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Accurate delineation of biogeographical regions depends on the use of an appropriate distance measure
Author(s) -
Gagné Sara A.,
Proulx Raphaël
Publication year - 2009
Publication title -
journal of biogeography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.7
H-Index - 158
eISSN - 1365-2699
pISSN - 0305-0270
DOI - 10.1111/j.1365-2699.2008.01990.x
Subject(s) - biogeography , hellinger distance , measure (data warehouse) , euclidean distance , cluster analysis , insular biogeography , cluster (spacecraft) , ecology , biology , statistics , mathematics , computer science , data mining , artificial intelligence , programming language
The use of analytical techniques to delineate biogeographical regions is becoming increasingly popular. One recent example, Heikinheimo et al . ( Journal of Biogeography , 2007, 34 , 1053–1064), applied the k ‐means clustering algorithm to define the biogeography of the European land mammal fauna. However, they used the Euclidean distance measure to cluster grid cells described by species‐occurrence data, which is inappropriate. The Euclidian distance yields misleading results when applied to species‐occurrence data because of the double‐zero problem and the species‐abundance paradox. We repeat their analysis using the Hellinger distance, a measure appropriate for species‐occurrence data and which has been shown to outperform other such measures. Our results differ substantially from those presented by Heikinheimo et al. We argue that the rigorous application of appropriate statistical techniques is of crucial concern within conservation biogeography.