Enteromorpha-microalgae competitive growth process simulation method
By constructing a simulation method for the competitive growth process of Ulva prolifera and microalgae, and using remote sensing image data and ecological dynamics models, the competitive process between Ulva prolifera and microalgae can be accurately simulated. This solves the problem of inaccurate simulation of Ulva prolifera green tides in existing technologies, and provides scientific prediction and control methods to protect marine ecology.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-13
- Publication Date
- 2026-03-31
AI Technical Summary
Existing technologies fail to comprehensively and systematically consider the competitive relationship between Ulva prolifera and microalgae at different growth stages, especially in the characterization of nutrient biogeochemical processes in complex environments, which is not precise enough, resulting in inaccurate simulation of Ulva prolifera green tides.
A method for simulating the competitive growth process of Ulva prolifera and microalgae was adopted. Three-dimensional spatiotemporal data were constructed by acquiring remote sensing image monitoring data, and grid division and data dimensionality reduction were performed. Numerical simulation was carried out by combining a nutrient-Ulva prolifera-microalgae-detritus ecological dynamics box model, adjusting environmental factor variables, and accurately simulating the growth process of Ulva prolifera and microalgae.
It has achieved accurate simulation of the growth process of Ulva prolifera, providing scientific basis for the prediction and prevention of Ulva prolifera green tide disasters, reducing disaster losses and protecting the marine ecological environment.
Smart Images
Figure CN120997450B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the fields of marine ecology and environmental science, and in particular to a method for simulating the competitive growth process of Ulva prolifera and microalgae. Background Technology
[0002] Enteromorpha ( Ulva prolifera As the main green algae causing green tides, the large-scale outbreak and rapid expansion of *Ulva prolifera* has brought about a series of serious consequences. The proliferation of *Ulva prolifera* not only significantly degrades water quality and damages the marine ecological environment, but also has a huge impact on fishery resources, seriously affecting the balance and sustainable development of the marine ecosystem. In-depth research has found that excessive nutrients in the water, especially the continuous supply of nitrogen and phosphorus, are key factors leading to the abnormally rapid growth of *Ulva prolifera*. Furthermore, the rampant growth of *Ulva prolifera* green tides also affects the normal growth of microalgae in the water, disrupting the original balance of the aquatic ecosystem.
[0003] In the existing technology, a variety of ecological models have been applied to simulate the growth process of Ulva prolifera and microalgae. However, most of the existing models have failed to comprehensively and systematically consider the competitive growth relationship between Ulva prolifera and microalgae in different growth stages in the actual complex environment, and the characterization of the nutrient biogeochemical processes in the competition between Ulva prolifera and microalgae is not accurate enough.
[0004] Therefore, there is an urgent need for a method to simulate the competitive growth process of Ulva prolifera and microalgae, so as to accurately simulate the nutrient biogeochemical processes during the competition between Ulva prolifera and microalgae. Summary of the Invention
[0005] Therefore, it is necessary to provide a method for simulating the competitive growth process of Ulva prolifera and microalgae to address the above-mentioned technical problems.
[0006] The present invention adopts the following technical solution:
[0007] This invention provides a method for simulating the competitive growth process of *Ulva prolifera* and microalgae, comprising:
[0008] Remote sensing image monitoring data of Ulva prolifera and microalgal biomass were obtained for the study area; and three-dimensional spatiotemporal data of Ulva prolifera coverage area were constructed based on the remote sensing image monitoring data.
[0009] The distribution area of the three-dimensional spatiotemporal data of the coverage area of *Ulva prolifera* is divided into grids according to the preset grid cell size; the grid cell number is used to replace the latitude and longitude of the three-dimensional spatiotemporal data of the coverage area of *Ulva prolifera* green tide, and the three-dimensional spatiotemporal distribution matrix is reduced to two-dimensional or one-dimensional data to obtain the dimensionality-reduced data of the coverage area of *Ulva prolifera*.
[0010] Convert the dimensionality-reduced data of Ulva prolifera coverage area into Ulva prolifera biomass;
[0011] Numerical simulations of the biomass generation and degradation of *Ulva prolifera* and microalgae were conducted using a nutrient- *Ulva prolifera*-microalgae-detritus ecodynamic box model to obtain simulated values of *Ulva prolifera* coverage area over time. The values of environmental variables in the nutrient- *Ulva prolifera*-microalgae-detritus ecodynamic box model were adjusted one by one using the controlled variable method, and the biomass generation and degradation of *Ulva prolifera* and microalgae were simulated to obtain simulated values of *Ulva prolifera* coverage area over time under different values for each environmental variable.
[0012] Based on the simulated values of the change in Ulva prolifera coverage area over time and the simulated values of the change in Ulva prolifera coverage area over time under different values of each environmental factor variable, the modulation effect value of each environmental factor is determined; based on the modulation effect value, the criticality of each environmental factor in controlling the competitive growth process of Ulva prolifera is determined.
