CN-121998187-A - Method for predicting variation response of offshore phytoplankton structure based on ecological power model

CN121998187ACN 121998187 ACN121998187 ACN 121998187ACN-121998187-A

Abstract

The invention relates to the technical field of marine ecological environment monitoring, in particular to a method for predicting the variation response of a close shore phytoplankton structure based on an ecological power model, which comprises the following steps of S1, collecting environmental data of a close shore area; S2, calculating the growth rate parameters of phytoplankton based on environmental data, S3, simulating the change of the phytoplankton structure by using an ecological power model, S4, predicting the response of the phytoplankton to a preset environmental change scene according to the simulated phytoplankton structure change, and outputting a predicted response report. According to the invention, the phytoplankton structure evolution is dynamically simulated through the ecological power model, and the accurate prediction of community response trend is realized by combining multi-scene comparison analysis, so that the prejudging capability of the change of the offshore ecological system and the scientificity of management decision are improved.

Inventors

WANG YIBIN
YIN XIAOFEI
WANG ZONGXING
DU NING
WANG JIANBU

Assignees

自然资源部第一海洋研究所

Dates

Publication Date: 20260508
Application Date: 20260126

Claims (10)

1. The method for predicting the variation response of the structure of the offshore phytoplankton based on the ecological power model is characterized by comprising the following steps of: S1, collecting environmental data of an offshore area, wherein the environmental data comprise water temperature, illumination intensity, nutrient salt concentration and water flow speed; s2, calculating the growth rate parameters of phytoplankton based on the environmental data, wherein the parameters comprise a maximum growth rate and a half-saturation constant; S3, simulating the change of the phytoplankton structure by using an ecological power model based on the growth rate parameters in the S2; S4, predicting the response of the phytoplankton to a preset environment change scene according to the simulated phytoplankton structure change, and outputting a predicted response report.
2. The method for predicting the response of the structure change of the offshore phytoplankton based on the ecological power model according to claim 1, wherein the step S1 specifically comprises: S11, distributing multi-parameter water quality sensor nodes at representative sampling points of a near-shore area, wherein the multi-parameter water quality sensor comprises a temperature sensor, an illumination sensor, a nutritive salt electrode sensor and a flowmeter; s12, measuring the water temperature value of the water body at a set depth layer through a temperature sensor; S13, acquiring the total-day illumination intensity distribution of the water surface layer through an illumination sensor; s14, synchronously collecting the concentrations of nitrate, nitrite and phosphate in the water body through a nutritive salt electrode sensor; S15, measuring the horizontal flow velocity and the vertical flow velocity of different tide level stages at each sampling point by a flowmeter, removing instantaneous fluctuation noise by adopting a 60-second moving average mode, and outputting a stable water flow velocity value.
3. The method for predicting the response of the structure change of the offshore phytoplankton based on the ecological power model according to claim 1, wherein the step S2 specifically comprises: s21, carrying out normalization processing on the environmental data obtained in the step S1, and respectively carrying out unit unification and time sequence smoothing on the water temperature, the illumination intensity, the nutrient salt concentration and the water flow speed to form an environmental input matrix; s22, correcting by utilizing a temperature sensitivity coefficient according to the deviation between the measured water temperature value and the standard growth temperature to obtain a specific growth rate after temperature correction; S23, using the measured nutrient salt concentration as an input variable, fitting the relation between the nutrient salt concentration and the growth rate by adopting a Monod kinetic equation, and calculating the maximum growth rate and the half-saturation constant; And S24, normalizing and synthesizing the growth rate after temperature correction and the nutrient salt response result, and outputting a comprehensive growth rate parameter set under the current environmental condition.
4. The method for predicting the response of the structure change of the offshore phytoplankton based on the ecological power model according to claim 1, wherein the step S3 specifically comprises: s31, constructing a biomass change equation of phytoplankton population according to the growth rate parameters obtained in the S2, setting the number and the proportion of initial population, and determining the initial state of community evolution; s32, inputting environmental data of water temperature, illumination intensity, nutrient salt concentration and water flow speed, and calculating growth and loss rates of each phytoplankton population in continuous time steps through an ecological power model to obtain biomass data at each moment; S33, calculating the proportion of each species to the total biomass of the community according to the biomass data output in the S32, and forming a proportion result of dominant species of the community; and S34, summarizing all the population biomass to obtain the community total biomass, and calculating a diversity index according to the population quantity distribution result.
