Nearshore phytoplankton community structure change response prediction method based on ecological dynamic model

Abstract

The invention relates to the technical field of marine ecological environment monitoring, in particular to a near-shore phytoplankton community structure change response prediction method based on an ecological dynamic model, comprising the following steps: S1, collecting environmental data of a near-shore area; s2, calculating growth rate parameters of the phytoplankton based on the environmental data; s3, simulating the change of a phytoplankton community structure by utilizing an ecological dynamic model; and S4, according to the simulated phytoplankton community structure change, predicting the response of the phytoplankton community to a preset environment change scene, and outputting a prediction response report. According to the invention, the evolution of the phytoplankton community structure is dynamically simulated through the ecological dynamic model, and the accurate prediction of the community response trend is realized in combination with multi-scene comparative analysis, so that the pre-judgment ability of the change of a near-shore ecological system and the scientificity of management decision are improved.

Description

Technical Field

[0001] This invention relates to the field of marine ecological environment monitoring technology, and in particular to a method for predicting the response of nearshore phytoplankton community structure changes based on an ecological dynamic model. Background Technology

[0002] Nearshore waters are important ecological areas for the frequent occurrence and succession of phytoplankton. The structure of phytoplankton communities directly affects primary productivity, trophic level structure, and the stability of nearshore fishery resources. Due to the combined effects of multiple factors such as land-based input, water exchange, and climate disturbances, environmental factors such as water temperature, light intensity, nutrient concentration, and hydrodynamics in nearshore areas fluctuate significantly in time and space, leading to rapid responses and structural reorganization of phytoplankton communities within a short period of time. Currently, ecological management and pollution control place higher demands on the prediction of abnormal phytoplankton proliferation (such as red tides), and there is an urgent need to establish dynamic prediction methods that can reflect the internal regulatory mechanisms of the ecosystem in order to predict the changing trends of community structure in advance.

[0003] Most existing methods rely on static monitoring or empirical statistical models, lacking quantitative descriptions of dynamic processes such as competition, growth, and resource utilization among phytoplankton populations. This makes it difficult to accurately predict community succession trends under different environmental change scenarios, and also fails to output the evolutionary paths of key ecological structure indicators such as dominant species ratios and diversity indices, thus limiting the scientific assessment and early warning of responses to complex ecological disturbances. Therefore, it is necessary to construct a method for predicting nearshore phytoplankton community structure changes based on an ecological dynamic model, thereby improving the accuracy and adaptability of community response prediction. Summary of the Invention

[0004] To achieve the above objectives, this invention provides a method for predicting the response of nearshore phytoplankton community structure changes based on an ecological dynamic model.

[0005] A method for predicting the response of nearshore phytoplankton community structure changes based on an ecological dynamic model includes the following steps: S1: Collect environmental data of the nearshore area, including water temperature, light intensity, nutrient concentration and water flow velocity; S2: Based on environmental data, calculate the growth rate parameters of phytoplankton, including the maximum growth rate and the half-saturation constant; S3: Based on the growth rate parameters in S2, the changes in phytoplankton community structure are simulated using an ecological dynamic model; S4: Based on the simulated changes in phytoplankton community structure, predict the response of the phytoplankton community to the preset environmental change scenario and output a predicted response report.

[0006] Optionally, S1 specifically includes: S11: Deploy multi-parameter water quality sensor nodes at representative sampling points in the nearshore area. The multi-parameter water quality sensor includes a temperature sensor, a light sensor, a nutrient electrode sensor, and a flow meter. S12: Measure the water temperature at a set depth using a temperature sensor; S13: Obtain the distribution of light intensity across the water surface throughout the day using a light sensor; S14: The concentrations of nitrate, nitrite and phosphate in the water are collected simultaneously using a nutrient electrode sensor; S15: The horizontal and vertical flow velocities at different tidal levels at each sampling point are measured using a flow meter, and a 60-second moving average method is used to remove instantaneous fluctuation noise and output a stable water flow velocity value.

