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“PATN should identify an outlier and this ‘group’ should only attract objects that are closer to it than any other group.”

Yes it did and assigned a group to the one object. I found this unsatisfactory given that all my other centroids represented 6+ objects.

“Not sure what Monte-Carlo process you are referring to”

I ran the PCC and Monte-Carlo (MCOA) routine in a heirachial clustering analysis of the imported group centroids derived from non-heirachial analysis. That’s alright is it not?

“The non-hierarchical process in PATN v3+ uses random seeds (see the help for the complete process). While this sounds odd, it works fine in practice.”

Yes I understand this and happy about it.

RE: CANOCO – I agree with your sentiment but I’m not a statistician and thus get a bit lost in the maths. I like the idea that environmental gradients should be examined by the species variables alone (in this case) rather than integrate other factors as intrinsics. It seems a bit odd to culminate a variety of measures into one analysis – bias could prevail (unless the data is standardised – but there could even be problems there, right?)