MALAPP Age Depth Modeling and Pollen Diagrams
Benjamin Davies, David K. Wright, Sarah J. Ivory, Jago J. Birk, Jeong-Heon Choi, Sabine Fiedler, Jacob Davis, Potiphar Kaliba, Jessica C. Thompson
This code is used to generate age-depth models and pollen diagrams for a forthcoming publication:
David K. Wright, Sarah J. Ivory, Jago J. Birk, Jeong-Heon Choi, Benjamin Davies, Sabine Fiedler, Jacob Davis, Potiphar Kaliba, Jessica C. Thompson.”Palaeoenvironmental data track Late Quaternary anthropogenic impacts on vegetation and landscapes in Mzimba, northern Malawi” Submitted to Frontiers in Environmental Archaeology, May 2023.
This is an R Markdown Notebook. When you execute code within the notebook, the results appear beneath the code.
Load packages
rm(list = ls())
library(rbacon)Loading required package: IntCallibrary(rioja)This is rioja 1.0-5AU3: Generate age depth model and assign pollen depths
Bacon(core="AU3",d.min=0,d.max=67,accept.suggestions = TRUE,verbose=FALSE)The run's files will be put in this folder: C:\Users\bdav_\Dropbox\Yale Paleo\AgeDepth\MALAPP_AgeDepthPollen\MALAPP_AgeDepthPollen_2JUN23\Cores/AU3
Reading C:\Users\bdav_\Dropbox\Yale Paleo\AgeDepth\MALAPP_AgeDepthPollen\MALAPP_AgeDepthPollen_2JUN23\Cores/AU3/AU3_15.bacon
Constant calibration curve.
IntCal20: Reading from file: C:\Users\bdav_\AppData\Local\R\win-library\4.2\IntCal\extdata/3Col_intcal20.14C
Marine20: Reading from file: C:\Users\bdav_\AppData\Local\R\win-library\4.2\IntCal\extdata/3Col_marine20.14C
SHCal20: Reading from file: C:\Users\bdav_\AppData\Local\R\win-library\4.2\IntCal\extdata/3Col_shcal20.14C
Added det: NA: 49.0+-964000.0 d=0 ResCorr= 0.0+-0.0 a=3 b=4 cc=SHCal20
Added det: Ua-69826: 49.0+-29.0 d=14 ResCorr= 0.0+-0.0 a=3 b=4 cc=SHCal20
Added det: Ua-69825: 267.0+-29.0 d=19 ResCorr= 0.0+-0.0 a=3 b=4 cc=SHCal20
Added det: Ua-69824: 964.0+-29.0 d=28 ResCorr= 0.0+-0.0 a=3 b=4 cc=SHCal20
Added det: NA: 964.0+-964000000.0 d=67 ResCorr= 0.0+-0.0 a=3 b=4 cc=SHCal20
BaconFixed: Bacon jumps model with fixed c's.
K=15, H=0, dim=17, Seed=161655, Dc=5.000000, c(0)=0.000000, c(K)=75.000000
twalk: 3740000 iterations to run, Sat Jun 3 07:54:09 2023
twalk thinning: 1 out of every 85 accepted iterations will be saved in file C:\Users\bdav_\Dropbox\Yale Paleo\AgeDepth\MALAPP_AgeDepthPollen\MALAPP_AgeDepthPollen_2JUN23\Cores/AU3/AU3_15.out
twalk: Finished, 2.2% of moved pars per iteration (ratio 83855.705883/3740000). Output in file C:\Users\bdav_\Dropbox\Yale Paleo\AgeDepth\MALAPP_AgeDepthPollen\MALAPP_AgeDepthPollen_2JUN23\Cores/AU3/AU3_15.out,
Sat Jun 3 07:54:13 2023
bacon: 184049 WarnBeyondLimits warnings:
bacon: WARNING: calibration attempted beyond MinYr= -96.000000 or MaxYr= 1000000.000000
bacon: burn in (initial iterations which will be removed): 17000
Eso es to...eso es to...eso es to...eso es toooodo amigos!
