Fish nutrition structure and hydrodynamic condition correlation analysis method and system

Abstract

The invention provides a correlation analysis method and system for a fish nutrition structure and hydrodynamic conditions. The method comprises the following steps: sampling and preprocessing fish and fish bait samples at sampling points preset in a target river reach; measuring the collected fish and fish bait samples by adopting a carbon-nitrogen stable isotope analysis method, and carrying out fish nutrition structure analysis including fish bait contribution rate calculation according to a measurement result; hydrodynamic simulation is conducted on the target river reach, correlation analysis is conducted on the fish bait contribution rate and hydrodynamic factors through a redundancy analysis method, and key hydrodynamic factors influencing the fish bait contribution rate are obtained. According to the method, a method of combining field investigation and theoretical analysis is adopted, analysis and research on the bait structure, the nutrition level, the food web structure and the ecological niche of the fish community are carried out, the correlation between the fish nutrition structure and different hydrodynamic conditions is established, and a scientific basis can be provided for water ecological environment protection and aquatic organism diversity protection.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy projects, and particularly relates to a method and system for analyzing the correlation between the fish nutritional structure and hydrodynamic conditions. Background Art

[0002] The fish nutritional structure is an organism that depends on and restricts each other through nutritional relationships among aquatic organisms, mainly including the fish diet structure, the trophic level of the fish community, the food web structure of the fish community, and the ecological niche of the fish community, etc. In recent years, the construction of water-related projects and the influence of human activities have led to changes in the nutritional structure of aquatic organisms, especially the fish community, and the competition for food and ecological niche among fish communities has shown an increasing trend. Therefore, conducting research on the changes in the nutritional structure of fish in different periods and clarifying the impact of hydrodynamic condition changes on the fish nutritional structure have theoretical value and practical significance for the protection of watershed water ecological environment and the promotion of aquatic biodiversity.

[0003] Existing research shows that the impact of hydrodynamic processes on aquatic ecosystems is comprehensive, but the correlation between hydrodynamic conditions and the fish nutritional structure still needs to be explored. Specifically, it is manifested in: (1) Existing research focuses on the impact of flow processes on aquatic organisms and aquatic environments, lacking a direct connection between nutritional structures such as fish trophic levels and diet structures and hydrodynamic conditions; (2) The bait effects brought about by changes in hydrodynamic conditions are complex. Changes in hydrodynamic conditions not only affect the water environment characteristics on which bait organisms depend for survival, but also directly affect the organisms themselves, but the key factors affecting the bait effect are still not clear enough. In summary, this application will establish the correlation between the fish nutritional structure and different hydrodynamic conditions, and analyze and clarify the hydrodynamic factors that have a greater impact on the fish nutritional structure. Summary of the Invention

[0004] This application provides a method and system for analyzing the correlation between the fish nutritional structure and hydrodynamic conditions to analyze the correlation between the fish nutritional structure and different hydrodynamic conditions, so as to clarify the hydrodynamic factors that affect the fish nutritional structure.

[0005] According to a first aspect, in one embodiment, a method for analyzing the correlation between the fish nutritional structure and hydrodynamic conditions is provided. The method includes:

[0006] Sampling and preprocessing fish and fish bait samples at sampling points preset in the target river section;

[0007] Using the carbon and nitrogen stable isotope analysis method to measure the collected fish and fish bait samples, and conducting fish nutritional structure analysis including calculating the fish bait contribution rate according to the measurement results;

[0008] Conduct hydrodynamic simulation on the target river section, and use redundancy analysis method to analyze the correlation between fish bait contribution rate and hydrodynamic factors to obtain the key hydrodynamic factors affecting fish bait contribution rate.

[0009] Furthermore, sample and preprocess fish and fish bait samples at the sampling points pre-set in the target river section, specifically including:

[0010] Determine that the collected fish samples include carnivorous fish, omnivorous fish, filter-feeding fish, and herbivorous fish, and determine that the collected fish bait samples include zooplankton, benthic animals, phytoplankton, periphyton, litter, and soil detritus.

[0011] Furthermore, sample and preprocess fish and fish bait samples at the sampling points pre-set in the target river section, specifically including:

[0012] Set multiple sampling points at different cross-section positions in the target river section, and conduct multiple samplings on different dates during the high-water period and the low-water period, including sampling soil detritus bait samples under completely submerged, partially submerged, and non-submerged conditions, as well as at different elevations and different soil layers under partially submerged conditions.

[0013] Furthermore, sample and preprocess fish and fish bait samples at the sampling points pre-set in the target river section, specifically including:

[0014] For fish samples: Take the back muscle samples of fish and place them in a petri dish, then place them in a drying oven for constant-temperature drying, grind them and sieve them, and finally put them into a sealed bag;

[0015] For zooplankton and phytoplankton samples: First, put the glass fiber filter membrane into an aluminum box, burn it in a muffle furnace to remove organic matter, acidify the filter membrane in concentrated hydrochloric acid after cooling, then dry it in an oven at a constant temperature, use a vacuum filtration machine to draw the pre-collected water sample onto the treated filter membrane to initially obtain zooplankton and phytoplankton samples, and finally dry the filtered filter membrane in a constant-temperature drying oven and seal it in a vacuum bag filled with silica gel for storage;

[0016] For benthic animal samples: Place the benthic animals selected with tweezers in a petri dish, wash them with distilled water and place them in a drying oven for constant-temperature drying, grind them and put them into a sealed bag;

[0017] For periphyton samples: Place the periphyton samples in a beaker, wait for the attached soil to precipitate, and then use a vacuum filtration machine to filter the periphyton samples onto the filter membrane, dry the filter membrane, and seal it in a vacuum bag filled with silica gel for storage;

[0018] For litter samples: Place the collected litter samples in an oven for drying, grind them into powder, sieve them, and finally put them into a sealed bag;

[0019] For soil debris samples: Take an appropriate amount of the ground and sieved soil sample in a small beaker, add hydrochloric acid and react for a period of time. Stir with a glass rod every preset time to make it react fully to completely remove the inorganic carbon in the soil. Let it stand, and then pour out the supernatant; stir and wash with distilled water, let it stand, and pour out the supernatant. Repeat several times to remove the excessive hydrochloric acid, and then dry it for standby. Place the acidified sample in a sampling dish, put it in a drying oven to dry, grind it through a sieve, and then seal it in a sealed plastic bag.

[0020] Furthermore, perform fish nutritional structure analysis including calculation of fish bait contribution rate based on the measurement results, specifically including:

[0021] Substitute the measured carbon and nitrogen stable isotope ratios of fish baits and fish into the Bayesian mixing model, use snails as the baseline organisms, and calculate the contribution rates of different fish baits to the different fish collected, including calculating the contribution rates of soil debris baits collected under different inundation conditions.

[0022] Furthermore, use the carbon and nitrogen stable isotope analysis method to measure the collected fish and fish bait samples, and perform fish nutritional structure analysis including calculation of fish bait contribution rate based on the measurement results, specifically including:

[0023] The fish nutritional structure analysis also includes fish trophic level, fish food web structure, and fish community niche analysis.

