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.

诸多种群所处的环境频繁经历生物与非生物因素的变化。然而，一个值得探究的问题是：即便在恒定环境中，进化种群的平均适合度是否能够无限提升、乃至不受任何上限限制？近期一项研究表明，在50000代的培养周期内，大肠杆菌（Escherichia coli）种群的适合度轨迹用幂律模型（power-law model）拟合效果优于双曲模型（hyperbolic model）。根据幂律模型，适合度的提升速率会随时间推移而下降，但适合度并无上限；而双曲模型则意味着适合度存在硬性上限。本研究旨在验证此前估算的幂律模型是否能够准确预测额外10000代后的适合度轨迹。为此，我们开展了超过1100组全新的竞争适合度测定实验。与此前的研究结果一致，相较于双曲模型，幂律模型对新实验数据的拟合效果更优。我们还分析了不同种群间的适合度变异情况，发现种群平均适合度存在细微但显著的异质性。其中部分（而非全部）变异可归因于随进化过程演化出的突变率差异。综上，我们的研究结果表明，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。