Z ‐scores unite pairwise indices of ecological similarity and association for binary data

Pairwise ecological resemblance, which includes compositional similarity between sites (beta diversity), or associations between species (co‐occurrence), can be measured by >70 indices. Classical examples for presence–absence data are Jaccard index or C ‐score. These can be expressed using contingency table matching components a , b , c , and d —the joint presences, presences at only one site/species, and joint absences. Using simulations of point patterns for two species with known magnitude of association, I demonstrate that most of the indices converge to a similar value and they describe the simulated association almost identically, as long as they are calculated as a Z ‐score, that is, as deviation of the index from a null expectation. Further, I show that Z ‐scores estimate resemblance on average better than raw forms of the indices, particularly in the face of confounding effects of spatial scale and conspecific aggregation. Finally, I show that any single of the matching components, when expressed as Z ‐score, can be used as an index that performs as good as the classical indices; this also includes joint absences. All this simplifies selection of the right resemblance index, it underscores the advantage of expressing resemblance as deviation from a null expectation, and it revives the potential of joint absences as a meaningful ecological quantity.