Acceptable Loss: Fitness Consequences of Salinity-Induced Cell Death in a Halotolerant Microalga.

AbstractEnvironmentally induced reductions in fitness components (survival, fecundity) are generally considered as passive, maladaptive responses to stress. However, there is also mounting evidence for active, programmed forms of environmentally induced cell death in unicellular organisms. While conceptual work has questioned how such programmed cell death (PCD) might be maintained by natural selection, few experimental studies have investigated how PCD influences genetic differences in longer-term fitness across environments. Here, we tracked the population dynamics of two closely related strains of the halotolerant microalga Dunaliella salina following transfers across salinities. We showed that after a salinity increase, only one of these strains displayed a massive population decline (-69% in 1 h), largely attenuated by exposure to a PCD inhibitor. However, this decline was followed by a rapid demographic rebound, characterized by faster growth than the nondeclining strain, such that sharper decline was correlated with faster subsequent growth across experiments and conditions. Strikingly, the decline was more pronounced in conditions more favorable to growth (more light, more nutrients, less competition), further suggesting that it was not simply passive. We explored several hypotheses that could explain this decline-rebound pattern, which suggests that successive stresses could select for higher environmentally induced death in this system.

Drosophilids with darker cuticle have higher body temperature under light.

Cuticle pigmentation was shown to be associated with body temperature for several relatively large species of insects, but it was questioned for small insects. Here we used a thermal camera to assess the association between drosophilid cuticle pigmentation and body temperature increase when individuals are exposed to light. We compared mutants of large effects within species (Drosophila melanogaster ebony and yellow mutants). Then we analyzed the impact of naturally occurring pigmentation variation within species complexes (Drosophila americana/Drosophila novamexicana and Drosophila yakuba/Drosophila santomea). Finally we analyzed lines of D. melanogaster with moderate differences in pigmentation. We found significant differences in temperatures for each of the four pairs we analyzed. The temperature differences appeared to be proportional to the differently pigmented area: between Drosophila melanogaster ebony and yellow mutants or between Drosophila americana and Drosophila novamexicana, for which the whole body is differently pigmented, the temperature difference was around 0.6 °C ± 0.2 °C. By contrast, between D. yakuba and D. santomea or between Drosophila melanogaster Dark and Pale lines, for which only the posterior abdomen is differentially pigmented, we detected a temperature difference of about 0.14 °C ± 0.10 °C. This strongly suggests that cuticle pigmentation has ecological implications in drosophilids regarding adaptation to environmental temperature.

Phenotypic plasticity evolves at multiple biological levels in response to environmental predictability in a long-term experiment with a halotolerant microalga.

Phenotypic plasticity, the change in the phenotype of a given genotype in response to its environment of development, is a ubiquitous feature of life, enabling organisms to cope with variation in their environment. Theoretical studies predict that, under stationary environmental variation, the level of plasticity should evolve to match the predictability of selection at the timing of development. However, the extent to which patterns of evolution of plasticity for more integrated traits are mirrored by their underlying molecular mechanisms remains unclear, especially in response to well-characterized selective pressures exerted by environmental predictability. Here, we used experimental evolution with the microalgae Dunaliella salina under controlled environmental fluctuations, to test whether the evolution of phenotypic plasticity in responses to environmental predictability (as measured by the squared autocorrelation ρ2) occurred across biological levels, going from DNA methylation to gene expression to cell morphology. Transcriptomic analysis indicates clear effects of salinity and ρ2 × salinity interaction on gene expression, thus identifying sets of genes involved in plasticity and its evolution. These transcriptomic effects were independent of DNA methylation changes in cis. However, we did find ρ2-specific responses of DNA methylation to salinity change, albeit weaker than for gene expression. Overall, we found consistent evolution of reduced plasticity in less predictable environments for DNA methylation, gene expression, and cell morphology. Our results provide the first clear empirical signature of plasticity evolution at multiple levels in response to environmental predictability, and highlight the importance of experimental evolution to address predictions from evolutionary theory, as well as investigate the molecular basis of plasticity evolution.

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.

Laplace's demon in biology: Models of evolutionary prediction.

Our ability to predict natural phenomena can be limited by incomplete information. This issue is exemplified by "Laplace's demon," an imaginary creature proposed in the 18th century, who knew everything about everything, and thus could predict the full nature of the universe forward or backward in time. Quantum mechanics, among other things, has cast doubt on the possibility of Laplace's demon in the full sense, but the idea still serves as a useful metaphor for thinking about the extent to which prediction is limited by incomplete information on deterministic processes versus random factors. Here, we use simple analytical models and computer simulations to illustrate how data limits can be captured in a Bayesian framework, and how they influence our ability to predict evolution. We show how uncertainty in measurements of natural selection, or low predictability of external environmental factors affecting selection, can greatly reduce predictive power, often swamping the influence of intrinsic randomness caused by genetic drift. Thus, more accurate knowledge concerning the causes and action of natural selection is key to improving prediction. Fortunately, our analyses and simulations show quantitatively that reasonable improvements in data quantity and quality can meaningfully increase predictability.