[0024] Furthermore, conduct hydrodynamic simulation on the target river section, specifically including:

[0025] Divide the target river section into multiple cross-sections, set boundary conditions and parameter rates, and construct a longitudinal one-dimensional hydrodynamic model to conduct hydrodynamic simulation on the target river section:

[0026] Continuity equation:

[0027]

[0028] Momentum equation:

[0029]

[0030] Where: A is the cross-sectional area of the flowing water, with the unit of m 2 ; t is the time, with the unit of s; Q is the flow rate, with the unit of m 3 / s; x is the longitudinal coordinate, with the unit of m; q is the tributary flow rate, with the unit of m 2 / s; v is the average cross-sectional velocity, with the unit of m / s; g is the acceleration due to gravity, with the unit of m 2 / s; z is the average cross-sectional water level, with the unit of m; S f is the friction slope; ql is the inflow of tributaries per unit river length, with the unit of m 2 / s;

[0031] The results of hydrodynamic simulation include changes in water level, flow rate, water depth, flow velocity, and water surface width, and the results of hydrodynamic simulation are verified by combining with measured data.

[0032] Furthermore, redundancy analysis is used to analyze the correlation between the fish bait contribution rate and hydrodynamic factors to obtain the key hydrodynamic factors affecting the fish bait contribution rate, specifically including:

[0033] Determine the hydrodynamic factors with inundation characteristics, including the mean flow rate, flow peak, flow rate change rate, mean flow velocity, mean water depth, inundation frequency, and inundation rate in a preset statistical period, and calculate the values of the hydrodynamic factors with inundation characteristics according to the results of hydrodynamic simulation.

[0034] Furthermore, redundancy analysis is used to analyze the correlation between the fish bait contribution rate and hydrodynamic factors to obtain the key hydrodynamic factors affecting the fish bait contribution rate, specifically including:

[0035] Redundancy analysis is performed on the soil debris bait contribution rates at different elevations and different soil layers under partial inundation conditions respectively with the hydrodynamic factors with inundation characteristics to obtain the extremely significant, significant influencing factors, and positive and negative correlation factors affecting the fish bait contribution rate.

[0036] According to the second aspect, in an embodiment, a correlation analysis system between fish nutritional structure and hydrodynamic conditions is provided, and the system includes:

[0037] A sample collection module for sampling and preprocessing fish and fish bait samples at sampling points preset in the target river section;

[0038] An isotope analysis module for measuring the collected fish and fish bait samples by carbon and nitrogen stable isotope analysis method, and performing fish nutritional structure analysis including calculating the fish bait contribution rate according to the measurement results;

[0039] A correlation analysis module for performing hydrodynamic simulation on the target river section, and using redundancy analysis to analyze the correlation between the fish bait contribution rate and hydrodynamic factors to obtain the key hydrodynamic factors affecting the fish bait contribution rate.

[0040] The present application provides a method and system for analyzing the correlation between the fish nutritional structure and hydrodynamic conditions. Sampling and preprocessing of fish and fish bait samples are carried out at sampling points preset in the target river section. The collected fish and fish bait samples are measured by carbon and nitrogen stable isotope analysis methods, and fish nutritional structure analysis including calculation of fish bait contribution rate is carried out according to the measurement results. Hydrodynamic simulation is carried out on the target river section, and redundancy analysis is used to analyze the correlation between the fish bait contribution rate and hydrodynamic factors to obtain the key hydrodynamic factors affecting the fish bait contribution rate. The present application adopts a method combining field investigation and theoretical analysis, conducts analysis and research on the bait structure, trophic level, food web structure and ecological niche of the fish community, establishes the correlation between the fish nutritional structure and different hydrodynamic conditions, and clarifies the hydrodynamic factors that have a greater impact on the fish nutritional structure, which can provide a scientific basis for water ecological environment protection and aquatic biodiversity protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a flowchart of a method for analyzing the correlation between the fish nutritional structure and hydrodynamic conditions provided by an embodiment of the present invention;

[0042] Figure 2 It is a technical roadmap of a method for analyzing the correlation between the fish nutritional structure and hydrodynamic conditions provided by an embodiment of the present invention;

[0043] Figure 3 It is a map of the research river section in a method for analyzing the correlation between the fish nutritional structure and hydrodynamic conditions provided by an embodiment of the present invention;

[0044] Figure 4 It is a map of the sample collection section position in a method for analyzing the correlation between the fish nutritional structure and hydrodynamic conditions provided by an embodiment of the present invention;

[0045] Figure 5 It is a schematic diagram of the setting of soil sample collection points in a method for analyzing the correlation between the fish nutritional structure and hydrodynamic conditions provided by an embodiment of the present invention;

[0046] Figure 6 It is a graph of the calculation results of fish trophic levels in a method for analyzing the correlation between the fish nutritional structure and hydrodynamic conditions provided by an embodiment of the present invention;

[0047] Figure 7 It is a graph of the aquatic food web structure of the research river section in a method for analyzing the correlation between the fish nutritional structure and hydrodynamic conditions provided by an embodiment of the present invention;

[0048] Figure 8 It is a graph of the research results of the ecological niche of the fish community in a method for analyzing the correlation between the fish nutritional structure and hydrodynamic conditions provided by an embodiment of the present invention;

[0049] Figure 9 Comparison between the simulated cross-section water level results and the measured results in the method for analyzing the correlation between fish nutritional structure and hydrodynamic conditions provided by an embodiment of the present invention;

[0050] Figure 10 Graph showing the analysis results of the contribution rate of detrital bait at different elevations and the redundancy analysis (RDA) of the inundation factor in the method for analyzing the correlation between fish nutritional structure and hydrodynamic conditions provided by an embodiment of the present invention;

[0051] Figure 11 Graph showing the analysis results of the contribution rate of detrital bait in different soil layers and the redundancy analysis (RDA) of the inundation factor in the method for analyzing the correlation between fish nutritional structure and hydrodynamic conditions provided by an embodiment of the present invention;

[0052] Figure 12 Schematic diagram of the logical structure of a system for analyzing the correlation between fish nutritional structure and hydrodynamic conditions provided by an embodiment of the present invention. Detailed implementation manners

[0053] The present invention will be further described in detail below in conjunction with the accompanying drawings through specific implementation manners. Similar elements in different implementation manners are labeled with related similar element numbers. In the following implementation manners, many detailed descriptions are provided to enable a better understanding of the present application. However, those skilled in the art can easily recognize that some of the features can be omitted in different situations, or can be replaced by other elements, materials, or methods. In some cases, some operations related to the present application are not shown or described in the specification to avoid overwhelming the core part of the present application with excessive descriptions. For those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations based on the description in the specification and general technical knowledge in the art.

[0054] In addition, the features, operations, or characteristics described in the specification can be combined in any appropriate manner to form various implementation manners. At the same time, the steps or actions in the method description can also be reordered or adjusted in an obvious manner by those skilled in the art. Therefore, the various sequences in the specification and drawings are only for clearly describing a certain embodiment and do not mean a necessary sequence, unless it is stated that a certain sequence must be followed.

[0055] The first embodiment of the present invention provides a method for analyzing the correlation between fish nutritional structure and hydrodynamic conditions. The following will be described in detail in conjunction with Figure 1 and Figure 2 for detailed description.

