CN102567621A - Method for building phytoplankton in lakes during historic period - Google Patents

Abstract

The invention relates to a method for building phytoplankton in lakes during historic period, which aims to solve the problem of deficiency of data of living species in the lakes during historic period. The method includes: utilizing wavelet analysis, genetic algorithm, a mathematic model of canonical correspondence analysis to build phytoplankton during historic period on the basis of physical and chemical factors of surface water of the lakes, aquatic phytoplankton and physical factors of sediments of the surface layer, and finally acquiring action relation of the phytoplankton and the physical and chemical factors in the lakes during historic period. Accordingly, the method for building phytoplankton in the lakes during historic period has significance to acquire lake environment evolution and states of ecosystems during historic period.

Description

A kind of method that makes up period of history lake phytoplankton

Technical field

The invention belongs to aquatic ecosystem monitoring field, relating to a kind of is foundation with lake table water physical and chemical factor, table water phytoplankton and surface deposit physical and chemical factor, utilizes mathematical model to make up the method for period of history lake phytoplankton.

Background technology

Phytoplankton is the important component part of plant community, and phytoplankton just is meant planktonic algae usually.Phytoplankton is primary producer important in the lake ecosystem, and it has started the food web in the lake, in energy Flow, material cycle and the information transmission of lake ecosystem, plays crucial effects.The structure of community of phytoplankton and diversity change can be responsive reflect that envirment factor is to Ecosystem System Influence.The cell wall structure of different phytoplanktons is difference to some extent; In deposition process; Diatom has been resisted the corrosion of soda acid with its siliceous cell wall; And phytoplanktons such as green alga, blue-green algae can be decomposed gradually, cause most of phytoplankton to preserve with the form of biological fossil, promptly can not differentiate the phytoplankton species through conventional Bioexperiment.Green alga, blue-green algae group can effectively indicate the nutrition degree in lake; The fluctuation of lake dissolved oxygen concentration can be reflected in dinoflagellate group; Therefore there is stronger response in diatom group to the water level and flow in lake, makes up period of history lake phytoplankton to understanding lake ecosystem and change and Environment Change having great significance.

In recent ten years the research of phytoplankton is progressively goed deep into.Domestic; There is the scholar to utilize the multi-dimentional scale analysis that abundance, biomass and the group's relation of Various Seasonal phytoplankton are studied; Because the growth temperature of diatom is low than green alga and blue-green algae; Simultaneously be the peak season of diatom breeding spring, thus the biomass of phytoplankton and abundance spring than obviously increasing (Wang Jianguo etc., 2011) other seasons; There is the scholar to utilize Pearson correlation analysis and model's correspondence analysis that the relation of growth and decline of phytoplankton quantity and envirment factor is inquired into; The result shows that water temperature, dissolved oxygen DO and transparency are the main affecting factors (Wang Liqing etc., 2011) of phytoplankton quantity growth and decline; There is the scholar to find that pH and total phosphorus concentration in the water body are the key factors (Shen Huitao etc., 2008) that influences the phytoplankton Distribution Pattern.Abroad; The Continuous Observation to phytoplankton and envirment factor is more paid attention in research; At first confirm the main phytoplankton species in zone; Find that through long term monitoring temperature is restriction phytoplankton figure's factor of determination (Alam et al; 2001); Being applied to predicting biomass and the turnout (

et al.) of lake phytoplankton based on the dynamic model of food web, is a trend (Popovich et al.) of research phytoplankton and envirment factor effect with the combination in duration sequence time and large scale space.The patent of invention that phytoplankton is measured is found in retrieval; Application number is 200910119854.0; Name is called a kind of phytoplankton composition quick determination method, and publication number is CN101561395, and this method comprises 3 steps: obtain phytoplankton fluorescence spectrum original spectrum storehouse; Make up the higher-dimension orthogonal intersection space, extract the phytoplankton fluorescence spectral characteristic; Phytoplankton fluorescent spectrum is carried out discriminance analysis, finally confirm the phytoplankton composition.