[0013] Preferably, based on remote sensing image monitoring data of *Ulva prolifera*, three-dimensional spatiotemporal data of *Ulva prolifera* coverage area are constructed, specifically including:
[0014] Normalized vegetation index was calculated from remote sensing image monitoring data of Ulva prolifera to determine the coverage area of Ulva prolifera green tide disaster blocks;
[0015] The latitude and longitude of the centroid of the Ulva prolifera green tide disaster area are extracted as the spatial location of the Ulva prolifera green tide disaster area;
[0016] Based on the spatial location of the Ulva prolifera green tide disaster area, the three-dimensional spatiotemporal distribution matrix of the Ulva prolifera green tide coverage area is determined by the following formula:
[0017] ;
[0018] In the formula, CA U x represents the area covered by the green tide of seaweed. lon and y lon , respectively, are the longitude and latitude of the centroid of the Ulva prolifera block, and t is the time when the Ulva prolifera block was monitored by satellite.
[0019] Preferably, the nutrient-Ulva prolifera-microalgae-detritus ecodynamic box model includes an Ulva prolifera sub-model and a microalgae sub-model; both the Ulva prolifera sub-model and the microalgae sub-model include: a nitrogen and phosphorus nutrient direct conversion module, a nitrogen and phosphorus nutrient biotransfer and conversion module, and an algal biogenesis and decomposition module; the construction process of the nutrient-Ulva prolifera-microalgae-detritus ecodynamic box model specifically includes:
[0020] Based on the cross-section of the study area where the dimensionality reduction data of Ulva prolifera coverage area is located, the study area where the dimensionality reduction data of Ulva prolifera coverage area is located is divided into three partitions, and each partition corresponds to a box model.
[0021] The cross-regional diversion coefficient is determined based on the drift speed of the green tide of *Ulva prolifera* across the zoning boundaries;
[0022] By connecting the three box models in series using the cross-regional diversion coefficient between adjacent partition boxes, a nutrient-Ulva prolifera-microalgae-detritus ecodynamic box model is obtained.
[0023] Preferably, the preset grid cell size is the minimum daily increment of the coverage area in the three-dimensional spatiotemporal data of the Ulva prolifera green tide coverage area.
[0024] Preferably, the latitude and longitude of the three-dimensional spatiotemporal data of the Ulva prolifera green tide coverage area are replaced by the grid cell number, thereby reducing the three-dimensional spatiotemporal distribution matrix into two-dimensional or one-dimensional data. Specifically, this includes:
[0025] The grid cell number of the Ulva prolifera patch is used to replace the longitude and latitude in the 3D data of the Ulva prolifera patch, thus reducing the 3D data to 2D data. The formula is as follows:
[0026] ;
[0027] In the formula, CA U The area covered by the green tide of *Ulva prolifera* is represented by S, which represents the grid cell number, and t is the time when the *Ulva prolifera* block was detected by satellite.
[0028] Applying nonlinear curve fitting and linear fitting to the two-dimensional data, the spatial location and temporal fit function of the *Ulva prolifera* patches are obtained, as shown in the formula:
[0029] ;
[0030] In the formula, S represents the spatial location grid cell number of the Ulva prolifera block, t is the time of appearance of the Ulva prolifera block, a is the maximum value, k is the growth rate, and c is the inflection point.
[0031] If the spatial location S changes monotonically with time t, then the two-dimensional data can be reduced to one-dimensional data using the following formula:
[0032] ;
[0033] If the spatial location S does not change monotonically with time t, then dimensionality reduction of the two-dimensional data is not performed.
[0034] Preferably, the formula for converting the dimensionality reduction of Ulva prolifera coverage area into Ulva prolifera biomass is as follows:
[0035] ;
[0036] In the formula, B U (t) represents the biomass of *Ulva prolifera* at time t, ξ is the conversion factor between *Ulva prolifera* biomass and its coverage area, and CA U (t) represents the area of vegetation coverage at time t.
[0037] Preferably, the numerical simulation formulas for the generation and degradation processes of *Ulva prolifera* biomass and microalgal biomass are as follows:
[0038] ;
[0039] ;
[0040] In the formula, This refers to the chlorophyll content of microalgae. The chlorophyll content of *Ulva prolifera* and These are the maximum growth rate constants for microalgae and Ulva prolifera, respectively. and These are the maximum mortality rate constants for microalgae and Ulva prolifera, respectively. This is the light limitation coefficient during the growth and decay of microalgae. This is the temperature limiting coefficient in the microalgae growth and digestion process. The nitrogen and phosphorus nutrient limitation coefficients during the microalgal growth and digestion process. This is the limiting factor in the microalgal respiration process. This is the light limitation coefficient during the growth and decay of *Ulva prolifera*. This is the temperature limiting coefficient during the growth and decay of *Ulva prolifera*. This is the limiting factor during the respiration process of *Ulva prolifera*. The limiting coefficients for nitrogen and phosphorus nutrients during the growth and decay of Ulva prolifera.
[0041] Preferably, the environmental variables in the nutrient-Ulva prolifera-microalgae-detritus ecodynamic box model include: the coverage area of the initial Ulva prolifera patch, sea surface temperature, light intensity, and nitrogen and phosphorus nutrient concentrations.
[0042] Preferably, the modulatory effect value of each environmental factor is determined based on the maximum simulated value of the annual change in Ulva prolifera coverage area over time and the magnitude of the change between the maximum simulated value of the annual change in Ulva prolifera coverage area over time for each environmental factor variable at different values. Specifically, this includes:
[0043] The maximum value of the simulated annual change in Ulva prolifera coverage area over time and the maximum value of the simulated annual change in Ulva prolifera coverage area over time for each environmental factor variable under different values were selected.
