tree<-read.nexus("C:/Users/feder/Desktop/MASTER UPO/_TFM/ANALISIS/bats_fixedMCC.tre")
plot(tree)
traits<- read.csv("MATRIXDATA.csv", sep="\t",header=T,row.names = "Cod2")
head(traits)
## check tree and traits match up
name.check(tree, traits)
rownames(traits)==tree$tip.label
traits<-traits[tree$tip.label,]
rownames(traits)==tree$tip.label

traits1 <- comparative.data(phy  = tree, data = traits, names.col = Cod, vcv = TRUE, na.omit = FALSE, warn.dropped = TRUE)

phylo.d(data=traits1, binvar = Presencia, permut = 1000)

tree$edge.length <- tree$edge.length * 100

fit6 <- binaryPGLMM(Presencia~ log10(Group_size) + log10(Forearm) + as.factor(swarming) + as.factor(migration) + as.factor(Inter_species2) + as.factor(refuge),data=traits, phy=tree)

tip<- c("Malc", "Mbra", "Mdas", "Mcfn", "Rmeh")
tree2<-drop.tip(phy=tree, tip)
plot(tree2)

traits3<- read.csv("MATRIXDATAn2.csv",sep=";", row.names = "Cod")
head(traits3)


phylosig(tree2, traits3$Perc_trans, method="lambda", test=TRUE)

phylosig(tree2, traits3$Perc_trans, nsim=1000,method="K", test=TRUE) 


name.check(tree2, traits3)

rownames(traits3)==tree2$tip.label
traits3<-traits3[tree2$tip.label,]
rownames(traits3)==tree2$tip.label

fit.bm<-phylolm(Perc_trans ~log10(as.numeric(Group_size)) + log10(as.numeric(Forearm)) + as.factor(swarming) + as.factor(migration) + as.factor(Inter_species2) + as.factor(refuge),data=traits3, phy=tree2, model=c("BM"))
summary(fit.bm)

fit.lam<-phylolm(Perc_trans ~log10(as.numeric(Group_size)) + log10(as.numeric(Forearm)) + as.factor(swarming) + as.factor(migration) + as.factor(Inter_species2) + as.factor(refuge),data=traits3, phy=tree2, model=c("lambda"))
summary(fit.lam)

fit.ou<-phylolm(Perc_trans ~log10(as.numeric(Group_size)) + log10(as.numeric(Forearm)) + as.factor(swarming) + as.factor(migration) + as.factor(Inter_species2) + as.factor(refuge),data=traits3, phy=tree2, model=c("OUfixedRoot"))
summary(fit.ou)



# compare aic
fit.bm$aic
fit.lam$aic
fit.ou$aic


### bm seems to be best

# perform model dredging (stepwise aic)
str(summary(fit.bm))
fit.dredge<-dredge(fit.bm, rank = "AICc")
subset(fit.dredge, delta<4) # show all models with AICc>=4

fit.dredge<-dredge(fit.lam, rank = "AICc")
subset(fit.dredge, delta<4) # show all models with AICc>=4

fit.dredge<-dredge(fit.ou, rank = "AICc")
subset(fit.dredge, delta<4) # show all models with AICc>=4


# visualize importance of variables
par(mar = c(2,5,14,4))
plot(fit.dredge, labAsExpr = TRUE, col = "green", col2="yellow") # here, each row is a model, with the best one on the top. coloured boxes are the variables included in the model

#get 'Best' model
summary(get.models(fit.dredge, 1)[[1]])
summary(get.models(fit.dredge, 2)[[1]])
summary(get.models(fit.dredge, 3)[[1]])

###variables correlations

nylas2<-read.csv("Nlas2.csv",header=T,sep=";")
nylas2[] <- lapply(nylas2,as.integer)
sjp.corr(nylas2[,c(2,4,10,11)],show.legend = TRUE)


ppyg2<-read.csv("Ppyg2.csv",header=T,sep="\t")
ppyg2[] <- lapply(ppyg2,as.integer)
sjp.corr(ppyg2[,c(2,4,9,10)],show.legend = TRUE)

pkuh2<-read.csv("Pkuh3.csv",header=T,sep=";")
pkuh2[] <- lapply(pkuh2,as.integer)
sjp.corr(pkuh2[,c(2,5,10,11)],show.legend = TRUE)


nl4<-glmer(Presencia ~ Sexo2 + Av + (1|LOCALIDAD),data = nylas2, family="binomial")
pp4<-glmer(Presencia ~ Sexo2 + Av2 + (1|LOCALIDAD),data = ppyg2, family="binomial")
pk4<-glmer(Presencia ~ Sexo2 + Av2 + (1|LOCALIDAD),data = pkuh2, family="binomial")
ggplot(pkuh2, aes(x=as.factor(Presencia), y=Av)) + 
  geom_boxplot(fill="slateblue", alpha=0.3) + 
  xlab("Presence AdV") + ylab("Forearm (mm)")

##MANTEL###
disloc_nl<-(dist(cbind(nylas_mant$Longitude, nylas_mant$Latitude)))
distpor_nl<-(dist(nylas_mant$Perc))
disloc_nl
as.matrix(disloc_nl)
as.matrix(distpor_nl)
mantel.rtest(disloc_nl,distpor_nl, nrepet=999)ù
p1<-plot(distpor_nl~disloc_nl, xlab="geographic distance", ylab="percentage difference (%)",ylim=c(0,40),xlim=c(0,7), bty="n")
nl<-lm(distpor_nl~disloc_nl)
abline(nl,col="black", lwd=2)


ppyg_mant<-read.csv("Ppyg_mantel510.csv",header=T,sep=";")
head(ppyg_mant)
disloc_pp<-dist(cbind(ppyg_mant$Longitude, ppyg_mant$Latitude))
distpor_pp<-dist(ppyg_mant$Perc)
disloc_pp
as.matrix(disloc_pp)
as.matrix(distpor_pp)
mantel.rtest(disloc_pp,distpor_pp, nrep=999)
p1<-plot(distpor_pp~disloc_pp, xlab="geographic distance", ylab="percentage difference (%)",ylim=c(0,40), xlim=c(0,4), bty="n")
abline(mod_man, col="black", lwd=2)

disloc_pk<-dist(cbind(pkuh_mant$Longitude, pkuh_mant$Latitude))
distpor_pk<-dist(pkuh_mant$Perc)
as.matrix(disloc_pk)
as.matrix(distpor_pk)
mpk<-mantel.rtest(disloc_pk,distpor_pk)
mpk
mantel.randtest(disloc_pk,distpor_pk)
pk<-plot(distpor_pk~disloc_pk, xlab="geographic distance", ylab="percentage difference (%)",ylim=c(0,40),xlim=c(0,5) ,bty="n")
pk<-lm(distpor_pk~disloc_pk)
summary(pk)
abline(pk,col="black", lwd=2)