I still find DCA a useful, and reasonably robust, technique. The arch effect has been shown to be not only present in CA but in many other orthogonal techniques. Although they are not a direct measure of cumulative variance, as they are for PCA, they work much the same way. I think of them as an expression of the closeness of the data to the block structure for which the technique is looking. It is mostly useful as a means of determining whether the ordination is worth keeping or not.
Eigenvalues always decrease with successive axes.
Most vegetation datasets collected with reasonable care will yield eigenvalues between .65 and .95. This is not bad, just the case. Low eigenvalues will also be found in data sets with a short first axis. Ordination Interpretation Interpretation of an ordination is the same regardless of technique. These two values determine the direction and length of the biplot vector respectively.