Adaptation to an extraordinary environment by evolution of phenotypic plasticity and genetic assimilation

Adaptation to a sudden extreme change in environment, beyond the usual range of background environmental fluctuations, is analysed using a quantitative genetic model of phenotypic plasticity. Generations are discrete, with time lag τ between a critical period for environmental influence on individual development and natural selection on adult phenotypes. The optimum phenotype, and genotypic norms of reaction, are linear functions of the environment. Reaction norm elevation and slope (plasticity) vary among genotypes. Initially, in the average background environment, the character is canalized with minimum genetic and phenotypic variance, and no correlation between reaction norm elevation and slope. The optimal plasticity is proportional to the predictability of environmental fluctuations over time lag τ. During the first generation in the new environment the mean fitness suddenly drops and the mean phenotype jumps towards the new optimum phenotype by plasticity. Subsequent adaptation occurs in two phases. Rapid evolution of increased plasticity allows the mean phenotype to closely approach the new optimum. The new phenotype then undergoes slow genetic assimilation, with reduction in plasticity compensated by genetic evolution of reaction norm elevation in the original environment.     

EVOLUTION OF PHENOTYPE–ENVIRONMENT ASSOCIATIONS BY GENETIC RESPONSES TO SELECTION AND PHENOTYPIC PLASTICITY IN A TEMPORALLY AUTOCORRELATED ENVIRONMENT

Covariation between population‐mean phenotypes and environmental variables, sometimes termed a “phenotype–environment association” (PEA), can result from phenotypic plasticity, genetic responses to natural selection, or both. PEAs can potentially provide information on the evolutionary dynamics of a particular set of populations, but this requires a full theoretical characterization of PEAs and their evolution. Here, we derive formulas for the expected PEA in a temporally fluctuating environment for a quantitative trait with a linear reaction norm. We compare several biologically relevant scenarios, including constant versus evolving plasticity, and the situation in which an environment affects both development and selection but at different time periods. We find that PEAs are determined not only by biological factors (e.g., magnitude of plasticity, genetic variation), but also environmental factors, such as the association between the environments of development and of selection, and in some cases the level of temporal autocorrelation. We also describe how a PEA can be used to estimate the relationship between an optimum phenotype and an environmental variable (i.e., the environmental sensitivity of selection), an important parameter for determining the extinction risk of populations experiencing environmental change. We illustrate this ability using published data on the predator‐induced morphological responses of tadpoles to predation risk.     

Predictability rather than amplitude of temperature fluctuations determines stress resistance in a natural population of Drosophila simulans

The adaptability of organisms to novel environmental conditions depends on the amount of genetic variance present in the population as well as on the ability of individuals to adjust their phenotype through phenotypic plasticity. Here, we investigated the phenotypic plasticity induced by a single generation's exposure to three different temperature regimes with respect to several life‐history and stress‐resistance traits in a natural population of Drosophila simulans. We studied a constant as well as a predictably and an unpredictably fluctuating temperature regime. We found high levels of phenotypic plasticity among all temperature regimes, suggesting a strong influence of both temperature fluctuations and their predictability. Increased heat tolerance was observed for flies developed in both types of fluctuating thermal environments compared with flies developed in a constant environment. We suggest that this was due to beneficial hardening when developing in either fluctuating temperature environment. To our surprise, flies that developed in constant and predictably changing environments were similar to each other in most traits when compared to flies from the unpredictably fluctuating environment. The unpredictably changing thermal environment imposed the most stressful condition, resulting in the lowest performance for stress‐related traits, even though the absolute temperature changes never exceeded that of the predictably fluctuating environment. The overall decreased stress resistance of flies in the unpredictably fluctuating environment may be the consequence of maladaptive phenotypic plasticity in this setting, indicating that the adaptive value of plasticity depends on the predictability of the environment.     

