Aquatic organism integrity evaluation method and system and application thereof

Abstract

The invention belongs to the technical field of aquatic organism resource protection, and particularly relates to an aquatic organism integrity evaluation method and system and application thereof. The method comprises the following steps: investigating aquatic organisms in an evaluation area; the method comprises the following steps: acquiring the following biological indexes in investigation: periphytic algae biomass, benthonic animal biomass, the amount of catches per unit fishing effort, a Pielou uniformity index and a Shannon-Wiener index; calculating the weight of each biological index by adopting an information entropy weight method; and by adopting a linear weighted combination method, multiplying the normalized biological index and the information entropy weight of the normalized biological index, and then accumulating to obtain the aquatic organism integrity index. The invention further provides a system for implementing the method. The method and the system have the advantages of being accurate, objective and comprehensive, and have good application prospects in the fields of aquatic organism resource protection, ecological system evaluation or environment monitoring.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of aquatic organism resource protection, and particularly relates to an aquatic organism integrity evaluation method and system and use thereof. BACKGROUND

[0002] With the rapid development of social economy, the influence of human activities on aquatic ecosystems is increasingly significant, and the survival environment of aquatic organisms is facing many threats, such as habitat destruction, pollution, overfishing, and invasion of alien species. In order to effectively protect aquatic organism resources and maintain ecological balance, it is particularly important to accurately evaluate the integrity of aquatic organisms.

[0003] Traditional methods of evaluating the integrity of aquatic organisms mostly rely on single biological indicators or environmental indicators, which are difficult to comprehensively and systematically reflect the overall condition of aquatic ecosystems. For example, only through the number of fish species or specific water quality parameters to evaluate the integrity of aquatic organisms often ignores other biological groups and the complex interactions of the ecosystem. In addition, some existing evaluation methods have great subjectivity in the selection of reference points, the screening of core parameters, and the determination of weights, which may lead to inaccurate and objective evaluation results, and cannot truly reflect the actual level of the integrity of aquatic organisms.

[0004] In the practice of aquatic organism investigation and evaluation, there is an urgent need for an aquatic organism integrity evaluation method that can comprehensively consider multiple biological groups and environmental factors, reduce subjective influence, and improve evaluation accuracy and objectivity. SUMMARY

[0005] In view of the problems of the prior art, the present application provides an aquatic organism integrity evaluation method, system and use thereof.

[0006] An aquatic organism integrity evaluation method, comprising the following steps:

[0007] Step 1: conducting an aquatic organism investigation in the evaluation area;

[0008] Step 2: obtaining the following biological indicators in the investigation: periphytic algal biomass, benthic animal biomass, unit fishing effort catch, Pielou evenness index, and Shannon-Wiener index;

[0009] Step 3: calculating the weight of each biological indicator by using the information entropy weight method;

[0010] Step 4: multiplying the normalized biological indicators by their information entropy weights and then accumulating to obtain the aquatic organism integrity index by using the linear weighted combination method.

[0011] Preferably, in step 1, the investigation of fish is performed according to SC / T9429, Qinghai Provincial Local Standard DB63 / T1217; the investigation of periphyton is performed according to SC / T9402; and the investigation of benthic animals is performed according to HJ710.8.

[0012] Preferably, in step 2, the obtained biological indicators are subjected to quality control and cross-checking by using an exploratory spatial interpolation function, and the environmental variables with data missing and RMSE>0.6 are excluded.

[0013] Preferably, the method further comprises the following steps:

[0014] In step 5, according to the obtained aquatic organism integrity index, the aquatic organism integrity index of the reference point is evaluated by using the 25% quantile method, and greater than 25% quantile represents the optimal state, and the distribution range less than 25% quantile is divided into four equal parts, which respectively represent different states, and the states from high to low are excellent, good, medium, poor and very poor.

[0015] Preferably, the reference point is selected from a point with a human footprint index less than or equal to 12, and the human footprint index is calculated according to European Wilderness Quality Standard and Audit System Version 1.8.

[0016] Preferably, the weight of periphyton biomass is 0.24, the weight of benthic animal biomass is 0.20, the weight of catch per unit fishing effort is 0.15, the weight of Pielou evenness index is 0.22, and the weight of Shannon-Wiener index is 0.19.

