Biodiversity and Conservation 13: 659–660, 2004. © 2004 Kluwer Academic Publishers. Printed in the Netherlands. Research note 1,∗ 2 RALPH MAC NALLY and CHRISTOPHER J. WALSH Australian Centre for Biodiversity: Analysis, Policy and Management, Monash University, Victoria 3800, Australia; Water Studies Centre and Cooperative Research Centre for Freshwater Ecology, Monash University, Victoria 3800, Australia; Author for correspondence (e-mail: Ralph.MacNally@sci.monash.edu.au; fax: +61-3-9905-5613) Ecologists and conservation biologists rely heavily on multiple regression (MR) for inferring probable causes of patterns affecting species distributions or numbers. Mac Nally (2000) argued the case for use of hierarchical par- titioning as an important means to identify those predictor variables having the most independent inﬂuence on the response variable (typically species richness or probability of occurrence). Use of randomization of the data ma- trix also allows the quantiﬁcation of relative ‘effect sizes’ associated with the partitioning by allowing one to estimate the Z-score for each predictor vari- able (Mac Nally 2002); this aids in deciding variables to maintain in models. Here we alert readers to a recent port of the hierarchical partitioning software to the public-domain package, R, which provides wider availability for the conservation community. The second author ported both the hierarchical partitioning algorithm and
Biodiversity and Conservation – Springer Journals
Published: Oct 18, 2004
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