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.

诸多生物种群栖息于频繁遭遇生物性与非生物性环境变化的环境中。尽管如此，我们仍可提出一个颇具研究价值的问题：即便身处恒定环境，不断演化的种群的平均适合度（fitness）是否能够无限增长、乃至突破一切上限？近期一项研究表明，针对大肠杆菌（Escherichia coli）种群历经50000代演化的适合度轨迹，幂律模型（power-law model）的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逐渐降低，但适合度不存在上限；而双曲模型则意味着存在严格的上限。在本研究中，我们旨在验证此前估算的幂律模型是否能够准确预测额外10000代的适合度轨迹。为此，我们开展了超过1100次全新的竞争适合度测定实验。与此前的研究结果一致，幂律模型对新实验数据的拟合效果依旧优于双曲模型。我们同时分析了不同种群间的适合度差异，发现其平均适合度存在细微但显著的异质性。此类差异中的一部分（而非全部），可归因于随演化进程改变的突变率差异。综合来看，我们的研究结果表明，即便身处恒定环境，生物的适应性演化与种群分化均可持续进行——至少可在极长的时间跨度内持续。