[0017] The application also provides a system for performing the aquatic organism integrity evaluation method, comprising:

[0018] The input module is configured to input the following biological indicators: periphyton biomass, benthic animal biomass, catch per unit fishing effort, Pielou evenness index and Shannon-Wiener index.

[0019] The weight calculation module is configured to calculate the weight of each biological indicator by using an information entropy weight method.

[0020] The aquatic organism integrity index calculation module is configured to obtain the aquatic organism integrity index by using a linear weighted combination method, multiplying the normalized biological indicators by the information entropy weights and then accumulating.

[0021] Preferably, the system further comprises:

[0022] The evaluation module is configured to evaluate the aquatic organism integrity index according to the 25th percentile method of the aquatic organism integrity index of the reference point, wherein an index greater than the 25th percentile represents an optimal state, and an index less than the 25th percentile is divided into four equal parts, each of which represents a different state, from high to low, as excellent, good, medium, poor, and very poor.

[0023] The application further provides an electronic device comprising a processor, a memory, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the aquatic organism integrity evaluation method when executing the program.

[0024] The application further provides a computer-readable storage medium having a computer program stored thereon, wherein the program implements the steps of the aquatic organism integrity evaluation method when executed by a processor.

[0025] The application further provides the use of the system in the field of aquatic organism resource protection, ecosystem evaluation, or environmental monitoring.

[0026] The application provides a comprehensive evaluation index system for the evaluation of aquatic organism integrity, and can obtain accurate evaluation results by constructing a calculation process of evaluation indexes and optimizing biological indexes for calculation.

[0027] In addition, the application uses an information entropy weight method to take the contribution of each index to the overall information entropy as a weight, thereby further avoiding the influence of the subjectivity of weight determination on the final result.

[0028] In the preferred embodiment, the application uses a human footprint index to quantitatively evaluate the degree of disturbance of the research area by human activities, selects a survey site with the least human disturbance as a reference site, avoids the subjectivity of the selection of reference points in the traditional method, and makes the evaluation results more objective and comparable.

[0029] In summary, the aquatic organism integrity evaluation method and system of the application have the advantages of accuracy, objectivity, and comprehensiveness, and have good application prospects in the field of aquatic organism resource protection, ecosystem evaluation, or environmental monitoring.

[0030] Obviously, according to the above content of the present application, other various forms of modification, replacement or change can be made according to the common technical knowledge and usual means in the art without departing from the above basic technical idea of the present application.

[0031] The above content of the present application will be further explained in detail by a specific embodiment in the form of examples. However, it should not be understood that the above subject matter of the present application is limited to the following examples. Any technology realized based on the above content of the present application belongs to the scope of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0032] Figure 1 Spatial pattern of epilithic algal biomass (a) and its relationship with latitude (b) and longitude (c);

[0033] Figure 2 Relationship of epilithic algal biomass (Zao) with elevation (DEM), river profile curvature (Slope), human footprint (HFP) and cumulative flow (Flow);

[0034] Figure 3 Spatial pattern of benthic animal biomass (a) and its relationship with latitude (b) and longitude (c);

[0035] Figure 4 Relationship of benthic animal biomass (DiB) with elevation (DEM), river profile curvature (Slope), human footprint (HFP) and cumulative flow (Flow);

[0036] Figure 5 Spatial pattern of catch per unit effort (CPUE, unit: g / 1000m 2 of fixed gill net / 24h) and its relationship with latitude (b) and longitude (c);

[0037] Figure 6 Relationship of catch per unit effort (Fis) with elevation (DEM), river profile curvature (Slope), human footprint (HFP) and cumulative flow (Flow);

[0038] Figure 7 Spatial pattern of Pielou evenness index (a) and its relationship with latitude (b) and longitude (c);

[0039] Figure 8 Relationship of Pielou evenness index (Pie) with elevation (DEM), river profile curvature (Slope), human footprint (HFP) and cumulative flow (Flow);

[0040] Figure 9Spatial pattern of Shannon-Wiener index (a) and its relationship with latitude (b) and longitude (c);

[0041] Figure 10 Relationship of Shannon-Wiener index with elevation (DEM), river profile curvature (Slope), human footprint (HFP) and cumulative flow (Flow);

