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Script umbratus Dorsal skull.R
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Script umbratus Dorsal skull.R
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########################################################################
########################################################################
#R script of "Geographic variation in select species of the bat genus Platyrrhinus"
#by Velazco, Ly, McAllister and Esquivel
# THERYA, 2023, Vol 14(1):121-130. DOI:10.12933/therya-23-2208
# This Script contains the following functions:
## Load raw data coordinates and classifiers
## GPA
## PCA and LDA/CVA
## Allometry
## Size and shape analysis
## Graphics
#########################################################################
########################################################################
# Load packages
# install.packages("")
dir()
require(geomorph)
require(MASS)
require(ggplot2)
require(car)
require(vegan)
require(Morpho)
########################################################################
# Upload data (.tps)
load("umbratus_dorsal.RData")
dim(tps)
dim(plan)
groups<-as.factor(plan[,2])
sex<-as.factor(plan[,3])
########################################################################
# GPA (Generalized Procrustes Analysis)
gpa.object<-gpagen(tps)
shape<-gpa.object$coords
size<-gpa.object$Csize
plotAllSpecimens(shape)
########################################################################
# Find outliers. This step can be skipped.
plotOutliers(shape)
plotOutliers(shape,groups = groups,inspect.outliers=TRUE)
# Remove specimens/outliers.
tps<-tps[,,-c(29,32)]
plan<-plan[-c(29,32),]
########################################################################
# Shape visualization
# Mean shape
ref<-mshape(shape)
# Define links between landmarks
# links<-define.links(ref,ptsize=1)# Manually
links<-
matrix(c(1,3,3,4,4,5,5,6,6,7,1,2),nrow=6,ncol=2,byrow=T)
# Plot specimens against mean shape
GP1<-gridPar(pt.bg="gray",link.col="gray",link.lty=1)
plotRefToTarget(ref,shape[,,11],links=links,method="TPS",mag = 1)
plotRefToTarget(ref,shape[,,11],links=links,method="vector", mag = 1)
plotRefToTarget(ref,shape[,,11],links=links,method="points",gridPars=GP1) # target = black, reference = gray
plotAllSpecimens(shape,mean=TRUE,links=links)
############################ Size Analyses ##############################
plan$CS<-size # size per each group
nigellus<-plan[plan$Species=="nigellus",]
umbratus<-plan[plan$Species=="umbratus",]
##### 1.1 Does the size differ between Females and Males? (considering all as one group) ####
boxplot(size~sex,ylab="CENTROID SIZE")
#Vizualization of size differences between sexes
boxplot(log(size)~sex,ylab="LOG CENTROID SIZE")
#tiff("1. Boxplot log (size) vs sex.tiff", units="in", width=5, height=5, res=300)
#dev.off()
# Test
shapiro.test(log(size))
leveneTest(log(size),group = sex,center = "median")
t.test(log(size)~sex,var.equal = TRUE)
##### 1.2 Does the size differ between Females and Males inside each subpopulation or groups/species? (2 groups) ####
par(mfrow=c(1,2))
boxplot(log(nigellus$CS)~nigellus$Sex,ylab="LOG CENTROID SIZE")
boxplot(log(umbratus$CS)~umbratus$Sex,ylab="LOG CENTROID SIZE")
# Test: Variation in size inside species (considering two separate groups)
shapiro.test(log(nigellus$CS))
leveneTest(log(nigellus$CS),group = nigellus$Sex,center = "median")
t.test(log(nigellus$CS)~nigellus$Sex,var.equal = TRUE)
##### 1.3 Sexual dimorphism in size? ####
boxplot(size~groups*sex,ylab="CENTROID SIZE")
boxplot(log(size)~groups*sex,ylab="LOG CENTROID SIZE")
# Test: ANOVA with interaction
# Unbalanced designs
anova<-aov(log(size)~sex*groups)
Anova(anova, type = "II")
TukeyHSD(anova)
gdf <- geomorph.data.frame(shape= shape,size=size,
sex=plan$Sex,species=groups)
fit.size.sp.int <- lm.rrpp(size ~ sex * species,iter = 10000,RRPP = TRUE,
SS.type = "II", data = gdf,
print.progress = FALSE) # Does size differ between species, while accounting for size covarying with sexes?
