Sources of Order Dependence Order dependence of the input is disconcerting. It cannot always be avoided in practice. In most applications, your calculator will give you an answer which is just fine for practical purposes. Differences between large numbers is a famous calculator example and it is helpful to have a sense of when things can go wrong. In ordinations based on Eigen methods, close eigenvalues is a place to be careful.
Ecological data are often very difficult to gather with precision.
Ideally the analyses we perform should be resiliant to such noise. Analysis of the convergence problems in DCA A problem in DCA is that twins go undetected, because the eigenvalue problem solved for the first axis, for example, differs from the eigenvalue problem solved for the second axis. The second eigenvalue in the eigenvalue problem for axis 1 may be equal to the first eigenvalue, whereas the second DCA axis is much lower. Unstable axes in DCA may therefore go unnoticed. The Shuffle software available off the Oksanen web page may help detecting such unstable eigenvalues.