The analysis of species co-occurrence patterns continues to be a main pursuit of ecologists, primarily because the coexistence of species is fundamentally important in evaluating various theories, principles and concepts. Examples include community assembly, equilibrium versus non-equilibrium organization of communities, resource partitioning and ecological character displacement, the local–regional species diversity relationship, and the metacommunity concept. Traditionally, co-occurrence has been measured and tested at the level of an entire species presence–absence matrix wherein various algorithms are used to randomize matrices and produce statistical null distributions of metrics that quantify structure in the matrix. This approach implicitly recognizes a presence–absence matrix as having some real ecological identity (e.g. a set of species exhibiting nestedness among a set of islands) in addition to being a unit of statistical analysis. An emerging alternative is to test for non-random co-occurrence between paired species. The pairwise approach does not analyse matrix-level structure and thus views a species pair as the fundamental unit of co-occurrence. Inferring process from pattern is very difficult in analyses of co-occurrence; however, the pairwise approach may make this task easier by simplifying the analysis and resulting inferences to associations between paired species.