Bootstrap confidence intervals for biodiversity measures based on Gini index and entropy

Bootstrap confidence intervals for biodiversity measures based on Gini index and entropy Monitoring the richness and the diversity of species living in an ecosystem is an important goal of ecology. To this purpose, measures of biodiversity have been introduced as statistical summaries of the abundance vector. In particular, we take into consideration the Gini–Simpson and the Shannon–Wiener indices, along with the effective number of species calculated through these measures, proposed, respectively, by Laakso and Taagepera (Comp Polit Stud 12:3–25, 1979) and Leti (Statistica descrittiva, Bologna, Il Mulino, 1983). It is an open question how to associate to these indices a measure of uncertainty. In this paper we compare confidence intervals based on these measures, calculated through three different bootstrap methods: percentile, -t and accelerated bias-corrected percentile. We recommend to practitioners to use the percentile procedure, as it is straightforward and computationally feasible, providing results very close to those obtained by more complex techniques. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

Bootstrap confidence intervals for biodiversity measures based on Gini index and entropy

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Publisher
Springer Netherlands
Copyright
Copyright © 2016 by Springer Science+Business Media Dordrecht
Subject
Social Sciences; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
D.O.I.
10.1007/s11135-016-0443-x
Publisher site
See Article on Publisher Site

Abstract

Monitoring the richness and the diversity of species living in an ecosystem is an important goal of ecology. To this purpose, measures of biodiversity have been introduced as statistical summaries of the abundance vector. In particular, we take into consideration the Gini–Simpson and the Shannon–Wiener indices, along with the effective number of species calculated through these measures, proposed, respectively, by Laakso and Taagepera (Comp Polit Stud 12:3–25, 1979) and Leti (Statistica descrittiva, Bologna, Il Mulino, 1983). It is an open question how to associate to these indices a measure of uncertainty. In this paper we compare confidence intervals based on these measures, calculated through three different bootstrap methods: percentile, -t and accelerated bias-corrected percentile. We recommend to practitioners to use the percentile procedure, as it is straightforward and computationally feasible, providing results very close to those obtained by more complex techniques.

Journal

Quality & QuantitySpringer Journals

Published: Oct 17, 2016

References

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