[0056] As Figure 1As shown in the figure, in step S100, fish and fish bait samples are collected and pretreated at sampling points preset in the target river section.

[0057] The above steps specifically include:

[0058] In this embodiment, it is determined that the collected fish samples include carnivorous fish, omnivorous fish, filter-feeding fish, and herbivorous fish, and it is determined that the collected fish bait samples include zooplankton, benthic animals, phytoplankton, epiphytic algae, litter, and soil debris.

[0059] In this embodiment, multiple sampling points are set at different cross-section positions in the target river section, and multiple samplings are carried out on different dates during the high-water period and the low-water period, including sampling of soil debris bait samples under fully submerged, partially submerged, and non-submerged conditions, as well as different elevations and different soil layers under partially submerged conditions.

[0060] 1. Selection of research area: In this embodiment, the section from downstream of the Xiangjiaba Dam to Yibin is selected as the research section of the lower reaches of the Jinsha River. The length of this section is 33 km, which is a section of the National Nature Reserve for Rare and Endemic Fishes in the Upper Reaches of the Yangtze River, and it is also the only relatively intact continuous river section in the main stream of the upper reaches of the Yangtze River that has not been developed for hydropower. The maintenance of its water ecological health has important ecological significance and social value for the ecological protection of the upper reaches of the Yangtze River. Selecting this section to study the impact of river hydrological processes on fish bait structure and other issues has significant typicality and representativeness, and the research results have a leading and exemplary role in the maintenance of river water ecological health. The lower reaches of the Jinsha River start from the confluence of the Yalong River and end at the confluence of the three rivers in Yibin, with a total length of about 768 km. This section is located in the transition zone between the Qinghai-Tibet Plateau and the Sichuan Basin, and 4 cascade hydropower stations including Wudongde, Baihetan, Xiluodu, and Xiangjiaba have been built. The research area is as Figure 3 shown.

[0061] 2. Investigation and analysis of water environment factors: Field investigations and laboratory analyses are carried out on water environment factors (including water temperature, PH, dissolved oxygen, conductivity, turbidity, NH3-N) at different sampling cross-sections in May and October. After investigation and analysis, the water quality status of the research area is good, and all indicators meet the Class III water quality standard of the Surface Water Environment Quality Standard (GB3838 - 2002).

[0062] 3. Sampling time and location: Field collection and analysis are carried out on typical fish and 6 kinds of their baits in the research river section. A total of two samplings are carried out, and each sampling includes 4 typical cross-sections, as Figure 4As shown in the figure. In May 2022, the collection time of fish samples was from May 17th to May 31st, and the collection time of fish bait was on May 18th and May 19th; in October 2022, the collection time of fish samples was from October 20th to 27th, and the bait collection time was on October 27th and October 28th. The specific sampling situations of fish and bait are shown in Table 1.

[0063] Table 1 Sampling Information of Fish and Bait

[0064]

[0065]

[0066] 4. Sampling Methods and Sampling Results:

[0067] 1) Fish Samples

[0068] Generally, the way to capture fish for scientific research is to use fishing boats for large-scale fishing. In this study, the fish samples were obtained by means such as gillnets and bottom cages using the local fishery administration boats. According to the feeding characteristics of fish, the collected fish samples were divided into carnivorous fish, omnivorous fish, filter-feeding fish, and herbivorous fish. A total of 151 fish samples of 33 species were collected in May and October 2022. Among them, there were 20 fish samples of 5 herbivorous fish species, 87 fish samples of 15 omnivorous fish species, 16 fish samples of 4 filter-feeding fish species, and 28 fish samples of 9 carnivorous fish species.

[0069] 2) Fish Bait

[0070] In this study, both endogenous organic matter and exogenous organic matter sources were selected as the research objects of fish bait structure. Among them, endogenous organic matter includes plankton and benthic organisms, and exogenous organic matter sources are composed of litter and soil debris. Six main types of fish bait, including zooplankton, benthic animals, phytoplankton, periphytic algae, litter, and soil debris, were collected in this example.

[0071] Refer to the "Technical Requirements for Aquatic Ecosystem Detection - Freshwater Zooplankton (Trial)" (China National Environmental Monitoring Centre, 2022) to collect zooplankton. Use a No. 25 plankton net (mesh diameter 0.064 mm, the net is conical, and there is an outlet switch piston at the bottom of the net). Move in an "∞" shape reciprocally at a speed of 0.2 - 0.3 m / s on the water surface at the sampling point shore. After dragging for about 30 s, lift it out of the water. At this time, the zooplankton has gathered at the bottom of the plankton net. Open the bottom piston and collect the zooplankton in a 50 ml polyethylene bottle for preservation.

[0072] Use a handheld D-shaped net (0.25 m × 0.25 m) to collect benthic animals at the bank of the sampling point. When sampling, the net mouth faces upstream. Vibrate the riverbed sediment with the legs to gather zooplankton into the net. After repeating 4 times, pass through a 60-mesh sieve, and use tweezers to pick out the macro-benthic animals and place them in a 50-ml polyethylene bottle. Finally, fix them with 95% alcohol and bring them back to the laboratory for pretreatment.

[0073] Refer to the "Technical Specifications for Freshwater Plankton Survey (SCT 9402-2010)" to collect phytoplankton. At each sampling point, collect 1000 ml of surface (0.5 m) water samples. Use a vacuum filtration machine to filter the water samples. What remains on the filter membrane is the preliminary phytoplankton sample.

[0074] When collecting attached algae, select several stones submerged in water. Use the brush head of a toothbrush to scrape the surface of the stones and flush them into a bucket with a wash bottle. Finally, pour the mixture in the bucket into a seal for preservation and bring it back to the laboratory for the next step of treatment.

[0075] When collecting litter, mainly collect the remaining dead branches and leaves, animal organs, herbaceous litter, and animal remains on the beach area, put them into a sealed bag and bring them back to the laboratory for the next step of treatment.

[0076] Detrital bait usually consists of silt, sand grains, and organic matter. When collecting detrital bait, it is necessary to collect soil at different elevations and different depths. The specific sampling process is to use a core sampler to take samples of surface (0-10 cm) and deep (10-20 cm) soil at different sampling points. To avoid accidental errors, multiple soil samples should be collected at the same elevation to avoid accidental errors. In this embodiment, 3 parallel samples of soil samples at the same elevation are taken immediately and put into a sealed bag for preservation, and then brought back to the laboratory for the next step of treatment.

[0077] For soil detrital bait, in this embodiment, soil detrital bait samples under different inundation conditions, different elevations, and different soil layers are sampled. The sampling points of soil detrital bait are as Figure 5 shown. The specific method for setting sampling points is as follows: Set one sampling point in the completely inundated area, without distinguishing between surface and deep soil, and define this point as GS; Set 3 sampling points in the partially inundated area. Starting from the place just inundated by water, each elevation interval of 1 m is a sampling point. At each sampling point, collect surface soil samples (0-10 cm) and deep soil samples (10-20 cm). The sampling points are defined as G1-1, G1-2, G2-1, G2-2, G3-1, G3-2 in sequence; Set one sampling point in the non-inundated area, without distinguishing between surface and deep soil, and define this point as GW. Finally, a total of 8 soil samples are collected at each sampling point, and a total of 32 soil samples are collected in a single sampling.