Plasticity across levels: Relating epigenomic, transcriptomic, and phenotypic responses to osmotic stress in a halotolerant microalga.

Phenotypic plasticity, the ability of a given genotype to produce alternative phenotypes in response to its environment of development, is an important mechanism for coping with variable environments. While the mechanisms underlying phenotypic plasticity are diverse, their relative contributions need to be investigated quantitatively to better understand the evolvability of plasticity across biological levels. This requires relating plastic responses of the epigenome, transcriptome, and organismal phenotype, and investigating how they vary with the genotype. Here we carried out this approach for responses to osmotic stress in Dunaliella salina, a green microalga that is a model organism for salinity tolerance. We compared two strains that show markedly different demographic responses to osmotic stress, and showed that these phenotypic responses involve strain- and environment-specific variation in gene expression levels, but a relative low-albeit significant-effect of strain × environment interaction. We also found an important genotype effect on the genome-wide methylation pattern, but little contribution from environmental conditions to the latter. However, we did detect a significant marginal effect of epigenetic variation on gene expression, beyond the influence of genetic differences on epigenetic state, and we showed that hypomethylated regions are correlated with higher gene expression. Our results indicate that epigenetic mechanisms are either not involved in the rapid plastic response to environmental change in this species, or involve only few changes in trans that are sufficient to trigger concerted changes in the expression of many genes, and phenotypic responses by multiple traits.

Frequency dependence and the predictability of evolution in a changing environment.

Frequency-dependent (FD) selection, whereby fitness and selection depend on the genetic or phenotypic composition of the population, arises in numerous ecological contexts (competition, mate choice, crypsis, mimicry, etc.) and can strongly impact evolutionary dynamics. In particular, negative frequency-dependent selection (NFDS) is well known for its ability to potentially maintain stable polymorphisms, but it has also been invoked as a source of persistent, predictable frequency fluctuations. However, the conditions under which such fluctuations persist are not entirely clear. In particular, previous work rarely considered that FD is unlikely to be the sole driver of evolutionary dynamics when it occurs, because most environments are not static but instead change dynamically over time. Here, we investigate how FD interacts with a temporally fluctuating environment to shape the dynamics of population genetic change. We show that a simple metric introduced by Lewontin, the slope of frequency change against frequency near equilibrium, works as a key criterion for distinguishing microevolutionary outcomes, even in a changing environment. When this slope D is between 0 and -2 (consistent with the empirical examples we review), substantial fluctuations would not persist on their own in a large population occupying a constant environment, but they can still be maintained indefinitely as quasi-cycles fueled by environmental noise or genetic drift. However, such moderate NFDS buffers and temporally shifts evolutionary responses to periodic environments (e.g., seasonality). Stronger FD, with slope D < -2, can produce self-sustained cycles that may overwhelm responses to a changing environment, or even chaos that fundamentally limits predictability. This diversity of expected outcomes, together with the empirical evidence for both FD and environment-dependent selection, suggests that the interplay of internal dynamics with external forcing should be investigated more systematically to reach a better understanding and prediction of evolution.

Using phenotypic plasticity to understand the structure and evolution of the genotype-phenotype map.

Deciphering the genotype-phenotype map necessitates relating variation at the genetic level to variation at the phenotypic level. This endeavour is inherently limited by the availability of standing genetic variation, the rate of spontaneous mutation to novo genetic variants, and possible biases associated with induced mutagenesis. An interesting alternative is to instead rely on the environment as a source of variation. Many phenotypic traits change plastically in response to the environment, and these changes are generally underlain by changes in gene expression. Relating gene expression plasticity to the phenotypic plasticity of more integrated organismal traits thus provides useful information about which genes influence the development and expression of which traits, even in the absence of genetic variation. We here appraise the prospects and limits of such an environment-for-gene substitution for investigating the genotype-phenotype map. We review models of gene regulatory networks, and discuss the different ways in which they can incorporate the environment to mechanistically model phenotypic plasticity and its evolution. We suggest that substantial progress can be made in deciphering this genotype-environment-phenotype map, by connecting theory on gene regulatory network to empirical patterns of gene co-expression, and by more explicitly relating gene expression to the expression and development of phenotypes, both theoretically and empirically.

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.

Fluctuating optimum and temporally variable selection on breeding date in birds and mammals.