Selection in a fluctuating environment leads to decreased genetic variation and facilitates the evolution of phenotypic plasticity

Changes in the environment are expected to induce changes in the quantitative genetic variation, which influences the ability of a population to adapt to environmental change. Furthermore, environmental changes are not constant in time, but fluctuate. Here, we investigate the effect of rapid, continuous and/or fluctuating temperature changes in the seed beetle Callosobruchus maculatus, using an evolution experiment followed by a split-brood experiment. In line with expectations, individuals responded in a plastic way and had an overall higher potential to respond to selection after a rapid change in the environment. After selection in an environment with increasing temperature, plasticity remained unchanged (or decreased) and environmental variation decreased, especially when fluctuations were added; these results were unexpected. As expected, the genetic variation decreased after fluctuating selection. Our results suggest that fluctuations in the environment have major impact on the response of a population to environmental change; in a highly variable environment with low predictability, a plastic response might not be beneficial and the response is genetically and environmentally canalized resulting in a low potential to respond to selection and low environmental sensitivity. Interestingly, we found greater variation for phenotypic plasticity after selection, suggesting that the potential for plasticity to evolve is facilitated after exposure to environmental fluctuations. Our study highlights that environmental fluctuations should be considered when investigating the response of a population to environmental change.     

The selective advantage of reaction norms for environmental tolerance

A tolerance curve defines the dependence of a genotype's fitness on the state of an environmental gradient. It can be characterized by a mode (the genotype's optimal environment) and a width (the breadth of adaptation). It seems possible that one or both of these characters can be modified in an adaptive manner, at least partially, during development. Thus, we extend the theory of environmental tolerance to include reaction norms for the mode and the width of the tolerance curve. We demonstrate that the selective value of such reaction norms increases with increasing spatial heterogeneity and between-generation temporal variation in the environment and with decreasing within-generation temporal variation. Assuming that the maintenance of a high breadth of adaptation is costly, reaction, norms are shown to induce correlated selection for a reduction in this character. Nevertheless, regardless of the magnitude of the reaction norm, there is a nearly one to one relationship between the optimal breadth of adaptation and the within-generation temporal variation perceived by the organism. This suggests that empirical estimates of the breadth of adaptation may provide a useful index of this type of environmental variation from the organism's point of view.     

Evolution of the environmental component of the phenotypic variance: stabilizing selection in changing environments and the cost of homogeneity

Quantitative traits show abundant genetic, environmental, and phenotypic variance, yet if they are subject to stabilizing selection for an optimal phenotype, both the genetic and environmental components are expected to decline. The mechanisms that determine the level and maintenance of phenotypic variance are not yet fully understood. While there has been extensive study of mechanisms maintaining genetic variability, it has generally been assumed that environmental variance is not dependent on the genotype and therefore not subject to change. However, accumulating data suggest that the environmental variance is under some degree of genetic control. In this study, it is assumed accordingly that both the genotypic value (i.e., mean phenotypic value) and the variance of phenotypic value given genotypic value depend on the genotype. Two models are investigated as potentially able to explain the protected maintenance of environmental variance of quantitative traits under stabilizing selection. One is varying environment among generations, such that both the optimal phenotype and the strength of the stabilizing selection vary between generations. The other is the cost of homogeneity, which is based on an assumption of an engineering cost of minimizing variability in development. It is shown that a small homogeneity cost is enough to maintain the observed levels of environmental variance, whereas a large amount of temporal variation in the optimal phenotype and the strength of selection would be necessary.     

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.     

Plastic inducible morphologies are not always adaptive: The importance of time delays in a stochastic environment

Summary We present a mathematical model for predicting the expected fitness of phenotypically plastic organisms experiencing a variable environment. We assume that individuals experience two discrete environments probabilistically in time (as a Markov process) and that there are two different phenotypic states, each yielding the highest fitness in one of the two environments. We compare the expected fitness of a phenotypically fixed individual to that of an individual whose phenotype is induced to produce the better phenotype in each environment with a time lag between experiencing a new environment and realization of the new phenotype. Such time lags are common in organisms where phenotypically plastic, inducible traits have been documented. We find that although plasticity is generally adaptive when time lags are short (relative to the time scale of environmental variability), plasticity can be disadvantageous for longer lag times. Asymmetries in environmental change probabilities and/or the relative fitnesses of each phenotype strongly influence whether plasticity is favoured. In contrast to other models, our model does not require costs for plasticity to be disadvantageous; costs affect the results quantitatively, not qualitatively.     

