4.5 Article

Additive partitioning of Rao's quadratic diversity: a hierarchical approach

Journal

ECOLOGICAL MODELLING
Volume 183, Issue 4, Pages 365-371

Publisher

ELSEVIER SCIENCE BV
DOI: 10.1016/j.ecolmodel.2004.08.020

Keywords

concavity; Euclidean distance matrices; Simpson's diversity; taxonomic diversity; taxonomic trees

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Ecologists have long recognized three different components of species diversity: alpha or within-community diversity (alpha), beta or between-community diversity (beta) and gamma or total species diversity in a region (gamma). In this framework, beta-diversity has been traditionally linked to the other diversity components through a multiplicative model so that it can be expressed as the ratio between gamma-diversity and average alpha-diversity in a set of plots. Yet, more recently, ecologists are starting to partition diversity using the lesser known approach that alpha- and beta-diversities sum to give the gamma-diversity. This additive diversity partitioning is based on the decomposition of concave diversity measures for which the total diversity in a pooled set of communities exceeds (or equals) the average diversity within communities. In this paper, first, I shortly revise additive diversity partitioning for traditional diversity measures that are computed from species relative abundances. Next, I show that, under some specific circumstances, the same model can be extended to Rao's quadratic entropy, a measure that combines species relative abundances and pairwise interspecies differences. Finally, in the framework of taxonomic diversity, Rao's quadratic entropy has another decomposition: the sum over the Simpson indices at all the taxonomic levels. Thus, I show that, combining both partitioning models, the contribution of each level in the taxonomic hierarchy to the alpha- beta- and gamma-diversity components of Rao's quadratic entropy is made explicit. The proposed diversity decomposition is illustrated with a worked example on data from a plant community on ultramatic soils in Tuscany (central Italy). (c) 2004 Elsevier B.V. All rights reserved.

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