[0044] Based on the maximum value of the simulated annual variation of Ulva prolifera coverage area over time and the amplitude of the variation of the maximum value of the simulated annual variation of Ulva prolifera coverage area over time for each environmental factor variable under different values, the modulating effect value of the control factor for Ulva prolifera green tide disaster is determined, using the following formula:
[0045] ;
[0046] In the formula, Re represents the modulation effect value of environmental factors, and S Re_max S represents the simulated value of the annual Ulva prolifera coverage area over time for each environmental variable at different values. Ac_max This represents the maximum value of the simulated annual change in the area covered by *Ulva prolifera* over time.
[0047] Preferably, the modulation effect value is proportional to the degree to which environmental factors control the competitive growth process of Ulva prolifera.
[0048] The above-mentioned at least one technical solution adopted in this invention can achieve the following beneficial effects:
[0049] In the simulation method for the competitive growth process of *Ulva prolifera* and microalgae provided in this invention, the growth and decay process of *Ulva prolifera* is simulated through multi-dimensional data processing and complex model construction. A three-dimensional spatiotemporal distribution matrix is constructed, gridded, and data dimensionality reduction is performed to simplify the complex three-dimensional spatiotemporal information of *Ulva prolifera* coverage area into easily analyzable two-dimensional or one-dimensional data. This is combined with the marine nutrient-*Ulva prolifera*-microalgae-detritus ecodynamic model (NUPD). Ulva prolifera The Phytoplankton Detritus process-coupled box model performs numerical simulations on the dimensionality-reduced data, comprehensively considering multiple processes such as nitrogen and phosphorus nutrient migration and transformation, algal growth and decay, etc., to determine which model best fits the actual ecosystem and can accurately simulate the growth and decay process of Ulva prolifera. It calculates the simulated value of the temporal change in Ulva prolifera coverage area, providing scientific and accurate data support and model basis for the prediction and prevention of Ulva prolifera green tide disasters, helping relevant departments to plan response strategies in advance and reduce disaster losses.
[0050] Numerical experiments using the NUPD box model focused on analyzing the impact of environmental factors on the growth of *Ulva prolifera*, characterized by a comprehensive consideration of multiple control factors. This method can scientifically and systematically analyze the regulatory role of various environmental factors in the formation and dissipation of *Ulva prolifera* disasters, clarify the degree and pattern of influence of different factors on *Ulva prolifera* growth, and thus formulate targeted prevention and control measures for *Ulva prolifera* green tide disasters. By regulating key environmental factors, excessive growth of *Ulva prolifera* can be inhibited, reducing the likelihood and severity of disasters, and protecting the marine ecological environment and the production and living order of coastal areas. Attached Figure Description
[0051] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:
[0052] Figure 1 A schematic diagram of a method for simulating the competitive growth process of Ulva prolifera and microalgae provided by the present invention;
[0053] Figure 2 A logical framework diagram of the NUPD process coupling box model for simulating the competitive growth process of Ulva prolifera-microalgae provided by this invention;
[0054] Figure 3 The image shows the monitoring and data reconstruction results of the green tide coverage area of *Ulva prolifera*, which is a simulation method for the competitive growth process of *Ulva prolifera* and microalgae provided by this invention.
[0055] Figure 4 The present invention provides a method for simulating the competitive growth process of Ulva prolifera-microalgae, which modulates the coverage area of Ulva prolifera green tides by environmental factors. Detailed Implementation
[0056] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments in the specification without creative effort are within the scope of protection of this application.
[0057] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.
[0058] Figure 1 This is a schematic diagram of a method for simulating the competitive growth process of *Ulva prolifera* and microalgae in this invention, specifically including the following steps:
[0059] S101: Obtain remote sensing image monitoring data of Ulva prolifera and microalgal biomass in the study area; and construct three-dimensional spatiotemporal data of Ulva prolifera coverage area based on the remote sensing image monitoring data.
[0060] Optionally, based on remote sensing image monitoring data, a three-dimensional spatiotemporal distribution matrix of the *Ulva prolifera* green tide coverage area is constructed. Specifically, this includes: calculating the normalized vegetation index (NDI) from the remote sensing image monitoring data to determine the coverage area of the *Ulva prolifera* green tide disaster area; extracting the latitude and longitude of the centroid of the *Ulva prolifera* green tide disaster area as its spatial location; and determining the three-dimensional spatiotemporal distribution matrix of the *Ulva prolifera* green tide coverage area based on the spatial location of the disaster area, using the following formula:
[0061] ;
[0062] In the formula, CA U x represents the area covered by the green tide of seaweed. lon and y lon , respectively, are the longitude and latitude of the centroid of the Ulva prolifera block, and t is the time when the Ulva prolifera block was monitored by satellite.
[0063] Specifically, based on remote sensing image monitoring data of *Ulva prolifera*, the normalized vegetation index (NDI) method was applied to estimate the coverage area of *Ulva prolifera* green tide disaster zones. The latitude and longitude of the centroid points of *Ulva prolifera* zones in the remote sensing images were obtained using ArcGIS. These latitude and longitude points were then used to represent the spatial location of *Ulva prolifera*, constructing a three-dimensional spatiotemporal matrix of *Ulva prolifera* coverage area.
[0064] S102: The distribution area of the three-dimensional spatiotemporal data of the coverage area of *Ulva prolifera* is divided into grids according to the preset grid cell size; the grid cell number is used to replace the latitude and longitude of the three-dimensional spatiotemporal data of the coverage area of *Ulva prolifera*, and the three-dimensional spatiotemporal distribution matrix is reduced to two-dimensional or one-dimensional data to obtain the dimensionality-reduced data of the coverage area of *Ulva prolifera*.
