I would like to know if compositional data (percentage) can be used in the explanatory data matrix of a redundancy analysis (RDA) or canonical correspondance analysis (CCA) (not to be confused with canonical correlation analysis). I appologize if this question has already been asked but I have searched the web far and wide and have yet to find an answer...
Here is a little background info: My objective is to determine which environmental variables explain my species abundances. So there is one response matrix and one explanatory matrix.
My first matrix is composed of species abundance (that I plan to transform using the Hellinger transfomration as suggested by Legendre & Legendre 2012). This is my response matrix, it has sites as rows, and species as columns. My second matrix contains the proportions of every habitat type found in a 100m buffer around my sampling sites (my explanatory matrix). Here the rows are once again the sites, but the columns are the proportions of the different habitat types.
I've read that centered log ratio transformation might be the way to go to eliminate problems of colinearity within my environmental data (Aitchison, 1986) but I still haven't read anywhere that this is a valid method for RDA or CCA. Also if this is a valid method can I include other environmental variables in my analysis?
Any hints will be appreciated.