The purpose of the present study was (1) to test the ability of six alternative methods to detect random and non-random patterns of overall association in artificial presence/absence data sets, and (2) to analyse overall associations and effects of sampling heterogeneity in four empirical presence/absence data sets of helminths of the common shrew Sorex araneus. In the null model, the expected distribution was created by means of a randomisation procedure. Application of methods on artificial data sets indicated a generally low probability of type I statistical error. All methods were more likely to detect positive non-randomness than negative non-randomness of comparable strength, which may partly explain the predominance of positive overall associations in empirical data sets. The analyses based on artificial data sets indicated slight differences between methods in their ability to detect non-randomness of known strength (type II error). However, some of the methods failed to detect strong overall association when the artificial assemblages consisted of roughly equal numbers of positive and negative pairwise interactions. The structure of the artificial data sets always disappeared when the expected distribution was constrained to account for "sampling heterogeneity", i.e. varying prevalence of species among subsamples. The patterns of overall association in real helminth communities were variable, depending on the locality and association method used, but not usually on the simulation constraint used. Of the four empirical data sets analysed, one showed an unequivocal positive structure, in one the structure depended on the method used, and two data sets from the same locality were unequivocally unstructured (random). We discuss the applicability of various association measures, and the possible causes of positive overall associations in parasites.
Keywords: Interspecific associations; Key words Helminths; Null models; Shrews; Sorex.