A reconstruction method and system of coastal wetland based on sedimentary palynology

Abstract

The application discloses a reconstruction method and system of a coastal wetland based on sedimentary palynology, and belongs to the technical field of wetland reconstruction. 210 Pb and 137 Cs dating technology is used for dating analysis on the sediment column soil sample; the sediment column soil sample after the dating analysis is extracted, identified and treated, the types and quantities of spores are counted, and the concentrations of different types of plant spores are selected as response variables; based on the sediment column soil sample after the dating analysis, environmental data corresponding to years are determined and collected, and the environmental data set is determined as an explanatory variable; a multiple regression model is established, and key environmental factors are screened; and the contribution rate of the spore distribution influencing factor of the target coastal wetland is determined based on the key environmental factors, so that the reconstruction of the coastal wetland is realized.

Description

Technical Field

[0001] This invention relates to the field of wetland reconstruction technology, and more specifically to a method and system for reconstructing coastal wetlands based on sedimentary palynology. Background Technology

[0002] Coastal wetlands, as ecosystems interacting with both land and sea, are highly active yet extremely fragile areas, possessing ecological value in maintaining biodiversity, protecting coastlines, and regulating climate. However, due to their unique geographical location, coastal wetlands are particularly vulnerable to global warming, sea-level rise, storms, and human activities, leading to severe losses and functional degradation. Therefore, restoring wetland habitats is crucial for regional ecological security and the maintenance of ecosystem functions. Under current levels of human activity, if wetlands are not protected or restored, the structure and function of wetland ecosystems will deteriorate rapidly. To restore and protect ecosystem services and functions, it is necessary to record and understand the past dynamics of wetlands, providing reference conditions for wetland restoration.

[0003] Currently, the core technologies for nature-based wetland restoration include the following: (1) Restoration, which mainly relies on the natural resilience of the ecosystem itself. (2) Repair, which involves repairing the original damaged or degraded ecosystem. (3) Replacement, which uses another ecosystem to replace the original, but irrecoverable, ecosystem. (4) Reconstruction, which involves selecting a suitable area for artificial reconstruction of the ecosystem. Overall, wetland restoration is mainly achieved by changing physical environmental conditions (such as hydrology, topography, and sediment properties), relying on the ecosystem's own resilience, and utilizing the coupling of physical and biological processes. However, regardless of the restoration technology, the baseline for wetland restoration must be known. Paleoecological sediments use untouched wetland ecological conditions as the baseline environment for wetland restoration. This method is widely used to describe the baseline parameters for ecosystem restoration and to provide a long-term ecological context for understanding ecosystem progress. In the absence of long-term monitoring data in historical periods, pollen preserved in sediments can be used to reflect wetland vegetation succession. Current research on pollen in coastal wetlands mainly focuses on using deep pollen assemblages to infer Holocene climate change and human history. However, it lacks research on shallow pollen drilling on a centennial scale and the impact and mechanism of coastal wetland sedimentary environment on the distribution of different types of pollen, thus affecting the reconstruction results of coastal wetlands.

[0004] Therefore, how to provide a method and system for the reconstruction of coastal wetlands based on sedimentary palynology is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, the present invention provides a method and system for the reconstruction of coastal wetlands based on sedimentary palynology, which can determine reference conditions for the restoration of coastal wetlands based on sedimentary palynology.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A method for reconstructing coastal wetlands based on sedimentary palynology includes the following steps:

[0008] Sediments from the target coastal wetland were sampled to obtain soil samples from the sediment column;

[0009] use 210 Pb and 137 Cs dating technology is used to date soil samples from sedimentary columns.

[0010] Soil samples from sedimentary columns after dating analysis were extracted, identified, and processed. Pollen species and quantities were counted, and pollen concentrations of different plant types were selected as response variables.

[0011] Based on the sedimentary column soil samples after dating analysis, environmental data for the corresponding years were measured and collected, and the environmental dataset was determined as an explanatory variable.

[0012] Establish a multiple regression model to screen key environmental factors;

[0013] Based on key environmental factors, the contribution rate of influencing factors on pollen distribution in the target coastal wetland was determined, and the target coastal wetland was reconstructed.

[0014] Optionally, sediments from the target coastal wetland may be sampled to obtain sediment column soil samples, including:

[0015] Obtain maps showing the changes in the course of the river as recorded in historical materials and documents;

[0016] Determine the location of historical river channels in different time periods, set up sampling points in and on both sides of the river channel, and determine the latitude and longitude of the sampling points in the target coastal wetland.

[0017] Multiple sedimentary soil samples were obtained by selecting borehole columns and taking multiple samples.

[0018] Optional, utilize 210 Pb and 137 Cs dating technology is used to date soil samples from sedimentary columns, including:

[0019] S11: Analysis of soil samples from sedimentary columns using radiometric gamma-ray spectroscopy. 210 Pb, 226 Ra and 137 Measurement of the radioactivity intensity of Cs;

[0020] S12: By means of total 210 Subtract from the Pb radioactivity 226 Ra radioactivity intensity obtained 210 Pb ex The strength is calculated using the following formula:

[0021] 210 Pb ex = 210 Pb total - 210 Pb sup ;

[0022] In the formula: 210 Pb ex Indicates the radioactivity intensity of the remaining Pb; 210 Pb total For measurement 210 Total strength of Pb; 210 Pb sup For the sample 226 Ra decay formation 210 The radioactivity intensity of Pb;

[0023] S13: Based on soil samples from each sedimentary column 210 Pb ex This corresponds to the depths. 210 Pb ex The formula for calculating the age of each sample layer is as follows:

[0024] 210 Pb ex = 210 Pb (0) *e -λt ;

[0025] In the formula, 210 Pb ex The radioactivity intensity of 210Pb at depth x; 210 Pb (0) For the surface layer 210 Pb radioactivity intensity; λ is 210 The half-life constant of Pb; t is the age at depth x;

[0026] S14: Based on the era and nuclides of the peak nuclear testing period 137 The distribution of Cs in sediments corresponds to the age of peak positions, and the depositional rates and ages of recent sediments are calculated and verified. 210 The accuracy of Pb dating.