[0078] 5. Pretreatment method:

[0079] Fish dorsal muscle sample: Place the fish dorsal muscle sample in a petri dish, place it in a drying oven and dry it at a constant temperature of 60 °C for 48 h. Immediately after that, grind it and pass it through a 60-mesh sieve. Finally, put it into a sealed bag.

[0080] Plankton and benthos: First, pretreat a Whatman GF / F glass fiber filter membrane with a pore size of 0.45 μm and a diameter of 25 mm. Put the filter membrane into an aluminum box and calcine it in a muffle furnace at 450 °C for 3 h to remove organic matter. After the filter membrane cools down, put it into 1 mol / L concentrated hydrochloric acid and acidify it for 24 h. Then dry it in an oven at a constant temperature of 60 °C for 48 h. Use a vacuum filtration machine to draw water samples onto the pretreated filter membrane to initially obtain plankton and benthos samples. Finally, dry the above-filtered filter membrane in a constant temperature drying oven (60 °C) for 24 h, and put it into a vacuum bag filled with silica gel for drying and seal it for storage.

[0081] Benthic animals: Place the benthic animals selected with tweezers in a petri dish, wash them with distilled water, place them in a drying oven and dry them at a constant temperature of 50 °C for 24 h. Grind them and put them into a sealed bag.

[0082] Epiphytic algae: Place the epiphytic algae sample in a beaker. After the attached soil precipitates, use a vacuum filtration machine to filter the epiphytic algae sample onto a filter membrane. Dry the filter membrane at 60 °C for 24 h, and put it into a vacuum bag filled with silica gel for drying and seal it for storage.

[0083] Litter: Place the collected litter sample in an oven and dry it at 60 °C for 48 h, grind it into powder, pass it through a 60-mesh sieve, and finally put it into a sealed bag.

[0084] Organic detritus (soil): For soil samples, acid treatment is required before drying to eliminate the influence of inorganic carbon in the soil samples. Take an appropriate amount of ground and sieved soil sample in a small beaker, add 0.5 mol / L hydrochloric acid, and the reaction time should be 6 h. Stir it with a glass rod every 1 h to make it react fully to completely remove the inorganic carbon in the soil. Let it stand, and then pour out the supernatant; stir and wash it with distilled water, let it stand, and pour out the supernatant. Repeat this 3 - 4 times to remove the excess hydrochloric acid, and then dry it for standby. Place the acidified soil sample to be measured in a sampling dish, dry it in a drying oven at 70 °C for 48 h, grind it and pass it through a 60-mesh or 80-mesh sieve, and then put it into a sealed plastic bag and seal it.

[0085] As Figure 1 shown, in step S200, the carbon and nitrogen stable isotope analysis method is used to measure the collected fish and fish bait samples, and fish nutritional structure analysis including fish bait contribution rate calculation is carried out according to the measurement results.

[0086] The above steps specifically include: substituting the measured carbon and nitrogen stable isotope ratios of fish baits and fish into the Bayesian mixing model, using snails as the baseline organisms, and calculating the contribution rates of different fish baits to the different fish collected, including calculating the contribution rates of soil detritus baits collected under different inundation conditions.

[0087] In this embodiment, carbon and nitrogen stable isotope determinations were performed on the dorsal muscle samples of fish in May and October 2022. Carbon and nitrogen stable isotopes of six types of fish baits, namely zooplankton, benthos, phytoplankton, periphyton, litter, and soil detritus baits, were measured. According to the sampling situation and combined with the literature research situation, since snail organisms can reflect the spatio-temporal variation characteristics of fish baits, they can be selected as the baseline organisms. The measured δ 13 C of the snail organisms was -27.31‰, and δ 15 N was 8.60‰.

[0088] For the carbon and nitrogen stable isotope tests in this study, the instrument used to measure δ 13 C was the G2121-i Isotope and Gas Concentration Analyzer, and the instrument used to measure δ 15 N was the G5131-i Isotope and Gas Concentration Analyzer. The principle is that through combustion, C and N in the sample are converted into CO2 and N2O, and the concentrations of CO2 and N2O are measured to determine the contents of δ 13 C and δ 15 N in the sample. The precision of measuring δ 13 C in CO2 is better than 0.4‰ (typically 0.2–0.3‰), and the precision of measuring δ 15 N in N2O is better than 0.5‰. The carbon and nitrogen stable isotopes characterize a ratio, usually represented by δ, and the calculation formula is:

[0089] δX = [(R sample / R standard ) - 1] × 10 3 ‰

[0090] In the formula: X is 13 C or 15 N, R is 13 C / 12 C or 15 N / 14 N.

[0091] Carbon and nitrogen stable isotope analyses were respectively compared with the national standard materials PDB (Pee Dee Belnite) and the standard atmospheric carbon and nitrogen. The analysis results are expressed as δ 13 C and δ 15 N.

[0092] The carbon and nitrogen stable isotope model was first proposed by McKinney et al. (1950), and the basic equation of the model is as follows:

[0093] δ M = f1(δ1 + γ1) + f2(δ2 + γ2) + … + f n (δ n + γ n )

[0094] Where: δ M and δ i are the stable isotope values of the consumer (fish) and the food source (bait), respectively; f i is the contribution ratio of the i-th food source to the consumer, and γ i are the fractionation values between the i-th food source and its consumer, respectively.

[0095] The stable isotope model adopted in this embodiment is the Bayesian mixing model. The Bayesian mixing model, (Bayesian mixing models), is based on the mass-balance mixing model, uses statistical distributions to characterize the food source and consumer isotope values, and determines the probability distribution of the contribution ratio of the food source to the mixture within the Bayesian framework, fully estimating the uncertainty of the food source contribution.

[0096] In this embodiment, the Bayesian mixing model is created and run through Mix SIAR. Six types of fish baits, namely zooplankton, benthos, phytoplankton, epiphytic algae, litter, and detritus bait, are collected. At the same time, fish are classified according to herbivorous, omnivorous, filter-feeding, and carnivorous, and the contribution rates of various baits to fish with different feeding habits are calculated.

[0097] After calculation and analysis, the results are as follows: In May 2022, the contribution rates of various baits to herbivorous fish, from large to small, were periphytic algae, phytoplankton, zooplankton, benthic animals, litter, and detritus bait; in October 2022, the contribution rates, from large to small, were periphytic algae, phytoplankton, detritus bait, litter, zooplankton, and benthic animals. The bait intake of omnivorous fish was relatively uniform, and all six baits were ingested to varying degrees. There were obvious changes in the bait structure from May to October, with the intake of zooplankton and benthic animals decreasing significantly, while the intake of litter and detritus bait increasing significantly. In May, zooplankton and phytoplankton had the highest contribution rates to filter-feeding fish, while benthic animals and periphytic algae had relatively low contribution rates; in October, litter and detritus bait were the main food sources for filter-feeding fish, and the contribution rates of periphytic algae and benthic animals were relatively low. Zooplankton and benthic animals in May were the largest bait sources for carnivorous fish, while litter and detritus bait in October became the main baits for carnivorous fish, with contribution rates reaching 25% and 20% respectively.

