Resolving Environmental Disputes: A Statistical Method for Choosing Among Competing Cluster Models

The protection of whole assemblages of species requires that such assemblages be identified in some nonarbitrary, quantitative manner. Clustering methods can be used to identify groups or clusters of observations (i.e., sites, transects, quadrats, etc.) on the basis of multivariate assemblage data, where each species is a variable. There are many kinds of cluster analyses, all potentially providing different outcomes, that is, different clusters of the multivariate observations. The wide choice of clustering methods and the necessarily subjective choice of which method and measure of similarity to use for a particular data set is problematic. It can lead (and has led) to disputes about the way multivariate observations should be grouped, causing conflicts in making environmental decisions. We present a statistical test for choosing among competing cluster models and demonstrate its use with a case in point. The method provides an objective way to discriminate among competing models in order to determine the model that best fits the available data. Provided each party in a dispute identifies and articulates the cluster model it supports, the method can give a nonarbitrary judgment concerning the best model. This method provides an important tool for the resolution of environmental disputes concerning the presence of a particular community at a particular place and time, which may be impacted by a proposed development.