The contribution of maternal effects to selection response: an empirical test of competing models

Maternal effects can dramatically influence the evolutionary process, in some cases facilitating and in others hindering adaptive evolution. Maternal effects have been incorporated into quantitative genetic models using two theoretical frameworks: the variance-components approach, which partitions variance into direct and maternal components, and the trait-based approach, which assumes that maternal effects are mediated by specific maternal traits. Here, we demonstrate parallels between these models and test their ability to predict evolutionary change. First, we show that the two approaches predict equivalent responses to selection in the absence of maternal effects mediated by traits that are themselves maternally influenced. We also introduce a correction factor that may be applied when such cascading maternal effects are present. Second, we use several maternal effect models, as well as the standard breeder's equation, to predict evolution in response to artificial selection on flowering time in American bellflower, Campanulastrum americanum. Models that included maternal effects made much more accurate predictions of selection response than the breeder's equation. Maternal effect models differed somewhat in their fit, with a version of the trait-based model providing the best fit. We recommend fitting such trait-based models when possible and appropriate to make the most accurate evolutionary predictions.

母体效应（Maternal effects）可以显著影响进化过程，在部分情境下促进适应性进化，在另一些情境下则会阻碍其发生。目前学界已通过两种理论框架将母体效应纳入数量遗传模型（quantitative genetic models）：其一为方差组分法（variance-components approach），即将表型方差划分为直接组分与母体组分；其二为基于性状的方法（trait-based approach），该框架假定母体效应由特定母体性状所介导。本研究阐明了这两类模型之间的共通之处，并检验了它们预测进化变化的能力。首先，我们证实，当不存在由自身受母体调控的性状所介导的母体效应时，这两种方法所预测的选择响应完全一致。此外，我们提出了一种校正因子，可在存在此类级联母体效应时使用。其次，我们借助多种母体效应模型以及标准育种者方程（breeder's equation），对美洲风铃草（Campanulastrum americanum）开花时间的人工选择响应所引发的进化过程进行预测。相较于标准育种者方程，纳入母体效应的模型对选择响应的预测精度显著更高。不同母体效应模型的拟合度存在一定差异，其中基于性状的模型的某一变体展现出最优拟合效果。我们建议，在条件允许且适宜的情况下，应拟合此类基于性状的模型，以获得最为精准的进化预测结果。