THE CONTRIBUTION OF MATERNAL EFFECTS TO SELECTION RESPONSE: AN EMPIRICAL TEST OF COMPETING MODELS

Maternal effects can dramatically influence the evolutionary process, in some cases facilitating and in others hindering adaptive evolution. Maternal effects have been incorporated into quantitative genetic models using two theoretical frameworks: the variance‐components approach, which partitions variance into direct and maternal components, and the trait‐based approach, which assumes that maternal effects are mediated by specific maternal traits. Here, we demonstrate parallels between these models and test their ability to predict evolutionary change. First, we show that the two approaches predict equivalent responses to selection in the absence of maternal effects mediated by traits that are themselves maternally influenced. We also introduce a correction factor that may be applied when such cascading maternal effects are present. Second, we use several maternal effect models, as well as the standard breeder's equation, to predict evolution in response to artificial selection on flowering time in American bellflower, Campanulastrum americanum. Models that included maternal effects made much more accurate predictions of selection response than the breeder's equation. Maternal effect models differed somewhat in their fit, with a version of the trait‐based model providing the best fit. We recommend fitting such trait‐based models when possible and appropriate to make the most accurate evolutionary predictions.     

Plasticity and evolution in correlated suites of traits

When organisms are faced with new or changing environments, a central challenge is the coordination of adaptive shifts in many different phenotypic traits. Relationships among traits may facilitate or constrain evolutionary responses to selection, depending on whether the direction of selection is aligned or opposed to the pattern of trait correlations. Attempts to predict evolutionary potential in correlated traits generally assume that correlations are stable across time and space; however, increasing evidence suggests that this may not be the case, and flexibility in trait correlations could bias evolutionary trajectories. We examined genetic and environmental influences on variation and covariation in a suite of behavioural traits to understand if and how flexibility in trait correlations influences adaptation to novel environments. We tested the role of genetic and environmental influences on behavioural trait correlations by comparing Trinidadian guppies (Poecilia reticulata) historically adapted to high‐ and low‐predation environments that were reared under native and non‐native environmental conditions. Both high‐ and low‐predation fish exhibited increased behavioural variance when reared under non‐native vs. native environmental conditions, and rearing in the non‐native environment shifted the major axis of variation among behaviours. Our findings emphasize that trait correlations observed in one population or environment may not predict correlations in another and that environmentally induced plasticity in correlations may bias evolutionary divergence in novel environments.     

The danger of applying the breeder's equation in observational studies of natural populations

The breeder's equation, which predicts evolutionary change when a phenotypic covariance exists between a heritable trait and fitness, has provided a key conceptual framework for studies of adaptive microevolution in nature. However, its application requires strong assumptions to be made about the causation of fitness variation. In its univariate form, the breeder's equation assumes that the trait of interest is not correlated with other traits having causal effects on fitness. In its multivariate form, the validity of predicted change rests on the assumption that all such correlated traits have been measured and incorporated into the analysis. Here, we (i) highlight why these assumptions are likely to be seriously violated in studies of natural, rather than artificial, selection and (ii) advocate wider use of the Robertson–Price identity as a more robust, and less assumption‐laden, alternative to the breeder's equation for applications in evolutionary ecology.     

Environmental stress correlates with increases in both genetic and residual variances: A meta‐analysis of animal studies

Adaptive evolutionary responses are determined by the strength of selection and amount of genetic variation within traits, however, both are known to vary across environmental conditions. As selection is generally expected to be strongest under stressful conditions, understanding how the expression of genetic variation changes across stressful and benign environmental conditions is crucial for predicting the rate of adaptive change. Although theory generally predicts increased genetic variation under stress, previous syntheses of the field have found limited support for this notion. These studies have focused on heritability, which is dependent on other environmentally sensitive, but nongenetic, sources of variation. Here, we aim to complement these studies with a meta‐analysis in which we examine changes in coefficient of variation (CV) in maternal, genetic, and residual variances across stressful and benign conditions. Confirming previous analyses, we did not find any clear direction in how heritability changes across stressful and benign conditions. However, when analyzing CV, we found higher genetic and residual variance under highly stressful conditions in life‐history traits but not in morphological traits. Our findings are of broad significance to contemporary evolution suggesting that rapid evolutionary adaptive response may be mediated by increased evolutionary potential in stressed populations.     

