#Hellinger transformation of mite data and PCA
mite.spe.hel <- decostand(mite.spe, method="hellinger")
mite.spe.h.pca <- rda(mite.spe.hel)
 
#What are the significant axes?  
ev <- mite.spe.h.pca$CA$eig 
ev[ev>mean(ev)]
n <- length(ev)
barplot(ev, main="Eigenvalues", col="grey", las=2)
abline(h=mean(ev), col="red") 
legend("topright", "Average eigenvalue", lwd=1, col=2, bty="n") 

#Output summary/results    
summary(mite.spe.h.pca, display=NULL) 
windows()

#Plot the biplot
plot(mite.spe.h.pca, scaling=1, type="none", 
     xlab=c("PC1 (%)", round((mite.spe.h.pca$CA$eig[1]/sum(mite.spe.h.pca$CA$eig))*100,2)),
     ylab=c("PC2 (%)", round((mite.spe.h.pca$CA$eig[2]/sum(mite.spe.h.pca$CA$eig))*100,2))) 
points(scores(mite.spe.h.pca, display="sites", choices=c(1,2), scaling=1),
       pch=21, col="black", bg="steelblue", cex=1.2)
text(scores(mite.spe.h.pca, display="species", choices=c(1), scaling=1),
     scores(mite.spe.h.pca, display="species", choices=c(2), scaling=1),
     labels=rownames(scores(mite.spe.h.pca, display="species", scaling=1)),
     col="red", cex=0.8)