The patent of invention that phytoplankton detects is found in retrieval, and application number is 201010184785.4, and name is called a kind of detection method of phytoplankton structure, and publication number is CN101838700A, and this method comprises 4 steps: the phytoplankton water sampling; DNA extraction; Use sex change ladder gel electrophoresis technology to carry out the phytoplankton structure analysis; Graphics process and data analysis confirm that finally the structure of phytoplankton is formed, and help the outburst early warning of phytoplankton red tide.

This shows that we can make up period of history lake phytoplankton by mathematical model, understand environmental aspect and the evolution process of lake period of history, have important directive significance for the health of keeping modern lake ecosystem.

Summary of the invention

The period of history structure of lake phytoplankton comprises three parts: screening part, analysis part and structure part.The screening part is that the utilization wavelet analysis is selected crucial table water physical and chemical factor and surface deposit physical and chemical factor; Analysis part is to utilize the quantitative relationship of setting up two kinds of crucial physical and chemical factors based on the regression equation of genetic algorithm, uses the response relation of crucial physical and chemical factor of model's correspondence analysis exploration table water and phytoplankton simultaneously; Making up part is by the crucial physical and chemical factor value of extrapolating of corresponding period of history table water, the construction period of history lake phytoplankton, with the diatom species of each section layer of sedimentary column model is verified.

Description of drawings

Period of history lake phytoplankton structure process flow diagram

Embodiment

1) screens crucial physical and chemical factor

The variation tendency of utilization Haar wavelet decomposition pattern analysis physical and chemical factor; The slickness of Haar small echo is relatively poor; But has good locality; Can analyze the general trend of fluctuation signal, the physical and chemical factor that local oscillation is bigger shows that its variation has conspicuousness, and the variable gradient of physical and chemical factor helps to analyze the structural change of phytoplankton greatly

Haar small echo:

Wherein, basic small echo of θ (t) expression

The wavelet mother function formula: ${\mathrm{\ψ}}_{a,b}\left(x\right)={\left|a\right|}^{1/2}\mathrm{\ψ}\left(\frac{x-b}{a}\right)$

Wherein, a, b ∈ R, a ≠ 0, a is a scale factor, b is the distance along belt transect

Work as a=2 ^j, b=k2 ^j, the SI got 1 o'clock, and the wavelet filtering function is: g _{J, k}(x)=2 ^-j/2G (2 ^-jX-k)

J wherein, k ∈ N, j are the number of levels of wavelet decomposition, k is a sampling number

The wavelet transformation function that then carries out decomposing for j time is:

Wherein, x=1,2,3 ..., k, s (x) is the signal data sequence

2) correlativity of analysis of key physical and chemical factor

Surface deposit is that the particle in the table water of lake forms through deposition; There are the certain function relation in physical and chemical factor in the table water and the physical and chemical factor in the surface deposit; The utilization regression equation is set up the correlativity of two kinds of crucial physical and chemical factors; Introduce genetic algorithm and confirm the coefficient of regression equation, can effectively reduce the manual intervention degree in the regretional analysis

A, setting regression equation: y=β ₁X+ β ₂x ²+ β ₃x ³+ ...+β _nx ⁿ

Wherein the crucial physical and chemical factor of water is shown in the x representative, and y represents the crucial physical and chemical factor of surface deposit

B, coefficient to be determined arranged in order constitute individual chromosome vector and be { β ₁, β ₂..., β _n, each component of chromosome vector becomes a gene, several genes { β ₁, β ₂..., β _mBeing compiled into individuality, a plurality of individualities constitute one group of population, and the number of population is set, and are even number

C, the probability that hybridization is set and makes a variation

The process of hybridization: selected two father's vectors $\mathrm{Father}1=\{{\mathrm{\β}}_1^1,{\mathrm{\β}}_2^1,...,{\mathrm{\β}}_k^1\},$ $\mathrm{Father}2=\{{\mathrm{\β}}_1^2,{\mathrm{\β}}_2^2,...,{\mathrm{\β}}_k^2\},$ Generate n (0,1) interval several a at random ₁, a ₂..., a _n, two offsprings are defined as

