A food web evolution analysis method based on a lake water ecological-food web model
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-06-24
- Publication Date
- 2026-08-11
AI Technical Summary
然而，现有长期观测资料有限或缺失，尤其是涉及湖泊水生生物等关键生态变量的连续观测资料不足，这给湖泊食物网模型的历史重建带来了极大挑战
[0011]本发明的有益效果为：本发明基于改进的湖泊水生态模型，对湖泊水质及水生生物要素进行模拟与率定，并将湖泊水生态模型的输出结果作为食物网模型的输入数据，构建湖泊食物网模型，从而分析湖泊历史食物网的演变规律。研究成果可为研究湖泊区域资源管理、水生生物保护以及湖泊生态系统健康发展提供科学依据与理论支持。有效解决了当前湖泊生态研究中观测数据不足的问题，实现了数据缺失条件下湖泊历史食物网演变过程的定量分析。
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Abstract
Description
Technical Field
[0001] This invention relates to the field of food web evolution analysis, and specifically to a food web evolution analysis method based on a lake aquatic ecosystem-food web model. Background Technology
[0002] Lakes are an important component of natural ecosystems. As key water resource carriers, lakes play a vital role in flood control, water purification, wetland protection, and maintaining biodiversity. Over the past few decades, human activities have intensified, and the morphology of lakes and their relationships with other waterways have undergone significant changes due to multiple human activities, including land reclamation, fisheries development, and the construction of large-scale water conservancy projects. The frequency and intensity of regional watershed drought events have increased significantly, and water levels have continued to decline during the dry season. Rising temperatures and decreasing precipitation have led to more frequent extreme hydrological events such as lake floods and droughts. Against this backdrop, conducting simulation analyses of lake habitats, organisms, and food webs is of significant scientific and practical value for systematically revealing the ecological response mechanisms of lakes under multiple stresses, quantifying the characteristics of ecosystem evolution under different scenarios, and providing theoretical basis and decision-making support for lake ecological protection and management.
[0003] Current trends in lake model research mainly focus on two aspects: First, optimizing the model's structure by improving its mechanism settings, parameter adjustments, and module functions to enhance flexibility and simulation accuracy, adapting to the simulation needs of different lake types and complex environmental conditions. Second, constructing coupled models, such as hydrological-aquatic ecosystem models and aquatic ecosystem-food web models, combining the advantages of different models to achieve comprehensive simulation and system evaluation of physical, chemical, and biological processes within lake systems.
[0004] In lake ecosystem analysis, historical ecological evolution is crucial for understanding long-term lake patterns and assessing the impacts of environmental disturbances. However, existing long-term observational data is limited or lacking, particularly continuous observational data concerning key ecological variables such as lake aquatic organisms. This poses a significant challenge to the historical reconstruction of lake food web models. Therefore, how to reasonably infer the state of lake food webs in historical periods and conduct quantitative analysis in the absence of data remains an urgent problem to be solved in the field of lake ecological modeling research. Summary of the Invention
[0005] To address the aforementioned shortcomings of existing technologies, this invention provides a method for analyzing food web evolution based on a lake aquatic ecosystem-food web model.
[0006] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows: A method for analyzing food web evolution based on a lake aquatic ecosystem-food web model is provided, which includes the following steps: S1: Collect meteorological data, hydrological data, water quality data, biological data, and inflow data of the lake area, and preprocess them to obtain a preprocessed dataset; S2: Construct the lake water ecology model WET. The input data required for the lake water ecology model WET includes preprocessed datasets and model calibration data. The model calibration data is used as the target variable to simulate the lake water ecology model WET, adjust the parameters of the target variable, and output the simulation results. S3: Based on the simulation results of the lake water ecological model WET, daily biomass data of various organisms in the biological functional groups are extracted and converted into area biomass data; the Ecopath model is used as the lake food web model, and the lake food web model is balanced and adjusted to ensure that the ecosystem energy represented by the lake food web model is conserved. S4: Based on the energy conservation model of the lake food web, the simulation results of the lake food web model are derived, the changes in total flow of each trophic level in the lake food web are obtained, and the changes in energy transfer efficiency of the lake food web at different periods are analyzed.