[0027] Optionally, soil samples from the sedimentary column after dating analysis are extracted, identified, and processed to statistically analyze pollen species and quantities, and pollen concentrations of different plant types are selected as response variables, including:

[0028] S21: Weigh the soil samples from the sedimentary column according to lithology;

[0029] S22: Add indicator pollen to soil samples from sedimentary columns;

[0030] S23: Pollen extraction was performed using the hydrofluoric acid-sieving method;

[0031] S24: Identify the extracted pollen until each sedimentary column soil sample reaches the preset target;

[0032] S25: Calculate pollen parameters for different types;

[0033] S26: Tilia 3.0.1 software was used to process pollen parameters and draw pollen maps. Pollen assemblage zones were divided using the CONISS clustering program with the principle of minimum variance constrained by stratigraphy, and the pollen concentrations of different types of plants were selected as response variables.

[0034] Optional, S25: Calculate pollen parameters for different types, including:

[0035] The formula for calculating the mass concentration of pollen is:

[0036]

[0037] In the formula, Pc is the mass concentration of pollen of any family or genus; L is the number of sap pine spores added to the sample; M is the number of sap pine spores in the sample; N is the amount of pollen of any family or genus in the sample; and S is the mass of the sample.

[0038] Optional, S25: Calculating pollen parameters for different types, also includes:

[0039] The percentage content of pollen from terrestrial families is calculated using the following formula:

[0040]

[0041] In the formula, A is the percentage of pollen content of any family in the sample, n is the number of pollen identified in that family, and n is the total number of pollen identified in all terrestrial families and genera in the sample.

[0042] Optional, S25: Calculating pollen parameters for different types, also includes:

[0043] The formula for calculating the pollen content of aquatic plants, algae, and ferns is as follows:

[0044]

[0045] In the formula, B is the percentage content of a certain aquatic or algal or fern spore pollen in the sample, m is the identification statistic of that aquatic or algal or fern spore pollen, and N is the total identification statistic of all terrestrial family spore pollen in the sample.

[0046] Optionally, based on the soil samples from the sedimentary column after dating analysis, environmental data for the corresponding years are measured and collected to determine the environmental dataset as explanatory variables, including:

[0047] S31: Measurement of pH and salinity;

[0048] S32: Determination of particle size components and particle size parameters, including: pretreatment of sedimentary column soil samples with 30% H2O2 and 1 mol / L HCl to decompose organic matter and remove carbonates; uniform dispersion of sedimentary column soil samples by ultrasonic vibration; particle size measurement using Mastersizer2000; calculation of separation coefficient σ, skewness Sk, and kurtosis kg. The calculation formula is as follows:

[0049]

[0050]

[0051]

[0052] Φ represents the particle size at different frequencies;

[0053] S33: According to 210 Pb and 137 Deposition rates are calculated based on the age and depth determined by Cs.

[0054] S34: Obtain the environmental dataset based on S31-S33 and use it as an explanatory variable.

[0055] Optionally, a multiple regression model can be established to screen key environmental factors, including:

[0056] S41: Pollen concentration is used as the response variable, and environmental dataset is used as the explanatory variable;

[0057] S42: Perform logarithmic transformation on the response variable;

[0058] S43: Use a multiple linear regression model to calculate the effects of sediment composition, grain size index, sedimentary environment, sedimentation rate, annual average sediment load, annual average runoff, and water flow time on pollen concentration.

[0059] S44: Key environmental factors are obtained by stepwise regression based on the Akaike Information Criterion (AIC) to screen the independent variables of the model.

[0060] A coastal wetland reconstruction system based on sedimentary palynology, comprising:

[0061] Sampling module: Samples sediments from the target coastal wetland to obtain soil samples from the sediment column;

[0062] Year analysis module: using 210 Pb and 137 Cs dating technology is used to date soil samples from sedimentary columns.

[0063] Pollen extraction module: Extracts, identifies and processes soil samples from sedimentary columns after dating analysis, counts pollen types and quantities, and selects pollen concentrations of different plant types as response variables;

[0064] Environmental data measurement module: Based on the sedimentary column soil samples after dating analysis, measure and collect environmental data for the corresponding year, and determine the environmental dataset as an explanatory variable;

[0065] Screening module: Establishes a multiple regression model to screen key environmental factors;

[0066] Reconstruction Module: Based on key environmental factors, determine the contribution rate of factors influencing pollen distribution in the target coastal wetland, and reconstruct the target coastal wetland.

