Cladistic Data for Dicranograptid Graptolites from Song and Zhang (2014)

Description

Character matrix and majority-rule cladogram for 12 dicranograptid (and outgroup) graptoloids, taken from Song and Zhang (2014). Included here for use with functions related to character change.

Format

Loading this dataset adds two objects to the R environment. charMatDicrano is a data.frame object composed of multiple factors, with NA values representing missing values (states coded as '?'), read in with readNexus from package phylobase. cladogramDicrano is a cladogram, formatted as a phylo class object for use with package ape, without branch-lengths (as this was a consensus tree from a maximum-parsimony analysis).

Details

This example dataset is composed of a small cladistic character data for 12 taxa and 24 characters, taken from Song and Zhang (2014). Note that character 22 is a biostratigraphic character, which was not included in all analyses by Song and Zhang.

The included cladogram is the majority-rule consensus of a maximum-parsimony analysis on 12 taxa with 24 characters, including a biostratigraphic character. This tree is included (among the four depicted) as it appeared to be the basis for the majority of Song and Zhang's discussion of dicranograptid systematics.

Both the matrix and the tree were entered by hand from their flat graphic depiction in Song and Zhang's manuscript.

Source

Song, Y., and Y. Zhang. 2014. A preliminary study on the relationship of the early dicranograptids based on cladistic analysis. GFF 136(1):243-248.

Examples

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## Not run: 

 

require(ape)
require(phylobase)

charMatDicrano<-readNexus(file.choose(),type="data",SYMBOLS = " 0 1 2")

cladogramDicrano<-read.nexus(file.choose())

save(charMatDicrano,cladogramDicrano,file="SongZhangDicrano.rdata")


## End(Not run)

data(SongZhangDicrano)

# calculate a distance matrix from the character data
char<-charMatDicrano
charDist<-matrix(,nrow(char),nrow(char))
rownames(charDist)<-colnames(charDist)<-rownames(char)
for(i in 1:nrow(char)){for(j in 1:nrow(char)){
	charDiff<-logical()
	for(k in 1:ncol(char)){
		selectPair<-char[c(i,j),k]
		if(all(!is.na(selectPair))){
			#drop states that are missing			
			isSame<-identical(selectPair[1],selectPair[2])
			charDiff<-c(charDiff,isSame)
			}
		}
	charDist[i,j]<-1-sum(charDiff)/length(charDiff)
	}}

#####
# the following is mostly stolen from the example code for my graptDisparity dataset

#now, is the diagonal zero? (it should be)
all(diag(charDist)==0)

#now, is the matrix symmetric? (it should be)
isSymmetric(charDist)

#can apply cluster analysis
clustRes <- hclust(as.dist(charDist))
plot(clustRes)

#can apply PCO (use lingoes correction to account for negative values
   #resulting from non-euclidean matrix
pco_res <- pcoa(charDist,correction="lingoes")

#relative corrected eigenvalues
rel_corr_eig <- pco_res$values$Rel_corr_eig
layout(1:2)
plot(rel_corr_eig)
#cumulative
plot(cumsum(rel_corr_eig))

#well let's look at those PCO axes anyway
layout(1)
pco_axes <- pco_res$vectors
plot(pco_axes[,1],pco_axes[,2],pch=16,
   xlab=paste("PCO Axis 1, Rel. Corr. Eigenvalue =",round(rel_corr_eig[1],3)),
   ylab=paste("PCO Axis 2, Rel. Corr. Eigenvalue =",round(rel_corr_eig[2],3)))

#######

# plot majority rule tree from Song and Zhang
plot(cladogramDicrano,
main="MajRule_24charX12Taxa_wBiostratChar")

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