Expected relative fitness and the adaptive topography of fluctuating selection

Wright's adaptive topography describes gene frequency evolution as a maximization of mean fitness in a constant environment. I extended this to a fluctuating environment by unifying theories of stochastic demography and fluctuating selection, assuming small or moderate fluctuations in demographic rates with a stationary distribution, and weak selection among the types. The demography of a large population, composed of haploid genotypes at a single locus or normally distributed phenotypes, can then be approximated as a diffusion process and transformed to produce the dynamics of population size, N, and gene frequency, p, or mean phenotype, . The expected evolution of p or is a product of genetic variability and the gradient of the long-run growth rate of the population, , with respect to p or . This shows that the expected evolution maximizes , the mean Malthusian fitness in the average environment minus half the environmental variance in population growth rate. Thus, as a function of p or represents an adaptive topography that, despite environmental fluctuations, does not change with time. The haploid model is dominated by environmental stochasticity, so the expected maximization is not realized. Different constraints on quantitative genetic variability, and stabilizing selection in the average environment, allow evolution of the mean phenotype to undergo a stochastic maximization of . Although the expected evolution maximizes the long-run growth rate of the population, for a genotype or phenotype the long-run growth rate is not a valid measure of fitness in a fluctuating environment. The haploid and quantitative character models both reveal that the expected relative fitness of a type is its Malthusian fitness in the average environment minus the environmental covariance between its growth rate and that of the population.     

Evolution and maintenance of the environmental component of the phenotypic variance: benefit of plastic traits under changing environments

How environmental variances in quantitative traits are influenced by variable environments is an important problem in evolutionary biology. In this study, the evolution and maintenance of phenotypic variance in a plastic trait under stabilizing selection are investigated. The mapping from genotypic value to phenotypic value of the quantitative trait is approximated by a linear reaction norm, with genotypic effects on its phenotypic mean and sensitivity to environment. The environmental deviation is assumed to be decomposed into environmental quality, which interacts with genotypic value, and residual developmental noise, which is independent of genotype. Environmental quality and the optimal phenotype of stabilizing selection are allowed to randomly fluctuate in both space and time, and individuals migrate equally before development and reproduction among different niches. Analyses show that phenotypic plasticity is adaptive within variable environments if correlations have become established between the optimal phenotype and environmental quality in space and/or time. The evolved plasticity increases with variances in optimal phenotypes and correlations between optimal phenotype and environmental quality; this further induces increases in mean fitness and the environmental variance in the trait. Under certain circumstances, however, the environmental variance may decrease with increase in variation in environmental quality.     

How stress selects for reversible phenotypic plasticity

Stress occurring in periods shorter than life span strongly selects for reversible phenotypic plasticity, for maximum reliability of stress indicating cues and for minimal response delays. The selective advantage of genotypes that are able to produce adaptive reversible plastic phenotypes is calculated by using the concept of environmental tolerance. Analytic expressions are given for optimal values of mode and breadth of tolerance functions for stress induced and non-induced phenotypes depending on (1) length of stress periods, (2) response delay for switching into the induced phenotype, (3) response delay for rebuilding the non-induced phenotype, (4) intensity of stress, i.e. mean value of the stress inducing environment, (5) coefficient of variation of the stress environment and (6) completeness of information available to the stressed organism. Adaptively reversible phenotypic plastic traits will most probably affect fitness in a way that can be described by simultaneous reversible plasticity in mode and breadth of tolerance functions.     

The combined effects of temporal autocorrelation and the costs of plasticity on the evolution of plasticity

Adaptive phenotypic plasticity is an important source of intraspecific variation, and for many plastic traits, the costs or factors limiting plasticity seem cryptic. However, there are several different factors that may constrain the evolution of plasticity, but few models have considered costs and limiting factors simultaneously. Here we use a simulation model to investigate how the optimal level of plasticity in a population depends on a fixed maintenance fitness cost for plasticity or an incremental fitness cost for producing a plastic response in combination with environmental unpredictability (environmental fluctuation speed) limiting plasticity. Our model identifies two mechanisms that act, almost separately, to constrain the evolution of plasticity: (i) the fitness cost of plasticity scaled by the nonplastic environmental tolerance, and (ii) the environmental fluctuation speed scaled by the rate of phenotypic change. That is, the evolution of plasticity is constrained by the high cost of plasticity in combination with high tolerance for environmental variation, or fast environmental changes in combination with slow plastic response. Qualitatively similar results are found when maintenance and incremental fitness costs of plasticity are incorporated, although a larger degree of plasticity is selected for with an incremental cost. Our model highlights that it is important to consider direct fitness costs and phenotypic limitations in relation to nonplastic environmental tolerance and environmental fluctuations, respectively, to understand what constrains the evolution of phenotypic plasticity.     