Predicting evolutionary potential: A numerical test of evolvability measures

Despite sophisticated mathematical models, the theory of microevolution is mostly treated as a qualitative rather than a quantitative tool. Numerical measures of selection, constraints, and evolutionary potential are often too loosely connected to theory to provide operational predictions of the response to selection. In this paper, we study the ability of a set of operational measures of evolvability and constraint to predict short‐term selection responses generated by individual‐based simulations. We focus on the effects of selective constraints under which the response in one trait is impeded by stabilizing selection on other traits. The conditional evolvability is a measure of evolutionary potential explicitly developed for this situation. We show that the conditional evolvability successfully predicts rates of evolution in an equilibrium situation, and further that these equilibria are reached with characteristic times that are inversely proportional to the fitness load generated by the constraining characters. Overall, we find that evolvabilities and conditional evolvabilities bracket responses to selection, and that they together can be used to quantify evolutionary potential on time scales where the G‐matrix remains relatively constant.     

Integrating correlation between traits improves spatial predictions of plant functional composition

Functional trait composition is increasingly recognized as key to better understand and predict community responses to environmental gradients. Predictive approaches traditionally model the weighted mean trait values of communities (CWMs) as a function of environmental gradients. However, most approaches treat traits as independent regardless of known tradeoffs between them, which could lead to spurious predictions. To address this issue, we suggest jointly modeling a suit of functional traits along environmental gradients while accounting for relationships between traits. We use generalized additive mixed effect models to predict the functional composition of alpine grasslands in the Guisane Valley (France). We demonstrate that, compared to traditional approaches, joint trait models explain considerable amounts of variation in CWMs, yield less uncertainty in trait CWM predictions and provide more realistic spatial projections when extrapolating to novel environmental conditions. Modeling traits and their co‐variation jointly is an alternative and superior approach to predicting traits independently. Additionally, compared to a ‘predict first, assemble later’ approach that estimates trait CWMs post hoc based on stacked species distribution models, our ‘assemble first, predict later’ approach directly models trait‐responses along environmental gradients, and does not require data and models on species’ distributions, but only mean functional trait values per community plot. This highlights the great potential of joint trait modeling approaches in large‐scale mapping applications, such as spatial projections of the functional composition of vegetation and associated ecosystem services as a response to contemporary global change.     

Selection on skewed characters and the paradox of stasis

Observed phenotypic responses to selection in the wild often differ from predictions based on measurements of selection and genetic variance. An overlooked hypothesis to explain this paradox of stasis is that a skewed phenotypic distribution affects natural selection and evolution. We show through mathematical modeling that, when a trait selected for an optimum phenotype has a skewed distribution, directional selection is detected even at evolutionary equilibrium, where it causes no change in the mean phenotype. When environmental effects are skewed, Lande and Arnold's (1983) directional gradient is in the direction opposite to the skew. In contrast, skewed breeding values can displace the mean phenotype from the optimum, causing directional selection in the direction of the skew. These effects can be partitioned out using alternative selection estimates based on average derivatives of individual relative fitness, or additive genetic covariances between relative fitness and trait (Robertson–Price identity). We assess the validity of these predictions using simulations of selection estimation under moderate sample sizes. Ecologically relevant traits may commonly have skewed distributions, as we here exemplify with avian laying date — repeatedly described as more evolutionarily stable than expected — so this skewness should be accounted for when investigating evolutionary dynamics in the wild.     

Selection and evolutionary potential of spring arrival phenology in males and females of a migratory songbird

The timing of annual life‐history events affects survival and reproduction of all organisms. A changing environment can perturb phenological adaptations and an important question is if populations can evolve fast enough to track the environmental changes. Yet, little is known about selection and evolutionary potential of traits determining the timing of crucial annual events. Migratory species, which travel between different climatic regions, are particularly affected by global environmental changes. To increase our understanding of evolutionary potential and selection of timing traits, we investigated the quantitative genetics of arrival date at the breeding ground using a multigenerational pedigree of a natural great reed warbler (Acrocephalus arundinaceus) population. We found significant heritability of 16.4% for arrival date and directional selection for earlier arrival in both sexes acting through reproductive success, but not through lifespan. Mean arrival date advanced with 6 days over 20 years, which is in exact accordance with our predicted evolutionary response based on the breeder's equation. However, this phenotypic change is unlikely to be caused by microevolution, because selection seems mainly to act on the nongenetic component of the trait. Furthermore, demographical changes could also not account for the advancing arrival date. Instead, a strong correlation between spring temperatures and population mean arrival date suggests that phenotypic plasticity best explains the advancement of arrival date in our study population. Our study dissects the evolutionary and environmental forces that shape timing traits and thereby increases knowledge of how populations cope with rapidly changing environments.     

Environment‐dependent variation in selection on life history across small spatial scales

Variation in life‐history traits is ubiquitous, even though genetic variation is thought to be depleted by selection. One potential mechanism for the maintenance of trait variation is spatially variable selection. We explored spatial variation in selection in the field for a colonial marine invertebrate that shows phenotypic differences across a depth gradient of only 3 m. Our analysis included life‐history traits relating to module size, colony growth, and phenology. Directional selection on colony growth varied in strength across depths, while module size was under directional selection at one depth but not the other. Differences in selection may explain some of the observed phenotypic differentiation among depths for one trait but not another: instead, selection should actually erode the differences observed for this trait. Our results suggest selection is not acting alone to maintain trait variation within and across environments in this system.     

