Which line to follow? The utility of different line-fitting methods to capture the mechanism of morphological scaling
Which line to follow? The utility of different line-fitting methods to capture the mechanism of morphological scaling
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遵循哪条线？
DOI：
10.1101/216960
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
A. Shingleton
中科院分区：
文献类型：
--
作者：
A. Shingleton
双变量形态尺度关系描述了两个性状的大小如何在群体中的成年人中共同变化。由于身体形状是由身体内各种性状的相对大小反映的，因此形态尺度关系捕捉了身体形状如何随大小而变化，因此已被广泛用作物种内和物种间形态变化的描述符。尽管它们的广泛使用，有持续的讨论，线拟合方法应该用来描述线性形态缩放关系。在这里，我认为，“最好的”线拟合方法是一个最准确地捕捉到近端的发展机制，产生缩放关系。使用数学建模，我表明，“最好的”线拟合方法取决于个人之间的发展机制，调节性状大小的变化模式，这种模式的发展变化产生的形态变化。对于果蝇性状，这种变异模式表明主轴回归是最好的线性拟合方法。然而，对于其他动物的形态特征，其他线性拟合方法可能更准确。我为研究人员提供了一个简单的基于Web的应用程序，以探索不同的线拟合方法如何在他们自己的形态数据上执行。
Bivariate morphological scaling relationships describe how the size of two traits co-varies among adults in a population. In as much as body shape is reflected by the relative size of various traits within the body, morphological scaling relationships capture how body shape varies with size, and therefore have been used widely as descriptors of morphological variation within and among species. Despite their extensive use, there is continuing discussion over which line-fitting method should be used to describe linear morphological scaling relationships. Here I argue that the ‘best’ line-fitting method is the one that most accurately captures the proximate developmental mechanisms that generate scaling relationships. Using mathematical modeling, I show that the ‘best’ line-fitting method depends on the pattern of variation among individuals in the developmental mechanisms that regulate trait size, and the morphological variation this pattern of developmental variation produces. For Drosophila traits, this pattern of variation indicates that major axis regression is the best line-fitting method. For morphological traits in other animals, however, other line-fitting methods may be more accurate. I provide a simple web-based application for researchers to explore how different line-fitting methods perform on their own morphological data.
影响因子：
7.2
作者：
Irvine KD;Harvey KF
通讯作者：
Harvey KF