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检测自然选择表型估计中环境诱导的偏差:理论与实践。

Testing for environmentally induced bias in phenotypic estimates of natural selection: theory and practice.

作者信息

Stinchcombe John R, Rutter Matthew T, Burdick Donald S, Tiffin Peter, Rausher Mark D, Mauricio Rodney

机构信息

Department of Biology, Duke University, Durham, North Carolina 27708-0338, USA.

出版信息

Am Nat. 2002 Oct;160(4):511-23. doi: 10.1086/342069.

DOI:10.1086/342069
PMID:18707526
Abstract

Measuring natural selection has been a fundamental goal of evolutionary biology for more than a century, and techniques developed in the last 20 yr have provided relatively simple means for biologists to do so. Many of these techniques, however, share a common limitation: when applied to phenotypic data, environmentally induced covariances between traits and fitness can lead to biased estimates of selection and misleading predictions about evolutionary change. Utilizing estimates of breeding values instead of phenotypic data with these methods can eliminate environmentally induced bias, although this approach is more difficult to implement. Despite this potential limitation to phenotypic methods and the availability of a potential solution, little empirical evidence exists on the extent of environmentally induced bias in phenotypic estimates of selection. In this article, we present a method for detecting bias in phenotypic estimates of selection and demonstrate its use with three independent data sets. Nearly 25% of the phenotypic selection gradients estimated from our data are biased by environmental covariances. We find that bias caused by environmental covariances appears mainly to affect quantitative estimates of the strength of selection based on phenotypic data and that the magnitude of these biases is large. As our estimates of selection are based on data from spatially replicated field experiments, we suggest that our findings on the prevalence of bias caused by environmental covariances are likely to be conservative.

摘要

一个多世纪以来,测量自然选择一直是进化生物学的一个基本目标,过去20年里开发的技术为生物学家提供了相对简单的方法来实现这一目标。然而,这些技术中的许多都有一个共同的局限性:当应用于表型数据时,环境诱导的性状与适合度之间的协方差会导致对选择的估计产生偏差,并对进化变化做出误导性预测。使用这些方法时,利用育种值估计而不是表型数据可以消除环境诱导的偏差,尽管这种方法更难实施。尽管表型方法存在这种潜在局限性,并且有潜在的解决方法,但关于环境诱导偏差在选择的表型估计中的程度,几乎没有实证证据。在本文中,我们提出了一种检测选择的表型估计中偏差的方法,并通过三个独立的数据集展示了其用法。从我们的数据中估计出的近25%的表型选择梯度受到环境协方差的偏差影响。我们发现,环境协方差引起的偏差似乎主要影响基于表型数据的选择强度的定量估计,并且这些偏差的幅度很大。由于我们对选择的估计基于空间重复田间实验的数据,我们认为我们关于环境协方差引起的偏差普遍性的发现可能是保守的。

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