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基于面积的费舍尔对数级数法对物种丰富度进行稀疏化和外推

Rarefaction and extrapolation of species richness using an area-based Fisher's logseries.

作者信息

Chen Youhua, Shen Tsung-Jen

机构信息

Department of Renewable Resources University of Alberta Edmonton AB Canada.

Chengdu Institute of Biology Chinese Academy of Sciences Chengdu China.

出版信息

Ecol Evol. 2017 Oct 23;7(23):10066-10078. doi: 10.1002/ece3.3509. eCollection 2017 Dec.

Abstract

Fisher's logseries is widely used to characterize species abundance pattern, and some previous studies used it to predict species richness. However, this model, derived from the negative binomial model, degenerates at the zero-abundance point (i.e., its probability mass fully concentrates at zero abundance, leading to an odd situation that no species can occur in the studied sample). Moreover, it is not directly related to the sampling area size. In this sense, the original Fisher's alpha (correspondingly, species richness) is incomparable among ecological communities with varying area sizes. To overcome these limitations, we developed a novel area-based logseries model that can account for the compounding effect of the sampling area. The new model can be used to conduct area-based rarefaction and extrapolation of species richness, with the advantage of accurately predicting species richness in a large region that has an area size being hundreds or thousands of times larger than that of a locally observed sample, provided that data follow the proposed model. The power of our proposed model has been validated by extensive numerical simulations and empirically tested through tree species richness extrapolation and interpolation in Brazilian Atlantic forests. Our parametric model is data parsimonious as it is still applicable when only the information on species number, community size, or the numbers of singleton and doubleton species in the local sample is available. Notably, in comparison with the original Fisher's method, our area-based model can provide asymptotically unbiased variance estimation (therefore correct 95% confidence interval) for species richness. In conclusion, the proposed area-based Fisher's logseries model can be of broad applications with clear and proper statistical background. Particularly, it is very suitable for being applied to hyperdiverse ecological assemblages in which nonparametric richness estimators were found to greatly underestimate species richness.

摘要

费希尔对数级数被广泛用于描述物种丰度模式,一些先前的研究用它来预测物种丰富度。然而,这个从负二项式模型推导出来的模型,在零丰度点会退化(即其概率质量完全集中在零丰度,导致一种奇怪的情况,即在研究样本中不会出现任何物种)。此外,它与采样面积大小没有直接关系。从这个意义上说,原始的费希尔α(相应地,物种丰富度)在不同面积大小的生态群落之间是不可比的。为了克服这些局限性,我们开发了一种新的基于面积的对数级数模型,该模型可以考虑采样面积的复合效应。新模型可用于基于面积的物种丰富度稀疏化和外推,其优点是能够准确预测一个比局部观测样本面积大数百或数千倍的大区域中的物种丰富度,前提是数据符合所提出的模型。我们提出的模型的有效性已通过广泛的数值模拟得到验证,并通过巴西大西洋森林中树种丰富度的外推和内插进行了实证检验。我们的参数模型数据简洁,因为当仅提供局部样本中的物种数量、群落大小或单物种和双物种数量的信息时,它仍然适用。值得注意的是,与原始的费希尔方法相比,我们基于面积的模型可以为物种丰富度提供渐近无偏方差估计(因此有正确的95%置信区间)。总之,所提出的基于面积的费希尔对数级数模型具有清晰且恰当的统计背景,可广泛应用。特别是,它非常适合应用于超多样的生态组合,在这种组合中,发现非参数丰富度估计器会大大低估物种丰富度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff4e/5723611/5bb41c4f1dfa/ECE3-7-10066-g001.jpg

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