Ordination is a widely used and useful tool in ecological data analysis. It has been derived and adapted from a range of disciplines ranging from engineering through social sciences and so the applications of existing software from other research disciplines need to be carefully examined before application to ecological questions. If the data consist of a single table of potentially correlated variables, and one is just interested in patterns ofsamples and variables, then unconstrained ordinations are generally used to display these patterns. The choice of transformation and implicit or explicit multivariate distance measures will determine the success of the ordination in identifying the dominant pattern. If data are composed of two distinct sets of variables, such as fish and habitat or site, then constrained ordinations (as well as unconstrained) ordinations are generally employed. If the independent variables are continuous, then redundancy analysis, canonical correlation, and canonical CA are appropriate. If the dependent variables are categorical (e.g., site, experimental treatment), then canonical discriminant analysis is appropriate to best display these relationships. Some ordinations (e.g., CDA) are an implicit part of dissecting the results of a statistical hypothesis test. In general they are a means of displaying common trends among different samples, and perhaps attributing these differences to changes in the values of dependent variables. If an ordination does not yield an ecologically interpretable solution, it is probably because the variables were not tightly correlated to begin with. As with all ecological analysis, it is up to the researcher to apply the reality check to ensure the analysis is commensurate with the ecological question and the data structure.

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