Temporal variation in natural selection is predicted to strongly impact the evolution and demography of natural populations, with consequences for the rate of adaptation, evolution of plasticity, and extinction risk. Most of the theory underlying these predictions assumes a moving optimum phenotype, with predictions expressed in terms of the temporal variance and autocorrelation of this optimum. However, empirical studies seldom estimate patterns of fluctuations of an optimum phenotype, precluding further progress in connecting theory with observations. To bridge this gap, we assess the evidence for temporal variation in selection on breeding date by modeling a fitness function with a fluctuating optimum, across 39 populations of 21 wild animals, one of the largest compilations of long-term datasets with individual measurements of trait and fitness components. We find compelling evidence for fluctuations in the fitness function, causing temporal variation in the magnitude, but not the direction of selection. However, fluctuations of the optimum phenotype need not directly translate into variation in selection gradients, because their impact can be buffered by partial tracking of the optimum by the mean phenotype. Analyzing individuals that reproduce in consecutive years, we find that plastic changes track movements of the optimum phenotype across years, especially in bird species, reducing temporal variation in directional selection. This suggests that phenological plasticity has evolved to cope with fluctuations in the optimum, despite their currently modest contribution to variation in selection.

How does the strength of selection influence genetic correlations?

Genetic correlations between traits can strongly impact evolutionary responses to selection, and may thus impose constraints on adaptation. Theoretical and empirical work has made it clear that without strong linkage and with random mating, genetic correlations at evolutionary equilibrium result from an interplay of correlated pleiotropic effects of mutations, and correlational selection favoring combinations of trait values. However, it is not entirely clear how change in the overall strength of stabilizing selection across traits (breadth of the fitness peak, given its shape) influences this compromise between mutation and selection effects on genetic correlation. Here, we show that the answer to this question crucially depends on the intensity of genetic drift. In large, effectively infinite populations, genetic correlations are unaffected by the strength of selection, regardless of whether the genetic architecture involves common small-effect mutations (Gaussian regime), or rare large-effect mutations (House-of-Cards regime). In contrast in finite populations, the strength of selection does affect genetic correlations, by shifting the balance from drift-dominated to selection-dominated evolutionary dynamics. The transition between these domains depends on mutation parameters to some extent, but with a similar dependence of genetic correlation on the strength of selection. Our results are particularly relevant for understanding how senescence shapes patterns of genetic correlations across ages, and genetic constraints on adaptation during colonization of novel habitats.

Age-dependent phenological plasticity in a wild bird.

Life-history traits are often plastic in response to environmental factors such as temperature or precipitation, and they also vary with age in many species. Trait variation during the lifetime could thus be partly driven by age-dependent plasticity in these traits. We study whether plasticity of a phenological trait-the egg-laying date-with respect to spring temperature, varies with age, and explore whether this variation relates to changes in breeding success throughout the life cycle. We use data from a four-decade long-term monitoring of a wild population of blue tits in Corsica, to estimate age-dependent plasticity of reproductive phenology and annual reproductive success. We show that both laying date plasticity and annual reproductive success vary with age: young and old females are less plastic, and fledge fewer offspring, than middle-age females. Furthermore, in contrast to young and prime-age females, in old females fledging success does not depend on laying date. Phenological plasticity is a major mechanism for coping with rapid environmental variation. Our results suggest that understanding its role in adaptation to climate change and population persistence requires integrating the age structure of the population.

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.

Fluctuations in lifetime selection in an autocorrelated environment.

Most natural environments vary stochastically and are temporally autocorrelated. Previous theory investigating the effects of environmental autocorrelation on evolution mostly assumed that total fitness resulted from a single selection episode. Yet organisms are likely to experience selection repeatedly along their life, in response to possibly different environmental states. We model the evolution of a quantitative trait in organisms with non-overlapping generations undergoing several episodes of selection in a randomly fluctuating and autocorrelated environment. We show that the evolutionary dynamics depends not directly on fluctuations of the environment, but instead on those of an effective phenotypic optimum that integrates the effects of all selection episodes within each generation. The variance and autocorrelation of the integrated optimum shape the variance and predictability of selection, with substantial qualitative and quantitative deviations from previous predictions considering a single selection episode per generation. We also investigate the consequence of multiple selection episodes per generation on population load. In particular, we identify a new load resulting from within-generation fluctuating selection, generating the death of individuals without significance for the evolutionary dynamics. Our study emphasizes how taking into account fluctuating selection within lifetime unravels new properties of evolutionary dynamics, with crucial implications notably with respect to responses to global changes.

Where is the optimum? Predicting the variation of selection along climatic gradients and the adaptive value of plasticity. A case study on tree phenology.

