pca or nmds (with which normalization and distance ) for abundance data ?

Claire, Here some small comments

Dear all,



a)      Which ordination method would be better for my data : PCA knowing
that the represented inertia is 35.62% or NMDS  with a stress value about
0.22? 

These numbers cannot be used to say which of these methods is better. You need other criteria. Some people may have strong opinions on the choice here, but these opinions cannot be based on these numbers -- they are based on something else (I do have such an opinion, but I abstain from expressing my opinion).

b)      If NMDS is more adapted which one is the better? with Hellinger
normalization and Bray-Curtis distance, or with the normalization
recommended by Legendre and Legendre  and Kulcynski distance ?

Hellinger transformation was suggested for Euclidean metric, and normally it is used in PCA/RDA (which are based on Euclidean metric although they do not explicitly calculate Euclidean distances). I haven't heard of any advantages of Hellinger transformation with Bray-Curtis dissimilarity. I suggest you don't use it with Bray-Curtis. I don't know if Kulczy?ski dissimilarity is any better than, say, S?rensen dissimilarity (and both seem to be difficult to spell), but certainly it belongs to the same group of usually well behaving dissimilarities as variants of Bray-Curtis or Jaccard.

c)       Is there other method to apply? I?m going to try co-inertia with
ade4 package

Certainly there is a high number of methods you can apply, but why? What you try to analyse? What are your questions?

Cheers, Jari Oksanen

Jari Oksanen, Dept Biology, Univ Oulu, 90014 Finland
jari.oksanen at oulu.fi, Ph. +358 400 408593, http://cc.oulu.fi/~jarioksa