[0007] Furthermore, the specific method for constructing the WET lake water ecological model is as follows: S21: Configure the food web in the lake water ecological model WET, divide the aquatic organisms in the lake into different biological functional groups, including four fish groups with different diets, two zooplankton, three phytoplankton, benthic animals and macroaquatic plants, and set the different biological functional groups according to their diets in the configuration file fabm.yaml of the lake water ecological model WET, and generate the energy transfer relationship between the different biological functional groups. S22: Calibrate and validate the lake water ecology model WET. Based on the Pasac automatic calibration program and using the stepwise calibration method, adjust the parameters of various target variables in the order of physical-chemical-biological, simulate the lake water ecology model WET, and output the simulation results of the lake water ecology model WET.
[0008] Further, step S3 includes: S31: Based on the simulation results of the lake water ecology model WET, daily biomass data of fish groups with different diets, two zooplankton species, three phytoplankton species, benthic animals and macroaquatic plants are extracted, and the daily biomass data are converted into area biomass data, and then converted into annual biomass data by multi-day averaging. S32: Calculating detrital biomass data using primary productivity and water transparency in lake aquatic ecosystems. ; ; in, As the primary productive force of biological functional groups, Average transparency; S33: Using the Ecopath model as a lake food web model to calculate detrital biomass data. The P / B coefficient and Q / B coefficient of each biological functional group are calculated by summing the annual biomass data and then adjusting the lake food web model based on the food matrix DC to ensure the energy conservation of the ecosystem represented by the lake food web model.
[0009] Furthermore, the P / B coefficient represents the primary productivity of each biological functional group. Divide by detrital biomass data The Q / B coefficient, summed with annual biomass data, represents the prey load for each biological functional group. Divide by detrital biomass data The sum of annual biomass data.
[0010] Furthermore, the balance adjustment of the lake food web model is based on the principle of energy input and output balance in the ecosystem, and the ecological efficiency coefficients of each biological functional group are used. Ee Ecological efficiency coefficient is an indicator used to determine the energy balance between energy input and output in an ecosystem. Ee Indicates the prey volume of each biological functional group Fishing volume Other output losses The sum of production volume The ratio; ; in, g As the numbering of biological functional groups, For the first g Ecological efficiency coefficient of each biological functional group; When performing equilibrium adjustments on a lake food web model, the energy balance constraints that need to be satisfied are: ; ; in, For the first g Respiratory volume of each biological functional group For the first g The assimilation amount of each biological functional group.
[0011] The beneficial effects of this invention are as follows: Based on an improved lake aquatic ecosystem model, this invention simulates and calibrates lake water quality and aquatic biological elements, and uses the output results of the lake aquatic ecosystem model as input data for a food web model to construct a lake food web model, thereby analyzing the evolutionary patterns of historical lake food webs. The research results can provide a scientific basis and theoretical support for research on lake regional resource management, aquatic life protection, and the healthy development of lake ecosystems. It effectively solves the problem of insufficient observational data in current lake ecological research, and realizes quantitative analysis of the evolutionary process of historical lake food webs under conditions of data deficiency. Attached Figure Description
[0012] Figure 1 This is a generalized diagram of the WET lake water ecology model.
[0013] Figure 2 The flowchart for modeling the WET (West Lake Ecosystem) model. Figure 3 This is a schematic diagram of the Poyang Lake basin.
[0014] Figure 4 The figure shows the simulation results of the WET lake aquatic ecosystem model for water temperature, dissolved oxygen, and chlorophyll.
[0015] Figure 5 Figure 1 shows the simulation results of the WET model for total phosphorus, total nitrogen, and fish biomass in a lake.
[0016] Figure 6 This is a diagram of the food web structure of Poyang Lake from 1990 to 2004.
[0017] Figure 7 This is a diagram of the Poyang Lake food web structure from 2005 to 2017.
[0018] Figure 8 This is a diagram of the Poyang Lake food web structure for 2018-2019.
[0019] Figure 9 This is a diagram of the Poyang Lake food web structure from 2020 to 2022. Figure 10 This is a graph showing the changes in the total flow transfer efficiency of the Poyang Lake ecosystem at different times.
[0020] Figure 11 This is a graph showing the aggregated trophic level energy flow in Poyang Lake from 1990 to 2004.
[0021] Figure 12 This is a graph showing the aggregated trophic level energy flow in Poyang Lake from 2005 to 2017.
[0022] Figure 13 This is a graph showing the aggregated trophic level energy flow in Poyang Lake from 2018 to 2019.
[0023] Figure 14 This is a graph showing the aggregated trophic level energy flow in Poyang Lake from 2020 to 2022. Detailed Implementation
[0024] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.