[0067] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a method and system for the reconstruction of coastal wetlands based on sedimentary palynology, which determines the influencing factors of palynological concentration of different vegetation types, and can accurately predict the past interdecadal evolution of coastal wetlands by using palynology, fill the gap in palynological data on a centennial scale since the formation of coastal wetlands, obtain ecological data on a centennial scale sequence, and determine a reference condition for the restoration of coastal wetlands based on sedimentary palynology, which is conducive to the reconstruction of coastal wetlands. Attached Figure Description

[0068] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0069] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0070] Figure 2 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0071] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0072] The purpose of this invention is to provide a method and system for reconstructing coastal wetlands based on sediment palynology. The method includes: sampling sediments from the target coastal wetland to obtain sediment column soil samples; and utilizing... 210 Pb and 137 Cs dating technology was used to date sedimentary column soil samples. The dated soil samples were then extracted, identified, and processed to statistically analyze pollen species and quantities, and pollen concentrations of different plant types were selected as response variables. Based on the dated soil samples, environmental data for the corresponding years were measured and collected, and the environmental dataset was identified as an explanatory variable. A multiple regression model was established to screen key environmental factors. Based on these key environmental factors, the contribution rate of factors influencing pollen distribution in the target coastal wetland was determined, and the target coastal wetland was reconstructed. This study provides favorable reference conditions for determining a coastal wetland restoration method based on sedimentary palynology and facilitates the reconstruction of coastal wetlands.

[0073] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0074] Example 1

[0075] See Figure 1 As shown, Embodiment 1 of the present invention discloses a method for reconstructing coastal wetlands based on sedimentary palynology, comprising:

[0076] Sediments from the target coastal wetland were sampled to obtain soil samples from the sediment column;

[0077] use 210 Pb and 137 Cs dating technology is used to date soil samples from sedimentary columns.

[0078] Soil samples from sedimentary columns after dating analysis were extracted, identified, and processed. Pollen species and quantities were counted, and pollen concentrations of different plant types were selected as response variables.

[0079] Based on the sedimentary column soil samples after dating analysis, environmental data for the corresponding years were measured and collected, and the environmental dataset was determined as an explanatory variable.

[0080] Establish a multiple regression model to screen key environmental factors;

[0081] Based on key environmental factors, the contribution rate of influencing factors on pollen distribution in the target coastal wetland was determined, and the target coastal wetland was reconstructed.

[0082] Specifically, key environmental factors are selected from sediment composition (clay, silt, sand), grain size indices (mean grain size Mz, separation coefficient σ, skewness Sk, kurtosis Kg), sedimentation depth, sedimentation environment (salinity, pH), sedimentation rate, annual average sediment load, annual average runoff, and travel time.

[0083] In one specific embodiment, sediments from the target coastal wetland are sampled to obtain a sediment column soil sample, including:

[0084] A diagram showing the changes in the course of the river based on historical records and documents;

[0085] ArcGIS was used to determine the location of historical river channels in different time periods, and sampling points were set up in and on both sides of the river channel to determine the latitude and longitude of the sampling points.

[0086] During on-site sampling, unsuitable sampling points located near roads, farmland, villages, factories, and aquaculture ponds were eliminated based on the actual site conditions. Priority was given to sampling points less affected by human activities and with more representative natural vegetation. A 1m borehole was used for three sampling operations to ensure a core recovery rate of over 90%.

[0087] Specifically, the sediment column was sliced ​​at 2cm intervals for dating, and pollen identification and physicochemical index testing were performed at 10cm intervals. The slices were numbered, freeze-dried, and stored in a cool place.

[0088] In one specific embodiment, utilizing 210 Pb and 137 Cs dating techniques are used to date soil samples from sedimentary columns, including: using 210 Pb and 137 Cs dating was used to date soil samples from sedimentary columns. Soil samples were taken in 2cm strata, dried at 60℃ to constant weight, ground and mixed thoroughly, and then passed through a 20-mesh sieve. Samples were aliquoted into 7ml centrifuge tubes, sealed, and allowed to stand for at least 21 days to allow for soil aging. 226 Ra- 222 Rn- 210 After Pb reaches decay equilibrium, the activity of the nuclide in the dating instrument is measured. The dating analysis specifically includes the following steps:

[0089] S11: The sampling instrument used was an ORTEC probe (GWL-120-15) from the USA, with a well-type high-purity germanium crystal and a coaxial instrument. Radiometric gamma-ray spectroscopy was employed to analyze the soil samples from the sedimentary column. 210 Pb, 226 Ra and 137 Measurement of the radioactivity intensity of Cs.

[0090] S12: By means of total 210 Subtract from the Pb radioactivity 226 Ra radioactivity intensity obtained 210 Pb ex The strength of is calculated using the following formula:

[0091] 210 Pb ex = 210 Pb total - 210 Pb sup ;

[0092] In the formula: 210 Pb ex Indicates the radioactivity intensity of the remaining Pb; 210 Pb total For measurement 210 Total strength of Pb; 210 Pb sup For the sample 226 Ra decay formation 210 The radioactivity intensity of Pb.

[0093] S13: Based on each sample layer 210 Pb ex This corresponds to the depths. 210 Pb ex Then, the age of each sample layer is calculated using the dating formula, which is as follows:

[0094] 210 Pb ex = 210 Pb (0) *e -λt

[0095] in, 210 Pb ex The radioactivity intensity of 210Pb at depth x; 210 Pb (0) For the surface layer 210 Pb radioactivity intensity; λ is 210 The half-life constant of Pb; t is the age at depth x.

[0096] S14: Based on the era and nuclides of the peak nuclear testing period 137The distribution of Cs in sediments corresponds to the age of peak positions, thereby determining the deposition rate and age of recent sediments for verification. 210 The accuracy of Pb dating.