[0098] In this embodiment, the contribution rates of soil detritus bait samples collected under different inundation conditions, different elevations, and different soil layers were also calculated. Specifically, one fish species with a relatively high intake of soil detritus bait was selected from each of the four types of fish with different feeding habits, and the contribution rates of soil detritus bait to different feeding habits of fish under different inundation conditions were calculated. In this embodiment, Megalobrama skolkovii was selected as the representative herbivorous fish, Carassius auratus as the representative omnivorous fish, Aristichthys nobilis as the representative filter-feeding fish, and Leiocassis longirostris as the representative carnivorous fish. The results of the contribution rates of detritus bait for representative fish with different feeding habits are shown in Table 2.

[0099] Table 2 Summary of Calculation Results of Contribution Rates of Detritus Bait (Unit: %)

[0100]

[0101] In this embodiment, the fish nutritional structure analysis further includes fish trophic level, fish food web structure, and fish community niche analysis.

[0102] 1) Fish trophic level

[0103] The fish trophic level (Trophic Level, TL) is an important indicator to measure the position of fish in the ecosystem and community structure, which characterizes the nutritional level of fish in the fish food web structure of the aquatic ecosystem, reflects the vertical structure relationship of different fish in the food web, and is an important indicator to characterize the characteristics of the fish nutritional structure.

[0104] The carbon and nitrogen stable isotope analysis method has been widely used in the study of fish trophic level. Its principle is to utilize 15 the continuous enrichment of N stable isotopes along the food web (chain), and according to different fish 15The enrichment degree of N stable isotopes determines the corresponding trophic level. As the food chain extends, the δ 13 C and δ 15 N also enrich along the food web. The enrichment degree of δ 13 C between consumers and their food sources is between 1‰ - 2‰. Therefore, the δ 13 C value of specific tissues of fish and the δ 13 C value of its bait are usually used to establish a food source contribution model to determine the contribution rate. The enrichment degree of δ 15 N between different consumers is about 3‰ - 4‰. Therefore, δ 15 N can generally be used to determine the trophic levels of different consumers. An important prerequisite for determining the trophic level of consumers is to reasonably select the δ 15 N enrichment degree and baseline organisms. The baseline organisms are generally the initial trophic level, and their trophic level is generally 1. In the actual research process, snail organisms are usually used as baseline organisms to calculate the trophic level. On the one hand, choosing snails to calculate the trophic level can make the calculation results more accurate. On the other hand, due to the large number of snails in the study river section, they are easily accessible and have strong representativeness.

[0105] Taking into account the advantages and disadvantages of different methods for calculating trophic levels, this embodiment selects the carbon and nitrogen stable isotope analysis method as the method for studying the trophic level of fish, and selects snails as the baseline organisms.

[0106] The trophic level calculation formula is as follows:

[0107] TL consumer =[(δ 15 N consumer -δ 15 N baseline ) / Δδ 15 N]+λ

[0108] In the formula: TL consumer represents the trophic level of the consumer (fish); δ 15 N consumer represents the nitrogen stable isotope ratio of the consumer (fish); δ 15 N baseline represents the nitrogen stable isotope ratio of the baseline organism. In this study, snails are selected as the baseline organism; Δδ 15 N represents the nitrogen stable isotope enrichment degree, which is generally taken as 3.4‰ in the freshwater ecosystem; λ is the trophic level of the baseline organism. If the baseline organism is primary productivity, λ takes the value of 1. If it is a primary consumer, λ takes the value of 2. Since the snail is a primary consumer, in this embodiment, λ takes the value of 2.

[0109] This embodiment calculates and analyzes the nutritional levels of typical fish in different periods from Xiangjiaba to Yibin sections of the Jinsha River. The trophic level calculation results are as Figure 6as shown

[0110] 2) Fish food web structure

[0111] The food web usually expresses the "predation - prey" relationship between different species. The study of the fish food web structure characterizes the temporal and spatial relationship between fish and their baits. Since the carbon and nitrogen stable isotopes integrate the relationships of fish nutritional levels and predation characteristics in multiple spaces and scales, in this embodiment, the carbon and nitrogen stable isotopes of fish and their baits are characterized in one graph to reflect the competitive or nutritional relationships between fish species and between fish and baits. The fish food web structures in the study reaches in May and October are as Figure 7 as shown

[0112] 3) Fish community niche

[0113] The niche is one of the simplest and clearest means to study community structure. The basic idea of studying community structure by niche is to combine the variation ranges of δ 13 C and δ 15 N of the fish community at different times with the temporal and spatial positions of the fish community in the aquatic ecosystem, and use the variations of δ 13 C and δ 15 N to characterize the connections and changes of the fish community. Generally, six quantitative indexes of isotope trophic structure are used to characterize the size of the community niche. These quantitative indexes have two main ecological significances. One is to characterize the niche breadth of the fish community, that is, to quantify the degree of occupation of space and bait resources by the fish community. The other is to characterize the degree of niche overlap within the community, which can quantify the competition intensity for different resources.

[0114] The six quantitative indexes of isotope trophic structure are respectively the range of δ 13 C (Range of δ 13 C, CR), the range of δ 15 N (Range of δ 15 N, NR), the total area of the convex hull (TA), the mean distance to centroid (CD), the mean nearest neighbor distance (MNND), and the standard deviation of nearest neighbor distance (SDNND). Their ecological significances are as follows:

[0115] (1) CR: The difference between the maximum and minimum values of δ 13 C in the fish community, which can generally be used to characterize the richness of the bait sources of the studied fish community;

[0116] (2) NR: The difference between the maximum and minimum values of δ 15 N in the fish community, which can generally be used to characterize the trophic length of the fish community;

[0117] (3) TA: The convex polygon of δ 13 C and δ 15 N enclosed by all fish, and the area of the convex polygon represents the total interval of the ecological niche of the fish community; 13 C-δ 15 N;

[0118] (4) CD: The average Euclidean distance between each sample point and the center point in the δ 13 C-δ 15 N two-dimensional diagram enclosed by all fish, which characterizes the average degree of the trophic structure diversity of the fish food web; 13 C-δ 15 N;

[0119] (5) MNND: The average value of the distances between each sample point and the nearest sample point in the δ 13 C-δ 15 N two-dimensional diagram enclosed by all fish, which characterizes the density of species aggregation in the community; 13 C-δ 15 N;

[0120] (6) SDNND: The standard deviation of the Euclidean distances between each sample point and the nearest sample point in the δ 13 C-δ 15 N two-dimensional diagram enclosed by all fish, which characterizes the evenness of species aggregation in the community. 13 C-δ 15 N;

[0121] Since the TA value is significantly affected by the sample size, the corrected standard ellipse area (SEAc) that corrects for the sample size is generally used to reflect the core ecological niche. The corrected TA value is less affected by the sample size and is relatively stable.