[0025] Example 1 A method for analyzing food web evolution based on a lake aquatic ecosystem-food web model includes the following steps: S1: Collect meteorological data, hydrological data, water quality data, biological data, and inflow data of the lake area, and preprocess them to obtain a preprocessed dataset; In this embodiment, the method for preprocessing meteorological data, hydrological data, water quality data, biological data, and lake runoff data specifically includes the following steps: For meteorological data, the temperature, wind speed, air pressure and humidity data collected from multiple stations in the lake area are averaged using the arithmetic mean method. The averaging method for temperature data is as follows, and the averaging method for other meteorological data is the same. ; in: The average temperature (°C) of the area where the lake is located over a certain period of time. The first in the area where the lake is located i Temperature records from each weather station, i The weather station number (°C) is used. n The number of weather stations in the area where the lake is located. The first in the area where the lake is located n Temperatures recorded at each weather station.
[0026] Regarding the hydrological data, which includes water level data, only the water level sequence of Xingzi Hydrological Station is used as the representative water level of the entire lake; For the inflow data into the lake, the daily flow series of the hydrological stations corresponding to all the tributaries flowing into the lake are summed to obtain the total inflow data of the lake. (m) 3 / s); ; in, u The numbers are assigned to the tributaries flowing into the lake. U The number of tributaries flowing into the lake, For the first u The inflow rate (m³) recorded by the hydrological station on the tributary where the tributary flows into the lake. 3 / s).
[0027] For water quality data, the model accepts total phosphorus and total nitrogen data as input. The water quality data of each tributary flowing into the lake are weighted and averaged according to the corresponding inflow rate to form the lake's water quality data input sequence. Taking total phosphorus data as an example (the calculation method for total nitrogen data is the same as that for total phosphorus data). ; in, The total phosphorus concentration (mg / L) of the tributaries flowing into the lake. For the first u The phosphorus concentration (mg / L) recorded at the hydrological station of the tributary where the tributary flows into the lake. For the first U The phosphorus concentration (mg / L) recorded at the hydrological station of the tributary where the tributary flows into the lake.
[0028] This embodiment uses Poyang Lake as an example to construct a lake water ecological model (WET). A schematic diagram of the Poyang Lake basin is shown below. Figure 3 As shown in Table 1, the collected meteorological data, hydrological data, water quality data, and biological data are as follows; Table 1 Data used in the research
[0029] S2: Construct the lake water ecology model WET. The input data required for the lake water ecology model WET includes preprocessed datasets and model calibration data. The model calibration data is used as the target variable to simulate the lake water ecology model WET, adjust the parameters of the target variable, and output the simulation results. Model calibration data includes two categories: water quality calibration data and biological calibration data. The water quality calibration data consists of water quality data such as water temperature, dissolved oxygen, nitrogen, and phosphorus measured by monitoring stations in the lake area. The biological calibration data consists of historical fish biomass data of the lake. All of the above data are based on daily scale data.
[0030] The Lake Ecosystem Model (WET) encompasses multiple ecological modules, including fish, zooplankton, phytoplankton, benthic animals, and abiotic components of water and sediment. These modules can be customized based on the characteristics of the study area rather than being configured as a fixed food web.
[0031] S3: Based on the simulation results of the lake water ecological model WET, daily biomass data of various organisms in the biological functional group are extracted and converted into area biomass data; the Ecopath model is used as the lake food web model, and the lake food web model is balanced and adjusted to ensure that the ecosystem energy represented by the lake food web model is conserved.
[0032] Step S3 specifically includes: S31: Based on the simulation results of the lake water ecology model WET, daily biomass data of fish groups with different diets, two zooplankton species, three phytoplankton species, benthic animals and macroaquatic plants are extracted, and the daily biomass data are converted into area biomass data, and then converted into annual biomass data by multi-day averaging. Since daily biomass data are mostly expressed as concentration per unit volume (mg / L), a conversion between volume and area units is necessary. Multiply the daily biomass data by the multi-year average water level during the study period. Area biomass data in t / km² were obtained. B 1; ; in, C This is daily biomass data.
[0033] S32: Calculating detrital biomass data using primary productivity and water transparency in lake aquatic ecosystems. ; ; in, This represents the detrital biomass data (t / km²) of various organisms in the biological functional group. Primary productivity of biological functional groups (tC / km² / y), i.e., phytoplankton production. P This is expressed as the amount of carbon produced per km² in a year. Average transparency (m); S33: Using the Ecopath model as a lake food web model to calculate detrital biomass data. The P / B coefficient and Q / B coefficient of each biological functional group are calculated by summing the annual biomass data and then adjusting the lake food web model based on the food matrix DC to ensure the energy conservation of the ecosystem represented by the lake food web model.