[0097] In one specific embodiment, soil samples from the sedimentary column after dating analysis were extracted, identified, and processed to count pollen species and quantities. Pollen concentrations of different plant types were selected as response variables. This included: pollen extraction using conventional acid-alkali treatment and heavy liquid flotation methods; followed by identification and counting of pollen species and quantities under a Leitz biological microscope; and calculation of the abundance of trees, shrubs, terrestrial herbaceous vegetation, and aquatic / wetland herbaceous plants based on the percentage concentration of pollen. The specific methods are as follows:

[0098] S21: Weighing: The weight of the sample is determined based on the lithology: 100g for clay and peat samples, 200g for soil samples, and ≥400g for sand samples.

[0099] S22: Add indicator pollen: Add one lycophyte spore tablet to the beaker as indicator pollen. One tablet contains 27637±563 lycophyte spores / tablet.

[0100] S23: Hydrofluoric acid-sieving method was used for pollen extraction. The specific method is as follows: Add dilute hydrochloric acid to a beaker, heat in a water bath for 30 minutes to remove carbonate impurities from the sample. The water bath temperature is set to 70℃. After the reaction is complete, add water and remove from the water bath. Place the sample in a fume hood and change the water until the sample is neutral. Add hydrofluoric acid to a beaker, heat in a water bath for 8 hours to remove silicate impurities from the sample. The water bath temperature is set to 70℃, and stir slowly and evenly until there is no gritty feeling. Change the water until the sample is neutral. Add concentrated hydrochloric acid, heat in a water bath for 30 minutes at 100℃. Stir the sample continuously during heating. After the reaction is complete, add water and remove from the water bath until the sample is neutral. Extract the supernatant, vibrate the sample in an ultrasonic bath, and filter out fine impurities smaller than the pollen diameter using a 5μm sieve. Pour into a 100ml small glass beaker for precipitation. Precipitation time is at least 8 hours. Extract the supernatant from the small glass beaker, pour the remaining sample into a test tube, and then centrifuge the test tube containing the sample (centrifuge speed 3000 r / min, centrifugation time 13 min). Pour the sample from the small glass beaker into the test tube in multiple batches until the sample in the beaker is completely rinsed out. After all the test tube samples have been processed, arrange the test tubes in the identification chamber in order, ready for microscopic identification.

[0101] S24: Pollen identification. Specific methods include: identifying and counting extracted pollen grains under an Olympus optical microscope at 400x magnification, or identifying and counting pollen species and quantities (grains) under a Leitz biological microscope, until each sample contains at least 200 pollen grains. Generally, identification can only be performed up to the genus level, some can only be identified up to the family level, and very rarely up to the species level.

[0102] S25: Pollen concentration and percentage calculation, including:

[0103] The mass concentration of pollen is calculated by adding a known number of pine spore fragments using the following formula:

[0104]

[0105] In the formula, Pc is the mass concentration of pollen of a certain family and genus; L is the number of sap pine spores added to the sample; M is the number of sap pine spores in the sample; N is the amount of pollen of a certain family and genus in the sample; and S is the mass of the sample.

[0106] The percentage content of terrestrial pollen is calculated based on the total amount of terrestrial pollen and is expressed as:

[0107]

[0108] Where A is the percentage of pollen content of a certain family in the sample, n is the number of pollen identified in that family, and n is the total number of pollen identified in all terrestrial families and genera in the sample.

[0109] The pollen content of aquatic plants, algae, and ferns is calculated as follows:

[0110]

[0111] Where B is the percentage content of a certain aquatic, algae, or fern spore pollen in the sample, m is the identification statistic of that aquatic, algae, or fern spore pollen, and N is the total identification statistic of all terrestrial genera and genus spore pollen in the sample.

[0112] S26: Use Tilia 3.0.1 software to process pollen data, draw pollen maps, and use the CONISS clustering program to classify pollen assemblage zones according to the principle of minimum variance constrained by stratigraphy.

[0113] In one specific embodiment, based on the soil samples from the sedimentary column after dating analysis, environmental data for the corresponding year are measured and collected to determine the environmental dataset as explanatory variables. This includes: measuring and collecting important environmental factors as the environmental dataset used for modeling; the measured data includes pH, salinity, grain size composition, and sedimentation rate; the collected data includes self-diversion history, historical inflow runoff, sediment deposition load, and water flow duration. The steps for measuring the data are as follows:

[0114] S31: Determination of pH and salinity. Remove plant residues, stones, and other debris from soil samples in the laboratory. Then, dry the soil samples to constant weight at 60°C. Determine soil salinity and pH by passing the soil samples through a 20-mesh sieve. The salinity and pH of the soil-water supernatant were determined using a portable salt-alkali meter (Jenco 3010m) and a pH meter (Hanna HI 8424), respectively.

[0115] S32: Determination of particle size composition and particle size index. Sediment samples were pretreated with 30% H₂O₂ and 1 mol / L HCl to decompose organic matter and remove carbonates. After uniform dispersion by ultrasonic vibration, the particle size was measured using a Mastersizer 2000 (Malvern Corporation).

[0116] Specifically, the calculation methods for the separation coefficient (σ), skewness (Sk), and kurtosis (kg) are as follows:

[0117]

[0118]

[0119]

[0120] Specifically, the particle size for different frequencies is represented by Φ. Particle sizes are categorized as sand (>63μm), silt (63~4μm), and clay (<4μm).

[0121] S33: According to 210 Pb and 137 The deposition rate is calculated based on the age and depth determined by Cs.

[0122] Specifically, factors that influence the formation and deposition of different types of pollen concentrations were selected as explanatory variables for subsequent modeling.

[0123] See Figure 2 As shown, in one specific embodiment, a classification is established, and factors that influence the formation and deposition of different types of pollen concentrations are screened as explanatory variables for subsequent modeling. The screening of key environmental factors specifically includes the following steps:

[0124] S41: Pollen concentration is used as the response variable, and environmental dataset is used as the explanatory variable.

