A lot of questions, some responses below...

2013/4/19 Aurélie Boissezon <Aurelie.Boissezon@unige.ch>

> The main purpose is to understand how disturbance gradient affect the
> composition of the macrophyte community, in particular the distribution of
> Charophytes ("V3" mission in Anderson et al 2011).
>

Basically, to address this kind of question, you need constrained
ordination.


>  I want to ignore double zero because there is no reason to consider that
> double zeros indicate similarity.--> avoid euclidean-distance based method
> such as PCA and RDA
>

Again: with appropriate transformations, such as Hellinger, double zeros
are not taken into account in RDA!


> The succession of a high number of species generated numerous zero in my
> species dataset (long environmental gradient). --> one more argument
> against RDA
>  Finally vegetation was well sampled so rarest species were truly rare in
> the water body. Nevertheless I am not particularly interest by those rare
> species so I deleted them before multivariate analysis.
>

Bad idea. RDA on Hellinger-transformed cover data is not that much
sensitive to rare (unfrequent) species, contrary to CCA. My advice is to
keep all species in your dataset.


>
> For all these reasons, I firstly I tried CCA ordination. But I did not
> tried dbRDA. Should I on the basis of my practical limits? Would it be
> really best than CCA ? I guess I have to try following Pierre's method. The
> main positive point for dbRDA is that I can use any dissimilarity matrix
> (if I understand well), hellinger or bray curtis for example.
>

dbRDA on Bray-Curtis dissimilarity matrix is an acceptable alternative to
RDA on Hellinger-transformed data. CCA is based on a double standardization
of sites and species and is known to give high weight to rare species: if
you are not primarily interested by the indication of these species, forget
CCA.


>
> Why not explore unconstrained ordination methods and went further with
> NMDS ("V2" mission in Anderson et al 2011)?
>

Just because your purpose is to explain community structure by
environmental variables (a regression-oriented question). Direct gradient
analysis (especially with RDA and adjusted R-square) is in this case more
powerful than indirect gradient analysis (from NMDS or any other
unconstrained ordination).


>  I understood that I was wrong when using Bray-Curtis distance on
> hellinger transformed data before NMDS, I have to choose. But that I am
> right when superimposing vector or gam surface on NMDS ordinations.
>

That's right, but you can fit a GAM model on RDA results as well!

Cheers,

François


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