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Inferences on the Number of Unseen Species and the Number of Abundant/Rare Species

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  • Hongmei Zhang

Abstract

This paper focuses on estimating the number of species and the number of abundant species in a specific geographic region and, consequently, draw inferences on the number of rare species. The word 'species' is generic referring to any objects in a population that can be categorized. In the areas of biology, ecology, literature, etc, the species frequency distributions are usually severely skewed, in which case the population contains a few very abundant species and many rare ones. To model a such situation, we develop an asymmetric multinomial-Dirichlet probability model using species frequency data. Posterior distributions on the number of species and the number of abundant species are obtained and posterior inferences are induced using MCMC simulations. Simulations are used to demonstrate and evaluate the developed methodology. We apply the method to a DNA segment data set and a butterfly data set. Comparisons among different approaches to inferring the number of species are also discussed in this paper.

Suggested Citation

  • Hongmei Zhang, 2007. "Inferences on the Number of Unseen Species and the Number of Abundant/Rare Species," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(6), pages 725-740.
    • Handle: RePEc:taf:japsta:v:34:y:2007:i:6:p:725-740
    DOI: 10.1080/02664760701237010
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    Cited by:

    1. Zhang, Hongmei & Ghosh, Kaushik & Ghosh, Pulak, 2012. "Sampling designs via a multivariate hypergeometric-Dirichlet process model for a multi-species assemblage with unknown heterogeneity," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2562-2573.

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