[R-sig-eco] Removing non significant response variable in rda analysis with forward selection?

[amelie_can]

Hello all, 

My problem is somewhat similar to Vit Syrovatka posted on July 23th and
titled “Species fit in ordination”.

In my project, I am doing an rda between species abundances (response
variable – about 130 species) and type of sites (explanatory/environmental
variable – one variable). When I finish my analysis & plot it, I have a lot
of species present and I suspected that several of them did not contribute
significantly to the analysis. 

Consequently, I decided to do a forward selection analysis. Usually, a
forward selection analysis is used to remove environmental variable that
don’t relate as well with the response variable. But in my case, I only have
one environmental variable, so I basically switch around my response
variable (which are now my types of sites) and my explanatory variable
(which is now my species abundances) for the forward selection analysis. So,
basically, the forward selection shows me which species explains
significantly the types of sites found. Then I reran my rda analysis to
found that including the 20 species that were significant in the forward
analysis would explain as much the variation of my rda axis as when I had
all of my species. 

Is this correct? My supervisor raised question about the fact that I used my
response variable in forward analysis instead of environmental variable….  ?
If not, how can we remove species that are not significant? 

I thought of trying to find which species are correlated to one another. I
know one can use the cor.test function or the vif function, but it is
problematic to me, as we can only check two species per analysis. Since I
have about 130 species, checking all of those permutations by hand is just
too long. I also thought about doing a partial rda analysis, one species at
the time to see its significance in the model, but again, seemed too long. 

Thank you all for your time, 

Amelie D’Astous
Laval university
Quebec

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