Results using simulated data used to conduct power analyses

Spatial covariance between genotypic and environmental influences on phenotypes (CovGE) can result in the nonrandom distribution of genotypes across environmental gradients and is a potentially important factor driving local adaptation. However, a framework to quantify the magnitude and significance of CovGE has been lacking. We develop a novel quantitative/analytical approach to estimate and test the significance of CovGE from reciprocal transplant or common garden experiments, which we validate using simulated data. We demonstrate how power to detect CovGE changes over a range of experimental designs. We confirm an inverse relationship between gene-by-environment interactions (GxE) and CovGE, as predicted by first principles, but show how phenotypes can be influenced by both. The metric provides a way to measure how phenotypic plasticity covaries with genetic differentiation and highlights the importance of understanding the dual influences of CovGE and GxE on phenotypes in studies of local adaptation and species’ responses to environmental change.

基因型与环境对表型的影响之间的空间协方差（CovGE）可导致基因型在环境梯度上呈现非随机分布，是驱动本地适应的潜在关键因素。然而，此前一直缺乏可量化CovGE大小与显著性的研究框架。本研究开发了一种全新的定量分析方法，可基于互易移植实验或同质园实验对CovGE进行估计并检验其显著性，且通过模拟数据验证了该方法的有效性。研究揭示了检测CovGE的检验效力随实验设计参数变化的规律，证实了如第一性原理所预测的基因-环境互作（GxE）与CovGE之间的负相关关系，同时表明表型可同时受二者调控。该协方差度量方法可用于衡量表型可塑性与遗传分化之间的协变关系，并凸显了在本地适应及物种响应环境变化的研究中，厘清CovGE与GxE对表型的双重影响的重要性。