### File S4: Code for the data analysis in R.
### Authors: Abrego N, García-Baquero G, Halme P, Ovaskainen O & Salcedo I
### Manuscript title: Community turnover of wood-inhabiting fungi across hierarchical spatial scales
### Corresponding author: nerea.abrego@ehu.es  
################################################################################


################################################################################
###Figure 1->Species and dead wood abundance distributions

# Load vegan package
library(vegan)


##Figure 1a->The species abundance distributions based on the total number of observations
##made on each species


# Count how many of species have occurred once, twice...in different forest types
Fungi <- read.csv("FileS5.csv", header = TRUE, sep=";")
spe <- Fungi[,c(1:285)]
Nobssp<-colSums(spe)#Total number of observations of each species

# Split the data matrices in two according to the management factor
Managed<-Fungi[c(1:40),c(1:285)]
SpManag<-colSums(Managed)
SpManag<-(SpManag==1|SpManag>1)
Natural<-Fungi[c(41:80),c(1:285)]
SpNat<-colSums(Natural)
SpNat<-(SpNat==1|SpNat>1)
Presence<-SpNat-SpManag# As a factor:0=species that occur in both forest types, 1= species that only
#occur in natural forests, -1=species that only occur in managed forests

# Plot the graph
spocc<-t(rbind(Nobssp,Presence))


maxNobssp<-max(Nobssp)
nocc<-matrix(0,3,maxNobssp)

for(pres in -1:1){
	Nobssp_subset<-subset(spocc,Presence==pres)[,1]
	for (i in 1:maxNobssp){
		nocc[pres+2,i]<-sum(Nobssp_subset==i)
				
	}
}


tiff("Figure 1 A.tiff", width = 4, height = 4, units = 'in', res = 300)
barplot(nocc, ylab = "number of species", xlab="number of observations", ylim=c(0, 100), xlim=c(1,100),col=c("blue","yellow","green"),border=NA)
axis(1,seq(0,100,10))
text(5, 95, "A",font=2)
text(12,85,"rare species",cex=0.75,font=3)
text(67,10,"abundant species",cex=0.75, font=3)
legend("topright", legend=c("managed forests", "natural and managed forests","natural forests"), ncol=1, fill=c("blue","yellow","green"), border=NA, cex=0.75)
dev.off()

##Figure 1b-> The number of plots in which each species was recorded

# Count how many species have occurred in one plot, two...
Fungi <- read.csv("FileS5.csv", header = TRUE, sep=";")
spe <- Fungi[,c(1:285)]
spe[spe>1]<-1#convert to presence absence data

speplot<-colSums(spe)#occurrence of each species per plot
speplot<-(cbind(speplot,Presence))

# Plot the graph
maxspeplot<-max(speplot)
pocc<-matrix(0,3,maxspeplot)
for (pres in -1:1){
	pocc_subset<-subset(speplot,Presence==pres)[,1]
	for (i in 1:maxspeplot){
		pocc[pres+2,i]<-sum(pocc_subset==i)
	}
}

tiff("Figure 1 B.tiff", width = 4, height = 4, units = 'in', res = 300)
barplot((pocc), ylab = "number of species", xlab="number of plots with species occurrence", ylim=c(0, 100), xlim=c(1,80),col=c("blue","yellow","green"),border=NA)
axis(1,seq(0,80,10))
text(5, 95, "B",font=2)
text(20,80,"infrequent species",cex=0.75,font=3)
text(67,10,"common species",cex=0.75,font=3)
dev.off()


##Figure 1c->Dead wood abundance distributions based on the total number of observations
#made on each dead wood type