[0125] S42: To ensure the normality of the data, the response variable (pollen distribution) is logarithmically transformed.

[0126] S43: Use a multiple linear regression model to calculate the effects of sediment composition (clay, silt, sand), grain size parameters (mean grain size Mz, separation coefficient σ, skewness Sk, kurtosis Kg), sedimentation depth, sedimentation environment (salinity, pH), sedimentation rate, annual sediment load, annual runoff, and travel time on pollen concentration.

[0127] S44: Independent variables in the model were selected using stepwise regression based on the Akaike Information Criterion (AIC) (stepAIC function). The relaimpo package was used to calculate the relative importance of each variable. The ggplot package was used for visualization of the analysis results. R4.0.3 was used for both data analysis and plotting.

[0128] Specifically, the multiple linear regression model in this embodiment is a multiple linear regression model based on the R package.

[0129] For example, the formula for Terrestrial pollen is: m =

[0130] lm(Terrestrials~Mz+Sen+Sk+Kg+Clay+Silt+Salinity+pH+Sediment.load+Depth+Runoff+Deposition.rate+Traveltime,data=all)

[0131] The Arbors pollen formula is:

[0132] m=lm(Arbors~Mz+Sen+Sk+Kg+Clay+Silt+Salinity+pH+Sediment.load+Depth+Runoff+Deposition.rate+Traveltime,data=all)

[0133] Similarly, the other three types are also like this.

[0134] More specifically, the results obtained from the multiple linear regression model have some influencing factors with insignificant p-values, so the stepwise regression model is used to screen for factors with significant p-values.

[0135] The following is part of the modeling code:

[0136] stepAIC(m)

[0137] m=lm(formula=Terrestrials~Sen+Salinity+pH+Depth+Runoff,

[0138] data=all)

[0139] vif(m)

[0140] crlm.1<-calc.relimp(m,type=c("lmg"),rela=T)

[0141] b=summary(m)

[0142] anova(m)

[0143] crlm.1<-calc.relimp(m,type=c("lmg"),rela=T)

[0144] plot(crlm.1)

[0145] crlm.1=data.frame(crlm.1$lmg)

[0146] crlm.1$variable=rownames(crlm.1)

[0147] colnames(crlm.1)[1]='value'

[0148] crlm.1$value=100*crlm.1$value

[0149] crlm.1$index='beta'

[0150] #crlm.1$variable=factor(crlm.1$variable,levels=c('pH','TOC','Cl','NO3','NH4','Temperature'))

[0151] crlm.1$variable=factor(crlm.1$variable,levels=crlm.1$variable)

[0152] p.hp=ggplot(crlm.1,aes(x=index,y=value,fill=variable,color=variable))+

[0153] geom_bar(stat="identity",position='stack',width=.7,size=.25,alpha=1)+

[0154] scale_y_continuous(limits=c(0,100.1),expand=c(0,0))+

[0155] scale_fill_npg()+

[0156] scale_color_npg()+

[0157] labs(y="Relativeeffects(%)")+

[0158] theme_classic()+

[0159] theme(legend.position='right',legend.title=element_blank(),legend.text=eleme nt_text(size=12),

[0160] text=element_text(colour='black'),

[0161] axis.title=element_text(size=17),axis.text=element_text(size=15),

[0162] strip.text=element_text(size=15),panel.grid=element_blank(),axis.text.x=element_blank(),

[0163] axis.ticks.x=element_blank(),

[0164] axis.title.x=element_blank())

[0165] b1=b$coefficients[-1,]#data.frame(b)[-4,]

[0166] b2=b$fstatistic

[0167] b2=pf(b2[1],b2[2],b2[3],lower.tail=FALSE)

[0168] b2=rep(b2,time=nrow(b1))

[0169] r=rep(b$r.squared,time=nrow(b1))

[0170] d=rownames(b1)

[0171] res.emf.ml0=data.frame(d,r,b2,b1)

[0172] for(iin1:nrow(res.emf.ml0)){

[0173] a=res.emf.ml0$Pr...t..[i]

[0174] if(a<=0.001){

[0175] l<-"***"

[0176] }

[0177] if(0.001<a&&a<=0.01){

[0178] l<-"**"

[0179] }

[0180] if(0.01<a&&a<0.05){

[0181] l<-"*"

[0182] }

[0183] if(0.05<a&&a<0.1){

[0184] l<-"°"

[0185] }

[0186] if(a>=0.1){

[0187] l<-""

[0188] }

[0189] res.emf.ml0$label[i]=l

[0190] }

[0191] #res.emf.ml0$label=c('*',”,”,”,'**',”)

[0192] #res.emf.ml0$label=c('*','**',”,”,”,”)

[0193] res.emf.ml0$d=factor(res.emf.ml0$d,levels=rev(crlm.1$variable))

[0194] p.estimate=ggplot(data=res.emf.ml0)+

[0195] geom_hline(yintercept=0,linetype="dashed",color="grey40")+

[0196] geom_point(aes(x=d,

[0197] y=Estimate,

[0198] color=d),

[0199] size=3.5)+

[0200] geom_errorbar(aes(x=d,

[0201] y=Estimate,

[0202] ymin=Estimate-Std..Error,

[0203] ymax=Estimate+Std..Error,

[0204] color=d),

[0205] width=0.0)+

[0206] geom_text(aes(x=d,

[0207] y=Estimate,

[0208] label=paste0(d,"\n",round(Estimate,4),"",label)),size=4,

[0209] vjust=-.3)+

[0210] #scale_y_continuous(limits=c(-1,1))+

[0211] scale_color_manual(values=c('#F39B7F','#3C5488','#00A087','#4DBBD5','#E64B35'))+

