Using the Bootstrap to Assess Statistical Significance in the Cluster Analysis of Species Abundance Data

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Abstract
Clustering techniques are frequently used to analyze species abundance data, despite a lack of objective criteria for assessing the results of such analyses. We show that a nonparametric statistical technique known as the "bootstrap" can, in certain circumstances, be used to overcome this deficiency. The bootstrap uses the distributional properties of a "bootstrap sample", i.e. a sample obtained by an appropriate resampling of the data, to make statistical inferences about the underlying population. The method is versatile and can be readily applied to complex hypothesis testing problems, since the statistical properties of a bootstrap sample can always be determined by simulation. To illustrate the application of the bootstrap to the cluster analysis of ecological data, we derive a test for the "statistical significance" of clusters of communities and show that two dendrograms can be compared by bootstrapping the Fowlkes–Mallow statistic.

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