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Thirty years of progeny from Chao's inequality : estimating and comparing richness with incidence data and incomplete sampling

机译:赵超不等式的后代三十年:估计丰富度并与发生率数据和不完全采样进行比较

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摘要

In the context of capture-recapture studies, Chao (1987) derived an inequality among capture frequency counts to obtain a lower bound for the size of a population based on individuals' capture/non-capture records for multiple capture occasions. The inequality has been applied to obtain a non-parametric lower bound of species richness of an assemblage based on species incidence (detection/non-detection) data in multiple sampling units. The inequality implies that the number of undetected species can be inferred from the species incidence frequency counts of the uniques (species detected in only one sampling unit) and duplicates (species detected in exactly two sampling units). In their pioneering paper, Colwell and Coddington (1994) gave the name "Chao2" to the estimator for the resulting species richness. (The "Chao1" estimator refers to a similar type of estimator based on species abundance data). Since then, the Chao2 estimator has been applied to many research fields and led to fruitful generalizations. Here, we first review Chao's inequality under various models and discuss some related statistical inference questions: (1) Under what conditions is the Chao2 estimator an unbiased point estimator? (2) How many additional sampling units are needed to detect any arbitrary proportion (including 100%) of the Chao2 estimate of asymptotic species richness? (3) Can other incidence frequency counts be used to obtain similar lower bounds? We then show how the Chao2 estimator can be also used to guide a non-asymptotic analysis in which species richness estimators can be compared for equally-large or equally-complete samples via sample-size-based and coverage-based rarefaction and extrapolation. We also review the generalization of Chao's inequality to estimate species richness under other sampling-without-replacement schemes (e.g. a set of quadrats, each surveyed only once), to obtain a lower bound of undetected species shared between two or multiple assemblages, and to allow inferences about undetected phylogenetic richness (the total length of undetected branches of a phylogenetic tree connecting all species), with associated rarefaction and extrapolation. A small empirical dataset for Australian birds is used for illustration, using online software SpadeR, iNEXT, and PhD.
机译:在捕获-捕获研究的背景下,Chao(1987)根据多个捕获场合的个体捕获/非捕获记录,得出了捕获频率计数之间的不等式,从而获得了人口规模的下限。根据多个采样单位中的物种发生率(检测/未检测)数据,将不等式应用于获取组合物种丰富度的非参数下限。不等式意味着可以从唯一性(仅在一个采样单元中检测到的物种)和重复项(恰好在两个采样单元中检测到的物种)的物种发生频率计数来推断未发现物种的数量。 Colwell和Coddington(1994)在其开创性论文中将“ Chao2”命名为估算器,以得出物种丰富度。 (“ Chao1”估算器是指基于物种丰度数据的类似类型的估算器)。从那时起,Chao2估计器已应用于许多研究领域,并带来了卓有成效的概括。在这里,我们首先回顾各种模型下的Chao不等式,并讨论一些相关的统计推断问题:(1)在什么条件下Chao2估计量是无偏点估计量? (2)需要多少个额外的采样单位来检测渐近物种丰富度的Chao2估计值的任意比例(包括100%)? (3)是否可以使用其他入射频率计数来获得相似的下限?然后,我们将展示Chao2估计量如何还可以用于指导非渐进分析,在该分析中,可以通过基于样本量和基于覆盖率的稀疏和外推法,对同等大小或同等样本的物种丰富度估计量进行比较。我们还回顾了Chao不等式的泛化,以估计其他抽样无替代方案(例如,一组四足动物,每只仅调查一次)下的物种丰富度,从而获得两个或多个集合之间共享的未被发现的物种的下限,并且可以推断出未检测到的系统发育丰富度(连接所有物种的系统发育树未检测到的分支的总长度),以及相关的稀疏和外推。使用在线软件SpadeR,iNEXT和PhD来举例说明澳大利亚鸟类的小型经验数据集。

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    Chao Anne;

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  • 年度 2017
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