# Count how many of dead wood pieces of each class have been observed
Wood <- read.csv("FileS7.csv", header = TRUE,sep=";")
Manag<-Wood[,19]
Wood <- Wood[,c(2:16)]
WoodManag<-cbind(Wood,Manag)
Nobwood<-colSums(Wood)#Total number of observations of each dead wood class
maxNobwood<-max(Nobwood)#Total number of examined dead wood pieces
WoodManaged<-subset(WoodManag,Manag=="MANAGED")
WoodManaged<-colSums(WoodManaged[,1:15])
WoodNatural<-subset(WoodManag,Manag=="NATURAL")
WoodNatural<-colSums(WoodNatural[,1:15])
wocc<-rbind(WoodManaged,WoodNatural)

# Plot the graph
tiff("Figure 1 C.tiff", width = 4, height = 4, units = 'in', res = 300)
barplot(wocc, ylab = "number of dead wood pieces", ylim=c(0, 3000), xlim=c(0,17),axes=TRUE,col=c("blue","green"),border=NA,names.arg=c("VFWD 1","VFWD 2","VFWD 3","VFWD 4","VFWD 5","FWD 1","FWD 2","FWD 3","FWD 4","FWD 5","CWD 1","CWD 2","CWD 3","CWD 4", "CWD 5"),cex.names=0.75,las=2)
text(1.25, 2900, "C", font=2)
dev.off()

################################################################################
###Figure 2-> Species accumulation curves

Fungi <- read.csv("FileS5.csv", header = TRUE, sep=";")
spe <- Fungi[,c(2:285)]
spe.env <- Fungi[,c(287:289)]

## A function for randomizing the species number from different plots within a given level

NoSpecies <- function(set,mode,n,spe.env,spe)
	{
		if(mode=="within"){
			site<-sample(set$Site,1)
			set1<-subset(spe.env,Site==site)
			plots<-sample(set1$OrPlot,n)
		}
		else
 		{
			plots<-sample(set$OrPlot,n)
		}
		nspec<-sum(colSums(spe[plots,])>0)
		return(nspec)
	}



##The five cases which assume that the plots were selected randomly either within the same randomly selected 
##managed forest site (case==1), the same randomly selected natural forest site (case==2), among all sites in 
##managed forests (case==3), among all sites in natural forests (case==4), or among all sites (case==5)

cols<-c(rainbow(6)[1],rainbow(6)[5],rainbow(6)[3],rainbow(6)[4],rainbow(6)[2])
tcols<-c(rainbow(6,alpha=0.3)[1],rainbow(6,alpha=0.3)[5],rainbow(6,alpha=0.3)[3],rainbow(6,alpha=0.3)[4],rainbow(6,alpha=0.3)[2])

pmax<-80#Maximum number of plots
mv<-c(min(pmax,5),min(pmax,5),min(pmax,40),min(pmax,40),pmax)
nrepls<-1000#Number of randomizations

res<-matrix(0,ncol=pmax,nrow=5)
low<-matrix(0,ncol=pmax,nrow=5)
high<-matrix(0,ncol=pmax,nrow=5)

for(case in 1:5)
	{
		m<-pmax
		if(case==1){
			set<-subset(spe.env,Treatment=="Managed")
			mode<-"within"
		}
		if(case==2){
			set<-subset(spe.env,Treatment=="Unmanaged")
			mode<-"within"
		}
		if(case==3){
			set<-subset(spe.env,Treatment=="Managed")
			mode<-"among"
		}
		if(case==4){
			set<-subset(spe.env,Treatment=="Unmanaged")
			mode<-"among"
		}
		
		if(case==5){
			set<-spe.env
			mode<-"among"
		}
		m<-mv[case]
		
		for(n in 1:m)##interquartile regions
			{
				tmp<-matrix(0,nrepls)
				for(i in 1:nrepls)
					{
						tmp[i]<-NoSpecies(set,mode,n,spe.env,spe)
					}
				res[case,n]<-mean(tmp)
				low[case,n]<-quantile(tmp,0.25)
				high[case,n]<-quantile(tmp,0.75)
			}
	}

