Summary
We present a method for assessing similarity between species maps of presence and absence or abundance, that emphasizes global features while ignoring minor local dissimilarities. The method arranges sites into small groups, or cliques, and allows controlled changes to be made within cliques to reduce the influence of local discrepancies. Resulting measures of similarity are visually more satisfactory than traditional indices. We show that the similarity indices are useful for model selection, by comparing observed spatial patterns with those predicted by different fitted models. Examples are provided for spatial distributions of oribatid mites (Acari, Oribatei), woodlarks (Lullula arborea), and red deer (Cervus elaphus).
Key words: Best attainable match; Matching coefficient; Maximum matching in bipartite graph; Minimum attainable distance; Species map.