[0065] Optionally, the preset grid cell size is the minimum daily increment of the coverage area in the three-dimensional spatiotemporal distribution matrix of the Ulva prolifera green tide coverage area.
[0066] Optionally, the dimensionality of the three-dimensional data of multiple *Ulva prolifera* blocks is reduced based on their spatial location numbers to obtain dimensionality-reduced *Ulva prolifera* block data. Specifically, this involves replacing the longitude and latitude of the three-dimensional data of the *Ulva prolifera* blocks with the grid cell numbers of those blocks, thus reducing the dimensionality of the three-dimensional data to two-dimensional data. The formula is as follows:
[0067] ;
[0068] In the formula, CA U The area covered by the green tide of *Ulva prolifera* is represented by S, which represents the grid cell number, and t is the time when the *Ulva prolifera* block was detected by satellite.
[0069] Applying nonlinear curve fitting and linear fitting to the two-dimensional data, the spatial location and temporal fit function of the *Ulva prolifera* patches are obtained, as shown in the formula:
[0070] ;
[0071] In the formula, the parameters are explained as follows: S represents the spatial location grid cell number of the Ulva prolifera block, t is the time of appearance of the Ulva prolifera block, a is the maximum value, k is the growth rate, and c is the inflection point.
[0072] If the spatial location S changes monotonically with time t, then the two-dimensional data can be reduced to one-dimensional data using the following formula:
[0073] ;
[0074] If the spatial location S does not change monotonically with time t, then dimensionality reduction of the two-dimensional data is not performed.
[0075] Specifically, based on the overlay of historical Ulva prolifera coverage areas, the distribution range of Ulva prolifera was determined to be approximately between 29°-38°N and 119°-128°E. The Ulva prolifera coverage area was divided into grids, with the minimum daily increase in coverage area used as the grid unit division standard. Each grid unit was approximately 0.05° × 0.05°. Considering the drift characteristics of Ulva prolifera, 29°N - 128°E was designated as the initial point, numbered N1E1, and the remaining grid units were numbered sequentially from N1E1 to N180E180. The grid units were sorted by multiplying the i and j values in the grid number NiEj, and the product S was used to locate the spatial position of the Ulva prolifera patch.
[0076] Specifically, the grid cell containing the centroid of the *Ulva prolifera* patch is used as the spatial location of the patch. The grid cell number S is used to replace latitude and longitude, thus reducing the 3D data to 2D data. Based on the 2D data of the patch occurrence date and spatial location number, nonlinear curves and linear fitting are applied to different years to obtain the adaptability of *Ulva prolifera* occurrence time and spatial location. A comparative analysis of the correspondence between *Ulva prolifera* occurrence time and spatial location since 2008 is conducted. If the spatial location of *Ulva prolifera* changes monotonically over time, then the 2D spatiotemporal variation of *Ulva prolifera* coverage area can be reduced to a 1D temporal variation.
[0077] S103: Convert the dimensionality-reduced data of Ulva prolifera coverage area into Ulva prolifera biomass; numerically simulate the generation and decay process of Ulva prolifera biomass and microalgae biomass using a nutrient-Ulva prolifera-microalgae-detritus ecodynamic box model to obtain simulated values of Ulva prolifera coverage area changing over time; adjust the values of environmental element variables in the box model one by one using the controlled variable method, and simulate the generation and decay process of Ulva prolifera biomass and microalgae biomass to obtain simulated values of Ulva prolifera coverage area changing over time under different values of each environmental element variable.
[0078] Specifically, a NUPD process-coupled box model logical architecture was constructed, comprising two major sub-models: the Ulva prolifera model and the microalgae model. Each sub-model includes three modules: direct conversion of nitrogen and phosphorus nutrients, biomigration and conversion of nitrogen and phosphorus nutrients, and algal biogenesis and decomposition. The main variables include nitrogen and phosphorus nutrients, Ulva prolifera biomass, microalgae biomass and detritus, as well as environmental constraints such as temperature and light.
[0079] Specifically, field experiments were conducted on target ecosystems / enriched nitrogen and phosphorus nutrients in culture bottles. The experiments included two major series: (1) a series of algae removal / enriched nutrient culture bottles corresponding to the direct conversion process of nitrogen and phosphorus nutrients; and (2) a series of Ulva and microalgae / enriched nutrient culture bottles corresponding to the biogenesis and biotransformation of Ulva and microalgae and the biological migration and transformation process of nitrogen and phosphorus nutrients. Field experiments on enriched nitrogen and phosphorus nutrients in culture bottles could be conducted near the coast of Nantong, Rizhao, and Qingdao. The variables measured in the experiments included NH4-N, NO2-N, NO3-N, TDN, TPN, PO4-P, TDP, TPP, and SiO3-Si. The uncertainty and random deviation of the field experimental data were quantitatively evaluated by the relative standard deviation (RSD) of the two samples and the volatility of TN and TP. The smaller the relative standard deviation and volatility, the more precise the experimental results.