[0122] The calculations of all the above ecological niche indicators are obtained using the SIBER package based on the R.4.2.3 platform. The SIBER package is a program package under the R platform. By performing elliptical fitting on the carbon and nitrogen stable isotope data sets of the studied community to establish a mixing model, it realizes the purpose of comparing the isotope differences between two communities, and its research results characterize the breadth of the ecological space of the community and the degree of species aggregation.

[0123] In this embodiment, the SIBER pakage in R language is used to calculate the isotope indexes of the fish community collected in both May and October and analyze the results of the fish community niche. The quantitative indexes of the fish community isotopes are shown in Table 3, and the niche structure diagram of the fish community is as Figure 8 shown. As can be seen from the table, except that the range of δ 13 C (CR) shows that October is greater than May, the range of δ 15 N (NR), total niche space (TA), average centroid distance (CD), standard deviation of nearest neighbor distance (SDNND), and average nearest neighbor distance (MNND) of the fish community all show that May is significantly greater than October.

[0124] Table 3 Quantitative indexes of fish community isotopes

[0125]

[0126] In this embodiment, by analyzing the nutritional structure characteristics of the fish community, the results are summarized as follows: The trophic level of the fish community in October is significantly lower than that in May, and the change range of the trophic level of the fish community in October is smaller; Carnivorous fish are at the top of the food web in both May and October, but their nutritional level decreases slightly in October. The degree of ecological space overlap of omnivorous fish in October is much higher than that in May, indicating that their competitiveness is stronger in October. Filter-feeding fish and herbivorous fish are far from the core aggregation area, and the community competition is small; The niche characteristics of the fish community show that except that the range of δ13C (CR) shows that October is greater than May, the range of δ15N (NR), total niche space (TA), average centroid distance (CD), standard deviation of nearest neighbor distance (SDNND), and average nearest neighbor distance (MNND) of the fish community all show that May is significantly greater than October, which indicates that the niche breadth of the fish community in May is significantly greater than that in October; The contribution rate characteristics of soil detritus bait show that the contribution rate is the largest under partial inundation conditions, followed by complete inundation, and the smallest under non-inundation.

[0127] As Figure 1 shown, in step S300, hydrodynamic simulation is carried out on the target river section, and redundancy analysis method is used to analyze the correlation between the fish bait contribution rate and hydrodynamic factors to obtain the key hydrodynamic factors affecting the fish bait contribution rate.

[0128] The above steps specifically include: dividing the target river section into multiple cross-sections, setting boundary conditions and parameter rates, and constructing a longitudinal one-dimensional hydrodynamic model to carry out hydrodynamic simulation on the target river section.

[0129] This embodiment aims to explore the response law of fish bait structure to changes in hydrodynamic conditions. In this chapter, hydrodynamic condition simulation is carried out on the section from Xiangjiaba to Yibin of the Jinsha River, and key inundation factors (hydrodynamic factors) are extracted. Combining with the research results of fish bait structure and the contribution rate of soil debris bait in the previous text, redundancy analysis is used to explore the impact of hydrodynamic conditions on fish bait and the correlation between soil debris bait and hydrodynamic conditions.

[0130] Longitudinal one-dimensional hydraulic model:

[0131] Continuity equation:

[0132]

[0133] Momentum equation:

[0134]

[0135] In the formula: A is the cross-sectional area of the water passage, with the unit of m 2 ; t is the time, with the unit of s; Q is the flow rate, with the unit of m 3 / s; x is the longitudinal coordinate, with the unit of m; q is the tributary flow rate, with the unit of m 2 / s; v is the average cross-sectional velocity, with the unit of m / s; g is the acceleration due to gravity, with the unit of m 2 / s; z is the average cross-sectional water level, with the unit of m; S f is the friction slope; q l is the tributary inflow per unit river length, with the unit of m 2 / s;

[0136] The hydrodynamic simulation results include changes in water level, flow rate, water depth, flow velocity, and water surface width, and the hydrodynamic simulation results are verified by combining with the measured data.

[0137] 1) Inundation condition simulation

[0138] Using a one-dimensional unsteady flow hydraulic model, the section from downstream of Xiangjiaba Dam to Yibin of the Jinsha River is divided into 16 sections.

[0139] 2) Boundary condition setting

[0140] Referring to the measured flow rate data of the river hydrological station downstream of Xiangjiaba Dam, the boundary condition at the upstream of the model is set as the flow rate process. Combining the elevation difference between the river bottom of the downstream hydrological station section and Section 1# and referring to the measured water level data, the water level condition at the downstream boundary of the model is set.

[0141] 3) Parameter calibration

[0142] The parameters of the channel roughness are calibrated using the daily water level process of the Xiangjiaba Hydrological Station (2 km downstream of the Xiangjiaba Dam) in 2021. When the channel roughness is 0.032, the water level simulation results are as Figure 9 shown. The mean absolute error between the simulated water level and the measured water level is 0.27 m, meeting the simulation requirements.

[0143] In this embodiment, a longitudinal one-dimensional hydrodynamic model is used to simulate the hydrodynamic process of the research reach from April 15, 2022 to October 30, 2022. The simulation results include the water level, flow rate, water depth, flow velocity, and water surface width changes at different cross-sections (L1, H1, R1, R2).

[0144] In this embodiment, the above steps further include: determining the hydrodynamic factors with inundation characteristics, including the mean flow rate, flow peak, flow rate change rate, mean flow velocity, mean water depth, inundation frequency, and inundation rate during a preset statistical period, and calculating the values of the hydrodynamic factors with inundation characteristics according to the hydrodynamic simulation results.

[0145] The periodic inundation of the floodplain by the river is an important way of material exchange between the river and the outside world. Reasonably describing the inundation process is an important prerequisite for exploring the impact of hydrodynamic conditions on the fish bait structure. Existing research shows that the recovery period of the benthic animal community structure is 30 days. Therefore, the longest research duration in this embodiment is set to 30 days before the sampling date. According to the simulation results, the hydrodynamic conditions change greatly within 30 days before the sampling date. Therefore, the research duration is further refined to 7 days, 5 days, and 3 days before the sampling date. The hydrodynamic factors characterizing the inundation characteristics are shown in Table 4.

[0146] Table 4 Hydrodynamic factors characterizing inundation characteristics and their ecological significance

[0147]

[0148] Based on the above hydrodynamic simulation results of the downstream reach of Xiangjiaba and in combination with the selection of inundation factors (hydrodynamic factors characterizing inundation characteristics), this embodiment extracts the key inundation factors at different cross-sections and different time periods respectively. Specifically, taking 3 days, 5 days, 7 days, and 30 days before the sampling date as the statistical periods, the statistical variables include key inundation factors such as the mean flow rate, flow peak, flow rate change rate, mean flow velocity, mean water depth, inundation frequency, and inundation rate during the above periods.

[0149] In this embodiment, the redundancy analysis is performed on the soil debris bait contribution rates at different elevations and different soil layers under partial inundation conditions and the hydrodynamic factors with inundation characteristics respectively to obtain the extremely significant, significant influencing factors, and positive and negative correlation factors affecting the fish bait contribution rate.