#Graph
tiff("Figure 2 A.tiff", width = 4, height = 4, units = 'in', res = 300)
order <- c(3,4,5,2,1)
maxy<-max(res)+5
plot(1,type="n",xlim=c(1,pmax),ylim=c(0,maxy),xlab="number of plots",ylab="number of species")
for(ca in 1:5)
	{
		case <- order[ca]
		xx<-c(1:mv[case],rev(1:mv[case]))
		yy<-c(low[case,c(1:mv[case])],rev(high[case,c(1:mv[case])]))
		polygon(xx,yy,col=tcols[case],border=NA)
	}
for(ca in 1:5)
	{
		case<-order[ca]
		lines(res[case,c(1:mv[case])],col=cols[case],lwd=2)
	}

text(5, 0.95*maxy, "A",font=2)
dev.off()

tiff("Figure 2 B .tiff", width = 4, height = 4, units = 'in', res = 300)
res2<-res[,1:5]
low2<-low[,1:5]
high2<-high[,1:5]
maxy<-max(high2)+5
plot(1,type="n",xlim=c(1,5),ylim=c(0,maxy),xlab="number of plots",ylab="number of species")

for(ca in 1:5)
	{
		case <- order[ca]
		xx<-c(1:5,rev(1:5))
		yy<-c(low2[case,c(1:5)],rev(high2[case,c(1:5)]))
		polygon(xx,yy,col=tcols[case],border=NA)
	}
for(ca in 1:5)
	{
		case <- order[ca]
		lines(res2[case,],col=cols[case],lwd=2)
	}
text(1.25, 0.95*maxy, "B",font=2)
legend("bottomright", legend=c("within managed sites", "within natural sites", "among managed sites", "among natural sites", "among all sites"), ncol=1, fill=cols,cex=0.7, border=NA,box.lwd = 0,box.col = NA)

dev.off()


################################################################################
### Figure 3-> Additive species diversity partitioning
Fungi <- read.csv("FileS5.csv", header = TRUE, sep=";")
spe <- Fungi[,c(1:285)]
spepa <- spe
spepa[spe>0]<-1
spe.env <- Fungi[,c(287:289)]
spe.env$OrPlot<-as.factor(spe.env$OrPlot)
head(spe.env)
str(spe.env)

adipart(spepa ~OrPlot + Site + Treatment, data=spe.env, index=c("richness"), weights=c("unif"), relative = FALSE, nsimul=999,method="r0")


# Create a data frame with the observed and expected species values
SpObs <- c(22.96,67.56,206,44.60,138.44,(285-206.44),285)
SpExp <- c(22.96,97,273,74.04,175.75,(285-273),0)
RowNames <- c("alpha[1]", "alpha[2]", "alpha[3]", "beta[1]", "beta[2]", "beta[3]", "gamma")
Partition <- data.frame(SpObs, SpExp, row.names = RowNames)
Partition

# Plot species partition
tiff("Figure 3.tiff", width = 4, height = 4, units = 'in', res = 300)
barplot(t(Partition), beside = TRUE, ylab = "species number", ylim=c(0, 300), names=expression(alpha[1],alpha[2],alpha[3],beta[1],beta[2],beta[3],gamma),col=c("gray50","black"),border=NA)

points(5, 110, pch = 8)
points(8, 283, pch = 8)
points(11, 84, pch = 8)
points(14, 186, pch = 8)
points(17, 89, pch = 8)

legend(11,300, legend=c("observed", "expected"), ncol=1, fill=c("gray50", "black"), border=NA, cex=0.75)
dev.off()

################################################################################
### Figure 4-> Prediction of fungal species similarity across resource similarity and a spatial distance gradient


library(vegan)

#vector for the overall dead wood similarity matrix
Wood<- read.csv("FileS7.csv", header = TRUE,sep=";")
Wood.sp <- Wood[,c(2:16)]
Wood.sq<-sqrt(Wood.sp)
X1<-as.vector(1-(vegdist(Wood.sq,method="bray")))


#Vector for the overall species similarity matrix 
Fungi <- read.csv("FileS5.csv", header = TRUE, sep=";")
Fungi.sp<- Fungi[,c(2:285)]
Fungi.sq<-sqrt(Fungi.sp)
Y<-as.vector(1-(vegdist(Fungi.sq, method="bray")))