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2 
core<-rep("AU3",9)
pollenNo<-c("POL11","POL10","POL9","POL8","POL12","POL13","POL16","POL14","POL15")
depth<-c(3,14,19,28,34,40,46,55,67)
ageBP<-c()
ageMin<-c()
ageMax<-c()
for (i in depth){
ageBP<-append(ageBP,round(mean(Bacon.Age.d(i))))
ageMin<-append(ageMin,round(min(Bacon.Age.d(i))))
ageMax<-append(ageMax,round(max(Bacon.Age.d(i))))
}
AUG3data<-data.frame(core,pollenNo,depth,ageBP,ageMin,ageMax)
print(AUG3data)write.csv(AUG3data,"AUG3data.csv",row.names = FALSE)AU7: Generate age depth model and assign pollen depths
Bacon(core="AU7",d.min=0,d.max=77,accept.suggestions = TRUE,verbose=FALSE)The run's files will be put in this folder: C:\Users\bdav_\Dropbox\Yale Paleo\AgeDepth\MALAPP_AgeDepthPollen\MALAPP_AgeDepthPollen_2JUN23\Cores/AU7
Reading C:\Users\bdav_\Dropbox\Yale Paleo\AgeDepth\MALAPP_AgeDepthPollen\MALAPP_AgeDepthPollen_2JUN23\Cores/AU7/AU7_17.bacon
Constant calibration curve.
IntCal20: Reading from file: C:\Users\bdav_\AppData\Local\R\win-library\4.2\IntCal\extdata/3Col_intcal20.14C
Marine20: Reading from file: C:\Users\bdav_\AppData\Local\R\win-library\4.2\IntCal\extdata/3Col_marine20.14C
SHCal20: Reading from file: C:\Users\bdav_\AppData\Local\R\win-library\4.2\IntCal\extdata/3Col_shcal20.14C
Added det: NA: 149.0+-2388000.0 d=0 ResCorr= 0.0+-0.0 a=3 b=4 cc=SHCal20
Added det: Ua-69829: 149.0+-29.0 d=15 ResCorr= 0.0+-0.0 a=3 b=4 cc=SHCal20
Added det: Ua-69526: 792.0+-27.0 d=32 ResCorr= 0.0+-0.0 a=3 b=4 cc=SHCal20
Added det: Ua-69527: 1233.0+-28.0 d=51 ResCorr= 0.0+-0.0 a=3 b=4 cc=SHCal20
Added det: Ua-69528: 927.0+-28.0 d=54 ResCorr= 0.0+-0.0 a=3 b=4 cc=SHCal20
Added det: Ua-69529: 2388.0+-37.0 d=77 ResCorr= 0.0+-0.0 a=3 b=4 cc=SHCal20
BaconFixed: Bacon jumps model with fixed c's.
K=17, H=0, dim=19, Seed=814200, Dc=5.000000, c(0)=0.000000, c(K)=85.000000
twalk: 4180000 iterations to run, Sat Jun 3 07:54:25 2023
twalk thinning: 1 out of every 95 accepted iterations will be saved in file C:\Users\bdav_\Dropbox\Yale Paleo\AgeDepth\MALAPP_AgeDepthPollen\MALAPP_AgeDepthPollen_2JUN23\Cores/AU7/AU7_17.out
twalk: Finished, 1.2% of moved pars per iteration (ratio 49705.526316/4180000). Output in file C:\Users\bdav_\Dropbox\Yale Paleo\AgeDepth\MALAPP_AgeDepthPollen\MALAPP_AgeDepthPollen_2JUN23\Cores/AU7/AU7_17.out,
Sat Jun 3 07:54:31 2023
bacon: 185239 WarnBeyondLimits warnings:
bacon: WARNING: calibration attempted beyond MinYr= -73.000000 or MaxYr= 1000000.000000
bacon: burn in (initial iterations which will be removed): 19000
Eso es to...eso es to...eso es to...eso es toooodo amigos!
