Data from: Fluc­tu­a­tion domains in adap­tive evo­lu­tion

We derive an expression for the variation between parallel trajectories in phenotypic evolution, extending the well known result that predicts the mean evolutionary path in adaptive dynamics or quantitative genetics. We show how this expression gives rise to the notion of fluctuation domains–parts of the fitness landscape where the rate of evolution is very predictable (due to fluctuation dissipation) and parts where it is highly variable (due to fluctuation enhancement). These fluctuation domains are determined by the curvature of the fitness landscape. Regions of the fitness landscape with positive curvature, such as adaptive valleys or branching points, experience enhancement. Regions with negative curvature, such as adaptive peaks, experience dissipation. We explore these dynamics in the ecological scenarios of implicit and explicit competition for a limiting resource.我们推导了表型进化（phenotypic evolution）中平行轨迹间差异的表达式，推广了自适应动力学（adaptive dynamics）与数量遗传学（quantitative genetics）领域中用于预测平均进化路径的经典结论。我们阐明了该表达式如何催生涨落域（fluctuation domains）这一概念：适合度景观（fitness landscape）可划分为两类区域，一类进化速率高度可预测（源于涨落耗散（fluctuation dissipation）），另一类进化速率则极具波动性（源于涨落增强（fluctuation enhancement））。涨落域的划分由适合度景观的曲率（curvature）决定：带有正曲率的区域（如适应性谷（adaptive valleys）或进化分支点（branching points））会产生涨落增强效应；带有负曲率的区域（如适应性峰（adaptive peaks））则会产生涨落耗散效应。我们针对限制性资源（limiting resource）的隐性竞争与显性竞争这两类生态场景，对上述动力学过程展开了探索。