[0042] Figure 11 Spatial pattern of aquatic organism integrity index (IBI) (a) and its relationship with latitude (b) and longitude (c);

[0043] Figure 12 Relationship of aquatic organism integrity index (IBI) with elevation (DEM), river profile curvature (Slope), human footprint (HFP) and cumulative flow (Flow);

[0044] Figure 13 Statistical histogram of aquatic organism integrity index (IBI) in the range of a river basin. DETAILED DESCRIPTION

[0045] It should be particularly noted that the algorithms of the data collection, transmission, storage and processing steps not specifically explained in the embodiments, and the hardware structure, circuit connection, etc. not specifically explained can be realized through the existing technology disclosed.

[0046] Embodiment 1 aquatic organism integrity evaluation method and system

[0047] The embodiment provides a system for aquatic organism integrity evaluation, comprising:

[0048] An input module configured to input the following biological indicators: periphyton biomass, benthic animal biomass, catch per unit effort, Pielou evenness index and Shannon-Wiener index;

[0049] A weight calculation module configured to calculate the weight of each biological indicator by using an information entropy weight method;

[0050] An aquatic organism integrity index calculation module configured to obtain an aquatic organism integrity index by using a linear weighted combination method, multiplying the normalized biological indicators by the information entropy weights and then accumulating them.

[0051] An evaluation module configured to evaluate the obtained aquatic organism integrity index by using a 25% quantile method of the aquatic organism integrity index of a reference point, wherein an index greater than the 25% quantile represents the best state, and the distribution range less than the 25% quantile is divided into four equal parts, each of which represents a different state, and the states from high to low are excellent, good, medium, poor and very poor.

[0052] When the aquatic organism integrity evaluation is performed by the system, the following steps are performed:

[0053] Step 1, aquatic organism investigation is performed in the evaluation area;

[0054] Step 2, the following biological indicators are obtained in the investigation: periphyton biomass, benthic animal biomass, catch per unit effort, Pielou evenness index, Shannon-Wiener index;

[0055] Step 3, the information entropy weight method is used to calculate the weight of each biological indicator;

[0056] Step 4, the linear weighted combination method is used to multiply the normalized biological indicators by their information entropy weights and then accumulate them to obtain the aquatic organism integrity index;

[0057] Step 5, according to the obtained aquatic organism integrity index, the 25% quantile method of the aquatic organism integrity index of the reference point is used for evaluation, and greater than 25% quantile represents the best state, and less than 25% quantile is divided into 4 equal parts, which respectively represent different states, from high to low, they are excellent, good, medium, poor, and very poor.

[0058] The application of the above system and method will be further described in detail through an actual aquatic organism integrity evaluation case:

[0059] 1. Evaluation range

[0060] The evaluation area this time is from the confluence of Xikequ of Qinghai Province to the entrance of Longyangxia Reservoir of the Yellow River, a total of 59 sample points are investigated, and the investigation area includes the main stream of the Yellow River in Henan Mongol Autonomous County, Zeku County, Gande County, Maqin County, Maduo County, Jiuzhi County, Guinan County, Xinghai County, Tongde County, Maqu County, and the main tributaries of Lüqu County, with an elevation span of 2603.1m-4171.3m.

[0061] 2. Specific description of the evaluation method

[0062] 2.1 Aquatic organism investigation

[0063] The investigation time of aquatic organisms is from May to August 2023, and the investigation section covers 59 sample points, with a survey frequency of 2 times for each sample point. All investigation data and field photos are uploaded through the APP designated by the project to facilitate the collection and statistical analysis of the investigation data. The aquatic organism investigation is carried out under the permission of the "Special Permit for Catching Aquatic Wildlife" (Approval No.: Qingye Catching Letter

[2023] No. 02) issued by the provincial fishery administration department. The investigation categories include fish, periphyton, and benthic animals. The investigation methods of each biological category are described as follows.

[0064] 2.1.1 Fish

[0065] Fish investigation refers to the "Freshwater Fishery Resources Investigation Standard Rivers" (SC / T9429), "Aquatic Organism Monitoring Technology General" (Qinghai Provincial Standard DB63 / T1217) for execution. Fish investigation includes fish community structure, unit fishing effort catch, resource density, diversity index.