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2 
core<-rep("AU7",9)
pollenNo<-c("POL36","POL37","POL38","POL39","POL40","POL41","POL42","POL43","POL44")
depth<-c(6,15,26,32,42,51,54,66,77)
ageBP<-c()
ageMin<-c()
ageMax<-c()
for (i in depth){
ageBP<-append(ageBP,round(mean(Bacon.Age.d(i))))
ageMin<-append(ageMin,round(min(Bacon.Age.d(i))))
ageMax<-append(ageMax,round(max(Bacon.Age.d(i))))
}
AUG7data<-data.frame(core,pollenNo,depth,ageBP,ageMin,ageMax)
print(AUG7data)write.csv(AUG7data,"AUG7data.csv",row.names = FALSE)AU8: Generate age depth model and assign pollen depths
#Run age-depth model between 0 and max depth
Bacon(core="AU8",d.min=0,d.max=146,depth.unit = "cm",accept.suggestions = TRUE,verbose=FALSE)The run's files will be put in this folder: C:\Users\bdav_\Dropbox\Yale Paleo\AgeDepth\MALAPP_AgeDepthPollen\MALAPP_AgeDepthPollen_2JUN23\Cores/AU8
Reading C:\Users\bdav_\Dropbox\Yale Paleo\AgeDepth\MALAPP_AgeDepthPollen\MALAPP_AgeDepthPollen_2JUN23\Cores/AU8/AU8_31.bacon
Constant calibration curve.
IntCal20: Reading from file: C:\Users\bdav_\AppData\Local\R\win-library\4.2\IntCal\extdata/3Col_intcal20.14C
Marine20: Reading from file: C:\Users\bdav_\AppData\Local\R\win-library\4.2\IntCal\extdata/3Col_marine20.14C
SHCal20: Reading from file: C:\Users\bdav_\AppData\Local\R\win-library\4.2\IntCal\extdata/3Col_shcal20.14C
Added det: NA: 1794.0+-15742000.0 d=0 ResCorr= 0.0+-0.0 a=3 b=4 cc=SHCal20
Added det: Ua-69524: 1794.0+-32.0 d=11 ResCorr= 0.0+-0.0 a=3 b=4 cc=SHCal20
Added det: Ua-69523: 7759.0+-124.0 d=59 ResCorr= 0.0+-0.0 a=3 b=4 cc=SHCal20
Added det: Ua-69828: 9690.0+-51.0 d=74 ResCorr= 0.0+-0.0 a=3 b=4 cc=SHCal20
Added det: Ua-69522: 15742.0+-59.0 d=86 ResCorr= 0.0+-0.0 a=3 b=4 cc=SHCal20
Added det: NA: 15742.0+-15742000000.0 d=146 ResCorr= 0.0+-0.0 a=3 b=4 cc=SHCal20
BaconFixed: Bacon jumps model with fixed c's.
K=31, H=0, dim=33, Seed=970277, Dc=5.000000, c(0)=0.000000, c(K)=155.000000
twalk: 7260000 iterations to run, Sat Jun 3 07:54:59 2023
twalk thinning: 1 out of every 165 accepted iterations will be saved in file C:\Users\bdav_\Dropbox\Yale Paleo\AgeDepth\MALAPP_AgeDepthPollen\MALAPP_AgeDepthPollen_2JUN23\Cores/AU8/AU8_31.out
twalk: Finished, 0.5% of moved pars per iteration (ratio 38029.303031/7260000). Output in file C:\Users\bdav_\Dropbox\Yale Paleo\AgeDepth\MALAPP_AgeDepthPollen\MALAPP_AgeDepthPollen_2JUN23\Cores/AU8/AU8_31.out,
Sat Jun 3 07:55:13 2023
bacon: 173536 WarnBeyondLimits warnings:
bacon: WARNING: calibration attempted beyond MinYr= -73.000000 or MaxYr= 1000000.000000
bacon: burn in (initial iterations which will be removed): 33000
Eso es to...eso es to...eso es to...eso es toooodo amigos!