[0080] Specifically, whether in algae-free / nutrient-enriched culture flasks or in Ulva prolifera and microalgae / nutrient-enriched culture flasks, the direct conversion of nitrogen and phosphorus nutrients, the biomigration and transformation process, and the growth / death process of Ulva prolifera and microalgae may all exhibit different segmented forms. Therefore, it is necessary to identify their segmentation nodes and comprehensively apply statistical methods such as Partial Mann-Kendall trend analysis to determine the significance of each process. For significant kinetic processes, the optimal kinetic equation form needs to be selected. In the direct conversion of nitrogen and phosphorus nutrients, NH4-N nitrification, NO2-N nitrification, and NO3-N denitrification often use first-order kinetic equations. In the biomigration and transformation process of nitrogen and phosphorus nutrients, the nutrient absorption of Ulva prolifera and microalgae often uses the Monod equation. The growth process of Ulva prolifera and microalgae is closely related to their absorption of nitrogen and phosphorus nutrients, and they are generally considered to conform to the "positive S-shaped" Logistic equation; while the death process of Ulva prolifera and microalgae is closely linked to the intracellular nitrogen and phosphorus degradation process, exhibiting the characteristics of the "inverse S-shaped" Logistic equation. The phenomena of DOM released by seaweed and microalgae cells, the biodebris generated, degradation, and DOM mineralization produced by cells can usually be described by first-order kinetic equations.
[0081] Specifically, the NUPD process-coupled box model is a multi-process box model that requires parameter calibration to optimize parameter values. The parameter calibration process includes regional correction of process kinetic equations, Monte Carlo analysis of parameter sensitivity, setting initial parameter values based on the model's parameter sensitivity analysis results, step-by-step simulation calculations, and evaluation of the accuracy of the simulation results. Low-to-medium sensitivity parameters can use authoritative values from the NOVECOM model or parameter values verified through marine surveys, while high-sensitivity parameters need to be determined based on the results of nonlinear fitting from field experiments. When evaluating the accuracy of the simulation results, the relative standard deviation (RSD) and similarity coefficient (SI) are used to measure the degree of agreement between the simulation results and experimental results. To simulate the temporal changes in the concentrations of different forms of nitrogen and phosphorus nutrients in the target ecosystem—nutrient-enriched culture bottles—the Runge-Kutta simulation method was used for parameter calibration. Since multiple processes may occur in different series of culture bottles, the model parameters were calibrated in order from simple to complex, and gradually adjusted based on experimental results. In the initial round of simulation calculations, the model parameters for the biodiversity and degradation of *Ulva prolifera* and microalgae, as well as the migration and transformation of nitrogen and phosphorus nutrients, were set to 0, and parameter calibration was performed on the direct conversion of nitrogen and phosphorus nutrients. Then, in the second round of simulation calculations, the results of a series of experiments with *Ulva prolifera* and microalgae in nutrient-enriched culture bottles were comprehensively considered, and parameter calibration was performed on all processes. Finally, through multiple cyclic approximations, simulation results were obtained that were consistent with the experimental data in all aspects with an error of less than 20% and a temporal similarity greater than 80%, and the parameters for each process were determined. The accuracy of the NUPD process coupling model was evaluated by comparing the simulation results of the temporal changes in *Ulva prolifera* / microalgae biomass in other "independent" field experiments with the actual experimental results.
[0082] Specifically, the NUPD model was used to simulate the occurrence and extinction process of *Ulva prolifera*. Based on the occurrence, development, and extinction process of *Ulva prolifera*, three boxes were set up in the NUPD model: Zone 1, Zone 2, and Zone 3. The boundaries between zones were defined by the cross-sections between Zone 1 and Zone 2, and Zones 2 and 3, respectively. The three NUPD boxes were connected in series using the inter-regional diversion coefficients. The inter-regional diversion coefficient is the proportion of *Ulva prolifera* biomass crossing the cross-sections between Zone 1 and Zone 2 and Zone 2 and Zone 3 daily, relative to the total *Ulva prolifera* biomass. The inter-regional drift coefficient is mainly determined by the drift velocity of the *Ulva prolifera* blocks; the average inter-regional drift coefficients for the cross-sections between Zone 1 and Zone 2, and Zone 2 and Zone 3, are 0.01 and 0.005, respectively. Based on the conversion factor (ξ) between *Ulva prolifera* biomass and coverage area, the simulated value of the temporal change in the *Ulva prolifera* green tide coverage area can be calculated using the following formula:
[0083] ;
[0084] In the formula, B U (t) represents the biomass of *Ulva prolifera* at time t (gC); ξ is the conversion factor between *Ulva prolifera* biomass and its coverage area (gC / m²). 2 ).
[0085] Based on environmental factors during the green tide outbreak in the target year, the time series results of the Ulva prolifera coverage area (i.e., biomass) were calculated. Using the ModelMaker 4.0 software tool, the biomass changes of Ulva prolifera were reconstructed using a three-box NUPD model. The uncertainty of the numerical reconstruction simulation was tested and evaluated using the relative deviation (RD) and similarity index (SI) between the simulation results and marine survey and monitoring results.
[0086] Specifically, in the nutrient-Ulva prolifera-microalgae-detritus ecodynamic model, the environmental limiting functions mainly include light intensity, temperature, and nutrient limitation, which can be described as:
[0087] (1) Illumination constraint function
[0088] ;
[0089] ;
[0090] in, Light intensity (W / m 2 ); The optimal light intensity for microalgae (W / m²) 2 ); Optimal light intensity for Ulva prolifera (W / m²) 2 ).