The fitness costs of developmental canalization and plasticity

Organisms are capable of an astonishing repertoire of phenotypic responses to the environment, and these often define important adaptive solutions to heterogeneous and unpredictable conditions. The terms ‘phenotypic plasticity’ and ‘canalization’ indicate whether environmental variation has a large or small effect on the phenotype. The evolution of canalization and plasticity is influenced by optimizing selection‐targeting traits within environments, but inherent fitness costs of plasticity may also be important. We present a meta‐analysis of 27 studies (of 16 species of plant and 7 animals) that have measured selection on the degree of plasticity independent of the characters expressed within environments. Costs of plasticity and canalization were equally frequent and usually mild; large costs were observed only in studies with low sample size. We tested the importance of several covariates, but only the degree of environmental stress was marginally positively related to the cost of plasticity. These findings suggest that costs of plasticity are often weak, and may influence phenotypic evolution only under stressful conditions.     

Genetic evolution,plasticity, and bet‐hedging as adaptive responses to temporally autocorrelated fluctuating selection: A quantitative genetic model

Adaptive responses to autocorrelated environmental fluctuations through evolution in mean reaction norm elevation and slope and an independent component of the phenotypic variance are analyzed using a quantitative genetic model. Analytic approximations expressing the mutual dependencies between all three response modes are derived and solved for the joint evolutionary outcome. Both genetic evolution in reaction norm elevation and plasticity are favored by slow temporal fluctuations, with plasticity, in the absence of microenvironmental variability, being the dominant evolutionary outcome for reasonable parameter values. For fast fluctuations, tracking of the optimal phenotype through genetic evolution and plasticity is limited. If residual fluctuations in the optimal phenotype are large and stabilizing selection is strong, selection then acts to increase the phenotypic variance (bet‐hedging adaptive). Otherwise, canalizing selection occurs. If the phenotypic variance increases with plasticity through the effect of microenvironmental variability, this shifts the joint evolutionary balance away from plasticity in favor of genetic evolution. If microenvironmental deviations experienced by each individual at the time of development and selection are correlated, however, more plasticity evolves. The adaptive significance of evolutionary fluctuations in plasticity and the phenotypic variance, transient evolution, and the validity of the analytic approximations are investigated using simulations.     

Habitat selection and the value of information in heterogenous landscapes

Despite the wide usage of the term information in evolutionary ecology, there is no general treatise between fitness (i.e. density‐dependent population growth) and selection of the environment sensu lato. Here we 1) initiate the building of a quantitative framework with which to examine the relationship between information use in spatially heterogeneous landscapes and density‐dependent population growth, and 2) illustrate its utility by applying the framework to an existing model of breeding habitat selection. We begin by linking information, as a process of narrowing choice, to population growth/fitness. Second, we define a measure of a population's penalty of ignorance based on the Kullback–Leibler index that combines the contributions of resource selection (i.e. biased use of breeding sites) and density‐dependent depletion. Third, we quantify the extent to which environmental heterogeneity (i.e. mean and variance within a landscape) constrains sustainable population growth of unbiased agents. We call this the heterogeneity‐based fitness deficit, and combine this with population simulations to quantify the independent contribution of information‐use strategies to the total population growth rate. We further capitalize on this example to highlight the interactive effects of information between ecological scales when fear affects individual fitness through phenotypic plasticity. Informed breeding habitat selection moderates the demographic cost of fear commensurate with density‐dependent information use. Thus, future work should attempt to differentiate between phenotypic plasticity (i.e. acute fear) and demographic responses (i.e. chronic changes in population size). We conclude with a broader discussion of information in alternative contexts, and explore some evolutionary considerations for information use. We note how competition among individuals may constrain the information state among individuals, and the implications of this constraint under environmental change.     

