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.

Methods
We exposed a mixture of two Dunaliella salina strains to fluctuating vs constant salinity (strains CCAP  19/12 : A and CCAP 19/15 : C), and tracked their frequencies through time by amplicon sequencing of the ITS2 and a chloroplast locus. Genomic DNA from 1071 samples was extracted using the Nucleospin® plant II (Macherey-Nagel).

In order to make efficient use of the sequencing data, we reduced all chloroplast and ITS2 reads to short haplotypes made of a succession of few linked SNPs that individually maximized the FST among pure, reference cultures of A and C. We estimated fluctuating selection by tracking the dynamics of the frequency p of strain C through time, by combining two sources of genetic information, from the ITS2 and the chloroplast locus. We considered that the frequencies measured at the ITS2 and chloroplast loci were two observations (with error) of a true, unobserved strain frequency p. This corresponds to a state-space model, and we wrote its explicit likelihood function in C++, and optimized it using the TMB package in R (v.3.5.2)