[0091] (2) Temperature limit function
[0092] ;
[0093] ;
[0094] Where T is temperature (°C); TG is temperature coefficient (°C). -1 ); and These represent the highest and lowest temperatures (°C) for the growth of Ulva prolifera.
[0095] (3) Nitrate nitrogen restriction function
[0096] ;
[0097] ;
[0098] in, , and These represent the concentrations of nitrate nitrogen, ammonia nitrogen, and inorganic nitrogen nutrients (μmol·L⁻¹). -1 ); , and The half-saturation constants (μmol·L⁻¹) for the absorption of nitrate nitrogen, ammonia nitrogen, and inorganic nitrogen by microalgae, respectively. -1 ).
[0099] (4) Ammonia nitrogen limiting function
[0100] ;
[0101] ;
[0102] (5) Limiting function of dissolved organic nitrogen
[0103] ;
[0104] ;
[0105] ;
[0106] ;
[0107] in, , and These represent the concentrations (μmol·L⁻¹) of terrestrial dissolved organic nitrogen, terrestrial recalcitrant dissolved organic nitrogen, and readily degradable dissolved organic nitrogen nutrients. -1 ); , , and The half-saturation constants (μmol·L⁻¹) for the absorption of terrestrial dissolved organic nitrogen by microalgae, the absorption of terrestrial dissolved organic nitrogen by Ulva prolifera, the absorption of readily degradable dissolved organic nitrogen by microalgae, and the absorption of readily degradable dissolved organic nitrogen by Ulva prolifera are respectively. -1 ).
[0108] (6) Maximum absorption / assimilation limitation function of different forms of nitrogen
[0109] ;
[0110] ;
[0111] (7) Phosphate restriction function
[0112] ;
[0113] ;
[0114] in, Phosphate nutrient concentration (μmol·L) -1 ); and The half-saturation constants (μmol·L⁻¹) for phosphate absorption by microalgae and Ulva prolifera, respectively. -1 ).
[0115] (8) Limiting function of dissolved organophosphorus compounds
[0116] ;
[0117] in, and These represent the concentrations of terrestrial dissolved organophosphorus and terrestrial recalcitrant dissolved organophosphorus nutrients (μmol·L⁻¹). -1 ); The half-saturation constant (μmol·L⁻¹) for the absorption of terrestrial dissolved organic phosphorus by *Ulva prolifera*. -1 ).
[0118] (9) Silicate restriction function
[0119]
[0120] in, Nutrient concentration of silicate (μmol·L) -1 ); The half-saturation constant of silicate absorption by microalgae (μmol·L⁻¹) -1 ).
[0121] (10) Nutrient constraint function
[0122] ;
[0123] ;
[0124] ;
[0125] ;
[0126] in, and These are the minimum nitrogen and phosphorus concentrations (μmol·L⁻¹) required for the growth of *Ulva prolifera*. -1 ); and The particulate nitrogen and phosphorus contents of *Ulva prolifera* (μmol·L⁻¹) are respectively. -1 ); Chlorophyll content of Ulva prolifera (μmol·L) -1 ).
[0127] (11) Respiratory restriction function
[0128] ;
[0129] ;
[0130] in, and These represent the contents of particulate nitrogen and chlorophyll in microalgae, respectively (μmol·L⁻¹). -1 ); and These are the half-saturation constants of respiration in microalgae and seaweed, respectively. The ratio of nitrogen to chlorophyll in *Ulva prolifera*.
[0131] In this model, the migration and transformation processes of nitrogen, phosphorus, and silicon nutrients in both the *Ulva prolifera* and microalgae modules are described by first-order differential kinetic equations, and each state variable can be described by the following equations:
[0132] ;
[0133] ;
[0134] ;
[0135] ;
[0136] ;
[0137] ;
[0138] ;
[0139] ;
[0140] ;
[0141] ;
[0142] ;
[0143] ;
[0144] ;
[0145] ;
[0146] ;
[0147] ;
[0148] ;
[0149] ;
[0150] ;
[0151] in, , , and Nitrogen and phosphorus content (μmol·L⁻¹) of debris generated from the degeneration of microalgae and Ulva prolifera, respectively. -1 ); The nitrification and denitrification rate constants (d) -1 ) The rate constants for nitrification and reduction (d) -1 ) and The mineralization rate constants (d) of terrestrial dissolved organic nitrogen and phosphorus, respectively. -1 ) and These are the mineralization rate constants (d) of readily degradable dissolved organic nitrogen and phosphorus from algae sources. -1 ) and The aging rate constant (d) for easily degradable dissolved organic nitrogen and phosphorus -1 ) The mineralization rate constant (d) for dissolving organic silicates -1 ) , and These are the maximum absorption rate constants (d) of nitrogen, phosphorus, and silicon nutrients by microalgae. -1 ) and These are the maximum absorption rate constants (d) of nitrogen and phosphorus nutrients by Ulva prolifera. -1 ) , , and Nitrogen and phosphorus release rate constants (d) for microalgae and seaweed, respectively. -1 ) , , and Detritus formation rate constants (d) for microalgae and Ulva prolifera, respectively. -1 ) , , and Decomposition rate constants (d) for microalgae and Ulva prolifera, respectively. -1 ).