Response to joint selection on germination and flowering phenology depends on the direction of selection

Flowering and germination time are components of phenology, a complex phenotype that incorporates a number of traits. In natural populations, selection is likely to occur on multiple components of phenology at once. However, we have little knowledge of how joint selection on several phenological traits influences evolutionary response. We conducted one generation of artificial selection for all combinations of early and late germination and flowering on replicated lines within two independent base populations in the herb Campanula americana. We then measured response to selection and realized heritability for each trait. Response to selection and heritability were greater for flowering time than germination time, indicating greater evolutionary potential of this trait. Selection for earlier phenology, both flowering and germination, did not depend on the direction of selection on the other trait, whereas response to selection to delay germination and flowering was greater when selection on the other trait was in the opposite direction (e.g., early germination and late flowering), indicating a negative genetic correlation between the traits. Therefore, the extent to which correlations shaped response to selection depended on the direction of selection. Furthermore, the genetic correlation between timing of germination and flowering varies across the trait distributions. The negative correlation between germination and flowering time found when selecting for delayed phenology follows theoretical predictions of constraint for traits that jointly determine life history schedule. In contrast, the lack of constraint found when selecting for an accelerated phenology suggests a reduction of the covariance due to strong selection favoring earlier flowering and a shorter life cycle. This genetic architecture, in turn, will facilitate further evolution of the early phenology often favored in warm climates.     

Evolution of quantitative traits in the wild: mind the ecology

Recent advances in the quantitative genetics of traits in wild animal populations have created new interest in whether natural selection, and genetic response to it, can be detected within long-term ecological studies. However, such studies have re-emphasized the fact that ecological heterogeneity can confound our ability to infer selection on genetic variation and detect a population''s response to selection by conventional quantitative genetics approaches. Here, I highlight three manifestations of this issue: counter gradient variation, environmentally induced covariance between traits and the correlated effects of a fluctuating environment. These effects are symptomatic of the oversimplifications and strong assumptions of the breeder''s equation when it is applied to natural populations. In addition, methods to assay genetic change in quantitative traits have overestimated the precision with which change can be measured. In the future, a more conservative approach to inferring quantitative genetic response to selection, or genomic approaches allowing the estimation of selection intensity and responses to selection at known quantitative trait loci, will provide a more precise view of evolution in ecological time.     

The effect of trait type and strength of selection on heritability and evolvability in an island bird population

The heritability (h²) of fitness traits is often low. Although this has been attributed to directional selection having eroded genetic variation in direct proportion to the strength of selection, heritability does not necessarily reflect a trait's additive genetic variance and evolutionary potential (“evolvability”). Recent studies suggest that the low h² of fitness traits in wild populations is caused not by a paucity of additive genetic variance (V_A) but by greater environmental or nonadditive genetic variance (V_R). We examined the relationship between h² and variance‐standardized selection intensities (i or β_σ), and between evolvability (I_A:V_A divided by squared phenotypic trait mean) and mean‐standardized selection gradients (β_μ). Using 24 years of data from an island population of Savannah sparrows, we show that, across diverse traits, h² declines with the strength of selection, whereas I_A and I_R (V_R divided by squared trait mean) are independent of the strength of selection. Within trait types (morphological, reproductive, life‐history), h², I_A, and I_R are all independent of the strength of selection. This indicates that certain traits have low heritability because of increased residual variance due to the age at which they are expressed or the multiple factors influencing their expression, rather than their association with fitness.     

Individual variation in growth trajectories: phenotypic and genetic correlations in ontogeny of the house finch (Carpodacus mexicanus)

We studied patterns of growth in a recently established natural population of the house finch (Carpodacus mexicanus) to examine whether phenotypic and genetic covariation among age‐specific trait values is likely to constrain morphological change favoured by selection acting on adults. We found variable patterns of allometric relationships during ontogeny, and documented relatively weak covariations among ages or among traits in individual growth trajectories. Frequent compensatory growth largely cancelled out the initial differences among nestlings, potentially enabling house finches to raise offspring under diverse and unpredictable environmental conditions. Moderate levels of additive genetic variance in morphological traits throughout ontogeny, and relatively low and fluctuating phenotypic and genetic covariation among ages imply strong potential for evolutionary change in morphological traits under selection. This conclusion is consistent with the profound population‐level divergence in morphological patterns that accompanied very successful colonization of most of North America by the house finch over the last 50 years.     