[0066] 2.1.2 Periphyton

[0067] The investigation method of periphyton refers to the "Freshwater Plankton Investigation Technical Specification" (SC / T9402) for execution. The investigation content includes the species, biomass density, and diversity index of periphyton.

[0068] 2.1.3 Benthic animals

[0069] The investigation method of benthic animals refers to the "Biodiversity Observation Technical Guidelines Freshwater Benthic Macroinvertebrates" (HJ710.8) for execution. The investigation content includes the species, biomass density, and diversity index of benthic animals.

[0070] 2.2 Establishing reference state

[0071] The reference state is constructed using the reference point method. The reference point refers to the point set in the area not or almost not disturbed by human activities. The human activity disturbance is quantitatively evaluated by the human footprint index. The human footprint refers to the pressure on the ecological environment caused by the change of ecological processes and natural landscape, and is a commonly used index for measuring human disturbance to natural ecosystems in biodiversity and conservation biology. The calculation method of human footprint adopts the European Wilderness Quality Standard and Audit System Version 1.8. The value range of human footprint is 1 to 50, and the larger the value, the greater the impact of human activities on the natural ecosystem. The ArcPy package of Python language is used to calculate the human footprint.

[0072] The human footprint values of 59 sampling points are shown in Table 1, and 5% quantile (59x5%=3) is used to select the three investigation sites (HH40, HH024, HH058) with the smallest human disturbance as the reference sites, representing the ideal state of the lowest human disturbance to the natural ecosystem of all sampling points.

[0073] Table 1 Human footprint values of each sampling point

[0074]

[0075]

[0076] 2.3 Establishing the list of candidate parameters

[0077] Three types of indices, species abundance, species diversity composition, and environmental variables, were selected as candidate parameters (Table 2).

[0078] Table 2. Candidate parameters and their responses to disturbance

[0079]

[0080] 2.4 Data collection

[0081] 2.4.1 Species abundance and species diversity

[0082] Data were collected through field surveys conducted from May 30 to June 14, 2023, July 1 to July 9, 2023, August 9 to August 17, 2023, and August 8 to August 14, 2023. Since the survey times were all located in the warm season and high water period of the same year, the average of the four measured values was taken as the candidate parameter. The exploratory spatial interpolation function of the ArcPy package in Python language was used for data quality control and cross-checking, and environmental variables with missing data and RMSE > 0.6 were excluded to exclude the influence of accidental errors and systematic errors on the results, and further ensure the data quality and accuracy of the results.

[0083] According to the above criteria, all collected candidate parameters were investigated, and the results showed that the retained candidate parameters were: periphytic algal biomass, benthic animal biomass, catch per unit effort, Pielou evenness index, and Shannon-Wiener index. This indicates that through these five biological indicators, the integrity of aquatic organisms in the evaluation area can be accurately reflected.

[0084] 2.4.2 Species abundance and species diversity

[0085] Elevation was derived from ALOS-12m topographic data from the Japan Aerospace Exploration Agency.

[0086] River slope curvature was calculated from ALOS-12m topographic data using the surface parameter function of the ArcPy package in Python language.

[0087] Cumulative flow was calculated from ALOS-12m topographic data using the flow, flow direction, and sink functions of the ArcPy package in Python language.

[0088] According to the industry standard "Small watershed division and coding specification" (SL 653-2013), the three-level watershed is used as the evaluation range and unit. The evaluation range selects the four three-level watersheds intersected by the Yellow River basin range and the survey points, i.e. Daxia River and Tao River, Maqu to Longyangxia, Yellow River source to Maqu, and Longyangxia to Lanzhou main stream. In order to avoid the influence of boundary effect on the results, the spatial analysis range of environmental variables is the 2km buffer zone of the above four three-level domain watershed boundaries.

[0089] 2.5 Construction of biological integrity index

[0090] 2.5.1 Data normalization

[0091] The evaluation indexes of biological integrity index have different properties and dimensions, and cannot be directly evaluated. Therefore, the "maximum-minimum" method is used to normalize each evaluation index after quality control and cross-validation in this embodiment, which can preserve the linear relationship before and after data normalization. Considering that the positive and negative correlation indexes of biological integrity have different influence directions on the results, the normalization methods for the indexes with decreasing and increasing responses are formula 1 and 2, respectively.