[0152] The differential equations for the growth and extinction processes of *Ulva prolifera* and microalgae in the NUPD coupled box model are expressed as follows:
[0153] ;
[0154] ;
[0155] in, and These are the maximum growth rate constants (d) for microalgae and Ulva prolifera, respectively. -1 ); and These are the maximum mortality rate constants (d) for microalgae and Ulva prolifera, respectively. -1 ).
[0156] S104: Based on the simulated values of the change in the coverage area of *Ulva prolifera* over time and the simulated values of the change in the coverage area of *Ulva prolifera* over time at different values for each environmental factor variable, determine the modulation effect value of each environmental factor; based on the modulation effect value, determine the criticality of each environmental factor in controlling the competitive growth process of *Ulva prolifera*.
[0157] Optionally, the modulatory effect value of each environmental factor is determined based on the variation amplitude between the maximum simulated value of the annual Ulva prolifera coverage area changing over time and the maximum simulated value of the annual Ulva prolifera coverage area changing over time for each environmental factor variable at different values. Specifically, this includes: selecting the maximum simulated value of the annual Ulva prolifera coverage area changing over time and the maximum simulated value of the annual Ulva prolifera coverage area changing over time for each environmental factor variable at different values; and determining the modulatory effect value of the Ulva prolifera green tide disaster control factor based on the variation amplitude between the maximum simulated value of the annual Ulva prolifera coverage area changing over time and the maximum simulated value of the annual Ulva prolifera coverage area changing over time for each environmental factor variable at different values. The formula is:
[0158] ;
[0159] In the formula, Re represents the modulation effect value of environmental factors, and S Re_max S represents the simulated value of the annual Ulva prolifera coverage area over time for each environmental variable at different values. Ac_max This represents the maximum value of the simulated annual change in the area covered by *Ulva prolifera* over time.
[0160] Optionally, the environmental variables in the nutrient-Ulva-microalgae-detritus ecodynamic box model include: the coverage area of the initial Ulva-inoculation block, sea surface temperature, light intensity, and nitrogen and phosphorus nutrient concentrations.
[0161] Optionally, the modulation effect value is proportional to the degree to which environmental factors control the competitive growth process of Ulva prolifera.
[0162] Specifically, because the growth of *Ulva prolifera* is regulated by multiple factors, changes in control factors such as the initial coverage area of *Ulva prolifera*, sea surface temperature, light intensity, and nitrogen and phosphorus nutrient concentrations typically lead to changes in the *Ulva prolifera* coverage area. The moderating effect of control factors refers to the regulatory role played during the occurrence and dissipation of *Ulva prolifera* disasters, which can be characterized by the magnitude of change in the maximum coverage area of the disaster. By statistically analyzing the interannual variations of key control factors in *Ulva prolifera*-occurring sea areas, and using the annual average values of environmental factors in these areas since 2008 as the scenario for numerical experiments, the moderating effect of control factors on *Ulva prolifera* green tide disasters can be tested and evaluated based on the magnitude of change in the maximum coverage area of the disaster under the annual average values of control factors.
[0163] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this invention.
Claims
1. A method of simulating a competitive growth process of Enteromorpha - microalgae, characterized by, The method comprises the following steps: acquiring monitoring data of Enteromorpha remote sensing images and microalgae biomass in a research area; constructing three-dimensional spatio-temporal data of Enteromorpha coverage area according to the monitoring data of Enteromorpha remote sensing images; dividing the distribution area of the three-dimensional spatio-temporal data of Enteromorpha coverage area into grids according to a preset grid unit size; using the serial number of the grid unit to replace the longitude and latitude of the three-dimensional spatio-temporal data of Enteromorpha green tide coverage area, reducing the dimensionality of the three-dimensional spatio-temporal data into two-dimensional or one-dimensional data to obtain reduced dimensionality data of Enteromorpha coverage area; converting the reduced dimensionality data of Enteromorpha coverage area into Enteromorpha biomass; numerically simulating the growth and decline process of Enteromorpha biomass and microalgae biomass by using a nutrient salt-Enteromorpha-microalgae-detritus ecological dynamics box model to obtain simulated values of Enteromorpha coverage area changing with time; adjusting the numerical values of environmental factor variables in the nutrient salt-Enteromorpha-microalgae-detritus ecological dynamics box model one by one by using the control variable method, and simulating the growth and decline process of Enteromorpha biomass and microalgae biomass to obtain simulated values of Enteromorpha coverage area changing with time under different numerical values of each environmental factor variable; wherein the nutrient salt-Enteromorpha-microalgae-detritus ecological dynamics box model comprises an Enteromorpha sub-model and a microalgae sub-model; both the Enteromorpha sub-model and the microalgae sub-model comprise a nitrogen and phosphorus nutrient salt direct conversion module, a nitrogen and phosphorus nutrient salt biological migration and conversion module, and an algae growth and decline module; the construction process of the nutrient salt-Enteromorpha-microalgae-detritus ecological dynamics box model specifically comprises: dividing the research area of the reduced dimensionality data of Enteromorpha coverage area into three partitions according to the cross section of the research area as the division limit, each partition corresponding to a box model; determining a cross-area diversion coefficient according to the drift speed of the Enteromorpha green tide across the partition limit; connecting the three box models through the cross-area diversion coefficient between adjacent partition boxes to obtain the nutrient salt-Enteromorpha-microalgae-detritus ecological dynamics box model; determining the adjustment effect value of each environmental factor according to the simulated values of Enteromorpha coverage area changing with time and the simulated values of Enteromorpha coverage area changing with time under different numerical values of each environmental factor variable; and determining the key degree of each environmental factor in controlling the competitive growth process of Enteromorpha according to the adjustment effect value.