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2 
#Create age-depth table
core<-rep("AU8",7)
pollenNo<-c("POL35","POL34","POL33","POL32","POL31","POL30","POL29")
depth<-c(11,45,59,74,86,110,146)
ageBP<-c()
ageMin<-c()
ageMax<-c()
for (i in depth){
ageBP<-append(ageBP,round(mean(Bacon.Age.d(i))))
ageMin<-append(ageMin,round(min(Bacon.Age.d(i))))
ageMax<-append(ageMax,round(max(Bacon.Age.d(i))))
}
AUG8data<-data.frame(core,pollenNo,depth,ageBP,ageMin,ageMax)
print(AUG8data)write.csv(AUG8data,"AUG8data.csv",row.names = FALSE)Generate pollen diagrams
Notes: 1) Terrestrial pollen data are expressed as percentages of all terrestrial species (excl. aquatics). Aquatics (Cyperaceae and Ascolepis) are plotted as percentage of all species (terrestrial + aquatics). 2) In order to view microcharcoal concentrations in the same plot as percentages, these values were transformed using log10, and then multiplied by 10 to exaggerate (e.g., conc of 10000 -> transformed log10(10000) = 4 -> exaggerated 4 * 10 = 40). The x-axis tick mark labels were then manually replaced with the appropriate scientific notation (e.g., 40 = 10000 = 10^4). 3) Age-depth modeling was not used for composite terrace sequence; ages are estimated using adjacent OSL samples (included as “TERRACEdata.csv”). 4) The convention used for the sequences is “ka”, and several of the dates are less than 1000 years and are expressed as decimals (e.g., 0.4 ka for 400 years). The rioja package does not seem to plot ages as non-integer values, so these were plotted here in terms of integer years (e.g., 400 for 400 years), but y-axis tick mark labels were edited manually to accommodate the convention. Y-axis titles (“Years ka”) were left off as well and were added manually. 5) A red line indicating disturbance in AUG7 was added manually in the published figure. A function is included here to mark the location of the disturbance, but this will look slightly different.
---
title: "MALAPP Age Depth Modeling and Pollen Diagrams"
author: "Benjamin Davies, David K. Wright, Sarah J. Ivory, Jago J. Birk, Jeong-Heon Choi, Sabine Fiedler, Jacob Davis, Potiphar Kaliba, Jessica C. Thompson"
output: html_notebook
---

This code is used to generate age-depth models and pollen diagrams for a forthcoming publication: 

David K. Wright, Sarah J. Ivory, Jago J. Birk, Jeong-Heon Choi, Benjamin Davies, Sabine Fiedler, Jacob Davis, Potiphar Kaliba, Jessica C. Thompson."Palaeoenvironmental data track Late Quaternary anthropogenic impacts on vegetation and landscapes in Mzimba, northern Malawi" Submitted to Frontiers in Environmental Archaeology, May 2023.

This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code. 

