Pedro,
I don't know why it's limiting your axes, but I see a couple of issues with
your analysis.

1. If you only have 9 samples you don't want to use 9 predictor variables.
My multivariate mentors try to stick to a minimum of 3 samples per
predictor included in the model.  I'm not sure where this rule of thumb
comes from, but it's worked pretty well for me.  I think McCune & Grace
(2002) recommend about 5 (If I recall correctly.  I don't have the book in
front of me).  More is always better.

2. If you are running a CCA, you shouldn't need to do the Hellinger
transform.  Log or square-root might reduce the skew if that is an issue,
but my understanding is that the Hellinger transform is used to linearize
the response data so that you can run an RDA on community data.  It has
advantages, such as a meaningful adjusted R^2, but you may still find
reviewers that balk at RDAs on community data.

If you find an alternative analysis method, I'd be interested in hearing
about it.  I have a similar dataset that I am dealing with.

Best,
Jesse

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