[0092]

[0093] In the formula, y ij represents the normalized value of the i-th grid of environmental variable j. xij represents the measured value of the i-th grid of environmental variable j. max(x j ) and min(x j ) are the maximum and minimum values of all grids of environmental variable j, respectively.

[0094] 2.5.2 Weight calculation

[0095] In the evaluation, the weight is used to measure the importance of a certain index in the index system, which affects the accuracy of the evaluation results. HJ1295 uses the equal weight method, which needs to exclude the influence of environmental variable collinearity. If the variable collinearity exists, HJ1295 requires "selecting the parameters with relatively large information quantity and strong importance as core parameters". How to judge "relatively large information quantity and strong importance", HJ1295 does not give a clear method, and users have a certain subjectivity in operation. In order to make up for this limitation, this embodiment uses the information entropy weight method (formula 3-6) to quantitatively evaluate the weight of environmental variables, which avoids the influence of variable collinearity on the results and avoids the subjectivity of variable selection, so as to fully utilize the actual data and comprehensively consider the relationship between multiple samples, and improve the accuracy of the evaluation results.

[0096]

[0097] In the formula, e jH is the information entropy; W j H is the information entropy; W ij y is the proportion of the jth index in the ith grid cell (normalized proportion), used to calculate the probability distribution weight of information entropy; y ij y is the normalized index value, representing the normalized value of the jth index layer in the ith grid cell.

[0098] 2.5.3 Aquatic organism integrity assessment

[0099] The normalized biological index and its information entropy weight were multiplied and accumulated by using linear weighted combination method to obtain the aquatic organism integrity index (IBI) (Formula 6). Then, the assessment was carried out according to the following standards:

[0100] According to the 25% quantile method of reference point IBI value, the state is optimal when it is greater than the 25% quantile, and the distribution range is divided into 4 equal parts when it is less than the 25% quantile, representing different states. From high to low, they are excellent, good, medium, poor, and very poor.

[0101] IBI = ∑y j × W j ( Formula 6 )

[0102] In the formula, y j is the biological index, W j is the information entropy weight.

[0103] 2.6 Data analysis

[0104] All data analysis was carried out using the Arcpy package of Python language.

[0105] 3. Results

[0106] 3.1 Selection of evaluation indexes

[0107] After quality control and cross-validation, 5 effective indexes were selected from the species abundance and diversity indexes in Table 2 for subsequent analysis. The 5 effective indexes are: periphytic algal biomass, benthic animal biomass, catch per unit effort, Pielou evenness index, and Shannon-Wiener index. The remaining indexes have higher accidental error and systematic error, indicating that the measured data cannot represent the overall situation of the study area. In order to ensure the accuracy of the analysis, they are discarded.

[0108] 3.2 Periphytic algal biomass

[0109] A total of 65 species and 44 genera of periphytic algae were measured in the evaluation area, with Cyanophyta and Chlorophyta as dominant species. The periphytic algal biomass was between 0.33-0.86 mg / L Figure 1a) The relationship with longitude is higher in the west and lower in the east. Figure 1 c) The relationship with latitude is higher in the south and lower in the north. Figure 1 (b) Biogeographical distribution pattern. The average value of the reference points is basically located at the midpoint of the latitude and longitude of the watershed, indicating that human activities have little impact on the biomass of attached algae. Correlation analysis ( Figure 2 Similarly, no significant collinearity was observed between algal biomass and human footprint, river profile curvature, and cumulative discharge (|P|<0.75). Attached algae showed a clear positive correlation with altitude (|P|=0.88>0.75). In summary, the main factors affecting algal biomass are latitude, longitude, and altitude, while human activities have a relatively small impact on algal biomass.

[0110] 3.3 Benthic animal biomass

[0111] A total of 27 species and 25 genera of benthic animals were identified within the evaluation area, with arthropods being the dominant group. Benthic animal biomass ranged from 0.68 to 3.15 g / m³. 2 between( Figure 3 a) The peak latitude is around 35 degrees north latitude. Figure 3 b), the relationship with longitude is higher in the west and lower in the east. Figure 3 c). The average value of the reference points is basically located at the midpoint of the longitude of the watershed, indicating that human activities have little impact on benthic biomass. Correlation analysis ( Figure 4 The results showed that benthic animal biomass did not exhibit significant collinearity with altitude, human footprint, river profile curvature, and cumulative discharge (|P|<0.75), but showed the strongest positive correlation with altitude (P=0.70). In conclusion, the main factors influencing benthic animal biomass are latitude, longitude, and altitude, while human activities have a relatively small impact on algal biomass.

