Data from: Sustained fitness gains and variability in fitness trajectories in the long-term evolution experiment with Escherichia coli

Many populations live in environments subject to frequent biotic and abiotic changes. Nonetheless, it is interesting to ask whether an evolving population’s mean fitness can increase indefinitely, and potentially without any limit, even in a constant environment. A recent study showed that fitness trajectories of Escherichia coli populations over 50,000 generations were better described by a power-law model than by a hyperbolic model. According to the power-law model, the rate of fitness gain declines over time but fitness has no upper limit, whereas the hyperbolic model implies a hard limit. Here, we examine whether the previously estimated power-law model predicts the fitness trajectory for an additional 10,000 generations. To that end, we conducted more than 1100 new competitive fitness assays. Consistent with the previous study, the power-law model fits the new data better than the hyperbolic model. We also analysed the variability in fitness among populations, finding subtle, but significant, heterogeneity in mean fitness. Some, but not all, of this variation reflects differences in mutation rate that evolved over time. Taken together, our results imply that both adaptation and divergence can continue indefinitely—or at least for a long time—even in a constant environment.诸多种群生存于频繁经受生物与非生物因子扰动的环境中。尽管如此，探究进化种群的平均适合度是否能够无限增长、乃至在恒定环境中亦无上限，仍是一个极具研究价值的科学问题。近期一项研究表明，相较于双曲线模型（hyperbolic model），幂律模型（power-law model）更适于描述大肠杆菌（Escherichia coli）种群历经50000代的适合度轨迹。依据幂律模型，适合度的提升速率随时间推移逐渐降低，但适合度并无上限；而双曲线模型则预示存在严格的上限。本研究旨在验证此前估算的幂律模型是否能够预测额外10000代的适合度轨迹。为此，我们开展了逾1100次全新的竞争适合度测定实验。与此前的研究结果一致，相较于双曲线模型，幂律模型仍能更好地拟合本次新获得的数据。我们同时分析了种群间的适合度变异，发现平均适合度存在细微但显著的异质性。其中部分（而非全部）变异可归因于进化过程中逐渐形成的突变率差异。综合来看，本研究结果表明，即便在恒定环境中，适应性进化与种群分化均能够无限持续——或至少可延续极长的时间。