[0007] Optionally, S2 specifically includes: S21: Normalize the environmental data obtained in S1 by unifying the units and smoothing the time series of water temperature, light intensity, nutrient concentration and water flow velocity to form an environmental input matrix. S22: Based on the deviation between the measured water temperature and the standard growth temperature, the temperature sensitivity coefficient is used for correction to obtain the temperature-corrected specific growth rate. S23: Using the measured nutrient concentration as the input variable, the Monod kinetic equation is used to fit the relationship between nutrient concentration and growth rate, and the maximum growth rate and half-saturation constant are calculated. S24: Normalize and synthesize the temperature-corrected growth rate and nutrient response results to output a set of comprehensive growth rate parameters under the current environmental conditions.

[0008] Optionally, S3 specifically includes: S31: Based on the growth rate parameters obtained in S2, construct the biomass change equation of the phytoplankton population, set the initial population size and its proportion, and determine the initial state of community evolution. S32: Input environmental data such as water temperature, light intensity, nutrient concentration and water flow velocity, and calculate the growth and loss rates of each phytoplankton population within a continuous time step using an ecological dynamic model to obtain biomass data at each time step. S33: Based on the biomass data output from S32, calculate the proportion of each species in the total community biomass to form the community dominant species proportion results; S34: Summarize the biomass of all populations to obtain the total community biomass, and calculate the diversity index based on the population size distribution results.

[0009] Optionally, S32 specifically includes: S321: Combine the growth rate parameters obtained in S2 with the biomass change equation established in S31, and input the water temperature, light intensity, nutrient concentration and water flow velocity data of the nearshore area to form the input parameter set of the ecological dynamic model. S322: Based on the set time step Numerical integration calculations were performed on each phytoplankton population over a continuous time interval to solve the biomass change equation and obtain the instantaneous growth rate and loss rate at each time step. S323: Subtract the growth rate from the loss rate to obtain the net change of each phytoplankton population at the current time step, and add it to the biomass value of the previous time step to update the new population biomass. S324: Iterate through the time steps until the preset simulation period is reached, and output the biomass data sequence at each time point.

[0010] Optionally, S33 specifically includes: S331: Extract the biomass data of each phytoplankton population from the output of S32 at the same time node, construct the biomass vector set at the current time, represented as... , where n is the number of phytoplankton species; S332: Sum the biomass of all species at the current time node to obtain the total community biomass. This is used as a benchmark value for percentage calculation; S333: For each species i, its biomass is compared with the total biomass. Divide them to calculate their relative proportion in the community. This results in the proportion of dominant species in the community. S334: The proportion results of all species are combined into a proportion distribution sequence, sorted from high to low according to the value, and the top few species with the highest proportion are marked as dominant species, forming the proportion result of dominant species in the community at the current moment.

[0011] Optionally, S34 specifically includes: S341: The sequence of community proportions of each phytoplankton species at the current time node, obtained from S33, denoted as... ,in The total number of species within the community; S342: Based on the proportion sequence, the Shannon diversity index is used to comprehensively assess the richness and evenness of community species composition. The calculation formula is as follows: ,in, The Shannon diversity index. Let be the community percentage of the i-th phytoplankton species. This represents the total number of species at the current moment. It is the natural logarithm; S343: Repeat the calculation of the diversity index at each time point to obtain the time series results of community diversity within the simulation period.

[0012] Optionally, S4 specifically includes: S41: Set up multiple environmental change scenarios, including water temperature rise, light change, nutrient concentration change and water flow velocity disturbance, and input the environmental factor values ​​under each scenario into the ecological dynamic model to replace the original environmental parameters as input conditions for prediction simulation. S42: Under various environmental change scenarios, call the ecological dynamic model established in S3, and based on the growth rate parameters obtained in S2, recalculate the biomass dynamic change process of phytoplankton populations, and output the biomass sequence and community structure evolution data of each species in the simulation period. S43: Extract the proportion of dominant species, total biomass and diversity index of the community obtained from the simulation in each scenario, and compare them with the corresponding values ​​in the benchmark scenario to generate community response trend data in each environmental scenario, including species succession, changes in community stability and biomass fluctuations. S44: Based on the community response trend data in S43, summarize and generate a predicted response report, and output it in the form of charts and data sequences.

