Predicting population genetic change in an experimental stochastic environment

Most natural environments exhibit a substantial component of random variation. Such environmental noise is expected to cause random fluctuations in natural selection, affecting the predictability of evolution. But despite a long-standing theoretical interest for understanding the population genetic consequences of stochastic environments, there has been a dearth of empirical validation and estimation of the underlying parameters of this theory. Indeed, tracking the genetics of a large number of replicate lines under a controlled level of environmental stochasticity is particularly challenging. Here, we tackled this problem by resorting to an automated experimental evolution approach. We used a liquid-handling robot to expose over a hundred lines of the micro-alga Dunaliella salina to randomly fluctuating salinity over a continuous range, with controlled mean, variance, and autocorrelation. We then tracked the frequency of one of two competing strains through amplicon sequencing of a nuclear and choloroplastic barcode sequences. We show that the magnitude of environmental fluctuations (variance), but also their predictability (autocorrelation), have large impacts on the average selection coefficient. Furthermore, the stochastic variance in population genetic change is substantially higher in a fluctuating environment. Reaction norms of selection coefficients and growth rates of single strains against the environment captured the mean response accurately, but failed to explain the high variance induced by environmental stochasticity. Overall, our results provide exceptional insights on the prospects for understanding and predicting genetic evolution in randomly fluctuating environments.