title: Similarity indices for spatial ecological data.
abstract: 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).
authors: School of Mathematics and Statistics, University of St. Andrews, Mathematical Institute, Fife, UK. r.fewster@auckland.ac.nz
authors: R M
authors: RM
authors: Fewster
authors: S T
authors: ST
authors: Buckland
mesh_terms: D000465:Algorithms; D000818:Animals; D003670:Deer; D004463:Ecology; D005843:Geography; D008925:Mites / Q000145:classification*; D015233:Models, Statistical; D016012:Poisson Distribution; D011156:Population Density; D012606:Scotland