Data and code from: Predicting population genetic change in an autocorrelated random environment: insights from a large automated experiment

Most natural environments exhibit a substantial component of random
variation, with a degree of temporal autocorrelation that defines the
color of environmental noise. Such environmental fluctuations cause random
fluctuations in natural selection, affecting the predictability of
evolution. But despite long-standing theoretical interest for population
genetics in stochastic environments, there is a dearth of empirical
estimation of underlying parameters of this theory. More importantly, it
is still an open question whether evolution in fluctuating environments
can be predicted indirectly using simpler measures, which combine
environmental time series with population estimates in constant
environments. Here we address these questions 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
frequencies 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), had large impacts on the average selection coefficient.
The stochastic variance in frequency change, which quantifies randomness
in population genetics, was substantially higher in a fluctuating
environment. The reaction norm of selection coefficients against constant
salinity yielded accurate predictions for the mean selection coefficient
in a fluctuating environment. This selection reaction norm was in turn
well predicted by environmental tolerance curves, with population growth
rate against salinity. However, both the selection reaction norm and
tolerance curves underestimated  the variance in selection caused
by random environmental fluctuations. Overall, our results provide
exceptional insights into the prospects for understanding and predicting
genetic evolution in randomly fluctuating environments.