5. The method for predicting the response to the structural change of the phytoplankton on the coast based on the ecological power model according to claim 4, wherein the step S32 specifically comprises: S321, combining the growth rate parameters obtained in the step S2 with the biomass change equation established in the step S31, and inputting data of water temperature, illumination intensity, nutrient salt concentration and water flow speed of a near-shore area to form an input parameter set of an ecological power model; S322, according to the set time step Performing numerical integration calculation on each phytoplankton population in a continuous time interval, and solving a biomass change equation to obtain an instantaneous growth rate and a loss rate in each time step; S323, subtracting the growth rate calculation result and the loss rate result to obtain the net variation of each phytoplankton population in the current time step, accumulating the net variation to the biomass value of the previous time step, and updating to obtain new population biomass; s324, performing time step iteration circularly until a preset simulation period is reached, and outputting biomass data sequences under all time nodes.
6. The method for predicting a response to a change in a structure of an offshore phytoplankton based on an ecological power model according to claim 5, wherein S33 specifically comprises: S331, extracting biomass data of each phytoplankton population output in S32 at the same time node, constructing a biomass vector set at the current moment, and representing the biomass vector set as Wherein n is the number of phytoplankton species; S332, summing up biomass of all species of the current moment node to obtain community total biomass A reference value for calculation as a duty ratio; S333, for each species i, mixing its biomass with the total biomass Dividing and calculating the relative proportion of the components in the community Forming a community dominant species proportion result; S334, the proportion distribution sequence is formed by the proportion results of all the species, the proportion distribution sequence is ordered according to the values from high to low, and a plurality of species with the highest proportion before the proportion is marked as dominant species, so that a community dominant species proportion result at the current moment is formed.
7. The method for predicting the response to the structural change of the phytoplankton on the coast based on the ecological power model according to claim 6, wherein the step S34 specifically comprises: S341, calling the community ratio sequence of each phytoplankton population obtained in S33 at the current time node, and marking as Wherein Is the total number of species in the community; S342, based on the duty ratio sequence, comprehensively evaluating the richness and uniformity of the group-falling object composition by adopting a Shannon diversity index calculation method, wherein the calculation formula is as follows: , wherein, For the Shannon diversity index, Is the community ratio of the ith phytoplankton, As the total number of species at the present moment, Is natural logarithm; S343, repeatedly executing the diversity index calculation of each time node to obtain a time sequence result of community diversity in the simulation period.
8. The method for predicting the response of the structure change of the offshore phytoplankton based on the ecological power model according to claim 1, wherein the step S4 specifically comprises: S41, setting a plurality of environment change scenes including water temperature rise, illumination change, nutrient salt concentration change and water flow speed disturbance, and inputting an environment factor value in each scene into an ecological power model to replace an original environment parameter to serve as an input condition of prediction simulation; S42, calling the ecological power model established in the S3 under each environment change scene, recalculating the biomass dynamic change process of the phytoplankton population based on the growth rate parameters obtained in the S2, and outputting biomass sequences and community structure evolution data of each species in a simulation period; S43, respectively extracting the proportion of dominant species, the total biomass and the diversity index of the community obtained by simulation in each scene, and comparing the proportion with the corresponding value in the reference scene to generate community response trend data in each environment scene, wherein the community response trend data comprises species replacement, community stability change and biomass fluctuation; and S44, generating a prediction response report based on community response trend data of the step S43, and outputting the prediction response report in a form of a chart and a data sequence.
9. The method for predicting the response to the structural change of the phytoplankton on shore based on the ecological power model according to claim 8, wherein S43 specifically comprises: S431, respectively extracting community structure data obtained by simulation under various environmental change scenes, including biomass of various phytoplankton populations at each moment Total biomass of community Ratio of community Diversity index Where s represents a scene number and t represents a time node; s432, community structure indexes under each scene and reference scenes are obtained The results under the corresponding time nodes are compared item by item to obtain a difference value and a change rate; S433, based on the dominant species sequencing list under each time node, counting the dissimilarity of the first k dominant species between each environmental scene and the reference scene, and if the sequence or composition of the specified species changes, recording as a species replacement event; s434, integrating the difference data, and outputting a phytoplankton response trend data set of each environmental scene compared with the reference scene, wherein the phytoplankton response trend data set comprises a biomass fluctuation curve, a dominant species change map and a diversity index change track.
10. The method for predicting the response of a variation in the structure of an offshore phytoplankton based on an ecological power model according to claim 8, wherein the predicted response report comprises environmental disturbance variables, a variation index of the structure of the phytoplankton, a variation amplitude of a key population and response time sequence.