[0034] The P / B coefficient represents the primary productivity of each biological functional group. Divide by detrital biomass data The Q / B coefficient, summed with annual biomass data, represents the prey load for each biological functional group. Divide by detrital biomass data The sum of annual biomass data.
[0035] In this embodiment, the input parameters of the Poyang Lake Ecopath model are shown in Table 2 below; Table 2 Input parameters of the Poyang Lake Ecopath model
[0036] In this embodiment, the data composition of the initial food matrix DC of the Poyang Lake Ecopath model is shown in Table 3 below. In Table 3, the vertical axis represents the prey and the horizontal axis represents the predators. Table 3. Data Composition of the Initial Food Matrix (DC) in the Poyang Lake Ecopath Model
[0037] The equilibrium adjustment of the lake food web model is based on the principle of conservation of energy input and output in the ecosystem, where the ecological efficiency coefficients of each biological functional group are considered. Ee Ecological efficiency is a core indicator for measuring whether a model has achieved energy balance. Ecological efficiency coefficient Ee Indicates the prey volume of each biological functional group Fishing volume Other output losses The sum of production volume The ratio; ; in, g As the numbering of biological functional groups, For the first g Ecological efficiency coefficient of each biological functional group; When performing equilibrium adjustments on a lake food web model, the energy balance constraints that need to be satisfied are: ; ; in, For the first g Respiratory volume (t / km²) of each biological functional group. For the first g Assimilation amount (t / km²) of each biological functional group.
[0038] To meet energy balance requirements, the production of each biological functional group should be sufficient to support both its utilization and losses. If If the consumption of the biological functional group exceeds its production capacity, the model is in an unbalanced state and the food matrix DC needs to be adjusted; otherwise, it indicates that the consumption of the biological functional group meets its production capacity. Further consideration should be given to whether the ratio of respiration to assimilation and the ratio of production to respiration meet the constraints. If both are met, it indicates that the Ecopath model meets the energy balance condition.
[0039] In this embodiment, the simulation output results of the Poyang Lake Ecopath model are shown in Table 4 below; Table 4 Output parameters of the Poyang Lake Ecopath model
[0040] S4: Based on the energy conservation model of the lake food web, the simulation results of the Ecopath model are derived to obtain the changes in total flow at each trophic level in the lake food web and to analyze the changes in energy transfer efficiency of the lake food web at different times.
[0041] In this embodiment, to analyze the changes in the Poyang Lake food web, as a specific case study, the Poyang Lake food web from 1990 to 2022 is analyzed in four stages. Figures 6-9 As shown.
[0042] Figures 6-9 The medium gray lines represent the boundaries between effective trophic levels 1, 2, 3, and 4 in the food web, distinguishing different trophic levels. The color of the lines connecting different functional groups indicates the predator-prey ratio, reflecting the proportion of energy flow between functional groups. The size of the blue circular nodes represents the biomass of each functional group; larger nodes indicate a higher proportion of biomass in the ecosystem.
[0043] From the perspective of the overall food web structure, the Poyang Lake food web consists of three energy transfer pathways, originating from phytoplankton, macroalgae, and detritus, respectively. The pathway originating from phytoplankton and macroalgae belongs to the pastoral food chain, where energy is transferred from primary producers to herbivorous functional groups, and then further to predators at higher trophic levels. The pathway originating from detritus is called the detritus food chain, where energy originates from organic detritus, enters the lake food web system via zooplankton or benthic functional groups, and is transferred step by step to higher trophic levels.
[0044] The Ecopath model further divides the Poyang Lake food web structure into four trophic levels for analysis. Trophic level I mainly includes detritus, phytoplankton, and macroalgae, representing primary producers; trophic level II is primarily composed of zooplankton, representing primary consumers; trophic level III includes benthic animals, planktonic fish, and herbivorous fish; and trophic level IV consists of omnivorous and carnivorous fish, representing higher trophic level predators in the food web. The total flow distribution of the Poyang Lake food web across different trophic levels from 1990 to 2022 is shown in Table 5 below.