The process of variation: each coefficient to be determined all has bound, and capping is c _i, be limited to d down _i, the random number k between the generation (0,1) then parent is β _jGene through the variation, filial generation is child=d _i+ k (c _i-d _i)

D, adaptive value evaluation function are with all independent variable sample value x _iBring regression equation calculation into and draw Y _i, then with Y _iWith actual dependent variable y _iSquared difference and ξ as evaluation criterion

Stop when obtaining the minimal value computing, confirm regression coefficient

3) response relation of crucial physical and chemical factor of table water and phytoplankton

Different phytoplankton species are variant to the responsiveness of physical and chemical factor; Phytoplankton mainly refers to planktonic algae; Mainly be divided into diatom, green alga, blue-green algae, dinoflagellate, chrysophyceae, latent Trentepohlia; Through discriminatory analysis choose each belong in percentage composition greater than 10% kind as representative species, utilization model correspondence analysis is visited the relation of interval scale kind and crucial physical and chemical factor

The ranking value of A, definite table hydromining sampling point

B, the ranking value of sampled point and the crucial physical and chemical factor of table water are combined with regression analysis, its regression equation is:

Z _j＝b ₀+bU _j

Wherein, Z _jRepresent the ranking value of j sampled point, b ₀Be intercept, b is the regression coefficient between sampled point and the crucial physical and chemical factor of table water, U _jThe crucial physical and chemical factor of representative table water is in the observed reading of j sampled point

The ranking value weighted mean of C, utilization sampled point is asked the ranking value of phytoplankton representative species

${\mathrm{Sp}}_k=\frac{\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{Z}_j\×{\mathrm{Abund}}_{\mathrm{kj}}}{\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{Abund}}_{\mathrm{kj}}}$

Wherein, Sp _kRepresent the ranking value of k kind phytoplankton, Abund _KjRepresent the richness of k kind phytoplankton at j sampled point, n represents the number of sampled point

The corresponding relation of D, the crucial physical and chemical factor of foundation table water and phytoplankton representative species

4) structure period of history phytoplankton and modelling verification

Measure the crucial physical and chemical factor in each section layer of sedimentary column; Quantitative relationship according to table water and surface deposit physical and chemical factor; Calculate crucial physical and chemical factor value in each section layer corresponding historical lake in period table water,, make up period of history lake phytoplankton based on the response relation of phytoplankton representative species with the crucial physical and chemical factor of table water; Diatom species in each section layer of discriminated union statistics sedimentary column are verified model result simultaneously.

Embodiment

The phytoplankton constructing method of this paper is applied to certain lake, the China north, because lake table water is longer swap time, relatively stable, significant change can not take place in phytoplankton species usually within a century.

1. choosing lake table water physical and chemical factor is temperature, dissolved oxygen DO, the green rope of leaf, transparency, pH, total nitrogen, total phosphorus; Choosing lake surface deposit physical and chemical factor is organic carbon, organic nitrogen, total phosphorus, conductivity, pH; Utilize wavelet analysis to choose crucial physical and chemical factor, original signal is broken down into high-frequency signal h on 3 levels _iWith low frequency signal l _i, wherein low frequency signal is the approximate signal of original signal, and high-frequency signal is the detail signal of original signal, and approximate signal and detail signal satisfy following relation under each level: the first horizontal S ₁=h ₁+ l ₁The second horizontal S ₂=h ₁+ l ₂+ h ₂(l ₁Be decomposed into l ₂And h ₂); The 3rd horizontal S ₃=h ₁+ h ₂+ l ₃+ h ₃(l ₂Be decomposed into l ₃And h ₃), according to analysis result, in table water physical and chemical factor; PH has obvious vibration on three levels, the degree of fluctuation of other factors is all less than three levels, in the surface deposit physical and chemical factor; Conductivity has obvious vibration on two levels; Therefore the degree of fluctuation of other factors chooses pH as the crucial physical and chemical factor of table water all less than two levels, chooses conductivity as the crucial physical and chemical factor of surface deposit.