## Load packages
```{r warning=FALSE}
rm(list = ls())
library(rbacon)
library(rioja)
```

### AU3: Generate age depth model and assign pollen depths
```{r message=FALSE}
Bacon(core="AU3",d.min=0,d.max=67,accept.suggestions = TRUE,verbose=FALSE)
core<-rep("AU3",9)
pollenNo<-c("POL11","POL10","POL9","POL8","POL12","POL13","POL16","POL14","POL15")
depth<-c(3,14,19,28,34,40,46,55,67)
ageBP<-c()
ageMin<-c()
ageMax<-c()
for (i in depth){
  ageBP<-append(ageBP,round(mean(Bacon.Age.d(i))))
  ageMin<-append(ageMin,round(min(Bacon.Age.d(i))))
  ageMax<-append(ageMax,round(max(Bacon.Age.d(i))))
}
AUG3data<-data.frame(core,pollenNo,depth,ageBP,ageMin,ageMax)
print(AUG3data)
write.csv(AUG3data,"AUG3data.csv",row.names = FALSE)
```

### AU7: Generate age depth model and assign pollen depths
```{r message=FALSE}
Bacon(core="AU7",d.min=0,d.max=77,accept.suggestions = TRUE,verbose=FALSE)

core<-rep("AU7",9)
pollenNo<-c("POL36","POL37","POL38","POL39","POL40","POL41","POL42","POL43","POL44")
depth<-c(6,15,26,32,42,51,54,66,77)
ageBP<-c()
ageMin<-c()
ageMax<-c()
for (i in depth){
  ageBP<-append(ageBP,round(mean(Bacon.Age.d(i))))
  ageMin<-append(ageMin,round(min(Bacon.Age.d(i))))
  ageMax<-append(ageMax,round(max(Bacon.Age.d(i))))
}
AUG7data<-data.frame(core,pollenNo,depth,ageBP,ageMin,ageMax)
print(AUG7data)
write.csv(AUG7data,"AUG7data.csv",row.names = FALSE)
```

## AU8: Generate age depth model and assign pollen depths
```{r message=FALSE}
#Run age-depth model between 0 and max depth
Bacon(core="AU8",d.min=0,d.max=146,depth.unit = "cm",accept.suggestions = TRUE,verbose=FALSE)
#Create age-depth table 
core<-rep("AU8",7)
pollenNo<-c("POL35","POL34","POL33","POL32","POL31","POL30","POL29")
depth<-c(11,45,59,74,86,110,146)
ageBP<-c()
ageMin<-c()
ageMax<-c()
for (i in depth){
  ageBP<-append(ageBP,round(mean(Bacon.Age.d(i))))
  ageMin<-append(ageMin,round(min(Bacon.Age.d(i))))
  ageMax<-append(ageMax,round(max(Bacon.Age.d(i))))
}
AUG8data<-data.frame(core,pollenNo,depth,ageBP,ageMin,ageMax)
print(AUG8data)
write.csv(AUG8data,"AUG8data.csv",row.names = FALSE)
```


### Generate pollen diagrams
Notes: 
1) Terrestrial pollen data are expressed as percentages of all terrestrial species (excl. aquatics). Aquatics (Cyperaceae and Ascolepis) are plotted as percentage of all species (terrestrial + aquatics).
2) In order to view microcharcoal concentrations in the same plot as percentages, these values were transformed using log10, and then multiplied by 10 to exaggerate (e.g., conc of 10000 -> transformed log10(10000) = 4 -> exaggerated 4 * 10 = 40). The x-axis tick mark labels were then manually replaced with the appropriate scientific notation (e.g., 40 = 10000 = 10^4). 
3) Age-depth modeling was not used for composite terrace sequence; ages are estimated using adjacent OSL samples (included as "TERRACEdata.csv").
4) The convention used for the sequences is "ka", and several of the dates are less than 1000 years and are expressed as decimals (e.g., 0.4 ka for 400 years). The rioja package does not seem to plot ages as non-integer values, so these were plotted here in terms of integer years (e.g., 400 for 400 years), but y-axis tick mark labels were edited manually to accommodate the convention. Y-axis titles ("Years ka") were left off as well and were added manually.
5) A red line indicating disturbance in AUG7 was added manually in the published figure. A function is included here to mark the location of the disturbance, but this will look slightly different. 