[0045] Table 5. Total flow distribution of aggregated trophic levels in Poyang Lake (t / km²) 2 / y)
[0046] The total flux of the food web at different trophic levels in Poyang Lake decreased with increasing trophic level, exhibiting a pyramidal structure, consistent with the basic law of energy decay at each trophic level in an ecosystem. For example... Figure 10As shown, over time, the energy transfer efficiency from primary producers to secondary consumers (from trophic level 1 to trophic level 2) has shown an upward trend over the past 30 years, indicating that energy at lower trophic levels is being more fully converted and utilized. In contrast, the energy transfer efficiency from trophic level 2 to 3, and from 3 to 4, has remained largely unchanged or shown a slow downward trend. Therefore, even with gradually improving ecological conditions, more energy remains at the middle and lower trophic levels, and the lake food web is in the process of gradual recovery.
[0047] like Figures 11-14 As shown, based on the analysis of the aggregated trophic level energy flow diagrams at different stages, the energy transfer process in the food web at different stages all begins with phytoplankton (P) and detritus (D). The proportion of the grazing pathway (the energy pathway starting from phytoplankton) in each stage remains basically stable at about 55% (based on...). Figure 11 For example, energy = 44.16 + 9.931 + 0.903 + 0.017), the overall structure did not change significantly, indicating that the basic energy input structure of the lake food web has not fundamentally changed. Although there are significant differences in fishing intensity and trophic status, the pastoral food chain remains the core pathway for maintaining the system's energy flow.
[0048] The proportion of energy produced by primary producers directly absorbed by the second trophic level was 22.2%, 21.9%, 30.9%, and 36.6% in the four periods, showing a clear upward trend. However, the proportion of energy from higher trophic levels (2, 3, and 4) in the pastoral food chain was 68.2%, 70.1%, 62.5%, and 63.2% in the four periods, showing an overall trend of first slightly increasing, then decreasing, and then stabilizing. This indicates that, despite changes in fishing intensity and trophic status, even though the overall food web's dominance in the pastoral food chain has not changed, the dependence of higher trophic level organisms on pastoral pathways is slowly decreasing.
[0049] This invention employs a combination of lake aquatic ecosystem models and food web models to analyze the spatiotemporal evolution characteristics of lake water quality, aquatic organisms, and food web structure, as well as the dynamic changes in biological communities and ecological habitats at different trophic levels. It identifies the main driving forces of factors such as climate change, nutrient input, and hydrological processes on the structure and function of the ecosystem, thereby deepening our understanding of the evolution mechanism of lake ecosystems and providing a scientific basis for lake water environment governance, aquatic ecological restoration, and ecological protection management.
[0050] Example 2 The difference between this embodiment and Embodiment 1 is that it provides a novel method for constructing the Lake Aquatic Ecosystem (WET) model, including the following steps: S21: Configure the food web in the WET lake ecosystem model, dividing aquatic organisms into different functional groups, including four different types of fish (carnivorous, omnivorous, herbivorous, and planktonic fish, which can be adjusted according to the actual application area), two types of zooplankton (cladocerans and copepods), three types of phytoplankton (cyanobacteria, green algae, and diatoms), benthic animals, and macrophytes. In the WET configuration file fabm.yaml, configure the different functional groups according to their feeding relationships, generating energy transfer relationships between the different functional groups. For example... Figure 1 As shown in the generalized diagram of the lake water ecology model WET constructed in this embodiment, the arrows indicate the direction of material and energy transfer in the lake ecosystem.
[0051] S22: Calibrate and validate the lake water ecology model WET. Based on the Pasac automatic calibration program and using the stepwise calibration method, adjust the parameters of various target variables in the order of physical-chemical-biological, simulate the lake water ecology model WET, and output the simulation results of the lake water ecology model WET.
[0052] The target variables in this embodiment include water temperature, dissolved oxygen, chlorophyll, total phosphorus, total nitrogen, and fish biomass. For example... Figure 2 As shown, the parameter adjustment process for each objective variable during the calibration and validation of the WET lake water ecology model is as follows: First, by optimizing the parameters related to heat exchange, vertical mixing, and diffusion among biological functional groups, the water temperature variable was calibrated to provide reliable physical environmental conditions for subsequent chemical and biological variables. After the water temperature is determined, the variables of dissolved oxygen, chlorophyll, total phosphorus and total nitrogen are calibrated in sequence. By adjusting parameters such as the mineralization decomposition constant of organic matter, the photosynthetic coefficient of phytoplankton and the nutrient salt use efficiency, the model can accurately simulate the changing characteristics of lake chemical indicators. By optimizing parameters such as respiration rate, assimilation efficiency, and interspecific predation preference coefficient of various organisms in different biological functional groups, a high-precision simulation of lake fish biomass was completed. All target variables are adjusted only after the previous target variable has been calibrated and verified. After all stepwise calibrations are completed, the target variables are integrated into a unified set of parameters for use in all subsequent scenario simulations.