[0212] theme_classic()+

[0213] theme(legend.position='none',legend.title=element_blank(),legend.text=element_text(size=12),

[0214] text=element_text(colour='black'),plot.title=element_text(size=19,hjust=.2),axis.title=element_text(size=17),axis.text=element_text(size=15),

[0215] strip.text=element_text(size=15), panel.grid=element_blank(), axis.text.y=elem ent_blank(),

[0216] axis.ticks.y=element_blank(),

[0217] axis.title.y=element_blank())+

[0218] coord_flip()+

[0219] labs(title="Terrestrials",y="Parameterestimate")

[0220] p.total1 <- p.hp + p.estimate +

[0221] plot_layout(design=c(area(l=0,r=20,t=0,b=1),

[0222] area(l=20,r=52,t=0,b=1)));p.total1

[0223] Embodiment 1 of the present invention also discloses a coastal wetland reconstruction system based on sedimentary palynology, comprising:

[0224] Sampling module: Samples sediments from the target coastal wetland to obtain soil samples from the sediment column;

[0225] Year analysis module: using 210 Pb and 137 Cs dating technology is used to date soil samples from sedimentary columns.

[0226] Pollen extraction module: Extracts, identifies and processes soil samples from sedimentary columns after dating analysis, counts pollen types and quantities, and selects pollen concentrations of different plant types as response variables;

[0227] Environmental data measurement module: Based on the sedimentary column soil samples after dating analysis, measure and collect environmental data for the corresponding year, and determine the environmental dataset as an explanatory variable;

[0228] Screening module: Establishes a multiple regression model to screen key environmental factors;

[0229] Reconstruction Module: Based on key environmental factors, determine the contribution rate of factors influencing pollen distribution in the target coastal wetland, and reconstruct the target coastal wetland.

[0230] The system apparatus disclosed in the embodiments is described simply because it corresponds to the method disclosed in the embodiments; relevant details can be found in the method section.

[0231] Example 2

[0232] This invention takes a coastal wetland as an example. The pollen in the sediments is constrained by factors such as its yield, transport mode, transport distance, ocean currents, runoff sediment transport, and sedimentary environment. This coastal wetland is affected by river diversion and reduced sediment inflow, leading to changes in its sedimentary environment and consequently, changes in pollen assemblage characteristics. Sediment grain size is a crucial parameter describing the sedimentary environment; variations in grain size dominate the sediment's absorption capacity and diffusion degree, further describing the regional sedimentary environment (such as provenance, hydrodynamics, and vegetation growth), thus revealing the sedimentary characteristics of the coastal wetland and further understanding its evolution. Past research on pollen in this area has mainly focused on using deep pollen assemblages to infer Holocene climate change and human history, lacking research on shallow pollen drilling at the centennial scale and the influence and mechanisms of the coastal wetland sedimentary environment on the distribution of different pollen types. Therefore, this invention utilizes a method for reconstructing the pattern and influencing mechanisms of coastal wetlands based on sedimentary palynology, specifically:

[0233] The age of the target coastal wetland varies with soil depth, with sedimentation rates gradually increasing from the surface to the bottom of the core. The furthest dating of the shallow drilled cores at 2-3 meters in this coastal wetland is the NH core near Ninghai, dating to 1921 at 2 meters, with sedimentation rates ranging from 1.69 to 2.23 cm / a. The CZG, YW, and ZMG cores at 2 meters are dated to 1947, 1934, and 1945, respectively, with sedimentation rates ranging from 1.15 to 6.67 cm / a, 0.54 to 8.89 cm / a, and 2.23 to 3.33 cm / a. The SXG core from the Shenxiangou flowway is dated to 1954 at 2 meters. Sedimentation rates were relatively low from 1964 to 1996, but reached 15.56 cm / a from 1964 to 1954. The DKH core from the Diaokou River channel dates to 1970 at a depth of 2m, with the deposition rate decreasing from 10.00 cm / a in 1970-1976 to 2.31 cm / a in 1996-2022. The three cores from the current Yellow River channel (BHBW, BLQ, and NA) have the deepest dates of 1990, 1974, and 1976, respectively, with deposition rates of 3.85-16.67 cm / a, 4.62-20.00 cm / a, and 3.08-11.00 cm / a, respectively.

[0234] Grain size analysis results indicate that the sediment types in this coastal wetland are mainly clay, silt, and sand. Different areas experienced different dynamic environments, resulting in variations in sediment grain size composition. Core NH, as the leading control point for water and sediment entering the sea in this area, is furthest from the current river estuary and exhibits a stable sedimentary environment. Cores CZG, ZMG, YW, DKH, SXG, BHBW, and NA are located in abandoned waste areas, indicating a relatively stable sedimentary environment. Core BLQ, located in the current river estuary area, is closest to the estuary and experiences the greatest disturbance, resulting in an unstable sedimentary environment.

[0235] Specifically, in this embodiment, all core samples refer to the abbreviations of the Chinese names of the core boreholes.

[0236] NH is a Chinese pinyin abbreviation that refers to the name of a region at the apex of a fan-shaped area in a northern coastal wetland.

[0237] CZG, YW, ZMG, SXG, DKH, BHBW, BLQ, and NA all represent different regional names of coastal wetlands, as well as diverted flow paths, etc.

