list and relative sequence abundance for each sampling point, and hydrodynamic parameters and geospatial data are collected simultaneously. S2, combining water flow velocity, flow rate, flow direction and geospatial data, performs reverse simulation of the upstream area of ​​the sampling point, calculates the eDNA signal migration probability distribution, and generates the species distribution probability density surface; S3, integrate water environment physicochemical index data and species life history characteristic data to construct a species niche model, perform weighted correction on eDNA detection amount, and calculate the fusion detection probability of each species at each sampling point; S4, combining positive control, negative control and genetic distance analysis, calculate the species detection confidence probability and correct the species sequence abundance and identification results; S5: Construct a species composition matrix, calculate the diversity index and fish biological integrity index, and generate an assessment report including a species distribution heat map, a spatial distribution map of the diversity index, and a classification map of ecological health status. S6 generates an incremental assessment dataset based on newly collected raw sequencing data, hydrodynamic parameters, geospatial data, water environment physicochemical index data, and species life history characteristic data. It also updates the assessment calculation parameters used in the calculation of species distribution probability density surface, fusion detection probability, corrected species sequence abundance, species detection confidence probability, corrected species identification results, species composition matrix, diversity index, and fish biological integrity index, and generates early warning information and pushes it to the management terminal.

2. The aquatic biodiversity assessment method based on the fusion of environmental DNA and ecological data according to claim 1, characterized in that, S1 includes: S11. Multiple sampling points are set up in the target water area according to a preset sampling grid, and the coordinates, sampling time and water depth information of each sampling point are recorded. A preset volume of water sample is collected at each sampling point as an environmental DNA sample. At the same time, hydrodynamic parameters of the corresponding location in the target water area are collected simultaneously, and geospatial data of the target water area is obtained. The hydrodynamic parameters include flow velocity, flow rate and flow direction. The geospatial data includes river system vector map, digital elevation model and sampling point and upstream catchment area range data. Finally, a sampling information dataset is generated. S12, Environmental DNA is extracted from the environmental DNA sample, and PCR amplification is performed using 12S rDNA gene primers or COI gene primers. A sequencing library is constructed and high-throughput sequencing is performed to obtain raw sequencing data. The raw sequencing data from each sampling point were normalized for sequencing depth, the normalized sequence abundance was calculated, and a normalized sequencing dataset was generated. S13, perform quality control on the standardized sequencing dataset to remove low-quality sequences, chimeric sequences, and adapter sequences to obtain valid sequence data. Align and annotate the valid sequence data with the reference database to obtain the species detection list for each sampling point and the relative sequence abundance of each species. Combine the sampling information dataset, hydrodynamic parameters, and geospatial data to generate a basic dataset.

3. The aquatic biodiversity assessment method based on the fusion of environmental DNA and ecological data according to claim 2, characterized in that, S13 includes: S131, perform quality control on the standardized sequencing dataset, calculate the average quality value of each sequencing sequence based on the error recognition probability of each base, determine the preset quality threshold based on the mean and standard deviation of the average quality values ​​of all sequencing sequences, determine the preset length range based on the expected length of the target amplification fragment corresponding to the universal primer and the allowable sequence length deviation, and sequentially remove sequencing sequences with an average quality value lower than the preset quality threshold, sequence length exceeding the preset length range, or including adapter sequences or having chimeric structures, and determine the retained sequencing sequences as valid sequences to generate a valid sequence dataset; S132, perform sequence alignment and annotation between the effective sequence dataset and the reference database, determine the corresponding species based on the best sequence matching result, count the number of effective sequences corresponding to each species at each sampling point, and calculate the relative sequence abundance of each species. S133, based on the species detection list and the relative sequence abundance of each species at each sampling point, establish an association relationship according to the unique identifier of the sampling point, and associate and bind the sampling information dataset, hydrodynamic parameters and geospatial data with the species detection list and relative sequence abundance of the corresponding sampling point, respectively, to form a basic dataset including sampling information, species detection information, hydrodynamic parameters and geospatial data.

