Within-species variation and measurement error in phylogenetic comparative methods
Within-species variation and measurement error in phylogenetic comparative methods
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DOI：
10.1080/10635150701313830
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发表时间：
2007-04-01
影响因子：
6.5
通讯作者：
Garland, Theodore, Jr.
中科院分区：
文献类型：
--
作者：
Ives, Anthony R.;Midford, Peter E.;Garland, Theodore, Jr.
大多数用于分析数量或连续变化的表型性状的基于遗传学的统计方法都假设物种内的变异不存在或至少可以忽略不计，这对许多性状来说是不现实的。种内变异有几个组成部分。同一物种的种群之间的差异可能代表系统发育分化或环境因素的直接影响，种群之间的差异（表型可塑性）。种群内变异也有助于种内变异，包括采样变异、仪器相关误差、行为或生理状态波动引起的低重复性、与年龄、性别、季节或一天中的时间相关的变异以及这些类别中的个体变异。在这里，我们开发的技术，包括物种内的变化，或“测量误差”，因为它通常被称为在统计文献中的遗传相关的数据进行分析。我们得出的方法（i）单变量分析，包括测量的“系统发育信号”，（ii）相关性和多个性状的主成分分析，（iii）多元回归，（iv）推断的“功能关系”，如减少主轴（RMA）回归。这些方法能够合并对于每个数据点不同的测量误差（物种或种群的平均值），但是它们可以针对对测量误差知之甚少的特殊情况进行修改（例如，当一个人愿意假设关于两个性状的测量误差的比率时）。我们发现，未能将测量误差可能会导致偏见和不精确（不太确定）的参数估计。即使是以前的方法，被认为是占测量误差，如传统的RMA回归，可以通过明确纳入测量误差和系统发育相关性进行改进。我们用例子和仿真来说明这些方法，并提供Matlab程序。【先祖重建;比较方法;估计的广义最小二乘;独立对比;最大似然;形态计量学;主成分分析;约化主轴;回归;限制最大似然]。
Most phylogenetically based statistical methods for the analysis of quantitative or continuously varying phenotypic traits assume that variation within species is absent or at least negligible, which is unrealistic for many traits. Within-species variation has several components. Differences among populations of the same species may represent either phylogenetic divergence or direct effects of environmental factors that differ among populations (phenotypic plasticity). Within-population variation also contributes to within-species variation and includes sampling variation, instrument-related error, low repeatability caused by fluctuations in behavioral or physiological state, variation related to age, sex, season, or time of day, and individual variation within such categories. Here we develop techniques for analyzing phylogenetically correlated data to include within-species variation, or "measurement error" as it is often termed in the statistical literature. We derive methods for (i) univariate analyses, including measurement of "phylogenetic signal," (ii) correlation and principal components analysis for multiple traits, (iii) multiple regression, and (iv) inference of "functional relations," such as reduced major axis (RMA) regression. The methods are capable of incorporating measurement error that differs for each data point (mean value for a species or population), but they can be modified for special cases in which less is known about measurement error (e.g., when one is willing to assume something about the ratio of measurement error in two traits). We show that failure to incorporate measurement error can lead to both biased and imprecise (unduly uncertain) parameter estimates. Even previous methods that are thought to account for measurement error, such as conventional RMA regression, can be improved by explicitly incorporating measurement error and phylogenetic correlation. We illustrate these methods with examples and simulations and provide Matlab programs. [Ancestor reconstruction; comparative methods; estimated generalized least-squares; independent contrasts; maximum likelihood; morphometrics; principal components analysis; reduced major axis; regression; restricted maximum likelihood].