[0112] 3.4 Catch per unit fishing effort

[0113] A total of 28 species and 21 genera of fish were detected in the evaluation area, with Cyprinidae and Cobitidae being the dominant groups. The catch per unit catch effort ranged from 133 to 16578 g / 1000m³. 2 Fixed barbed wire / between 24 hours Figure 5 a). The relationship with longitude is higher in the west and lower in the east. Figure 5 c) The relationship with latitude is higher in the south and lower in the north. Figure 5 b) Biogeographical distribution pattern. Correlation analysis ( Figure 6 The results showed that there was no significant collinearity between fish resources and altitude, human footprint, river profile curvature and cumulative discharge (|P|<0.75), but the correlation with altitude was the strongest, showing a positive correlation (P=0.69).

[0114] In summary, the main factors affecting fish resources are latitude, longitude, and altitude, while human activities have a relatively small impact on fish resources.

[0115] 3.5 Pielou uniformity index

[0116] The Pielou evenness index is a commonly used indicator for assessing biodiversity; a higher value reflects a greater evenness in species distribution within the assessed area. The Pielou evenness index ranges from 0.33 to 0.79. Figure 7 a) The relationship with longitude is lower in the west and higher in the east. Figure 7 c) The relationship with latitude is higher in the south and lower in the north. Figure 7 b) Biogeographical distribution pattern. Correlation analysis ( Figure 8 The results showed that the Pielou evenness index did not exhibit significant collinearity with altitude, human footprint, river profile curvature, and cumulative discharge (|P|<0.75), but showed the strongest negative correlation with altitude (P=-0.21). In conclusion, the main factors influencing the Pielou evenness index are latitude, longitude, and altitude, while human activities have a relatively small impact on the index.

[0117] 3.6 Shannon-Wiener index

[0118] The Shannon-Wiener index is a commonly used indicator for assessing biodiversity; a higher value reflects higher biodiversity in the assessed area. The Shannon-Wiener index ranges from 0.43 to 1.79. Figure 9 a) The relationship with longitude is lower in the west and higher in the east. Figure 9 , Figure 7 c) The relationship with latitude is higher in the south and lower in the north. Figure 7 b) Biogeographical distribution pattern. Correlation analysis ( Figure 10 The results showed that the Shannon-Wiener index did not exhibit significant collinearity with altitude, human footprint, river profile curvature, and cumulative discharge (|P|<0.75), but showed the strongest negative correlation with altitude (P=-0.45). In conclusion, the main factors influencing the Shannon-Wiener index are latitude, longitude, and altitude, while human activities have a relatively small impact on the index.

[0119] 3.7 Information Entropy Weight

[0120] Information entropy describes the contribution of each component to the overall information, specifically the importance of five biological indicators to aquatic life integrity: algal biomass, benthic biomass, catch per unit catch effort, Pielou evenness index, and Shannon-Wiener index. The results are shown in Table 3. The information entropy of each indicator ranged from 0.15 to 0.24, with a total of 1.00. Catch per unit catch effort had the lowest information entropy for aquatic life integrity, at 0.15. Algal biomass had the highest information entropy for aquatic life integrity, at 0.24.

[0121] Table 3 Information Entropy Weights of Candidate Parameters

[0122]

[0123] 3.8 Assessment of the integrity of aquatic organisms

[0124] Using information entropy as the weight, Figure 1 , Figure 3 , Figure 5 , Figure 7 , Figure 9 By summing up (Formula 6), the integrity of aquatic organisms is obtained. Figure 11 a). The relationship with longitude is higher in the west and lower in the east. Figure 11 c) The relationship with latitude is higher in the south and lower in the north. Figure 11 b) Biogeographical distribution pattern. Correlation analysis ( Figure 12 The results showed that the aquatic life integrity index did not exhibit significant collinearity with altitude, human footprint, river profile curvature, and cumulative discharge (|P|<0.75), but showed the strongest positive correlation with altitude (P=0.65). In conclusion, the main factors influencing the aquatic life integrity index are latitude, longitude, and altitude, while human activities have a relatively small impact on it.