```{r}

##AUG3
names<-c('Poaceae','Asteraceae',expression(italic('Podocarpus')),expression(italic('Pinus')),expression(italic('Brachystegia')), 'Cyperaceae',expression(italic('Ascolepis')),"Ferns","Trees","Herbs",expression(paste("Microcharcoal (#/cm"^3,")")))

colors<-c("gold","gold","forestgreen","forestgreen","forestgreen","darkcyan","darkcyan","darkgrey","forestgreen","gold","black")


AUG3DATES<-read.csv("AUG3data.csv",stringsAsFactors = FALSE)
ageBP<-AUG3DATES$ageBP
AUG3pollen<-read.csv("Aug3Pollen_1.csv",stringsAsFactors = FALSE)
char<-AUG3pollen$Microcharcoal
char<-(log10(char))*10
char[!is.finite(char)] <- 0
AUG3pollen$Microcharcoal<-char
pollenData<-AUG3pollen[,6:ncol(AUG3pollen)]
cs<-colSums(pollenData)
pollenData[is.na(pollenData)] <- 0
pollenData2 <- pollenData
ex<-c(FALSE,TRUE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE)
mult<-5


#png("AUG3pollen_FINAL.png",width=6,height=3,units="in",res=600)
par(oma=c(4,4,3,4))
strat.plot(pollenData2,yvar=round(ageBP), y.tks=c(0,1000,2000,3000), y.rev=TRUE, plot.line=FALSE, plot.poly=TRUE, plot.bar=FALSE, col.poly=colors, col.poly.line="white", lwd.poly=0.01, scale.percent=TRUE, xSpace=0.02,x.pc.inc=c(10,10,10,10,10,10,10,10,10,10,10), x.pc.omit0=TRUE,cex.xlabel=0.5,cex.axis=0.5,cex.yaxis=0.5,ylabel = "",cex.ylabel=0.5,srt.xlabel=45,las=3,mgp=c(3,0.6,0),exag=ex,exag.mult = 5,col.exag="auto",exag.alpha = 0.5,x.names=names)
#dev.off()


#AUG7
#Function to generate line marking disturbance at POL42 (~1.129 ka)
disturbedMark <- function(x, y, i, nm) {
  #tmp <- data.frame(x=y, y=x)
  #tmp <- na.omit(tmp)
  lines(c(0,100), c(1129,1129), col="red", lwd=1)
  if (i == 12){
  text(25,1200,labels="Disturbed",col="red",cex=0.5)
  }
}

names<-c('Poaceae','Asteraceae',expression(italic('Commelina')),expression(italic('Podocarpus')),expression(italic('Pinus')),expression(italic('Brachystegia')), 'Cyperaceae',expression(italic('Ascolepis')),"Ferns","Trees","Herbs",expression(paste("Microcharcoal (#/cm"^3,")")))

colors<-c("gold","gold","gold","forestgreen","forestgreen","forestgreen","darkcyan","darkcyan","darkgrey","forestgreen","gold","black")

AUG7DATES<-read.csv("AUG7data.csv",stringsAsFactors = FALSE)
ageBP<-AUG7DATES$ageBP
AUG7pollen<-read.csv("Aug7Pollen_1.csv",stringsAsFactors = FALSE)
char<-AUG7pollen$Microcharcoal
char<-(log10(char))*10
char[!is.finite(char)] <- 0
AUG7pollen$Microcharcoal<-char
pollenData<-AUG7pollen[,6:ncol(AUG7pollen)]
cs<-colSums(pollenData)
pollenData[is.na(pollenData)] <- 0
pollenData2 <- pollenData
ex<-c(FALSE,TRUE,FALSE,FALSE,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE)
mult<-5


#png("AU7pollen_FINAL.png",width=6,height=3,units="in",res=600)
par(oma=c(4,4,3,4))
strat.plot(pollenData2,yvar=round(ageBP), y.tks=c(0,500,1000,1500,2000,2500), y.rev=TRUE, plot.line=FALSE, plot.poly=TRUE, plot.bar=FALSE, col.poly=colors, col.poly.line="white", lwd.poly=0.01, scale.percent=TRUE, xSpace=0.02,x.pc.inc=c(10,10,10,10,10,10,10,10,10,10,10,10), x.pc.omit0=TRUE,cex.xlabel=0.5,cex.axis=0.5,cex.yaxis=0.5,ylabel = "",cex.ylabel=0.5,srt.xlabel=45,las=3,mgp=c(3,0.6,0),exag=ex,exag.mult = 5,col.exag="auto",exag.alpha = 0.5,x.names=names,fun2=distrurbedMark)