#Vector for the management type
#X2=MU if two plots are from different management types,
#X2=UU if the two plots are from natural forests, 
#X2=MM if the two plots are from managed forests

Fungi.env <- Fungi[,c(287:289)]
Regime<-c(Fungi.env$Treatment)


#For given X2=0, X2=1, X2=2 values:

tmp<-100+1:80
for(i in 1:80){
	if(Regime[i]==1) tmp[i]=0.001*tmp[i]
}
tmpEuc<-vegdist(tmp,method="euclidean")
classMM<-(tmpEuc<0.5)+0
classMU<-(tmpEuc>100)+0
classUU<-1-classMM-classMU
X2<-0*classMU+1*classUU+2*classMM
for(i in 1:length(X2)){
	if(X2[i]==0) X2[i]<-"MU"
	if(X2[i]==1) X2[i]<-"UU"
	if(X2[i]==2) X2[i]<-"MM"
}
X2<-factor(X2)


#Vector for the site (X3=1 if the two plots are from the same site, otherwise X3=0)
Site<-c(Fungi.env$Site)
SiteEuc<-vegdist(Site,method="euclidean")
SiteEuc<-matrix(SiteEuc)
SiteEuc<-c(SiteEuc)
X3<-1-(SiteEuc>0)+0

#Vector for the spatial similarity
Fungi.xy <- read.csv("FileS6.csv", header = TRUE, sep=";")
X4<-as.vector(log(vegdist(Fungi.xy,method="euclidean")))


##Model selection for modelling similarity in species composition:

model<-6#The best model

nrepl<-999

nov=c(6,3,3,2,2,4) #Number of variables in the model

if(model==1) M<-lm(Y~X1+X2+X3+X1*X2+X1*X3+X2*X3)
if(model==2) M<-lm(Y~X1+X2+X1*X2)
if(model==3) M<-lm(Y~X1+X2+X3)
if(model==4) M<-lm(Y~X2+X3)
if(model==5) M<-lm(Y~X1+X2)
if(model==6) M<-lm(Y~X1+X2+X3+X4)

F<-anova(M)$"F value"

permtable<-matrix(ncol=nov[model],nrow=nrepl)
for (i in 1:nrepl){
	perm<-sample(nrow(Fungi.sq))
	Fungiperm<-Fungi.sq[perm,]
	Yperm<-1-(vegdist(Fungiperm, method="bray"))
	
	if(model==1) Mperm<-lm(Yperm~X1+X2+X3+X1*X2+X1*X3+X2*X3)
	if(model==2) Mperm<-lm(Yperm~X1+X2+X1*X2)
	if(model==3) Mperm<-lm(Yperm~X1+X2+X3)
	if(model==4) Mperm<-lm(Yperm~X2+X3)
	if(model==5) Mperm<-lm(Yperm~X1+X2)
	if(model==6) Mperm<-lm(Yperm~X1+X2+X3+X4)
	Fperm<-anova(Mperm)$"F value"
	permtable[i,]<-Fperm[1:nov[model]]
}

#For testing the significance (permutation approach):
test<-matrix(0,3,nov[model])
for(i in 1:nov[model]){
	test[1,i]<-quantile(permtable[,i],0.05)
	test[2,i]<-quantile(permtable[,i],0.95)
	test[3,i]<-F[i]
}


pars<-M$coefficients
b0<-pars[1]
bX1<-pars[2]
bX2MU<-pars[3]
bX2UU<-pars[4]
bX3<-pars[5]
bX4<-pars[6]