[0013] Optionally, S43 specifically includes: S431: Extract the community structure data obtained from simulations under various environmental change scenarios, including the biomass of each phytoplankton population at each time step. Total community biomass Community proportion and diversity index , where s represents the scene number and t represents the time node; S432: Compare the community structure indicators in each scenario with the baseline scenario. The results at the corresponding time points are compared item by item to obtain the difference value and the rate of change. S433: Based on the dominant species ranking list at each time point, the differences between the top k dominant species in each environmental scenario and the baseline scenario are statistically analyzed. If the ranking or composition of the specified species changes, it is recorded as a species replacement event. S434: Integrate differential data to output a dataset of phytoplankton community response trends in each environmental scenario compared to the baseline scenario, including biomass fluctuation curves, dominant species change maps, and diversity index change trajectories.

[0014] Optionally, the predicted response report includes environmental disturbance variables, community structure change indicators, key population change magnitudes, and response time series.

[0015] The beneficial effects of this invention are: This invention achieves precise modeling of phytoplankton biomass changes by collecting key environmental factor data from nearshore areas and dynamically calculating phytoplankton growth rate parameters based on temperature correction and nutrient response models. It constructs an ecodynamic model that reflects the succession mechanism of phytoplankton community structure. This model can output key structural indicators such as the proportion of dominant species, total biomass, and diversity index, reflecting the evolutionary characteristics of the community under different environmental conditions.

[0016] This invention simulates the response process of phytoplankton communities under different disturbance conditions by setting up various environmental change scenarios. By comparing with the benchmark scenario, it quantifies the trends of species succession, stability changes and biomass fluctuations, and finally generates a structured prediction report, providing accurate data support and decision-making basis for the scientific assessment and risk warning of phytoplankton community ecological evolution. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the response prediction method according to an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the process of simulated phytoplankton community structure changes according to an embodiment of the present invention. Detailed Implementation

[0019] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should also be noted that, to make the embodiments more comprehensive, the following embodiments are the best and preferred embodiments, and those skilled in the art can use other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0020] like Figures 1-2 As shown, a method for predicting changes in the structure of nearshore phytoplankton communities based on an ecological dynamic model includes the following steps: S1: Collect environmental data of the nearshore area, including water temperature, light intensity, nutrient concentration and water flow velocity; S1 specifically includes: S11: Deploy multi-parameter water quality sensor nodes at representative sampling points in the nearshore area. The multi-parameter water quality sensors include temperature sensors, light sensors, nutrient electrode sensors, and flow meters. S12: Measure the water temperature at a set depth using a temperature sensor. The temperature sensor accuracy is no less than ±0.1℃, and the measurement frequency is once every 30 minutes. S13: Obtain the all-day light intensity distribution of the water surface through a light sensor, and use a digital quantum meter to record the total photosynthetically active radiation intensity per unit time at 10-minute intervals throughout the all-weather cycle. S14: The concentrations of nitrate, nitrite and phosphate in the water are collected synchronously by the nutrient electrode sensor. The obtained concentration data are converted into standard nutrient concentrations in mg / L after temperature and pH correction. S15: Horizontal and vertical flow velocities at different tidal levels at each sampling point are measured using a flowmeter, and instantaneous fluctuation noise is removed using a 60-second moving average method to output stable flow velocity values. Through the above steps, typical nearshore water areas can be covered spatially, and high-frequency acquisition of key parameters can be achieved temporally. By using unified sensor specifications and calibration mechanisms, the consistency and reliability of the collected water temperature, light intensity, nutrient concentration, and flow velocity data are ensured, providing accurate environmental input data support for subsequent growth rate parameter calculations and ecological model simulations.

