Data from: The fitness effect of mutations across environments: Fisher’s geometrical model with multiple optima

When are mutations beneficial in one environment and deleterious in another? More generally, what is the relationship between mutation effects across environments? These questions are crucial to predict adaptation in heterogeneous conditions in a broad sense. Empirical evidence documents various patterns of fitness effects across environments but we still lack a framework to analyse these multivariate data. In this paper, we extend Fisher’s geometrical model to multiple environments determining distinct peaks. We derive the fitness distribution, in one environment, among mutants with a given fitness in another and the bivariate distribution of random mutants’ fitnesses across two or more environments. The geometry of the phenotype-fitness landscape is naturally interpreted in terms of fitness trade-offs between environments. These results may be used to fit/predict empirical distributions or to predict the pattern of adaptation across heterogeneous conditions. As an example, we derive the genomic rate of substitution and of adaptation in a metapopulation divided into two distinct habitats in a high migration regime and show that they depend critically on the geometry of the phenotype-fitness landscape. --

为何突变在某一环境中呈现有益效应，却在另一环境中表现为有害？更一般地说，不同环境下的突变效应之间存在何种关联？此类问题对于预测广义异质环境下的生物适应过程至关重要。现有实验证据已揭示了不同环境下适合度（fitness）效应的多种分布模式，但目前仍缺乏可用于分析这类多元数据的理论框架。本文将费希尔几何模型（Fisher’s geometrical model）拓展至存在多个不同适应峰的多环境场景。我们推导了在某一环境中具备特定适合度的突变体，在另一环境中的适合度分布，以及随机突变体在两个或多个环境中的适合度双变量分布。表型-适合度景观的几何特征可自然地通过不同环境间的适合度权衡关系予以阐释。上述研究结果可用于拟合/预测实验观测得到的适合度分布，或是预测异质环境下的生物适应模式。作为示例，我们推导了被划分为两个不同生境的集合种群（metapopulation）在高迁移模式下的基因组替换率与适应速率，并证明其关键取决于表型-适合度景观的几何特征。