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Testing for community structure: A bayesian approach

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Abstract

Variance deficit in richness is not reliable evidence for niche limitation. The main problem is the effect of limitation to individual plant module packing at small quadrat sizes, though environmental and historical patchiness can also confound the results unless patch models are used. More reliable approaches to community structure (assembly rules) are those that take into account the characters of the species—guild proportionality, texture convergence and limiting similarity test.

A informally Bayesian approach to community structure is advocated, accepting that some hypotheses have greater prior probability, such as module-packing limitations, but using all the evidence available to estimate the likelihood of all the ecological hypotheses. The problem of environmental patchiness can be largely overcome by patch models. These models also overcome the problem of spatial autocorrelation, because they are conservative in such situations. For guild proportionality analyses, the possibility of sampling bias is a particular problem; this should be borne in mind during sampling, and checks made for the possibility in analyses. In devising Monte Carlo tests for community structure, permutation tests (i.e. randomisation tests, using sampling without replacement) are theoretically and practically preferable to bootstrap tests (i.e. using sampling with replacement). The best test is the simplest one that incorporates the intended null model and uses the intended test statistic.

Experiments have intuitive appeal, but field experiments have several severe drawbacks, including the inevitability of artefacts, and insoluble problems of when to record. In practice, field experiments have told us surprisingly little about community structure.

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  1. Botany Department, University of Otago, P.O. Box 56, Dunedin, New Zealand

    J. Bastow Wilson

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Wilson, J.B. Testing for community structure: A bayesian approach. Folia Geobot 30, 461–469 (1995). https://doi.org/10.1007/BF02803976

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