[0021] S2: Based on environmental data, calculate the growth rate parameters of phytoplankton, including the maximum growth rate and the half-saturation constant; S2 specifically includes: S21: Normalize the environmental data obtained in S1 by unifying the units and smoothing the time series of water temperature, light intensity, nutrient concentration and water flow velocity to form an environmental input matrix. S22: Based on the deviation between the measured water temperature and the standard growth temperature, a temperature sensitivity coefficient is used for correction to obtain the temperature-corrected specific growth rate. The calculation formula is as follows: ,in, This is the temperature-corrected growth rate. As the baseline growth rate, Here, T is the temperature sensitivity coefficient, and T is the measured water temperature. Standard growth temperature; S23: Using measured nutrient concentration as the input variable, the Monod kinetic equation is used to fit the relationship between nutrient concentration and growth rate, and the maximum growth rate and half-saturation constant are calculated. The equation formula is as follows: ,in, This is the actual specific growth rate. S represents the maximum growth rate, and S represents the nutrient concentration. It is the half-saturation constant; S24: The temperature-corrected growth rate and nutrient response results are normalized and synthesized to output a comprehensive growth rate parameter set under the current environmental conditions, which serves as the input for subsequent ecological dynamics models. Through the above steps, combined with the two-factor quantitative analysis of temperature and nutrients, the dynamic calculation of phytoplankton growth rate parameters is realized. The obtained maximum growth rate and half-saturation constant can accurately reflect the impact of environmental changes on the physiological characteristics of phytoplankton, providing scientific and quantifiable basic parameters for community structure simulation in ecological dynamics models.

[0022] S3: Based on the growth rate parameters in S2, the ecological dynamic model is used to simulate the changes in phytoplankton community structure. The ecological dynamic model describes the dynamic changes in phytoplankton biomass based on the mass balance equation. S3 specifically includes: S31: Based on the growth rate parameters obtained in S2, construct the biomass change equation of the phytoplankton population, set the initial population size and its proportion, and determine the initial state of community evolution. The equation for the biomass change of phytoplankton populations is: , where the biomass of the i-th phytoplankton; The actual specific growth rate of the i-th phytoplankton under the current environmental conditions is output by S24 in claim 3; This is the biomass loss term for the i-th phytoplankton species, including biomass loss caused by processes such as settling, feeding, and death; Let be the rate of change of biomass of the i-th phytoplankton species per unit time. This equation expresses the net change in biomass of the i-th phytoplankton species per unit time, which is equal to its growth rate under the influence of current environmental factors multiplied by the existing biomass, minus the loss caused by sedimentation, predation, etc. This dynamic structure reflects the dynamic balance between population growth and natural regulation mechanisms, and can dynamically simulate the continuous change of population size over time. S32: Input environmental data such as water temperature, light intensity, nutrient concentration and water flow velocity, and calculate the growth and loss rates of each phytoplankton population within a continuous time step using an ecological dynamic model to obtain biomass data at each time step. S33: Based on the biomass data output from S32, calculate the proportion of each species in the total community biomass to form the community dominant species proportion results; S34: The total biomass of all populations is summed to obtain the total community biomass, and the diversity index is calculated based on the population distribution results to reflect the complexity of the community structure. Through the above steps, the dynamic evolution characteristics of phytoplankton communities under different environmental conditions can be quantitatively described using an ecological dynamic model, accurately reflecting the changing trends of the proportion of dominant species, total biomass and diversity index, and providing an accurate structural data basis for subsequent ecological response prediction.

[0023] S32 specifically includes: S321: Combine the growth rate parameters obtained in S2 with the biomass change equation established in S31, and input the water temperature, light intensity, nutrient concentration and water flow velocity data of the nearshore area to form the input parameter set of the ecological dynamic model. S322: Based on the set time step Numerical integration was performed on each phytoplankton population over a continuous time interval to solve the biomass change equation, yielding the instantaneous growth rate and loss rate at each time step; the formula is: ,in, Let be the change in biomass of the i-th phytoplankton species in the current time step. This represents the biomass at the previous moment. For time step; S323: Subtract the growth rate from the loss rate to obtain the net change in biomass for each phytoplankton population at the current time step, and add this to the biomass value from the previous time step to update the new population biomass; the corresponding update formula is: ,in, For the updated biomass, This represents the biomass at the previous time step. This represents the current step biomass change value; S324: Iterate through the time steps until the preset simulation period is reached, and output the biomass data sequence at each time node. Through the above steps, the growth and loss process of phytoplankton populations can be continuously tracked in the time dimension, realizing the dynamic evolution calculation of biomass, ensuring that the model results are consistent and accurate in the time series, and providing a high-precision data foundation for the subsequent extraction of community structure indicators.