[0125] The aquatic life integrity scores for the reference points are: HH24 = 0.44, HH40 = 0.53, and HH58 = 0.65. Since the number of reference points (n = 3) is less than 4, the 25th percentile cannot be calculated. Therefore, the optimal state value for aquatic life integrity is set as: 1 / n quantile of reference points × 25% × n = 0.33. The aquatic life integrity within the watershed follows a normal distribution, with a mean of 0.50 > 0.33 (n = 43,399, STD = 0.11). Figure 13 The aquatic organism integrity level is excellent.

[0126] 4. Conclusion

[0127] On the basis of comprehensive and systematic investigation of 59 aquatic organism sections from the confluence of Xikocun to the entrance of Longyangxia Reservoir, the present study selected five biological indexes, including periphyton biomass, benthic animal biomass, catch per unit effort, Pielou species evenness index and Shannon-Wiener species diversity index, for aquatic organism integrity evaluation. The aquatic organism integrity evaluation method and system provided by the present application have the advantages of accuracy, objectivity and comprehensiveness, and have good application prospect in the field of aquatic organism resource protection, ecosystem evaluation or environmental monitoring.

Claims

1. A method for assessing the integrity of aquatic organisms, characterized in that, The method comprises the following steps: Step 1: performing aquatic organism investigation in an evaluation area; Step 2: obtaining the following biological indexes in the investigation: periphyton biomass, benthic animal biomass, catch per unit effort, Pielou evenness index, Shannon-Wiener index; Step 3: calculating the weight of each biological index by using information entropy weight method; Step 4: obtaining the aquatic organism integrity index by using linear weighted combination method to multiply the normalized biological index by the information entropy weight and then accumulating.

2. The method according to claim 1, c h a r a c t e r i z e d in that: In step 1, the fish investigation is performed according to SC / T9429 and Qinghai Provincial Local Standard DB63 / T1217; the periphyton investigation is performed according to SC / T9402; and the benthic animal investigation is performed according to HJ710.

8.

3. The method according to claim 1, characterized in that: In step 2, the obtained biological indexes are subjected to quality control and cross-checking by using an exploratory spatial interpolation function, and the environmental variables with data missing and RMSE>0.6 are excluded.

4. The method according to claim 1, characterized in that, The method further comprises the following steps: Step 5: performing evaluation by using the 25% quantile method of the aquatic organism integrity index of the reference point according to the obtained aquatic organism integrity index, wherein the aquatic organism integrity index greater than the 25% quantile represents the optimal state, and the distribution range less than the 25% quantile is divided into four equal parts, which represent different states from high to low, i.e., excellent, good, medium, poor and very poor.

5. The method according to claim 4, characterized in that: The reference point is selected from the point with a human footprint index less than or equal to 12, and the human footprint index is calculated according to European Wilderness Quality Standard and Audit System Version 1.

8.

6. The method according to claim 1, characterized in that: The weight of the periphyton biomass is 0.24, the weight of the benthic animal biomass is 0.20, the weight of the catch per unit effort is 0.15, the weight of the Pielou evenness index is 0.22, and the weight of the Shannon-Wiener index is 0.

19.

7. A system for performing the method of assessing the integrity of aquatic organisms according to any one of claims 1 to 6, characterized in that, The method comprises: an input module configured to input the following biological indexes: periphyton biomass, benthic animal biomass, catch per unit effort, Pielou evenness index, Shannon-Wiener index; a weight calculation module configured to calculate the weight of each biological index by using information entropy weight method; an aquatic organism integrity index calculation module configured to obtain the aquatic organism integrity index by using linear weighted combination method to multiply the normalized biological index by the information entropy weight and then accumulating.

8. An electronic device, comprising: The program is executed by the processor to implement the steps of the aquatic organism integrity evaluation method according to any one of claims 1-6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the steps of the aquatic organism integrity evaluation method according to any one of claims 1-6.

10. Use of the system according to claim 7 in the field of aquatic organism resource protection, ecosystem evaluation or environmental monitoring.

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