Rsq<-summary(M)$r.squared 


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

##Figure 4a-> Predicted similarity of the fungal composition according to resource similarity

cols<-c(rainbow(6)[1],rainbow(6)[5],rainbow(6)[3],rainbow(6)[4],rainbow(6)[2])

tiff("Figure 4 A.tiff", width = 4, height = 4, units = 'in', res = 300)
order <- c(3,4,5,2,1)

maxy<-0.6
miny<-0
plot(1,type="n",xlim=c(min(X1),max(X1)),ylim=c(miny,maxy),xlab="resource similarity",ylab="fungal similarity")


nX1<-length(X1)

mex<-rep(0,5)
mey<-rep(0,5)
mX4<-rep(0,5)
for(ca in 1:5){
	case <- order[ca]
	take<-rep(TRUE,nX1)
	for(i in 1:nX1){
		if(case==1) take[i]<-(X2[i]=="MM") && X3[i]==1	
		if(case==2) take[i]<-(X2[i]=="UU") && X3[i]==1	
		if(case==3) take[i]<-(X2[i]=="MM") && X3[i]==0	
		if(case==4) take[i]<-(X2[i]=="UU") && X3[i]==0	
		if(case==5) take[i]<-(X2[i]=="MU") && X3[i]==0		
	}
	points(X1[take],Y[take],cex=0.4,pch=19,col=cols[case])
	mX4[case]<-mean(X4[take])
	mex[case]<-mean(X1[take])
	mey[case]<-mean(Y[take])
}

for(case in 1:5) points(mex[case],mey[case],cex=2,pch=17,col=cols[case])

# Range X1 values for each case

cases<-data.frame(X1,X2,X3)

case1<-subset(cases,X3==1 & X2=="MM")
X1C1l<-seq(min(case1$X1),max(case1$X1),(((max(case1$X1)-min(case1$X1))/100)))

case2<-subset(cases,X3==1 & X2=="UU")
X1C2l<-seq(min(case2$X1),max(case2$X1),(((max(case2$X1)-min(case2$X1))/100)))

case3<-subset(cases,X3==0 & X2=="MM")
X1C3l<-seq(min(case3$X1),max(case3$X1),(((max(case3$X1)-min(case3$X1))/100)))

case4<-subset(cases,X3==0 & X2=="UU")
X1C4l<-seq(min(case4$X1),max(case4$X1),(((max(case4$X1)-min(case4$X1))/100)))

case5<-subset(cases,X3==0 & X2=="MU")
X1C5l<-seq(min(case5$X1),max(case5$X1),(((max(case5$X1)-min(case5$X1))/100)))

# Plot regression lines

lines(X1C1l,bX1*X1C1l+b0+bX3+(bX4*mX4[1]),col=cols[1],lwd=2)#two plots in the same managed site
lines(X1C2l,bX1*X1C2l+b0+bX2UU+bX3+(bX4*mX4[2]),col=cols[2],lwd=2)#two plots in the same natural forest site 
lines(X1C3l,bX1*X1C3l+b0+(bX4*mX4[3]),col=cols[3],lwd=2)#two different sites in managed forests
lines(X1C4l,bX1*X1C4l+b0+bX2UU+(bX4*mX4[4]),col=cols[4],lwd=2)#two different sites in natural forests
lines(X1C5l,bX1*X1C5l+b0+bX2MU+(bX4*mX4[5]),col=cols[5],lwd=2)#one site in a managed and one site in a natural forest 

text(0.3,0.55,"A",font=2)
dev.off()

################################################################################
##Figure 4b-> Predicted similarity of the fungal composition according to spatial distance

tiff("Figure 4 B.tiff", width = 4, height = 4, units = 'in', res = 300)
cols<-c(rainbow(6)[1],rainbow(6)[5],rainbow(6)[3],rainbow(6)[4],rainbow(6)[2])

maxy<-0.6
miny<-0
plot(1,type="n",xlim=c(min(X4),max(X4)),ylim=c(miny,maxy),xlab="log (spatial distance)",ylab="fungal similarity")

nX4<-length(X4)