[0024] S33 specifically includes: S331: Extract the biomass data of each phytoplankton population from the output of S32 at the same time node, construct the biomass vector set at the current time, represented as... , where n is the number of phytoplankton species; S332: Sum the biomass of all species at the current time node to obtain the total community biomass. This is used as a benchmark value for percentage calculation; S333: For each species i, its biomass is compared with the total biomass. Divide them to calculate their relative proportion in the community. The formula for determining the proportion of dominant species in a community is: ,in, Let be the community percentage of the i-th phytoplankton at that time point. Let be the biomass of the i-th population; S334: The proportion results of all species are compiled into a proportion distribution sequence, sorted from high to low, and the top few species with the highest proportions are marked as dominant species, forming the community dominant species proportion results at the current moment; through the above steps, the composition structure of the main contributing species in the nearshore phytoplankton community can be accurately identified in the time dimension, the dominance of dominant species in the overall community can be quantified, and key community structure basis can be provided for subsequent ecological evolution trend analysis and species response assessment.

[0025] S34 specifically includes: S341: The sequence of community proportions of each phytoplankton species at the current time node, obtained from S33, denoted as... ,in The total number of species within the community; S342: Based on the proportion sequence, the Shannon diversity index is used to comprehensively assess the richness and evenness of community species composition. The calculation formula is as follows: ,in, The Shannon diversity index. Let be the community percentage of the i-th phytoplankton species. This represents the total number of species at the current moment. It is the natural logarithm; S343: Repeat the calculation of diversity index at each time point to obtain the time series results of community diversity within the simulation period, which can be used for subsequent ecological response trend analysis. Through the above steps, the complexity and evenness of phytoplankton community structure can be quantitatively characterized based on the species proportion data within the community. The output diversity index can be used as the core evaluation index of community stability and ecological disturbance sensitivity, thereby improving the ecological explanatory power of community response prediction.

[0026] S4: Based on the simulated changes in phytoplankton community structure, predict the response of the phytoplankton community to the preset environmental change scenario and output a predicted response report. S4 specifically includes: S41: Set up multiple environmental change scenarios, including water temperature rise, light change, nutrient concentration change and water flow velocity disturbance, and input the environmental factor values ​​under each scenario into the ecological dynamic model to replace the original environmental parameters as input conditions for prediction simulation. S42: Under various environmental change scenarios, call the ecological dynamic model established in S3, and based on the growth rate parameters obtained in S2, recalculate the biomass dynamic change process of phytoplankton populations, and output the biomass sequence and community structure evolution data of each species in the simulation period. S43: Extract the proportion of dominant species, total biomass and diversity index of the community obtained from the simulation in each scenario, and compare them with the corresponding values ​​in the benchmark scenario to generate community response trend data in each environmental scenario, including species succession, changes in community stability and biomass fluctuations. S44: Based on the community response trend data in S43, a predictive response report is generated and output in the form of charts and data sequences. Through the above steps, the response path of phytoplankton communities under specific environmental disturbances can be systematically simulated, the impact trend of future environmental changes on community structure can be accurately assessed, and a visualized predictive report can be output in a structured form, providing a quantitative decision-making basis for ecological early warning and environmental management.

[0027] S43 specifically includes: S431: Extract the community structure data obtained from simulations under various environmental change scenarios, including the biomass of each phytoplankton population at each time step. Total community biomass Community proportion and diversity index , where s represents the scene number and t represents the time node; S432: Compare the community structure indicators in each scenario with the baseline scenario. The results at each corresponding time point were compared item by item to obtain the difference value and the rate of change; the formula for calculating the rate of change of total community biomass is as follows: ,in, Let be the rate of change of total community biomass at time point t. The total biomass of the community in scenario s. This is the corresponding value in the baseline scenario; S433: Based on the dominant species ranking list at each time point, statistically analyze the similarities and differences between the top k dominant species in each environmental scenario and the baseline scenario. If the ranking or composition of a specified species changes, it is recorded as a species succession event. Simultaneously, calculate the rate of change of the diversity index using the following formula: , used to reflect changes in community stability; S434: By integrating differential data, output a dataset of phytoplankton community response trends in each environmental scenario compared to the baseline scenario, including biomass fluctuation curves, dominant species change maps, and diversity index change trajectories. Through the above steps, the impact of different environmental change scenarios on phytoplankton community structure can be quantitatively assessed, accurately reflecting the response trends of key ecological indicators under disturbance, and providing data support for ecological risk identification and system regulation.

