Evolution in a static laboratory environment often proceeds via large-effect beneficial mutations that may become maladaptive in other environments. Conversely, natural settings require populations to endure environmental fluctuations. A sensible assumption is that the fitness of a lineage in a fluctuating environment is the time average of its fitness over the sequence of static conditions it encounters. However, transitions between conditions may pose entirely new challenges, which could cause deviations from this time average. To test this, we tracked hundreds of thousands of barcoded yeast lineages evolving in static and fluctuating conditions and subsequently isolated 900 mutants for pooled fitness assays in 15 environments. Here we find that fitness in fluctuating environments indeed often deviates from the time average, leading to fitness non-additivity. Moreover, closer examination reveals that fitness in one component of a fluctuating environment is often strongly influenced by the previous component. We show that this environmental memory is especially common for mutants with high variance in fitness across tested environments. We use a simple mathematical model and whole-genome sequencing to propose mechanisms underlying this effect, including lag time evolution and sensing mutations. Our results show that environmental fluctuations impact fitness and suggest that variance in static environments can explain these impacts.

静态实验室环境中的进化通常通过大效应有益突变进行，而这些突变在其他环境中可能会变得适应不良。相反，自然环境要求人们忍受环境波动。一个合理的假设是，谱系在波动环境中的适应度是其适应度在其遇到的一系列静态条件上的时间平均值。然而，条件之间的转换可能会带来全新的挑战，这可能会导致与该时间平均值的偏差。为了测试这一点，我们跟踪了数十万个在静态和波动条件下进化的条形码酵母谱系，随后分离出 900 个突变体，在 15 个环境中进行汇总适应性测定。在这里我们发现，波动环境中的适应度确实经常偏离时间平均值，导致适应度非可加性。此外，更仔细的检查表明，波动环境的一个组成部分的适应性往往会受到前一个组成部分的强烈影响。我们表明，这种环境记忆对于在测试环境中适应度差异较大的突变体尤其常见。我们使用简单的数学模型和全基因组测序来提出这种效应的机制，包括滞后时间进化和感知突变。我们的结果表明，环境波动会影响健康，并表明静态环境的变化可以解释这些影响。