Abstract
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 in 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 using 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 nuclear and choloroplastic barcode sequences. We show that the magnitude of environmental fluctuations (determined by their variance), but also their predictability (determined by their autocorrelation), had large impacts on the average selection coefficient. The 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.
Highlights
To what extent is evolution predictable? This question has received considerable interest from evolutionary biologists, and has become increasingly quantitative as relevant data have accumulated
Population genetic theory indicates that probabilistic predictions can still be made in this context, but the extent to which this holds empirically, and whether these predictions can be based on simple measurements, are still open questions
Tracking population genetics in an experimental stochastic environment We followed the frequency of one strain (CCAP 19/15, hereafter denoted as C) of the microalgae Dunaliella salina competing in a mixture with another strain (CCAP 19/12, hereafter denoted as A) during 37 transfers (~100 generations), in constant or randomly varying salinity
Summary
To what extent is evolution predictable? This question has received considerable interest from evolutionary biologists, and has become increasingly quantitative as relevant data have accumulated. Most natural environments exhibit random fluctuations— known as stochastic noise—characterized by their variance, which determines their magnitude, and autocorrelation (or color in power spectrum [19,20]), which determines their predictability. Such environmental noise causes randomly fluctuating selection at the genetic and phenotypic levels, which may reduce the predictability of evolution in a number of ways [17,21,22]. Environmental stochasticity contributes to chance in evolutionary trajectories, causing allele frequencies to undergo random walks, to genetic drift caused by the finiteness of populations [24,25,26,27,28,29,30]
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