[R-sig-eco] RE : CCA vs NMDS and ordisurf

[Gavin Simpson]

A contrary view in-lined below:

On Fri, 2013-04-19 at 15:19 +0200, François Gillet wrote:
> A lot of questions, some responses below...
<snip />
> > 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 think you need to justify the "more powerful" there! :-) I see uses
for both the constrained and unconstrained methods here. A comparison,
especially if your do PCA vs RDA (with Hellinger or similar
transformation) or PCoA vs capscale (with any distance measure) allows
you to investigate the degree to which your constraints relate to the
major patterns in the species responses.

These are complementary approaches and one would do well to use them
both.

> >  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!

You can, but the axes are still formed through linear functions of the
constraints. The constrained methods don't fit non-linear functions
(well you can introduce quadratic terms...) in the constraints.

I really don't see why this has to be an either/or situation.

G

> Cheers,
> 
> Franois
> 
> 
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