2. utilize the quantitative relationship of setting up pH and conductivity based on the regression equation of genetic algorithm, according to the sampling number of table water and surface deposit, the population number of setting genetic algorithm is 10; The probability of hybridization is 0.5, and the probability of variation is 0.5, produces number at random (0; 1) between; PH is as independent variable x, and conductivity is as dependent variable y, and regression equation is:

y＝0.034x ³+1.02x ²+5.49x+8.96

The relative coefficient R of regression equation ²Be 0.96, show that the relation of foundation has strong correlation.

3. show the response relation of crucial physical and chemical factor of water and phytoplankton

According to model's correspondence analysis result, the species that the crucial physical and chemical factor pH response of his-and-hers watches water is strong are as representative species, and response comprises and just responding and Negative Acknowledgment; Specialized range is strong response at [1 ,-0.5] ∪ [0.5,1]; Then selected representative species is 18 kinds, and wherein blue-green algae is 3 kinds, 3 kinds of green algas; 6 kinds in diatom, 2 kinds of dinoflagellates, 4 kinds of chrysophyceae.

4. the period of history phytoplankton structure

The sequential value of the crucial physical and chemical factor conductivity of measuring according to sedimentary column and the quantitative relationship of foundation; Calculate the sequential value of the crucial physical and chemical factor pH of corresponding period of history table water; Based on the response relation of crucial physical and chemical factor pH of table water and representative species, make up the phytoplankton of period of history, the diatom species in the microscopy discriminated union statistics sediment of utilization routine simultaneously; Empirical tests contains above-mentioned 6 kinds of diatoms in the sedimentary section layer of discovery 85%.

Claims (1)

1. one kind is utilized the lake to show the method that water physical and chemical factor, table water phytoplankton and surface deposit physical and chemical factor make up the period of history phytoplankton; It is characterized in that: the utilization wavelet analysis is selected in the table water and the crucial physical and chemical factor in the surface deposit; Confirm the quantitative relationship of two kinds of crucial physical and chemical factors by genetic algorithm; Thereby extrapolate crucial physical and chemical factor value in the corresponding period of history table water, explore the response relation of showing water physical and chemical factor and phytoplankton, based on crucial physical and chemical factor value and response relation in the period of history table water through model's correspondence analysis; Thereby make up period of history lake phytoplankton, concrete steps are following:

1) screens crucial physical and chemical factor

The variation tendency of utilization Haar wavelet decomposition pattern analysis physical and chemical factor; The slickness of Haar small echo is relatively poor; But has good locality; Can analyze the general trend of fluctuation signal, the physical and chemical factor that local oscillation is bigger shows that its variation has conspicuousness, and the variable gradient of physical and chemical factor helps to analyze the structural change of phytoplankton greatly

Haar small echo:

Wherein, basic small echo of θ (t) expression

The wavelet mother function formula: ${\mathrm{\ψ}}_{a,b}\left(x\right)={\left|a\right|}^{1/2}\mathrm{\ψ}\left(\frac{x-b}{a}\right)$

Wherein, a, b ∈ R, a ≠ 0, a is a scale factor, b is the distance along belt transect

Work as a=2 ^j, b=k2 ^j, the SI got 1 o'clock, and the wavelet filtering function is: g _{J, k}(x)=2 ^-j/2G (2 ^-jX-k)

J wherein, k ∈ N, j are the number of levels of wavelet decomposition, k is a sampling number

The wavelet transformation function that then carries out decomposing for j time is:

