

I have a data set for benthic cover made up of 4 species, over 9 years. I have been using the vegan package to create nMDS plots for this data, but am a little confused by some of my results.


Here is the data for 4 species (Sp 1-4) over the course of 9 years


Sp1<-c(27.76 ,25.44, 34.72, 41.28, 44.00, 31.28, 32.00, 38.72, 40.40)
Sp2<-c(36.72, 40.08, 36.96, 26.40, 34.40, 33.28, 31.28, 22.72, 14.00)
Sp3<-c(0.16, 0.08, 0.32, 1.28, 1.76, 1.84, 2.24, 4.40, 1.76)
Sp4<-c( 1.12,  2.24,  4.00 , 2.56,  4.40,  8.96, 12.24, 13.60, 23.36)

DF<-data.frame(Sp1,Sp2,Sp3,Sp4)

row.names(DF)<-c(2006:2014)



At first I was advised to transform my data to relative abundance like so:



DF<- decostand(DF, method='total')



set.seed(999)
mds<- metaMDS(DF, dist='bray',trymax=99)

envfitHab<-envfit(mds, DF,perm=999)

plot(mds,display='sites',type='t')

plot(envfitHab)

envfitHab









But I found that Species 3 seemed to be having a greater impact on the plot (as I judged by looking at the blue vectors, p values, and scores data) than I would have gathered. Considering Sp3 has a relatively low abundance and does not change much over time, I would not expect it to influence the dissimilarity matrix much at all. And Sp1, which has a higher abundance and changes significantly over time, seems to have a much smaller impact and larger p value.

I came to the conclusion that I should not be using relative abundance data, as I do care about total abundance. So I gave up on decostand and used raw abundance data instead. I was surprised to find that Sp3 played a similar if  even larger role in impacting the plot. Which runs counterintuitive to my (admittedly limited) understanding of nMDS. 

So my question is why is a species with low abundance that does not vary much from year to year impacting my nMDS plot so much?



 		 	   		  
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