2. The Enteromorpha-microalga competitive growth process simulation method according to claim 1, characterized in that, The construction of the three-dimensional spatio-temporal data of Enteromorpha coverage area according to the monitoring data of Enteromorpha remote sensing images specifically comprises: calculating the normalized difference vegetation index of the monitoring data of Enteromorpha remote sensing images to determine the coverage area of the Enteromorpha green tide disaster block; extracting the centroid point longitude and latitude of the Enteromorpha green tide disaster block as the spatial position of the Enteromorpha green tide disaster block; determining the three-dimensional spatio-temporal data of Enteromorpha green tide coverage area according to the spatial position of the Enteromorpha green tide disaster block, the formula being: ； where CA U represents the coverage area of green tide, x lon and y lon are the longitude and latitude of the centroid of the green tide block, respectively, and t is the time when the green tide block is monitored by the satellite.
3. The Enteromorpha-microalga competitive growth process simulation method according to claim 1, characterized by, the preset grid unit size is the minimum increment of the daily coverage area in the three-dimensional spatio-temporal data of Enteromorpha green tide coverage area.
4. The Enteromorpha-microalga competitive growth process simulation method according to claim 1, wherein, using the serial number of the grid unit to replace the longitude and latitude of the three-dimensional data of the Enteromorpha block to reduce the dimensionality of the three-dimensional data into two-dimensional data, the formula being: using the grid unit number of the Enteromorpha block to replace the longitude and latitude in the three-dimensional data of the Enteromorpha block to reduce the dimensionality of the three-dimensional data into two-dimensional data, the formula being: ； where CA U S represents the grid cell serial number, and t represents the time when the Enteromorpha zone was monitored by satellite. Applying non-linear curve fitting and linear fitting to the two-dimensional data, a spatial position and time adaptive function of the Enteromorpha block is obtained, and the formula is: ； In the formula, S represents the spatial position grid cell number of the Enteromorpha block, t is the appearance time of the Enteromorpha block, a is the maximum value, k is the growth rate, and c is the inflection point. If the spatial position S changes monotonically with time t, the two-dimensional data is reduced to one-dimensional data, and the formula is: ； If the spatial position S does not change monotonically with time t, the two-dimensional data is not reduced.
5. The Enteromorpha-microalga competitive growth process simulation method according to claim 1, characterized by, The conversion formula of the reduced dimension of the Enteromorpha coverage area to the Enteromorpha biomass is: ； In the formula, B U (t) is the biomass of Enteromorpha at time t, ξ is the conversion coefficient of Enteromorpha biomass and covered area, CA U (t) represents the covered area of Enteromorpha at time t.
6. The Enteromorpha-microalga competitive growth process simulation method according to claim 1, wherein, The numerical simulation formulas of the growth and decline processes of the Enteromorpha biomass and the microalgae biomass are respectively: ； ； wherein, is the microalgal chlorophyll content, is the Enteromorpha chlorophyll content, and are the maximum growth rate constants of the microalgae and Enteromorpha, respectively; and are the maximum mortality rate constants of the microalgae and Enteromorpha, respectively, is the light limitation coefficient in the microalgal growth and decay process, is the temperature limitation coefficient in the microalgal growth and decay process, is the nitrogen and phosphorus nutrient limitation coefficient in the microalgal growth and decay process, is the limitation coefficient in the microalgal respiration process, is the light limitation coefficient in the Enteromorpha growth and decay process, is the temperature limitation coefficient in the Enteromorpha growth and decay process, is the limitation coefficient in the Enteromorpha respiration process, is the nitrogen and phosphorus nutrient limitation coefficient in the Enteromorpha growth and decay process.
7. The Enteromorpha-microalga competitive growth process simulation method according to claim 1, wherein, The environmental factor variables in the nutrient salt-Enteromorpha-microalgae-debris ecological dynamics box model include: the initial coverage area of the Enteromorpha block, the sea surface temperature, the light intensity, and the nitrogen and phosphorus nutrient salt concentrations.
8. The Enteromorpha-microalga competitive growth process simulation method according to claim 1, wherein, The determination of the modulation effect value of each environmental factor according to the simulation value of the change of the Enteromorpha coverage area with time and the simulation value of the change of the Enteromorpha coverage area with time under different numerical values of each environmental factor variable, specifically includes: Selecting the maximum value of the simulation value of the change of the Enteromorpha coverage area with time each year and the maximum value of the simulation value of the change of the Enteromorpha coverage area with time each year under different numerical values of each environmental factor variable; According to the change amplitude of the maximum value of the simulation value of the change of the Enteromorpha coverage area with time each year and the maximum value of the simulation value of the change of the Enteromorpha coverage area with time each year under different numerical values of each environmental factor variable, the modulation effect value of the Enteromorpha green tide disaster control factor is determined, and the formula is: ； where Re represents the modulating effect value of the environmental element, S Re_max represents the simulated value of the annual coverage area of green algae over time for each environmental element variable at different values, S Ac_max represents the maximum value of the simulated value of the annual coverage area of green algae over time.
9. The Enteromorpha-microalga competitive growth process simulation method according to claim 1, characterized by, The modulation effect value is proportional to the key degree of the environmental factor control of the competitive growth process of the Enteromorpha.