[R-sig-eco] A beginner's question to constrained ordinations with vegan

[Tim Richter-Heitmann]

Hi there,

i have a typical ecological problem (modelling abiotic parameters to 
bacterial abundances - i have 9 of these explanatory variables (but also 
a variety of spatial and biotic parameters, who may serve as 
explanators), many bacterial species and hundreds of sites).

My species gradients seem to be very long in the DCA, so i began my 
analysis with CCA modelling all 9 abiotic parameters to the species 
matrix, and using the triplot as a final result.

However, i have two very distinct bacterial communities in the DCA with 
a huge gap on the x-axis between them (one community is defining 90% of 
all samples, and the smaller one is found in 10% of the samples), so i 
was fiddling around with performing rda's
(which i believe is recommended for small species gradients) on the two 
subsets.

Now, a colleague was actually recommending me to use unconstrained 
ordinations like PCA and use envfit to fit the explanatory variables later.

ord.OTU <- rda(OTU)
ef <- envfit(ord.OTU, Env, perm=999)

instead of

ord.OTU <- rda(OTU~., Env)

However, i fail to grasp the ideas and differences behind and between 
the two approaches - in my case, an envfitted PCA looked different than 
the "equivalent" RDA. As far as i have been taught, constrained 
ordination techniques like RDA or CCA search for the best explaining
variables in the direct gradients, so i would use those for problems 
like mine per default. So, what are the benefits in using the 
unconstrained techniques first?

Since i am new to the field, i lack the experience to evaluate this. Any 
advice would make me a very happy student.

Thank you very much, and my apologies if i have asked something that was 
asked many times before. In fact, i tried to find the answer online, but 
wasnt too successful.



-- 
Tim Richter-Heitmann