Wherein, x=1,2,3 ..., k, s (x) is the signal data sequence

2) correlativity of analysis of key physical and chemical factor

Surface deposit is that the fine deposition of crossing of the particle in the table water of lake forms; There are the certain function relation in physical and chemical factor in the table water and the physical and chemical factor in the surface deposit; The utilization regression equation is set up the correlativity of two kinds of crucial physical and chemical factors; Introduce genetic algorithm and confirm the coefficient of regression equation, can effectively reduce the manual intervention degree in the regretional analysis

A, setting regression equation: y=β ₁X+ β ₂x ²+ β ₃x ³+ ...+β _nx ⁿ

Wherein the crucial physical and chemical factor of water is shown in the x representative, and y represents the crucial physical and chemical factor of surface deposit

B, coefficient to be determined arranged in order constitute individual chromosome vector and be { β ₁, β ₂..., β _n, each component of chromosome vector becomes a gene, several genes { β ₁, β ₂..., β _mBeing compiled into individuality, a plurality of individualities constitute one group of population, and the number of population is set, and are even number

C, the probability that hybridization is set and makes a variation

The process of hybridization: selected two father's vectors $\mathrm{Father}1=\{{\mathrm{\β}}_1^1,{\mathrm{\β}}_2^1,...,{\mathrm{\β}}_k^1\},$ $\mathrm{Father}2=\{{\mathrm{\β}}_1^2,{\mathrm{\β}}_2^2,...,{\mathrm{\β}}_k^2\},$ Generate n (0,1) interval several a at random ₁, a ₂..., a _n, two offsprings are defined as

The process of variation: each coefficient to be determined all has bound, and capping is c _i, be limited to d down _i, produce the random number k between (0,1), then parent is β _jGene through the variation, filial generation is child=d _i+ k (c _i-d _i)

D, adaptive value evaluation function are with all independent variable sample value x _iBring regression equation calculation into and draw Y _i, then with Y _iWith actual dependent variable y _iSquared difference and ξ as evaluation criterion

Stop when obtaining the minimal value computing, confirm regression coefficient

3) response relation of crucial physical and chemical factor of table water and phytoplankton

Different phytoplankton species are variant to the responsiveness of physical and chemical factor; Phytoplankton mainly refers to planktonic algae; Mainly be divided into diatom, green alga, blue-green algae, dinoflagellate, chrysophyceae genus; Through discriminatory analysis choose each belong in percentage composition greater than 10% kind as representative species, utilization model correspondence analysis is inquired into the relation of representative species and crucial physical and chemical factor

The ranking value of A, definite table hydromining sampling point

B, the ranking value of sampled point and the crucial physical and chemical factor of table water are combined with regression analysis, its regression equation is:

Z _j＝b ₀+bU _j

Z _jRepresent the ranking value of j sampled point, b ₀Be intercept, b is the regression coefficient between sampled point and the crucial physical and chemical factor of table water, U _jThe crucial physical and chemical factor of representative table water is in the observed reading of j sampled point

The ranking value weighted mean of C, utilization sampled point is asked the ranking value of phytoplankton representative species

${\mathrm{Sp}}_k=\frac{\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{Z}_j\×{\mathrm{Abund}}_{\mathrm{kj}}}{\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{Abund}}_{\mathrm{kj}}}$

Sp _kRepresent the ranking value of k kind phytoplankton, Abund _KjRepresent the richness of k kind phytoplankton at j sampled point, n represents the number of sampled point

The corresponding relation of D, the crucial physical and chemical factor of foundation table water and phytoplankton representative species

4) structure period of history phytoplankton and modelling verification

Measure the crucial physical and chemical factor in each section layer of sedimentary column; Quantitative relationship according to table water and surface deposit physical and chemical factor; Calculate crucial physical and chemical factor value in each section layer corresponding historical lake in period table water,, make up period of history lake phytoplankton based on the response relation of phytoplankton representative species with the crucial physical and chemical factor of table water; Diatom species in each section layer of discriminated union statistics sedimentary column are verified model result simultaneously.

Images