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 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.

Reduced phenotypic plasticity evolves in less predictable environments.

Phenotypic plasticity is a prominent mechanism for coping with variable environments, and a key determinant of extinction risk. Evolutionary theory predicts that phenotypic plasticity should evolve to lower levels in environments that fluctuate less predictably, because they induce mismatches between plastic responses and selective pressures. However, this prediction is difficult to test in nature, where environmental predictability is not controlled. Here, we exposed 32 lines of the halotolerant microalga Dunaliella salina to ecologically realistic, randomly fluctuating salinity, with varying levels of predictability, for 500 generations. We found that morphological plasticity evolved to lower degrees in lines that experienced less predictable environments. Evolution of plasticity mostly concerned phases with slow population growth, rather than the exponential phase where microbes are typically phenotyped. This study underlines that long-term experiments with complex patterns of environmental change are needed to test theories about population responses to altered environmental predictability, as currently observed under climate change.

Interactions between Genetic and Ecological Effects on the Evolution of Life Cycles.

Sexual reproduction leads to an alternation between haploid and diploid phases, whose relative length varies widely across taxa. Previous genetical models showed that diploid or haploid life cycles may be favored, depending on dominance interactions and on effective recombination rates. By contrast, niche differentiation between haploids and diploids may favor biphasic life cycles, in which development occurs in both phases. In this article, we explore the interplay between genetical and ecological factors, assuming that deleterious mutations affect the competitivity of individuals within their ecological niche and allowing different effects of mutations in haploids and diploids (including antagonistic selection). We show that selection on a modifier gene affecting the relative length of both phases can be decomposed into a direct selection term favoring the phase with the highest mean fitness (due to either ecological differences or differential effects of mutations) and an indirect selection term favoring the phase in which selection is more efficient. When deleterious alleles occur at many loci and in the presence of ecological differentiation between haploids and diploids, evolutionary branching often occurs and leads to the stable coexistence of alleles coding for haploid and diploid cycles, while temporal variations in niche sizes may stabilize biphasic cycles.

Experimental evolution of environmental tolerance, acclimation, and physiological plasticity in a randomly fluctuating environment.

Environmental tolerance curves, representing absolute fitness against the environment, are an empirical assessment of the fundamental niche, and emerge from the phenotypic plasticity of underlying phenotypic traits. Dynamic plastic responses of these traits can lead to acclimation effects, whereby recent past environments impact current fitness. Theory predicts that higher levels of phenotypic plasticity should evolve in environments that fluctuate more predictably, but there have been few experimental tests of these predictions. Specifically, we still lack experimental evidence for the evolution of acclimation effects in response to environmental predictability. Here, we exposed 25 genetically diverse populations of the halotolerant microalgae Dunaliella salina to different constant salinities, or to randomly fluctuating salinities, for over 200 generations. The fluctuating treatments differed in their autocorrelation, which determines the similarity of subsequent values, and thus environmental predictability. We then measured acclimated tolerance surfaces, mapping population growth rate against past (acclimation) and current (assay) environments. We found that experimental mean and variance in salinity caused the evolution of niche position (optimal salinity) and breadth, with respect to not only current but also past (acclimation) salinity. We also detected weak but significant evidence for evolutionary changes in response to environmental predictability, with higher predictability leading notably to lower optimal salinities and stronger acclimation effect of past environment on current fitness. We further showed that these responses are related to the evolution of plasticity for intracellular glycerol, the major osmoregulatory mechanism in this species. However, the direction of plasticity evolution did not match simple theoretical predictions. Our results underline the need for a more explicit consideration of the dynamics of environmental tolerance and its underlying plastic traits to reach a better understanding of ecology and evolution in fluctuating environments.

Phenotypic memory drives population growth and extinction risk in a noisy environment.

Random environmental fluctuations pose major threats to wild populations. As patterns of environmental noise are themselves altered by global change, there is a growing need to identify general mechanisms underlying their effects on population dynamics. This notably requires understanding and predicting population responses to the colour of environmental noise, in other words its temporal autocorrelation pattern. Here, we show experimentally that environmental autocorrelation has a large influence on population dynamics and extinction rates, which can be predicted accurately provided that a memory of past environment is accounted for. We exposed nearly 1,000 lines of the microalgae Dunaliella salina to randomly fluctuating salinity, with autocorrelation ranging from negative to highly positive. We found lower population growth, and twice as many extinctions, under lower autocorrelation. These responses closely matched predictions based on a tolerance curve with environmental memory, showing that non-genetic inheritance can be a major driver of population dynamics in randomly fluctuating environments.

Evolution of haploid-diploid life cycles when haploid and diploid fitnesses are not equal.

Many organisms spend a significant portion of their life cycle as haploids and as diploids (a haploid-diploid life cycle). However, the evolutionary processes that could maintain this sort of life cycle are unclear. Most previous models of ploidy evolution have assumed that the fitness effects of new mutations are equal in haploids and homozygous diploids, however, this equivalency is not supported by empirical data. With different mutational effects, the overall (intrinsic) fitness of a haploid would not be equal to that of a diploid after a series of substitution events. Intrinsic fitness differences between haploids and diploids can also arise directly, for example because diploids tend to have larger cell sizes than haploids. Here, we incorporate intrinsic fitness differences into genetic models for the evolution of time spent in the haploid versus diploid phases, in which ploidy affects whether new mutations are masked. Life-cycle evolution can be affected by intrinsic fitness differences between phases, the masking of mutations, or a combination of both. We find parameter ranges where these two selective forces act and show that the balance between them can favor convergence on a haploid-diploid life cycle, which is not observed in the absence of intrinsic fitness differences.

A honey bee (Apis mellifera) light phase response curve.

The authors report a phase response curve (PRC) for individual honey bees (Apis mellifera) to single 1-h light pulses (1000 lux) using an Aschoff Type 1 protocol (n = 134). The bee PRC is a weak (Type 1) PRC with a maximum advance of 1.5 h between circadian time (CT) 18 and 3 and a maximum delay of 1.5 h between CT 12 and 18. This is the first published honey bee light PRC and provides an important resource for chronobiologists and honey bee researchers. It may also have practical applications for what is an economically important species frequently transported across different time zones.