[0028] The predictive response report includes environmental disturbance variables, community structure change indicators, key population change magnitudes, and response time series.

[0029] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0030] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for predicting the response of nearshore phytoplankton community structure changes based on an ecological dynamic model, characterized in that, Includes the following steps: S1: Collect environmental data of the nearshore area, including water temperature, light intensity, nutrient concentration and water flow velocity; S2: Based on environmental data, calculate the growth rate parameters of phytoplankton, including the maximum growth rate and the half-saturation constant; S3: Based on the growth rate parameters in S2, the changes in phytoplankton community structure are simulated using an ecological dynamic model; S4: Based on the simulated changes in phytoplankton community structure, predict the response of the phytoplankton community to the preset environmental change scenario and output a predicted response report.

2. The method for predicting the response of nearshore phytoplankton community structure change based on an ecological dynamic model according to claim 1, characterized in that, S1 specifically includes: S11: Deploy multi-parameter water quality sensor nodes at representative sampling points in the nearshore area. The multi-parameter water quality sensor includes a temperature sensor, a light sensor, a nutrient electrode sensor, and a flow meter. S12: Measure the water temperature at a set depth using a temperature sensor; S13: Obtain the distribution of light intensity across the water surface throughout the day using a light sensor; S14: The concentrations of nitrate, nitrite and phosphate in the water are collected simultaneously using a nutrient electrode sensor; S15: The horizontal and vertical flow velocities at different tidal levels at each sampling point are measured using a flow meter, and a 60-second moving average method is used to remove instantaneous fluctuation noise and output a stable water flow velocity value.

3. The method for predicting the response of nearshore phytoplankton community structure change based on an ecological dynamic model according to claim 1, characterized in that, S2 specifically includes: S21: Normalize the environmental data obtained in S1 by unifying the units and smoothing the time series of water temperature, light intensity, nutrient concentration and water flow velocity to form an environmental input matrix. S22: Based on the deviation between the measured water temperature and the standard growth temperature, the temperature sensitivity coefficient is used for correction to obtain the temperature-corrected specific growth rate. S23: Using the measured nutrient concentration as the input variable, the Monod kinetic equation is used to fit the relationship between nutrient concentration and growth rate, and the maximum growth rate and half-saturation constant are calculated. S24: Normalize and synthesize the temperature-corrected growth rate and nutrient response results to output a set of comprehensive growth rate parameters under the current environmental conditions.

4. The method for predicting the response of nearshore phytoplankton community structure change based on an ecological dynamic model according to claim 1, characterized in that, S3 specifically includes: S31: Based on the growth rate parameters obtained in S2, construct the biomass change equation of the phytoplankton population, set the initial population size and its proportion, and determine the initial state of community evolution. S32: Input environmental data such as water temperature, light intensity, nutrient concentration and water flow velocity, and calculate the growth and loss rates of each phytoplankton population within a continuous time step using an ecological dynamic model to obtain biomass data at each time step. S33: Based on the biomass data output from S32, calculate the proportion of each species in the total community biomass to form the community dominant species proportion results; S34: Summarize the biomass of all populations to obtain the total community biomass, and calculate the diversity index based on the population size distribution results.