4. The aquatic biodiversity assessment method based on the fusion of environmental DNA and ecological data according to claim 3, characterized in that, S2 includes: S21, based on the degradation process of environmental DNA in the target water area over time, the first-order degradation kinetics relationship is used to calculate the first-order degradation rate constant using the eDNA degradation rate constant. The remaining proportion of the eDNA signal of a species after it has migrated from the upstream spatial grid to the sampling point; S22, the target water area is divided into multiple upstream spatial grids, the water flow path between each upstream spatial grid and the sampling point is determined based on geospatial data, and the water flow velocity, flow rate and flow direction data are combined to perform reverse simulation to calculate the migration time and hydrodynamic transmission weight from each upstream spatial grid to the sampling point. S23, based on the hydrodynamic transport weight, calculate the... The migration probability of the eDNA signal of each species from each upstream spatial grid to the sampling point, and the migration probability of the first eDNA signal at each sampling point. The relative sequence abundance of each species is multiplied by the migration probability and back-mapped to the corresponding upstream spatial grid to obtain the species spatial distribution value. The corresponding species distribution probability density surface is generated based on the spatial distribution probability value of each upstream spatial grid.

5. The aquatic biodiversity assessment method based on the fusion of environmental DNA and ecological data according to claim 4, characterized in that, S3 includes: S31, using the species spatial distribution probability values ​​represented by the species distribution probability density surface as species distribution response data, and taking the water environment physicochemical index data corresponding to each spatial grid as environmental feature input, a species niche model corresponding to each species is constructed using the maximum entropy model, and the first... The species in the first The probability of environmental suitability in a spatial grid; S32, obtain the breeding season, egg-laying type, habitat preference, and migratory habits of each species, and calculate the effect of each life history characteristic on the eDNA release rate and intensity. The life history correction weights for each species during the sampling period, and the life history correction weights are used to adjust the life history of the species. The species in the first The eDNA detection levels at each sampling point were weighted and corrected. S33. Calculate the fusion detection probability of each species at each sampling point based on the spatial distribution probability of species in the corresponding spatial grid, the environmental suitability probability, and the eDNA detection amount corrected for life history characteristics.

6. The aquatic biodiversity assessment method based on the fusion of environmental DNA and ecological data according to claim 5, characterized in that, S4 includes: S41, in response to PCR amplification bias and false positive and false negative detection issues, establishes the amplification efficiency of each species using positive control samples, and corrects the species sequence abundance based on the amplification efficiency. At the same time, it calculates the species detection confidence probability by combining negative control samples, sequence alignment scores, database record frequency, detection frequency in similar studies, and environmental suitability probability of sampling points. S42. To address the uncertainty in species identification caused by the limitations of the reference database, genetic distance analysis is performed on the sequence alignment results. When the genetic distance difference between the best-matched species and the second-best-matched species is less than the preset genetic distance difference threshold and the reference database record is unique, the corresponding species is marked as pending confirmation, and its impact on diversity calculation is reduced in weight to obtain the corrected species identification results.

7. The aquatic biodiversity assessment method based on the fusion of environmental DNA and ecological data according to claim 6, characterized in that, S41 includes: S411 addresses the issue of sequence abundance deviation caused by differences in amplification efficiency among different species during PCR amplification. It sets up positive control samples with different concentration gradients of DNA templates synthesized by common species in the target region, obtains the number of sequencing sequences corresponding to each species at different template concentrations, establishes the relationship between amplification efficiency of each species, and corrects the sequence abundance of each species based on amplification efficiency. S412 addresses the issues of false positives and false negatives in eDNA detection. Based on negative control samples, positive control samples, and species detection-related information, it normalizes the sequence alignment score, the frequency of species records in the local database, the detection frequency in similar studies, and the environmental suitability probability of the sampling point. It also calculates the detection confidence probability of each species at each sampling point by combining the correction results of the control samples.

