Nipperess David A, Matsen Frederick A
Department of Biological Sciences, Faculty of Science, Macquarie University, NSW, 2109, Australia, + 61 2 9850 6950
Methods Ecol Evol. 2013 Jun 1;4(6):566-572. doi: 10.1111/2041-210X.12042.
Phylogenetic diversity (PD) depends on sampling depth, which complicates the comparison of PD between samples of different depth. One approach to dealing with differing sample depth for a given diversity statistic is to rarefy, which means to take a random subset of a given size of the original sample. Exact analytical formulae for the mean and variance of species richness under rarefaction have existed for some time but no such solution exists for PD.We have derived exact formulae for the mean and variance of PD under rarefaction. We confirm that these formulae are correct by comparing exact solution mean and variance to that calculated by repeated random (Monte Carlo) subsampling of a dataset of stem counts of woody shrubs of Toohey Forest, Queensland, Australia. We also demonstrate the application of the method using two examples: identifying hotspots of mammalian diversity in Australasian ecoregions, and characterising the human vaginal microbiome.There is a very high degree of correspondence between the analytical and random subsampling methods for calculating mean and variance of PD under rarefaction, although the Monte Carlo method requires a large number of random draws to converge on the exact solution for the variance.Rarefaction of mammalian PD of ecoregions in Australasia to a common standard of 25 species reveals very different rank orderings of ecoregions, indicating quite different hotspots of diversity than those obtained for unrarefied PD. The application of these methods to the vaginal microbiome shows that a classical score used to quantify bacterial vaginosis is correlated with the shape of the rarefaction curve.The analytical formulae for the mean and variance of PD under rarefaction are both exact and more efficient than repeated subsampling. Rarefaction of PD allows for many applications where comparisons of samples of different depth is required.
系统发育多样性(PD)取决于抽样深度,这使得不同深度样本之间的PD比较变得复杂。对于给定的多样性统计量,处理不同样本深度的一种方法是稀疏化,即从原始样本中随机抽取给定大小的子集。一段时间以来,已经存在关于稀疏化下物种丰富度均值和方差的精确解析公式,但对于PD还没有这样的解决方案。我们推导出了稀疏化下PD均值和方差的精确公式。通过将精确解的均值和方差与对澳大利亚昆士兰托希森林木本灌木茎干计数数据集进行重复随机(蒙特卡洛)二次抽样计算得到的结果进行比较,我们证实了这些公式是正确的。我们还通过两个例子展示了该方法的应用:确定澳大拉西亚生态区哺乳动物多样性的热点区域,以及描述人类阴道微生物群。在计算稀疏化下PD的均值和方差时,解析方法和随机二次抽样方法之间存在非常高的一致性,尽管蒙特卡洛方法需要大量随机抽样才能收敛到方差的精确解。将澳大拉西亚生态区哺乳动物PD稀疏化到25个物种的共同标准,揭示了生态区截然不同的排名顺序,表明与未稀疏化的PD相比,多样性热点区域有很大差异。将这些方法应用于阴道微生物群表明,用于量化细菌性阴道病的经典评分与稀疏化曲线的形状相关。稀疏化下PD均值和方差的解析公式既精确又比重复二次抽样更有效。PD的稀疏化允许在许多需要比较不同深度样本的应用中使用。