#dev.off()


#AUG 8

names<-c('Poaceae','Asteraceae',expression(italic('Podocarpus')),expression(italic('Pinus')),'Cyperaceae',expression(italic('Ascolepis')),"Ferns","Trees","Herbs",expression(paste("Microcharcoal (#/cm"^3,")")))

colors<-c("gold","gold","forestgreen","forestgreen","darkcyan","darkcyan","darkgrey","forestgreen","gold","black")

AUG8DATES<-read.csv("AUG8data.csv",stringsAsFactors = FALSE)
ageBP<-AUG8DATES$ageBP
AUG8pollen<-read.csv("Aug8Pollen_1.csv",stringsAsFactors = FALSE)
char<-AUG8pollen$Microcharcoal
char<-(log10(char))*10
char[!is.finite(char)] <- 0
AUG8pollen$Microcharcoal<-char
pollenData<-AUG8pollen[,6:ncol(AUG8pollen)]
cs<-colSums(pollenData)
pollenData[is.na(pollenData)] <- 0
pollenData2 <- pollenData
ex<-c(FALSE,FALSE,FALSE,FALSE,FALSE,TRUE,FALSE,FALSE,FALSE,FALSE)
mult<-5


#png("AU8pollen_FINAL.png",width=6,height=3,units="in",res=600)
par(oma=c(4,4,3,4))
strat.plot(pollenData2,yvar=round(ageBP), y.tks=c(0,10000,20000,30000), y.rev=TRUE, plot.line=FALSE, plot.poly=TRUE, plot.bar=FALSE, col.poly=colors, col.poly.line="white", lwd.poly=0.01, scale.percent=TRUE, xSpace=0.02,x.pc.inc=c(10,10,10,10,10,10,10,10,10,10), x.pc.omit0=TRUE,cex.xlabel=0.5,cex.axis=0.5,cex.yaxis=0.5,ylabel = "Years BP",cex.ylabel=0.5,srt.xlabel=45,las=3,mgp=c(3,0.6,0),exag=ex,exag.mult = 5,col.exag="auto",exag.alpha = 0.5,x.names=names)
#dev.off()



#TERRACES

names<-c('Poaceae','Asteraceae','Pinopsida undiff.',expression(italic('Brachystegia')),'Cyperaceae',"Ferns","Trees","Herbs",expression(paste("Microcharcoal (#/cm"^3,")")))

polycolors<-c("gold","gold","forestgreen","forestgreen","darkcyan","darkgrey","forestgreen","gold","black")


TERRACEDATES<-read.csv("TERRACEdata.csv",stringsAsFactors = FALSE)
ageKA<-TERRACEDATES$ageKA
TERRACEpollen<-read.csv("TerracePollen_1.csv",stringsAsFactors = FALSE)
char<-TERRACEpollen$Microcharcoal
char<-(log10(char))*10
char[!is.finite(char)] <- 0
TERRACEpollen$Microcharcoal<-char
pollenData<-TERRACEpollen[,6:ncol(TERRACEpollen)]
cs<-colSums(pollenData)
pollenData[is.na(pollenData)] <- 0
pollenData2 <- pollenData#[, which(cs > 5)]
ex<-c(FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,FALSE)
mult<-5


#png("TERRACEpollen_FINAL2.png",width=6,height=3,units="in",res=600)
par(oma=c(4,4,3,4))
strat.plot(pollenData2,yvar=round(ageKA), y.tks=c(0,2000,4000,6000,8000), y.rev=TRUE, plot.line=FALSE, plot.poly=TRUE, plot.bar=FALSE, col.poly=polycolors, col.poly.line="white", lwd.poly=0.01, scale.percent=TRUE, xSpace=0.02,x.pc.inc=c(10,10,10,10,10,10,10,10,10), x.pc.omit0=TRUE,cex.xlabel=0.5,cex.axis=0.5,cex.yaxis=0.5,ylabel = "",cex.ylabel=0.5,srt.xlabel=45,las=3,mgp=c(3,0.6,0),exag=ex,exag.mult = 5,col.exag="auto",exag.alpha = 0.5,x.names=names)
#dev.off()


```