anova(fit.size.sp.int)
boxplot(log(size)~groups,ylab="LOG CENTROID SIZE")
#tiff("Boxplot log (size) vs species.tiff", units="in", width=5, height=5, res=300)
#dev.off()
######################### Shape Analyses ################################
#### 2.1 Shape variation between species and sexes: sexual dimorphism? ####
new_shape<-two.d.array(shape)
gdf1 <- geomorph.data.frame(shape= new_shape,size=size,
sex=plan$Sex,species=groups)
fit.full.model <-lm.rrpp(shape ~ size * sex * species,iter = 10000,RRPP = TRUE,
SS.type = "II", data = gdf1,
print.progress = FALSE)
anova(fit.full.model,effect.type = "F")
#### PCA-Normal-WITHOUT SEXUAL DIMORPHISM ####
### Customize PCA
col.group<-c("#010305","#4271AE")
names(col.group)<-levels(groups)
col.group<-col.group[match(groups,names(col.group))]
PCA<-gm.prcomp(shape)
xlab<-"Principal Component 1 (37.35%)"
ylab<-"Principal Component 2 (17.96%)"
mat<-matrix(c(4,5,0,1,1,2,1,1,3),3) # Split the plot window
layout(mat, widths=c(3,2,2), heights=c(1,1,1))
par(mar=c(4, 4, 1, 1))
plot(PCA$x[,1],PCA$x[,2],pch=21,cex=3.5,cex.lab=1.8,bg=col.group,xlab=xlab,ylab=ylab,font.lab = 2,font.axis = 2,asp=T)
plotRefToTarget(ref,PCA$shapes$shapes.comp1$min,links=links,method="points",gridPars=GP1,mag = 2)
plotRefToTarget(ref,PCA$shapes$shapes.comp1$max,links=links,method="points",gridPars=GP1,mag = 2)
plotRefToTarget(ref,PCA$shapes$shapes.comp2$max,links=links,method="points",gridPars=GP1,mag = 2)
plotRefToTarget(ref,PCA$shapes$shapes.comp2$min,links=links,method="points",gridPars=GP1,mag = 2)
par(mfrow=c(1,1))
# 95% confidence ellipses
plot(PCA$x[,1],PCA$x[,2],pch=21,cex=2,bg=col.group,xlab=xlab,ylab=ylab,font.lab = 2,asp=T)
ordihull(PCA$x,group=groups,lwd = 1.5)
##### LDA / CVA ####
# LDA (Linear Discriminant Analysis)
cva<-lda(PCA$x[,1:6],groups)
plot(cva)
cva<-lda(PCA$x[,1:6],groups,CV=T) #LDA com Jackknife cross validation
tab<-table(groups,cva$class)
lda.p<-diag(tab)/summary(groups)*100
lda.p # providing correct classification for each group
##### Customize Figures #########
require(ggplot2)
fill <- c("#56B4E9","#4271AE")
Box_1<-ggplot(plan, aes(x=plan$Sex, y=log(plan$CS))) +
geom_boxplot(fill = fill,size = 1 )+
labs(title="Dorsal view: plot of Centroid Size by sex",x="Sex", y = "Log (Centroid Size)")+
theme_classic()+
theme(plot.title = element_text(size = 14, family = "Tahoma", face = "bold", hjust = 0.5),
axis.title.x = element_text(size=12, face="bold"),
axis.title.y = element_text(size=12, face="bold"),
axis.text.x = element_text(colour="black", size = 11),
axis.text.y = element_text(colour="black", size = 9))
ggsave("1. Boxplot log (size) vs sex.tiff",units="in",width=5, height=5, dpi=300)
#
Box_2<-ggplot(plan, aes(x=groups, y=log(size))) +
geom_boxplot(fill = fill,size = 1 )+
labs(title=NULL,x="Species", y = "Log (Centroid Size)")+
theme_classic()+
theme(plot.title = element_text(size = 14, family = "Tahoma", face = "bold", hjust = 0.5),
axis.title.x = element_text(size=12, face="bold"),
axis.title.y = element_text(size=12, face="bold"),
axis.text.x = element_text(colour="black", size = 11),
axis.text.y = element_text(colour="black", size = 9))
Box_2 + scale_x_discrete(labels=c("Platyrrhinus nigellus", "Platyrrhinus umbratus"))
ggsave("2. Boxplot log (size) vs species.tiff",units="in",width=5, height=5, dpi=300)