mex<-rep(0,5)
mey<-rep(0,5)
mX1<-rep(0,5)
for(ca in 1:5){
	case<-order[ca]
	take<-rep(TRUE,nX4)
	for(i in 1:nX4){
		if(case==1) take[i]<-(X2[i]=="MM") && X3[i]==1	
		if(case==2) take[i]<-(X2[i]=="UU") && X3[i]==1	
		if(case==3) take[i]<-(X2[i]=="MM") && X3[i]==0	
		if(case==4) take[i]<-(X2[i]=="UU") && X3[i]==0	
		if(case==5) take[i]<-(X2[i]=="MU") && X3[i]==0	
	}
	points(X4[take],Y[take],cex=0.4,pch=19,col=cols[case])
	mX1[case]<-mean(X1[take])
	mex[case]<-mean(X4[take])
	mey[case]<-mean(Y[take])
}

for(case in 1:5) points(mex[case],mey[case],cex=2,pch=17,col=cols[case])

#Minimum and maximum X4 values for each case

cases<-data.frame(X4,X2,X3)

case1<-subset(cases,X3==1 & X2=="MM")
X4C1l<-seq(min(case1$X4),max(case1$X4),(((max(case1$X4)-min(case1$X4))/100)))

case2<-subset(cases,X3==1 & X2=="UU")
X4C2l<-seq(min(case2$X4),max(case2$X4),(((max(case2$X4)-min(case2$X4))/100)))

case3<-subset(cases,X3==0 & X2=="MM")
X4C3l<-seq(min(case3$X4),max(case3$X4),(((max(case3$X4)-min(case3$X4))/100)))

case4<-subset(cases,X3==0 & X2=="UU")
X4C4l<-seq(min(case4$X4),max(case4$X4),(((max(case4$X4)-min(case4$X4))/100)))

case5<-subset(cases,X3==0 & X2=="MU")
X4C5l<-seq(min(case5$X4),max(case5$X4),(((max(case5$X4)-min(case5$X4))/100)))

# Plot regression lines

lines(X4C1l,bX4*X4C1l+b0+bX3+(bX1*mX1[1]),col=cols[1],lwd=2)#two plots in the same managed site
lines(X4C2l,bX4*X4C2l+b0+bX2UU+bX3+(bX1*mX1[2]),col=cols[2],lwd=2)#two plots in the same natural forest site 
lines(X4C3l,bX4*X4C3l+b0+(bX1*mX1[3]),col=cols[3],lwd=2)#two different sites in managed forests
lines(X4C4l,bX4*X4C4l+b0+bX2UU+(bX1*mX1[4]),col=cols[4],lwd=2)#two different sites in natural forests
lines(X4C5l,bX4*X4C5l+b0+bX2MU+(bX1*mX1[5]),col=cols[5],lwd=2)#one site in a managed and one site in a natural forest 

text(4.7,0.55,"B",font=2)
dev.off()


################################################################################
## Spatial structure of raw data and residuals from the model for fungal similarity

par(mfrow=c(2,2))

# Raw data

Fungi <- read.csv("FileS5.csv", header = TRUE, sep=";")
Fungi.sp<- Fungi[,c(2:285)]
Fungi.sq<-sqrt(Fungi.sp)
Y<-as.vector(1-(vegdist(Fungi.sq, method="bray")))
Fungi.xy <- read.csv("FileS6.csv", header = TRUE, sep=";")
Fungi.Y<-matrix(ncol=80,nrow=80,0)

index<-0
for (i in 1:79){
	for (j in (i+1):80){
		index<-index+1
		Fungi.Y[j,i]<-Y[index]
		Fungi.Y[i,j]<-Y[index]
	}
}

Fungi.correlog<-mantel.correlog((1-(Fungi.Y)),XY=Fungi.xy,n.class=25,nperm=999)
plot(Fungi.correlog)

text(30000,0.20,"species similarity")


# Residuals

M3<-lm(Y~X1+X2+X3)

Resid<-resid(M3)

Fungi.resid<-matrix(ncol=80,nrow=80,0)

index<-0
for (i in 1:79){
	for (j in (i+1):80){
		index<-index+1
		Fungi.resid[j,i]<-Resid[index]
		Fungi.resid[i,j]<-Resid[index]
	}
}