Description

Method for predicting variation response of offshore phytoplankton structure based on ecological power model Technical Field The invention relates to the technical field of marine ecological environment monitoring, in particular to a method for predicting the response of the variation of a close shore phytoplankton structure based on an ecological power model. Background The method is characterized in that the coastal water area is an important ecological area in which phytoplankton is carried out frequently, the phytoplankton structure directly influences the primary productivity, the nutrition level structure and the stability of the offshore fishery resource, the coastal area is influenced by the superposition of multiple factors such as land source input, water body exchange and climate disturbance, the space-time fluctuation of environmental factors such as water temperature, illumination, nutrient salt concentration and hydrodynamic force is obvious, so that the phytoplankton group is caused to quickly respond and restructure in a short time, the ecological management and pollution prevention and control at present have higher requirements on the prediction of abnormal proliferation (such as red tide) of the phytoplankton, and a dynamic prediction means capable of reflecting an internal regulation mechanism of an ecological system is needed to be established so as to pre-judge the change trend of the community structure in advance. Most of the existing methods rely on static monitoring or experience statistical models, lack quantitative description of dynamic processes such as competition, growth, resource utilization and the like among phytoplankton populations, are difficult to accurately predict community succession trends under different environmental change scenes, cannot output evolution paths of key ecological structure indexes such as dominant species proportion, diversity index and the like, and limit scientific evaluation and early warning of complex ecological disturbance response. Therefore, it is necessary to construct a method for predicting the response of the variation of the structure of the community of the offshore phytoplankton based on the ecological power model, so that the accuracy and the adaptability of the community response prediction are improved. Disclosure of Invention Based on the purposes, the invention provides a method for predicting the response of the variation of the structure of the close shore phytoplankton based on an ecological power model. An ecological power model-based method for predicting the variation response of the structure of an offshore phytoplankton comprises the following steps: S1, collecting environmental data of an offshore area, wherein the environmental data comprise water temperature, illumination intensity, nutrient salt concentration and water flow speed; s2, calculating the growth rate parameters of phytoplankton based on the environmental data, wherein the parameters comprise a maximum growth rate and a half-saturation constant; S3, simulating the change of the phytoplankton structure by using an ecological power model based on the growth rate parameters in the S2; S4, predicting the response of the phytoplankton to a preset environment change scene according to the simulated phytoplankton structure change, and outputting a predicted response report. Optionally, the S1 specifically includes: S11, distributing multi-parameter water quality sensor nodes at representative sampling points of a near-shore area, wherein the multi-parameter water quality sensor comprises a temperature sensor, an illumination sensor, a nutritive salt electrode sensor and a flowmeter; s12, measuring the water temperature value of the water body at a set depth layer through a temperature sensor; S13, acquiring the total-day illumination intensity distribution of the water surface layer through an illumination sensor; s14, synchronously collecting the concentrations of nitrate, nitrite and phosphate in the water body through a nutritive salt electrode sensor; S15, measuring the horizontal flow velocity and the vertical flow velocity of different tide level stages at each sampling point by a flowmeter, removing instantaneous fluctuation noise by adopting a 60-second moving average mode, and outputting a stable water flow velocity value. Optionally, the S2 specifically includes: s21, carrying out normalization processing on the environmental data obtained in the step S1, and respectively carrying out unit unification and time sequence smoothing on the water temperature, the illumination intensity, the nutrient salt concentration and the water flow speed to form an environmental input matrix; s22, correcting by utilizing a temperature sensitivity coefficient according to the deviation between the measured water temperature value and the standard growth temperature to obtain a specific growth rate after temperature correction; S23, using the measured nutrient salt conce