[0238] A total of 67 families and genera of plant pollen were discovered and identified in this coastal wetland. These included: pollen from 22 families and genera of woody plants, pollen from 28 families and genera of herbaceous plants, spores from 15 families and genera of ferns, one bryophyte *Reboulia*, and one algae *Sphaeroplea*.

[0239] The pollen of trees in coastal wetlands is mainly influenced by genera *Pinus*, *Quercus*, *Ulmus*, *Betula*, *Juglans*, *Quercus*, and *Picea*. The pollen of shrubs is primarily influenced by *Ephedra*, *Hippophae*, *Nitraria*, *Tamarix*, *Apocynum*, Rosaceae, *Salix*, and *Spiraea*. Terrestrial herbaceous plants include those from the Chenopodiaceae / Amaranthaceae families, Artemisia families, *Taraxacum*, *Aster-type*, *Humnus*, Compositae, and Cruciferae families. The pollen of aquatic / wetland herbaceous plants mainly belongs to the Poaceae, Cyperaceae, Typhaceae, Polygonum, Lythraceae, Nymphoides, and Onagraceae families. Ferns mainly belong to the Selaginellaceae, Polypodiaceae, and Hicriopteris genera.

[0240] The distribution of pollen assemblages in different soil profiles varies significantly from terrestrial to marine. Based on the spatiotemporal distribution of trees, shrubs, terrestrial herbs, aquatic / wet herbs, and ferns in coastal wetlands, and the age-related changes in the distribution of terrestrial and aquatic / wet herbs, it is evident that the sedimentary environment influences the distribution of pollen and spores in sediments. Deposited materials are continuously subjected to hydrodynamic and biological processes, leading to changes in their physicochemical properties, which are inevitably reflected in grain size parameters. The spatial distribution characteristics of sediment type and grain size composition are primarily controlled by the regional sedimentary environment (such as provenance, hydrodynamics, and vegetation growth). In this embodiment, it was found that the sediment type and grain size composition varied with depth in different regions. This may be due to differences in the sediment supply from river channels at different ages, with sediment supply and coastal hydrodynamics being the dominant factors.

[0241] In one specific embodiment, a multiple linear regression model was constructed to determine the influencing factors of pollen concentration for different vegetation types. Pollen concentration was positively affected by clay, silt, sedimentation depth, and water travel time, with influence values ​​of 0.57, 0.37, 0.87, and 0.80, respectively. On the other hand, mean particle size Mz and the separation coefficient σ had a negative impact on tree pollen concentration. Furthermore, sedimentation depth, annual sediment load Sk, and water travel time had significant positive effects on shrub pollen concentration, with influence values ​​of 0.60, 0.29, 1.8141, and 0.63, respectively. Annual average runoff was found to have a significant negative impact, with an effect value of -2.22. Salinity, pH, and sedimentation depth had significant positive effects on terrestrial herbaceous vegetation pollen concentration (0.50, 0.29, and 0.48, respectively), while the separation coefficient σ and annual runoff had significant negative effects. Pollen concentrations in aquatic and wetland herbaceous vegetation were influenced by sediment depth, clay content, salinity, annual sediment load, and water transport time, with effect values ​​of 0.77, 0.63, 0.32, 3.2743, and 0.91, respectively. However, average annual runoff had a negative impact. Fern pollen was significantly positively affected by skewness (Sk), clay, silt, settling rate, and water transport time, with effect values ​​of 0.24, 0.58, 0.44, 0.36, and 0.64, respectively. This embodiment aims to elucidate the pollen distribution and influencing factors of the target coastal wetland for reconstruction and to infer the interdecadal evolution of the coastal wetland using pollen.

[0242] This invention uses a multiple regression model to determine the influencing factors of pollen concentration distribution in different vegetation types, enabling accurate inference of past wetland vegetation types and dominant succession factors. This provides a relatively accurate historical reference for wetland restoration. The vegetation succession patterns and influencing factors revealed by this invention are beneficial for guiding wetland restoration.

[0243] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0244] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for reconstructing coastal wetlands based on sedimentary palynology, characterized in that, Includes the following steps: Sediments from the target coastal wetland were sampled to obtain soil samples from the sediment column; use 210 Pb and 137 Cs dating technology is used to date soil samples from sedimentary columns. Soil samples from sedimentary columns after dating analysis were extracted, identified, and processed. Pollen species and quantities were counted, and pollen concentrations of different plant types were selected as response variables. Based on the soil samples from the sedimentary column after dating analysis, environmental data for the corresponding years were measured and collected. The environmental dataset was determined as an explanatory variable, including: S31: Measurement of pH and salinity; S32: Determination of particle size components and particle size parameters, including: pretreatment of sedimentary column soil samples with 30% H2O2 and 1 mol / L HCl to decompose organic matter and remove carbonates; uniform dispersion of sedimentary column soil samples by ultrasonic vibration; particle size measurement using Mastersizer 2000; calculation of separation coefficient σ, skewness Sk, and kurtosis kg. The calculation formula is as follows: Kg= Φ represents the particle size at different frequencies; S33: According to 210 Pb and 137 Deposition rates are calculated based on the age and depth determined by Cs. S34: Obtain the environmental dataset based on S31-S33 and use it as an explanatory variable; Establish a multiple regression model to screen key environmental factors; Based on key environmental factors, the contribution rate of influencing factors on pollen distribution in the target coastal wetland was determined, and the target coastal wetland was reconstructed.

