A NEW METHOD FOR DETECTING SPECIES ASSOCIATIONS WITH SPATIALLY AUTOCORRELATED DATA

Many organisms display patchiness in their distribution patterns over a wide range of spatial scales. Patchy distribution patterns can be caused by processes such as growth, migration, reproduction, and mortality, which result in neighboring areas being more likely to contain a species than distant areas, a phenomenon known as positive spatial autocorrelation. When species are patchily distributed, the within-species spatial random- ness assumptions of the standard statistical tests for detecting species associations are seriously violated. Using these tests under such circumstances can lead to incorrect rejection of the null hypothesis. To address this problem we introduce a new test for detecting species associations—the random patterns test. This test takes into account spatial autocorrelation by including the characteristics of the spatial pattern of each species into the null model. A randomization procedure was used to generate the null distribution of the test statistic. The random patterns test is illustrated with data collected from an herbaceous understory community of a Eucalyptus forest near Canberra, Australia.

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