paleotree (version 3.4.7)

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

Description

Character matrix and two cladograms for 13 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. cladogramDicranoX12 and cladogramDicranoX13 are both cladograms, formatted as phylo class objects for use with package ape, without branch-lengths (as these was are, respectively, consensus tree and a maximum-parsimony tree from separate maximum-parsimony analyses).

Details

This example dataset is composed of a small cladistic character data for 13 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 first included cladogram cladogramDicranoX12 is the majority-rule consensus of a maximum-parsimony analysis on 12 taxa (excluding on taxa with incompletely known anatomy) with 24 characters, including a biostratigraphic character. This tree is included here as, among the four trees depicted, it appeared to be the basis for the majority of Song and Zhang's discussion of dicranograptid systematics.

The second cladogram cladogramDicranoX13 is a maximum-parsimony tree found by a maximum-parsimony analysis of 13 taxa with 24 characters, including a biostratigraphic character. This tree is much more resolved than the alternative majority-rule cladogram for 12 taxa.

The matrix and both trees were entered by hand from their flat graphic depiction in Song and Zhang's manuscript.

Examples

data(SongZhangDicrano)

# Examining morphospace with a distance matrix

# calculate a distance matrix from the morph character data
char <- charMatDicrano[,-22]	# remove strat character
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)
	}}

#####
# PCO of character distance matrix

#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 12 taxon majority rule tree from Song and Zhang
plot(cladogramDicranoX12,
	main = "MajRule_24charX12Taxa_wBiostratChar")

# plot 13 taxon MPT
plot(cladogramDicranoX13,
	main = "MPT_24charX13Taxa_wBiostratChar")

##############

if (FALSE) {
# Data was generated with following script:
require(ape)
require(phylobase)

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

cladogramDicranoX12 <- read.tree(file.choose())
cladogramDicranoX13 <- read.nexus(file.choose())

cladogramDicranoX13$tip.label <- rownames(
	 charMatDicrano)[c(13,8,7,9,12,10,1,4,6,2,3,11,5)]

save(charMatDicrano,cladogramDicranoX12,file = "SongZhangDicrano.rdata")
}