Department of Ecology, Evolution, and Behavior and Minnesota Center for Community Genetics, University of Minnesota, 100 Ecology Building, 1987 Upper Buford Circle, St. Paul, MN 55108, USA.
Evolution. 2010 Sep;64(9):2510-20. doi: 10.1111/j.1558-5646.2010.01010.x.
Since 1983, study of natural selection has relied heavily on multiple regression of fitness on the values for a set of traits via ordinary least squares (OLSs), as proposed by Lande and Arnold, to obtain an estimate of the quadratic relationship between fitness and the traits, the fitness surface. However, well-known statistical problems with this approach can affect inferences about selection. One key concern is that measures of lifetime fitness do not conform to a normal or any other standard sampling distribution, as needed to justify the usual statistical tests. Another is that OLS may yield an estimate of the sign of the fitness function's curvature that is opposite to the truth. We here show that the recently developed aster modeling approach, which explicitly models the components of fitness as the basis for inferences about lifetime fitness, eliminates these problems. We illustrate selection analysis via aster using simulated datasets involving five fitness components expressed in each of four years. We demonstrate that aster analysis yields accurate estimates of the fitness function in cases in which OLS misleads, as well as accurate confidence regions for directional selection gradients. Further, to evaluate selection when many traits are under consideration, we recommend model selection by information criteria and frequentist model averaging.
自 1983 年以来，通过普通最小二乘法（OLS）对适应度与一组特征值进行多元回归的研究，依赖于适应度与特征之间的二次关系，即适应度表面，这已成为自然选择研究的重要方法。然而，这种方法存在一些众所周知的统计问题，会影响对选择的推断。一个关键问题是，寿命适应度的度量不符合正态分布或任何其他标准抽样分布，这是通常的统计检验所必需的。另一个问题是，OLS 可能会产生与适应度函数曲率的真实符号相反的曲率估计。我们在这里表明，最近开发的 Aster 建模方法，通过将适应度的组成部分明确建模为寿命适应度推断的基础，消除了这些问题。我们使用涉及四个年份中每个年份表达的五个适应度成分的模拟数据集，通过 Aster 进行选择分析。我们证明，在 OLS 误导的情况下，Aster 分析可以准确估计适应度函数，并且可以准确确定定向选择梯度的置信区间。此外，为了评估在考虑许多特征时的选择，我们建议通过信息准则和频率主义模型平均进行模型选择。
