Hi Francisco,

I've had a simillar question and I found this:

" The length of first DCA axis > 4 S.D. indicates heterogeneous dataset on which unimodal methods should be used, while the length < 3 S.D. indicates homogeneous dataset for which linear methods are suitable (see Fig. 5. In the gray zone between 3 and 4 S.D., both linear and unimodal methods are OK. Note that while linear methods should not be used for heterogeneous data, unimodal methods can be used for homogeneous data, but linear methods, in this case, are more powerful and should be preferred. Alternatively, if your data are heterogeneous, but you still want to use linear ordination methods (PCA, RDA), apply them on Hellinger transformed species composition data to calculate ordination based on Hellinger distances (as recommended e.g. by Legendre & Gallagher (2001)). "

link: http://www.davidzeleny.net/anadat-r/doku.php/en:ordination

Legendre & Gallagher (2001): http://adn.biol.umontreal.ca/~numericalecology/Reprints/Legendre_&_Gallagher.pdf

All the best,

Jordi

Jordi-René Mor May I ask:which kind of data should DCA analysis apply on ? raw species-abundance data(every element is integer), or log1p() transformed raw data?