Variable Selection
subject to large errors, CCA will not yield satisfactory, or believable, results.
CCA is currently one of the most popular ordination techniques in community ecology.
It is, however, one of the most dangerous in the hands of people who do not take the
time to understand this relatively complex method.  The dangers lie principally in
several areas:
(1) Because it includes multiple regression of community gradients on environmental
variables, it is subject to all of the hazards of multiple regression.  These are well
documented in the statistical literature, but often not fully appreciated by newcomers
to multiple regression.  (2) As the number of environmental variables increases
relative to the number of observations, the results become increasingly dubious, even
though an appearance of very strong relationships is inevitable.
(3) Statistics indicating the "percentage of variance explained" can be calculated in
several ways, each for a different question, but users frequently confuse these
statistics when reporting their results.
CCA does not explicitly calculate a distance matrix.  But CCA, like CA and PCA, is
implicitly based on the chi squared distance measure where samples are weighted
according to their totals.  This gives high weight to species whose total abundance in
the data matrix is low, thus exaggerating the distinctiveness of samples containing
several rare species.
Copyright 2004  PISCES Conservation Ltd

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