Fungi.correlogII<-mantel.correlog((1-(Fungi.resid)),XY=Fungi.xy,n.class=25,nperm=999)
plot(Fungi.correlogII)

text(40000,0.07,"residuals")


################################################################################
## Model selection for modelling similarity in dead wood composition


model<-2#The best model
nrepl<-999

nov=c(4,2,1) #Number of variables in the model

if(model==1) M<-lm(X1~X2+X3+X2*X3+X4)
if(model==2) M<-lm(X1~X2+X3)
if(model==3) M<-lm(X1~X3+X4)
if(model==4) M<-lm(X1~X3)


F<-anova(M)$"F value"

permtable<-matrix(ncol=nov[model],nrow=nrepl)
for (i in 1:nrepl){
	perm<-sample(nrow(Wood.sq))
	Woodperm<-Wood.sq[perm,]
	X1perm<-(1-(vegdist(Woodperm, method="bray")))
	
	if(model==1) Mperm<-lm(X1perm~X2+X3+X2*X3+X4)
	if(model==2) Mperm<-lm(X1perm~X2+X3)
	if(model==3) Mperm<-lm(X1perm~X3+X4)
	if(model==4) Mperm<-lm(X1perm~X3)
	Fperm<-anova(Mperm)$"F value"
	permtable[i,]<-Fperm[1:nov[model]]
}

#For testing the significance:
test<-matrix(0,3,nov[model])
for(i in 1:nov[model]){
	test[1,i]<-quantile(permtable[,i],0.05)
	test[2,i]<-quantile(permtable[,i],0.95)
	test[3,i]<-F[i]
}

pars<-M$coefficients
b0<-pars[1]
bX2MU<-pars[2]
bX2UU<-pars[3]

Rsq<-summary(M)$r.squared 


################################################################################
## Spatial structure of raw data and residuals from the best model for dead wood similarity

Fungi.xy <- read.csv("FileS6.csv", header = TRUE, sep=";")

# Raw data
Wood.X1<-matrix(ncol=80,nrow=80,0)

index<-0
for (i in 1:79){
	for (j in (i+1):80){
		index<-index+1
		Wood.X1[j,i]<-X1[index]
		Wood.X1[i,j]<-X1[index]
	}
}

Wood.correlog<-mantel.correlog((1-Wood.X1),XY=Fungi.xy,n.class=25,nperm=999)
plot(Wood.correlog)
text(30000,0.08,"wood similarity")


# Residuals

M3b<-lm(X1~X2+X3)

Resid<-resid(M3b)
Wood.dist<-vegdist(Fungi.xy,method="euclidean")

Wood.resid<-matrix(ncol=80,nrow=80,0)

index<-0
for (i in 1:79){
	for (j in (i+1):80){
		index<-index+1
		Wood.resid[j,i]<-Resid[index]
		Wood.resid[i,j]<-Resid[index]
	}
}


Wood.correlogII<-mantel.correlog((1-(Wood.resid)),XY=Fungi.xy,n.class=25,nperm=999)
plot(Wood.correlogII)
text(40000,0.02,"residuals")



################################################################################
### Partitioning community variation by Permanova

# Load data and packages:
Fungi <- read.csv("FileS5.csv", header = TRUE, sep=";")
spe <- Fungi[,c(2:285)]
Fungi.sq<-sqrt(spe)
Wood <- read.csv("FileS7.csv", header = TRUE,sep=";")
Wood <- Wood[,c(2:16)]
Wood.sq<-sqrt(Wood)
Fungi.env <- Fungi[,c(287:289)]
library(BiodiversityR)
library(permute)

npmFungi.BC <- nested.npmanova(Fungi.sq~Treatment+Site, data=Fungi.env, method="bray",permutations=999)
npmWood.BC <- nested.npmanova(Wood.sq~Treatment+Site, data=Fungi.env, method="bray",permutations=999)