5. The method for predicting the response of nearshore phytoplankton community structure change based on an ecological dynamic model according to claim 4, characterized in that, Specifically, S32 includes: S321: Combine the growth rate parameters obtained in S2 with the biomass change equation established in S31, and input the water temperature, light intensity, nutrient concentration and water flow velocity data of the nearshore area to form the input parameter set of the ecological dynamic model. S322: Based on the set time step Numerical integration calculations were performed on each phytoplankton population over a continuous time interval to solve the biomass change equation and obtain the instantaneous growth rate and loss rate at each time step. S323: Subtract the growth rate from the loss rate to obtain the net change of each phytoplankton population at the current time step, and add it to the biomass value of the previous time step to update the new population biomass. S324: Iterate through the time steps until the preset simulation period is reached, and output the biomass data sequence at each time point.

6. The method for predicting the response of nearshore phytoplankton community structure change based on an ecological dynamic model according to claim 5, characterized in that, S33 specifically includes: S331: Extract the biomass data of each phytoplankton population from the output of S32 at the same time node, construct the biomass vector set at the current time, represented as... , where n is the number of phytoplankton species; S332: Sum the biomass of all species at the current time node to obtain the total community biomass. This is used as a benchmark value for percentage calculation; S333: For each species i, its biomass is compared with the total biomass. Divide them to calculate their relative proportion in the community. This results in the proportion of dominant species in the community. S334: The proportion results of all species are combined into a proportion distribution sequence, sorted from high to low according to the value, and the top few species with the highest proportion are marked as dominant species, forming the proportion result of dominant species in the community at the current moment.

7. The method for predicting the response of nearshore phytoplankton community structure change based on an ecological dynamic model according to claim 6, characterized in that, S34 specifically includes: S341: The sequence of community proportions of each phytoplankton species at the current time node, obtained from S33, denoted as... ,in The total number of species within the community; S342: Based on the proportion sequence, the Shannon diversity index is used to comprehensively assess the richness and evenness of community species composition. The calculation formula is as follows: ,in, The Shannon diversity index. Let be the community percentage of the i-th phytoplankton species. This represents the total number of species at the current moment. It is the natural logarithm; S343: Repeat the calculation of the diversity index at each time point to obtain the time series results of community diversity within the simulation period.

8. The method for predicting the response of nearshore phytoplankton community structure change based on an ecological dynamic model according to claim 1, characterized in that, S4 specifically includes: S41: Set up multiple environmental change scenarios, including water temperature rise, light change, nutrient concentration change and water flow velocity disturbance, and input the environmental factor values ​​under each scenario into the ecological dynamic model to replace the original environmental parameters as input conditions for prediction simulation. S42: Under various environmental change scenarios, call the ecological dynamic model established in S3, and based on the growth rate parameters obtained in S2, recalculate the biomass dynamic change process of phytoplankton populations, and output the biomass sequence and community structure evolution data of each species in the simulation period. S43: Extract the proportion of dominant species, total biomass and diversity index of the community obtained from the simulation in each scenario, and compare them with the corresponding values ​​in the benchmark scenario to generate community response trend data in each environmental scenario, including species succession, changes in community stability and biomass fluctuations. S44: Based on the community response trend data in S43, summarize and generate a predicted response report, and output it in the form of charts and data sequences.

9. A method for predicting the response of nearshore phytoplankton community structure changes based on an ecological dynamic model according to claim 8, characterized in that, Specifically, S43 includes: S431: Extract the community structure data obtained from simulations under various environmental change scenarios, including the biomass of each phytoplankton population at each time step. Total community biomass Community proportion and diversity index , where s represents the scene number and t represents the time node; S432: Compare the community structure indicators in each scenario with the baseline scenario. The results at the corresponding time points are compared item by item to obtain the difference value and the rate of change; S433: Based on the dominant species ranking list at each time point, the differences between the top k dominant species in each environmental scenario and the baseline scenario are statistically analyzed. If the ranking or composition of the specified species changes, it is recorded as a species replacement event. S434: Integrate differential data to output a dataset of phytoplankton community response trends in each environmental scenario compared to the baseline scenario, including biomass fluctuation curves, dominant species change maps, and diversity index change trajectories.

10. A method for predicting the response of nearshore phytoplankton community structure change based on an ecological dynamic model according to claim 8, characterized in that, The predicted response report includes environmental disturbance variables, community structure change indicators, key population change magnitudes, and response time series.

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