Developmental and genetic effects on behavioral and life‐history traits in a field cricket

A fundamental goal of evolutionary ecology is to identify the sources underlying trait variation on which selection can act. Phenotypic variation will be determined by both genetic and environmental factors, and adaptive phenotypic plasticity is expected when organisms can adjust their phenotypes to match environmental cues. Much recent research interest has focused on the relative importance of environmental and genetic factors on the expression of behavioral traits, in particular, and how they compare with morphological and life‐history traits. Little research to date examines the effect of development on the expression of heritable variation in behavioral traits, such as boldness and activity. We tested for genotype, environment, and genotype‐by‐environment differences in body mass, development time, boldness, and activity, using developmental density treatments combined with a quantitative genetic design in the sand field cricket (Gryllus firmus). Similar to results from previous work, animals reared at high densities were generally smaller and took longer to mature, and body mass and development time were moderately heritable. In contrast, neither boldness nor activity responded to density treatments, and they were not heritable. The only trait that showed significant genotype‐by‐environment differences was development time. It is possible that adaptive behavioral plasticity is not evident in this species because of the highly variable social environments it naturally experiences. Our results illustrate the importance of validating the assumption that behavioral phenotype reflects genetic patterns and suggest questions about the role of environmental instability in trait variation and heritability.     

EVOLUTION AND EXTINCTION IN A CHANGING ENVIRONMENT: A QUANTITATIVE-GENETIC ANALYSIS

Because of the ubiquity of genetic variation for quantitative traits, virtually all populations have some capacity to respond evolutionarily to selective challenges. However, natural selection imposes demographic costs on a population, and if these costs are sufficiently large, the likelihood of extinction will be high. We consider how the mean time to extinction depends on selective pressures (rate and stochasticity of environmental change, and strength of selection), population parameters (carrying capacity, and reproductive capacity), and genetics (rate of polygenic mutation). We assume that in a randomly mating, finite population subject to density-dependent population growth, individual fitness is determined by a single quantitative-genetic character under Gaussian stabilizing selection with the optimum phenotype exhibiting directional change, or random fluctuations, or both. The quantitative trait is determined by a finite number of freely recombining, mutationally equivalent, additive loci. The dynamics of evolution and extinction are investigated, assuming that the population is initially under mutation-selection-drift balance. Under this model, in a directionally changing environment, the mean phenotype lags behind the optimum, but on the average evolves parallel to it. The magnitude of the lag determines the vulnerability to extinction. In finite populations, stochastic variation in the genetic variance can be quite pronounced, and bottlenecks in the genetic variance temporarily can impair the population's adaptive capacity enough to cause extinction when it would otherwise be unlikely in an effectively infinite population. We find that maximum sustainable rates of evolution or, equivalently, critical rates of environmental change, may be considerably less than 10% of a phenotypic standard deviation per generation.     

Genetic variance of sexually selected traits in waxmoths: maintenance by genotype x environment interaction

When traits experience directional selection, such as that imposed by sexual selection, their genetic variance is expected to diminish. Nonetheless, theory and findings from sexual selection predict and demonstrate that male traits favored by female choice retain substantial amounts of additive genetic variance. We explored this dilemma through an ecological genetic approach and focused on the potential contributions of genotype x environment interaction (GEI) to maintenance of additive genetic variance for male signal characters in the lesser waxmoth, Achroia grisella (Lepidoptera: Pyralidae). We artificially selected genetic variants for two male signal characters, signal rate (SR) and peak amplitude (PA), that influence female attraction and then examined the phenotypic plasticity of these variants (high- and low-SR and high- and low-PA lines) under a range of environmental conditions expected in natural populations. Our split-family breeding experiments indicated that two signal characters, SR and PA, and several developmental characters in both high- and low-SR and high- and low-PA lines displayed considerable phenotypic plasticity among the environments tested. Moreover, strong GEIs leading to crossover between high- and low-SR lines were found for SR and developmental period. Therefore, neither high- nor low-SR genetic variants would achieve maximum attractiveness and fitness in every environment, and those variants producing unattractive signals with low SRs under normal conditions may remain in populations provided that gene flow across environments or generation overlap are sufficiently high. We speculate that the phenotypic plasticity for SR and developmental period is adaptive in A. grisella populations experiencing a range of temperature and density conditions. Females mating with attractive (high-SR) males may be assured of obtaining good genes because these males sire offspring that develop more rapidly and a crossover for developmental period may parallel that for SR. Such parallel crossovers may be expected wherever good-genes sexual selection mechanisms operate.