Many theoretical models predict when genetic evolution and phenotypic plasticity allow adaptation to changing environmental conditions. These models generally assume stabilizing selection around some optimal phenotype. We however often ignore how optimal phenotypes change with the environment, which limit our understanding of the adaptive value of phenotypic plasticity. Here, we propose an approach based on our knowledge of the causal relationships between climate, adaptive traits, and fitness to further these questions. This approach relies on a sensitivity analysis of the process-based model phenofit, which mathematically formalizes these causal relationships, to predict fitness landscapes and optimal budburst dates along elevation gradients in three major European tree species. Variation in the overall shape of the fitness landscape and resulting directional selection gradients were found to be mainly driven by temperature variation. The optimal budburst date was delayed with elevation, while the range of dates allowing high fitness narrowed and the maximal fitness at the optimum decreased. We also found that the plasticity of the budburst date should allow tracking the spatial variation in the optimal date, but with variable mismatch depending on the species, ranging from negligible mismatch in fir, moderate in beech, to large in oak. Phenotypic plasticity would therefore be more adaptive in fir and beech than in oak. In all species, we predicted stronger directional selection for earlier budburst date at higher elevation. The weak selection on budburst date in fir should result in the evolution of negligible genetic divergence, while beech and oak would evolve counter-gradient variation, where genetic and environmental effects are in opposite directions. Our study suggests that theoretical models should consider how whole fitness landscapes change with the environment. The approach introduced here has the potential to be developed for other traits and species to explore how populations will adapt to climate change.

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.

Selective Sweep at a QTL in a Randomly Fluctuating Environment.

Adaptation is mediated by phenotypic traits that are often near continuous, and undergo selective pressures that may change with the environment. The dynamics of allelic frequencies at underlying quantitative trait loci (QTL) depend on their own phenotypic effects, but also possibly on other polymorphic loci affecting the same trait, and on environmental change driving phenotypic selection. Most environments include a substantial component of random noise, characterized both by its magnitude and its temporal autocorrelation, which sets the timescale of environmental predictability. I investigate the dynamics of a mutation affecting a quantitative trait in an autocorrelated stochastic environment that causes random fluctuations of an optimum phenotype. The trait under selection may also exhibit background polygenic variance caused by many polymorphic loci of small effects elsewhere in the genome. In addition, the mutation at the QTL may affect phenotypic plasticity, the phenotypic response of given genotype to its environment of development or expression. Stochastic environmental fluctuations increase the variance of the evolutionary process, with consequences for the probability of a complete sweep at the QTL. Background polygenic variation critically alters this process, by setting an upper limit to stochastic variance of population genetics at the QTL. For a plasticity QTL, stochastic fluctuations also influences the expected selection coefficient, and alleles with the same expected trajectory can have very different stochastic variances. Finally, a mutation may be favored through its effect on plasticity despite causing a systematic mismatch with optimum, which is compensated by evolution of the mean background phenotype.

Maladaptive Shifts in Life History in a Changing Environment.

Many species facing climate change have complex life cycles, with individuals in different stages differing in their sensitivity to a changing climate and their contribution to population growth. We use a quantitative genetics model to predict the dynamics of adaptation in a stage-structured population confronted with a steadily changing environment. Our model assumes that different optimal phenotypic values maximize different fitness components, consistent with many empirical observations. In a constant environment, the population evolves toward an equilibrium phenotype, which represents the best compromise given the trade-off between vital rates. In a changing environment, however, the mean phenotype in the population will lag behind this optimal compromise. We show that this lag may result in a shift along the trade-off between vital rates, with negative consequences for some fitness components but, less intuitively, improvements in some others. Complex eco-evolutionary dynamics can emerge in our model due to feedbacks between population demography and adaptation. Because of such feedback loops, selection may favor further shifts in life history in the same direction as those caused by maladaptive lags. These shifts in life history could be wrongly interpreted as adaptations to the new environment, while in reality they only reflect the inability of the population to adapt fast enough.

Phenotypic plasticity in response to climate change: the importance of cue variation.

Phenotypic plasticity is a major mechanism of response to global change. However, current plastic responses will only remain adaptive under future conditions if informative environmental cues are still available. We briefly summarize current knowledge of the evolutionary origin and mechanistic underpinnings of environmental cues for phenotypic plasticity, before highlighting the potentially complex effects of global change on cue availability and reliability. We then illustrate some of these aspects with a case study, comparing plasticity of blue tit breeding phenology in two contrasted habitats: evergreen and deciduous forests. Using long-term datasets, we investigate the climatic factors linked to the breeding phenology of the birds and their main food source. Blue tits occupying different habitats differ extensively in the cues affecting laying date plasticity, as well as in the reliability of these cues as predictors of the putative driver of selective pressure, the date of caterpillar peak. The temporal trend for earlier laying date, detected only in the evergreen populations, is explained by increased temperature during their cue windows. Our results highlight the importance of integrating ecological mechanisms shaping variation in plasticity if we are to understand how global change will affect plasticity and its consequences for population biology. This article is part of the theme issue 'The role of plasticity in phenotypic adaptation to rapid environmental change'.