2. The method for reconstructing coastal wetlands based on sedimentary palynology according to claim 1, characterized in that, Sediments from the target coastal wetland were sampled to obtain soil samples from the sediment column, including: Obtain maps showing the changes in the course of the river as recorded in historical materials and documents; Determine the location of historical river channels in different time periods, set up sampling points in and on both sides of the river channel, and determine the latitude and longitude of the sampling points in the target coastal wetland. Multiple sedimentary soil samples were obtained by selecting borehole columns and taking multiple samples.

3. The method for reconstructing coastal wetlands based on sedimentary palynology according to claim 1, characterized in that, use 210 Pb and 137 Cs dating technology is used to date soil samples from sedimentary columns, including: S11: Soil samples from sedimentary columns were analyzed using radiometric gamma-ray spectroscopy. 210 Pb, 226 Ra and 137 Measurement of the radioactivity intensity of Cs; S12: By means of total 210 Subtract from the Pb radioactivity 226 Ra radioactivity intensity obtained 210 The strength of Pbex is calculated using the following formula: 210 Pb ex = 210 Pb total - 210 Pb sup ; In the formula: 210 Pb ex Indicates the radioactivity intensity of the remaining Pb; 210 Pb total For measurement 210 Total strength of Pb; 210 Pb sup For the sample 226 Ra decay formation 210 The radioactivity intensity of Pb; S13: Based on soil samples from each sedimentary column 210 Pb ex This corresponds to the depths. 210 Pb ex The formula for calculating the age of each sample layer is as follows: 210 Pb ex = 210 Pb （0） e -λt ; In the formula, 210 Pb ex The radioactivity intensity of 210Pb at depth x; 210 Pb(0) is the surface layer 210 Pb radioactivity intensity; λ is 210 The half-life constant of Pb; t is the age at depth x; S14: Based on the era and nuclides of the peak nuclear testing period 137 The distribution of Cs in sediments corresponds to the age of peak positions, and the depositional rates and ages of recent sediments are calculated and verified. 210 The accuracy of Pb dating.

4. The method for reconstructing coastal wetlands based on sedimentary palynology according to claim 1, characterized in that, Soil samples from the sedimentary column after dating analysis were extracted, identified, and processed. Pollen species and quantities were statistically analyzed, and pollen concentrations of different plant types were selected as response variables, including: S21: Weigh the soil samples from the sedimentary column according to lithology; S22: Add indicator pollen to soil samples from sedimentary columns; S23: Pollen extraction was performed using the hydrofluoric acid-sieving method; S24: Identify the extracted pollen until each sedimentary column soil sample reaches the preset target; S25: Calculate pollen parameters for different types; S26: Tilia 3.0.1 software was used to process pollen parameters and draw pollen maps. Pollen assemblage zones were divided using the CONISS clustering program and the principle of minimum variance constrained by stratigraphy. The concentration of pollen of different types of plants was selected as the response variable.

5. The method for reconstructing coastal wetlands based on sedimentary palynology according to claim 4, characterized in that, S25: Calculate pollen parameters for different types of pollen, including: The formula for calculating the mass concentration of pollen is: ； In the formula, Pc is the mass concentration of pollen of any family or genus; L is the number of sap pine spores added to the sample; M is the number of sap pine spores in the sample; N is the amount of pollen of any family or genus in the sample; and S is the mass of the sample.

6. The method for reconstructing coastal wetlands based on sedimentary palynology according to claim 4, characterized in that, S25: Calculating pollen parameters for different types, including: The percentage content of pollen from terrestrial families is calculated using the following formula: ； In the formula, A is the percentage of pollen content of any family in the sample, n is the number of pollen identified in that family, and n is the total number of pollen identified in all terrestrial families and genera in the sample.

7. The method for reconstructing coastal wetlands based on sedimentary palynology according to claim 4, characterized in that, S25: Calculating pollen parameters for different types, including: The formula for calculating the pollen content of aquatic plants, algae, and ferns is as follows: ； In the formula, B is the percentage content of a certain aquatic or algal or fern spore pollen in the sample, m is the identification statistic of that aquatic or algal or fern spore pollen, and N is the total identification statistic of all terrestrial family spore pollen in the sample.

8. The method for reconstructing coastal wetlands based on sedimentary palynology according to claim 1, characterized in that, Establish a multiple regression model to screen key environmental factors, including: S41: Pollen concentration is used as the response variable, and environmental dataset is used as the explanatory variable; S42: Perform logarithmic transformation on the response variable; S43: Use a multiple linear regression model to calculate the effects of sediment composition, grain size index, sedimentary environment, sedimentation rate, annual average sediment load, annual average runoff, and water flow time on pollen concentration. S44: Key environmental factors are obtained by stepwise regression based on the Akaike Information Criterion (AIC) to screen the independent variables of the model.

9. A reconstruction system for a coastal wetland reconstruction method based on sedimentary palynology according to any one of claims 1 to 8, characterized in that, include: Sampling module: Samples sediments from the target coastal wetland to obtain soil samples from the sediment column; Year analysis module: using 210 Pb and 137 Cs dating technology is used to date soil samples from sedimentary columns. Pollen extraction module: Extracts, identifies and processes soil samples from sedimentary columns after dating analysis, counts pollen types and quantities, and selects pollen concentrations of different plant types as response variables; Environmental data measurement module: Based on the sedimentary column soil samples after dating analysis, measure and collect environmental data for the corresponding year, and determine the environmental dataset as an explanatory variable; Screening module: Establishes a multiple regression model to screen key environmental factors; Reconstruction Module: Based on key environmental factors, determine the contribution rate of factors influencing pollen distribution in the target coastal wetland, and reconstruct the target coastal wetland.

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