Grabchak Michael, Marcon Eric, Lang Gabriel, Zhang Zhiyi
Department of Mathematics and Statistics, University of North Carolina at Charlotte. Charlotte, NC 28223, United States of America.
AgroParisTech, UMR EcoFoG, CNRS, CIRAD, INRA, Université des Antilles, Université de Guyane. BP 709, 97310 Kourou, France.
PLoS One. 2017 Mar 7;12(3):e0173305. doi: 10.1371/journal.pone.0173305. eCollection 2017.
Modern measures of diversity satisfy reasonable axioms, are parameterized to produce diversity profiles, can be expressed as an effective number of species to simplify their interpretation, and come with estimators that allow one to apply them to real-world data. We introduce the generalized Simpson's entropy as a measure of diversity and investigate its properties. We show that it has many useful features and can be used as a measure of biodiversity. Moreover, unlike most commonly used diversity indices, it has unbiased estimators, which allow for sound estimation of the diversity of poorly sampled, rich communities.
现代多样性测度满足合理的公理,通过参数化生成多样性概况,可表示为有效物种数以便于解释,并且配有估计器,使人们能够将其应用于实际数据。我们引入广义辛普森熵作为一种多样性测度,并研究其性质。我们表明它具有许多有用的特性,可作为生物多样性的一种测度。此外,与大多数常用的多样性指数不同,它具有无偏估计器,这使得能够对抽样不足的丰富群落的多样性进行合理估计。