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波动不对称性研究的样本是否充足?一种新的大小分布模型的影响。

Are fluctuating asymmetry studies adequately sampled? Implications of a new model for size distribution.

作者信息

Babbitt G A, Kiltie R, Bolker B

机构信息

Department of Zoology, University of Florida, P. O. Box 118525, Gainesville, Florida 32611, USA.

出版信息

Am Nat. 2006 Feb;167(2):230-45. doi: 10.1086/498621. Epub 2005 Dec 19.

Abstract

Previous work on fluctuating asymmetry (FA) has highlighted its controversial relationship with environmental stress and genetic architecture. While size-based measures of FA have been assumed to have half-normal distributions within populations, studies of model developmental mechanisms have suggested other plausible distributions for FA. We investigated the distribution of FA in large empirical data sets of wing shape and wing size asymmetry from three species of insects (cotton aphid Aphis gossipyii Glover, honeybee Apis mellifera, and long-legged fly Chrysosoma crinitus). Regardless of measurement method, FA was best described by a double Pareto-lognormal (DPLN) distribution or one of its limiting functional forms. To investigate convergence of mean sample FA to the population mean at various sample sizes, we sampled repeatedly under a DPLN distribution using parameter values that best fitted our data. Sample variances are much larger, and hence, convergence is slowed considerably with univariate or multivariate size-based measures of FA in contrast to a multivariate shape-based measure of FA. We suggest that much of the past work on FA may be undersampled, and we recommend using multivariate shape-based approaches or collecting larger data sets in future studies. We also discuss the implications of the DPLN distribution for understanding the developmental mechanisms underlying FA.

摘要

先前关于波动不对称性(FA)的研究突出了其与环境压力和遗传结构之间存在争议的关系。虽然基于大小的FA测量方法在种群中被假定具有半正态分布,但对模型发育机制的研究表明FA可能存在其他合理的分布。我们研究了三种昆虫(棉蚜Aphis gossipyii Glover、蜜蜂Apis mellifera和长足虻Chrysosoma crinitus)翅膀形状和翅膀大小不对称的大型实证数据集中FA的分布。无论测量方法如何,FA最好用双帕累托对数正态(DPLN)分布或其一种极限函数形式来描述。为了研究在不同样本量下平均样本FA向总体均值的收敛情况,我们使用最适合我们数据的参数值在DPLN分布下进行重复抽样。与基于形状的多变量FA测量方法相比,基于大小的单变量或多变量FA测量方法的样本方差要大得多,因此收敛速度会显著减慢。我们认为过去关于FA的许多研究可能抽样不足,建议在未来研究中使用基于形状的多变量方法或收集更大的数据集。我们还讨论了DPLN分布对于理解FA潜在发育机制的意义。

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