[0150] Redundancy analysis (RDA) is a common analysis method in statistics for analyzing the correlation between response variables (usually biological species data) and explanatory variables (usually environmental variables). Its basic principle is to determine the matrix relationship of multiple linear regression between response variables and explanatory variables, and at the same time reduce the variable dimension to intuitively display the correlation relationship in a two-dimensional plane. Since redundancy analysis can very intuitively and clearly display the correlation between response variables and explanatory variables, it has been widely used in the field of ecology. Redundancy analysis effectively simplifies the number of target variables through eigenvalue decomposition and screening, and finally realizes reflecting the relationship between response variables and explanatory variables under the same coordinate axis, so as to obtain the relationship between response variables and explanatory variables. In addition, redundancy analysis can also rank the contribution rates of multiple explanatory variables, so as to quantitatively analyze the influence degree of different explanatory variables on response variables. In this embodiment, redundancy analysis is used as the basic research method to obtain the correlation between fish bait structure and hydrodynamic conditions at sampling points, and find the most important influencing factors affecting fish bait structure and the contribution rate of detrital bait.

[0151] In this embodiment, the correlation analysis between the contribution rate of detrital bait and different inundation factors is carried out, including the RDA analysis of the contribution rate of detrital bait at different elevations under partial inundation conditions and the inundation factors, and the RDA analysis of the contribution rate of detrital bait in different soil layers and the inundation factors, so as to achieve the purpose of screening key inundation factors. The RDA test and analysis results are as follows:

[0152] (1) RDA analysis of the contribution rate of detrital bait at different elevations and inundation factors

[0153] Under partial inundation conditions, the RDA test results of the contribution rate of detrital bait at different elevations and inundation factors are shown in Table 5. The 30-day flow rise rate and 30-day inundation frequency are extremely significant influencing factors, and their explanation rates reach 57.1% and 18.9% respectively. The 7-day inundation rate and 30-day flow peak are significant influencing factors, and their explanation rates are 1.4% and 0.7% respectively. The RDA test results show that long-term flow scouring and inundation have a greater impact on the contribution rate of detrital bait at different elevations.

[0154] Table 5 RDA test results of the contribution rate of detrital bait at different elevations and inundation factors

[0155]

[0156] ** is extremely significantly correlated (p < 0.01), * is significantly correlated (0.01 < P < 0.05)

[0157] The RDA analysis results of the contribution rate of detrital bait and inundation factors under different elevation conditions are as Figure 10As shown in the figure. For cross-sections G1 and G2, the contribution rate of detrital bait is positively correlated with the peak flow in 30 days and the inundation frequency in 30 days, and negatively correlated with the flow rise rate in 30 days and the inundation rate in 7 days. The situation of G3 is just the opposite of that of G1 and G2. It is considered that G3 is more difficult to be inundated compared with G1 and G2, and has a higher requirement for the flow rise. Therefore, the long-term flow rise rate is the most important factor affecting its contribution rate.

[0158] (2) RDA analysis of the contribution rate of detrital bait in different soil layers and inundation factors

[0159] The RDA test results of the contribution rate of detrital bait in different soil layers and inundation factors are shown in Table 6. The flow rise rate in 30 days is an extremely significant influencing factor, with an explanatory rate of 58.6%, and the inundation frequency in 7 days is a significant influencing factor, with an explanatory rate of 14.8%. The RDA test results indicate that the long-term flow rise and the long-term inundation frequency are the most important factors affecting the contribution rate of detrital bait in different soil layers.

[0160] Table 6 RDA test results of the contribution rate of detrital bait in different soil layers and inundation factors

[0161]

[0162] ** indicates extremely significant correlation (p < 0.01), and * indicates significant correlation (0.01 < P < 0.05)

[0163] The analysis results of the contribution rate of detrital bait in different soil layers and RDA of inundation factors are as Figure 11 shown. The contribution rate of detrital bait in the surface soil is negatively correlated with both the flow rise rate in 30 days and the inundation rate in 7 days, while that in the deep soil is positively correlated. It is considered that with the continuous flow rise and inundation process, the detrital bait in the deep soil is gradually washed into the river, and the detrital bait in the river channel changes from mainly surface soil detrital bait before inundation to both surface soil and deep soil. Therefore, with the continuous scouring after the water level rises, the contribution rate of the deep soil increases.

[0164] Corresponding to the method for analyzing the correlation between fish nutritional structure and hydrodynamic conditions disclosed above, the embodiment of the present invention also discloses a system for analyzing the correlation between fish nutritional structure and hydrodynamic conditions, as Figure 12 shown, which specifically includes:

[0165] A sample collection module for sampling and preprocessing fish and fish bait samples at sampling points preset in the target river section;

[0166] An isotope analysis module for measuring the collected fish and fish bait samples by carbon and nitrogen stable isotope analysis method, and performing fish nutritional structure analysis including calculating the contribution rate of fish bait according to the measurement results;

[0167] A correlation analysis module is used to perform hydrodynamic simulation on a target river section, and adopt a redundancy analysis method to analyze the correlation between the fish bait contribution rate and hydrodynamic factors, so as to obtain the key hydrodynamic factors affecting the fish bait contribution rate.

[0168] It should be noted that the detailed description of a fish nutritional structure and hydrodynamic condition correlation analysis system provided in an embodiment of the present invention can refer to the relevant description of a fish nutritional structure and hydrodynamic condition correlation analysis method provided in an embodiment of the present application, which will not be elaborated here.

[0169] In addition, an embodiment of the present invention also provides an electronic device, which includes: a processor and a memory; the memory is used to store one or more program instructions; the processor is used to run one or more program instructions to execute the steps of a fish nutritional structure and hydrodynamic condition correlation analysis method as described in any one of the above.

[0170] It should be noted that the detailed description of an electronic device provided in an embodiment of the present invention can refer to the relevant description of a fish nutritional structure and hydrodynamic condition correlation analysis method provided in an embodiment of the present application, which will not be elaborated here.

[0171] In addition, an embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of a fish nutritional structure and hydrodynamic condition correlation analysis method as described in any one of the above.

[0172] It should be noted that the detailed description of a computer-readable storage medium provided in an embodiment of the present invention can refer to the relevant description of a fish nutritional structure and hydrodynamic condition correlation analysis method provided in an embodiment of the present application, which will not be elaborated here.

[0173] Those skilled in the art can understand that all or part of the functions of the various methods in the above embodiments can be implemented in a hardware manner or in a computer program manner. When all or part of the functions in the above embodiments are implemented in a computer program manner, the program can be stored in a computer-readable storage medium, which may include: read-only memory, random access memory, magnetic disks, optical disks, hard disks, etc. The above functions can be realized by a computer executing the program. For example, the program is stored in the memory of the device, and when the processor executes the program in the memory, the above all or part of the functions can be realized. In addition, when all or part of the functions in the above embodiments are implemented in a computer program manner, the program can also be stored in a storage medium such as a server, another computer, magnetic disk, optical disk, flash drive or mobile hard disk, and saved to the memory of the local device by downloading or copying, or the system of the local device is updated. When the processor executes the program in the memory, all or part of the functions in the above embodiments can be realized.

[0174] The above uses specific examples to elaborate on the present invention, which is only used to help understand the present invention and is not intended to limit the present invention. For those skilled in the art of the present invention, according to the idea of the present invention, several simple deductions, deformations or substitutions can also be made.