[0053] In this embodiment, meteorological, hydrological, water quality, and biological data collected from the Poyang Lake basin were used to calibrate and validate the lake's aquatic ecosystem model, WET. The calibration and validation results of the target variables, such as dissolved oxygen, chlorophyll, total phosphorus, total nitrogen, and fish biomass, are as follows: Figure 4 and Figure 5As shown, the simulation results of all objective variables meet the requirements, proving that the model can simulate the changes in water quality and ecological elements of Poyang Lake well, and the output results can be used as input for the lake food web model.
Claims
1. A food web evolution analysis method based on a lake water eco-food web model, characterized by, Includes the following steps: S1: Collect meteorological data, hydrological data, water quality data, biological data, and inflow data of the lake area, and preprocess them to obtain a preprocessed dataset; S2: Construct the lake water ecology model WET. The input data required for the lake water ecology model WET includes preprocessed datasets and model calibration data. The model calibration data is used as the target variable to simulate the lake water ecology model WET, adjust the parameters of the target variable, and output the simulation results. S3: Based on the simulation results of the lake water ecological model WET, daily biomass data of various organisms in the biological functional group are extracted and converted into area biomass data; The Ecopath model was used as a lake food web model, and the lake food web model was balanced and adjusted to ensure that the ecosystem energy represented by the lake food web model is conserved. S4: Based on the energy conservation model of the lake food web, the simulation results of the lake food web model are derived, the changes in total flow of each trophic level in the lake food web are obtained, and the changes in energy transfer efficiency of the lake food web at different periods are analyzed.
2. The food web evolution analysis method based on the lake aquatic ecosystem-food web model according to claim 1, characterized in that, The specific method for constructing the WET lake water ecological model is as follows: S21: Configure the food web in the lake water ecological model WET, divide the aquatic organisms in the lake into different biological functional groups, including four fish groups with different diets, two zooplankton, three phytoplankton, benthic animals and macroaquatic plants, and set the different biological functional groups according to their diets in the configuration file fabm.yaml of the lake water ecological model WET, and generate the energy transfer relationship between the different biological functional groups. S22: Calibrate and validate the lake water ecology model WET. Based on the Pasac automatic calibration program and using the stepwise calibration method, adjust the parameters of various target variables in the order of physical-chemical-biological, simulate the lake water ecology model WET, and output the simulation results of the lake water ecology model WET.
3. The food web evolution analysis method based on a lake water eco-food web model according to claim 2, characterized in that, Step S3 includes: S31: Based on the simulation results of the lake water ecology model WET, daily biomass data of fish groups with different diets, two zooplankton species, three phytoplankton species, benthic animals and macroaquatic plants are extracted, and the daily biomass data are converted into area biomass data, and then converted into annual biomass data by multi-day averaging. S32: Calculate detritus biomass data from primary productivity of the lake water ecosystem and water transparency ; ； wherein, is the primary productivity of the biological functional group, is the average transparency; S33: Calculate the detritus biomass data as a lake food web model by Ecopath model and the sum of annual biomass data, and then calculate the P / B coefficient and Q / B coefficient of each biological functional group, and balance the lake food web model based on the food matrix DC to make the ecosystem energy conservation represented by the lake food web model.
4. The food web evolution analysis method based on a lake water eco-food web model according to claim 3, characterized by, The P / B coefficient represents the primary productivity of each biological functional group Divided by detritus biomass data The Q / B coefficient represents the amount of predation of each biological functional group Divided by detritus biomass data The Q / B coefficient represents the amount of predation of each biological functional group 5.The lake water eco-food web model-based food web evolution analysis method according to claim 3, characterized in that, The equilibrium adjustment of the lake food web model is based on the principle of energy input and output balance in the ecosystem, and the ecological efficiency coefficients of each biological functional group are used. Ee Ecological efficiency coefficient is an indicator used to determine the energy balance between energy input and output in an ecosystem. Ee Indicates the prey volume of each biological functional group Fishing volume Other output losses The sum of production volume The ratio; ； wherein, g is the number of biological functional groups, is the eco-efficiency coefficient of the g th biological functional group. When performing equilibrium adjustments on a lake food web model, the energy balance constraints that need to be satisfied are: ； ； wherein, is the respiration of the g th biological functional group, is the assimilation of the g th biological functional group.