8. The aquatic biodiversity assessment method based on the fusion of environmental DNA and ecological data according to claim 7, characterized in that, S42 includes: S421, from the... Extracting the sequence alignment results of the nth sampling point For each candidate species, the best-matching reference sequence and the second-best-matching reference sequence are used to calculate the genetic distance between the sequence to be identified and the best-matching reference sequence and the second-best-matching reference sequence, respectively, and the genetic distance difference is calculated. S422, compare the genetic distance difference with a preset genetic distance difference threshold, and query the reference database. The number of reference sequence records for the best-matching species, when the genetic distance difference is less than a preset genetic distance difference threshold, and the reference database... When a best-matched species has only one reference sequence record, the identification result of the best-matched species is marked as pending confirmation; otherwise, the original species identification status is retained. S423, determine the first state variable to be confirmed based on the unconfirmed state variable. The sampling point of the nth sampling point The identification weights of each species are determined, and the sequence abundance or detection probability of the species participating in diversity calculation is reduced using the identification weights to obtain the corrected species identification results.

9. The aquatic biodiversity assessment method based on the fusion of environmental DNA and ecological data according to claim 8, characterized in that, S5 includes: S51, obtain the fusion detection probability of each species at each sampling point, the species sequence abundance after PCR amplification efficiency correction and species downweighting, and the corrected species identification results. Establish a correspondence between sampling points and species, and calculate the... The sampling point of the nth sampling point The species composition values ​​of each species are obtained, and a species composition matrix is ​​constructed from all species composition values; S52, based on the species composition matrix, calculate the species richness, Shannon-Wiener diversity index and Pielou evenness index of each sampling point, and calculate the fish biological integrity index according to the fish species composition, trophic structure and tolerance index. S53, associate the species composition values ​​in the species composition matrix with the geospatial locations of the corresponding sampling points, generate a species distribution heat map according to the spatial distribution of each species composition value, map the Shannon-Wiener diversity index, Pielou evenness index and fish biological integrity index of each sampling point to the corresponding spatial location to generate a diversity index spatial distribution map, then compare the fish biological integrity index with the preset ecological health grading threshold to determine the ecological health level of each sampling point and generate an ecological health status grading map. The ecological health level includes excellent, good, medium, poor, and very poor. Finally, an assessment report including the species distribution heat map, the diversity index spatial distribution map and the ecological health status grading map is generated.

10. The aquatic biodiversity assessment method based on the fusion of environmental DNA and ecological data according to claim 9, characterized in that, S6 includes: S61, acquire the periodic acquisition data formed in the current acquisition cycle, the periodic acquisition data includes raw sequencing data, hydrodynamic parameters, geospatial data, water environment physicochemical index data and species life history characteristic data, and obtain the periodic assessment results of the current acquisition cycle, the periodic assessment results include species distribution probability density surface, fusion detection probability, corrected species sequence abundance, species detection confidence probability, corrected species identification results, species composition matrix, diversity index and fish biological integrity index. According to the sampling point identifier and sampling time, associate the periodic assessment results of the current acquisition cycle with the periodic assessment results of historical acquisition cycles to form an incremental assessment dataset; S62, based on the incremental assessment dataset, update the assessment calculation parameters corresponding to the calculation of species distribution probability density surface, fusion detection probability, corrected species sequence abundance, species detection confidence probability, corrected species identification results, species composition matrix, diversity index and fish biological integrity index. The assessment calculation parameters include the influence weights corresponding to water environment physicochemical indicators, the influence weights corresponding to life history characteristics, PCR amplification efficiency, the weight coefficients used to calculate the species detection confidence probability, and the fish evaluation index weights used to calculate the fish biological integrity index. S63, compare the diversity index of each sampling point in the current assessment period with the preset diversity threshold, and determine whether an invasive alien species is detected based on the corrected species identification results, generating the first... For each sampling point, when the diversity index is lower than the preset diversity threshold or an invasive alien species is detected, an early warning information including the sampling point location, anomaly type, and corresponding assessment results is generated and pushed to the management terminal.

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