Claims

1. A method for analyzing the correlation between fish nutritional structure and hydrodynamic conditions, characterized in that: The method comprises: Sampling and pre-processing of fish and fish bait samples at sampling points pre-set in the target river section; The collected fish and fish bait samples were analyzed by carbon and nitrogen stable isotope analysis, and the fish nutritional structure analysis including the calculation of fish bait contribution rate was carried out based on the measurement results; The hydrodynamic simulation of the target river section was carried out, and the redundancy analysis method was used to analyze the correlation between the fish food contribution rate and the hydrodynamic factors to obtain the key hydrodynamic factors affecting the fish food contribution rate.

2. A method for analyzing the correlation between fish nutritional structure and hydrodynamic conditions according to claim 1, characterized in that: Fish and fish bait samples are collected and pre-processed at the sampling points pre-set in the target river section, including: The fish samples collected include carnivorous fish, omnivorous fish, filter-feeding fish and herbivorous fish; the fish bait samples collected include zooplankton, benthic animals, phytoplankton, attached algae, litter and soil debris.

3. A method for analyzing the correlation between fish nutritional structure and hydrodynamic conditions as claimed in claim 2, characterized in that: Fish and fish bait samples are collected and pre-processed at the sampling points pre-set in the target river section, including: Multiple sampling points were set up at different sections within the target river section, and multiple sampling was carried out on different dates during the flood season and the dry season, including sampling of soil debris bait samples at different elevations and soil layers under fully flooded, partially flooded and non-flooded conditions, as well as under partially flooded conditions.

4. A method for analyzing the correlation between fish nutritional structure and hydrodynamic conditions as claimed in claim 2, characterized in that: Fish and fish bait samples are collected and pre-processed at the sampling points pre-set in the target river section, including: For fish samples: Take fish back muscle samples and place them in a culture dish, then place them in a drying oven to dry at a constant temperature, grind them, sieve them, and finally put them into a sealed bag; For phytoplankton samples: first put the glass fiber filter membrane into an aluminum box, burn it in a muffle furnace to remove organic matter, put it into concentrated hydrochloric acid for acidification after cooling, and then dry it in a constant temperature oven. Use a vacuum filter to extract the pre-collected water sample onto the treated filter membrane to obtain a preliminary phytoplankton sample. Finally, dry the filtered filter membrane in a constant temperature drying oven, and put it into a vacuum bag filled with silica gel for sealing and preservation; For benthic animal samples: Place the benthic animals selected with tweezers in a petri dish, wash with distilled water, place in a drying oven to dry at a constant temperature, grind into powder and put into a sealed bag; For algae samples: place the algae samples in a beaker, and after the attached soil has settled, use a vacuum filter to filter the algae samples onto a filter membrane, dry the filter membrane, and seal and store it in a vacuum bag filled with silica gel; For litter samples: place the collected litter samples in an oven to dry, grind them into powder, sieve them, and finally put them into sealed bags; For soil debris samples: take an appropriate amount of ground and sieved soil sample into a small beaker, add hydrochloric acid to react for a period of time, stir it with a glass rod every preset time to allow it to react fully to completely remove the inorganic carbon in the soil, let it stand, and then pour out the supernatant; stir and wash with dedistilled water, let it stand, pour out the supernatant, repeat several times to remove excess hydrochloric acid, then dry it for use, put the acidified sample in a sampling dish, put it in a drying oven to dry, grind and sieve it, put it into a sealed plastic bag and seal it.

5. A method for analyzing the correlation between fish nutritional structure and hydrodynamic conditions according to claim 1, characterized in that: According to the test results, the fish nutrition structure analysis including the calculation of fish feed contribution rate is carried out, including: The measured carbon and nitrogen stable isotope ratios of fish bait and fish were entered into the Bayesian mixed model. Snails were used as baseline organisms to calculate the contribution rate of different fish baits to the different fish collected, including the contribution rate of soil debris bait collected under different flooding conditions.

6. A method for analyzing the correlation between fish nutritional structure and hydrodynamic conditions according to claim 1, characterized in that: The collected fish and fish feed samples were measured using carbon and nitrogen stable isotope analysis, and the fish nutritional structure analysis including the calculation of fish feed contribution rate was carried out based on the measurement results, including: The fish nutritional structure analysis also includes fish nutritional level, fish food web structure and fish community niche analysis.

7. A method for analyzing the correlation between fish nutritional structure and hydrodynamic conditions as claimed in claim 1, characterized in that: Conduct hydrodynamic simulation on the target river section, including: The target river section is divided into multiple sections, boundary conditions and parameter ratios are set, and a longitudinal one-dimensional hydrodynamic model is constructed to simulate the hydrodynamics of the target river section: Continuity equation: Momentum equation: Where: A is the cross-sectional area of ​​the water flow, in m 2 ; t is time, unit is s; Q is flow rate, unit is m 3 / s; x is the vertical coordinate, unit is m; q is the tributary flow, unit is m 2 / s; v is the average flow velocity of the cross section, in m / s; g is the acceleration due to gravity, in m 2 / s; z is the average water level of the section, in m; S f is the friction ratio; q l is the tributary inflow per unit river length, in m 2 / s; The hydrodynamic simulation results include changes in water level, flow, water depth, flow velocity and water surface width, and are verified in combination with measured data.

8. A method for analyzing the correlation between fish nutritional structure and hydrodynamic conditions as claimed in claim 1, characterized in that: The redundancy analysis method was used to conduct a correlation analysis between the fish feed contribution rate and the hydrodynamic factors to obtain the key hydrodynamic factors that affect the fish feed contribution rate, including: Determine the hydrodynamic factors with flooding characteristics, including the mean flow, peak flow, flow change rate, mean flow velocity, mean water depth, flooding frequency and flooding rate during the preset statistical period, and calculate the hydrodynamic factor values ​​with flooding characteristics based on the hydrodynamic simulation results.

9. A method for analyzing the correlation between fish nutritional structure and hydrodynamic conditions according to claim 8, characterized in that: The redundancy analysis method was used to conduct a correlation analysis between the fish feed contribution rate and the hydrodynamic factors to obtain the key hydrodynamic factors that affect the fish feed contribution rate, including: The contribution rates of soil debris bait at different elevations and soil layers under partial flooding conditions were subjected to redundancy analysis with hydrodynamic factors with flooding characteristics to obtain extremely significant and significant influencing factors as well as positive and negative correlation factors affecting the contribution rate of fish bait.

10. A fish nutrition structure and hydrodynamic conditions correlation analysis system, characterized in that: The system comprises: The sample collection module is used to collect and pre-process fish and fish bait samples at sampling points pre-set in the target river section; The isotope analysis module is used to measure the collected fish and fish bait samples using the carbon and nitrogen stable isotope analysis method, and to perform fish nutritional structure analysis including calculation of fish bait contribution rate based on the measurement results; The correlation analysis module is used to perform hydrodynamic simulation on the target river section and use the redundancy analysis method to perform correlation analysis on the fish food contribution rate and hydrodynamic factors to obtain the key hydrodynamic factors that affect the fish food contribution rate.

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