Evolutionary rates for multivariate traits: the role of selection and genetic variation

A fundamental question in evolutionary biology is the relative importance of selection and genetic architecture in determining evolutionary rates. Adaptive evolution can be described by the multivariate breeders'' equation (), which predicts evolutionary change for a suite of phenotypic traits () as a product of directional selection acting on them (β) and the genetic variance–covariance matrix for those traits (G). Despite being empirically challenging to estimate, there are enough published estimates of G and β to allow for synthesis of general patterns across species. We use published estimates to test the hypotheses that there are systematic differences in the rate of evolution among trait types, and that these differences are, in part, due to genetic architecture. We find some evidence that sexually selected traits exhibit faster rates of evolution compared with life-history or morphological traits. This difference does not appear to be related to stronger selection on sexually selected traits. Using numerous proposed approaches to quantifying the shape, size and structure of G, we examine how these parameters relate to one another, and how they vary among taxonomic and trait groupings. Despite considerable variation, they do not explain the observed differences in evolutionary rates.     

Constraints on the evolution of function‐valued traits: a study of growth in Tribolium castaneum

Growth trajectories often impact individual fitness. They are continuous by nature and so are amenable to analysis using a function‐valued (FV) trait framework to reveal their underlying genetic architecture. Previous studies have found high levels of standing additive genetic (co)variance for growth trajectories despite the expectation that growth should be responding to frequent strong directional selection. In this study, the FV framework is used to estimate the additive genetic covariance function for growth trajectories in larval Tribolium castaneum to address questions about standing additive genetic (co)variance and possible evolutionary constraints on growth and to predict responses to four plausible selection regimes. Results show that additive genetic (co)variance is high at the early ages, but decreases towards later ages in the larval period. A selection gradient function of the same size and in the same direction of the first eigenfunction of the G‐function should give the maximal response. However, evolutionary constraints may be acting to keep this maximal response from being realized, through either conflicting effects on survivability and fecundity of larger body size, few evolutionary directions having sufficient additive variance for a response, genetic trade‐offs with other traits or physiological regulatory mechanisms. More light may be shed on these constraints through the development of more sophisticated statistical approaches and implementation of additional empirical studies to explicitly test for specific types of constraints.     

ESTIMATING UNCERTAINTY IN MULTIVARIATE RESPONSES TO SELECTION

Predicting the responses to natural selection is one of the key goals of evolutionary biology. Two of the challenges in fulfilling this goal have been the realization that many estimates of natural selection might be highly biased by environmentally induced covariances between traits and fitness, and that many estimated responses to selection do not incorporate or report uncertainty in the estimates. Here we describe the application of a framework that blends the merits of the Robertson–Price Identity approach and the multivariate breeder's equation to address these challenges. The approach allows genetic covariance matrices, selection differentials, selection gradients, and responses to selection to be estimated without environmentally induced bias, direct and indirect selection and responses to selection to be distinguished, and if implemented in a Bayesian‐MCMC framework, statistically robust estimates of uncertainty on all of these parameters to be made. We illustrate our approach with a worked example of previously published data. More generally, we suggest that applying both the Robertson–Price Identity and the multivariate breeder's equation will facilitate hypothesis testing about natural selection, genetic constraints, and evolutionary responses.     

Genetic covariances promote climatic adaptation in Australian Drosophila*

Evolutionary potential for adaptation hinges upon the orientation of genetic variation for traits under selection, captured by the additive genetic variance-covariance matrix (G), as well as the evolutionary stability of G. Yet studies that assess both the stability of G and its alignment with selection are extraordinarily rare. We evaluated the stability of G in three Drosophila melanogaster populations that have adapted to local climatic conditions along a latitudinal cline. We estimated population- and sex-specific G matrices for wing size and three climatic stress-resistance traits that diverge adaptively along the cline. To determine how G affects evolutionary potential within these populations, we used simulations to quantify how well G aligns with the direction of trait divergence along the cline (as a proxy for the direction of local selection) and how genetic covariances between traits and sexes influence this alignment. We found that G was stable across the cline, showing no significant divergence overall, or in sex-specific subcomponents, among populations. G also aligned well with the direction of clinal divergence, with genetic covariances strongly elevating evolutionary potential for adaptation to climatic extremes. These results suggest that genetic covariances between both traits and sexes should significantly boost evolutionary responses to environmental change.     

Impacts of environmental heterogeneity on natural selection in a wild bird population*

Natural selection has been studied for several decades, resulting in the computation of thousands of selection estimates. Although the importance of environmental conditions on selection has often been suggested, published estimates rarely take into account the effects of environmental heterogeneity on selection patterns. Here, we estimated linear and nonlinear viability selection gradients on morphological traits of 12-day old nestlings in a wild population of tree swallows (Tachycineta bicolor) across a large-scale heterogeneous study system in southern Québec, Canada. We assessed the environmental drivers of nestling survival and evaluated their effects on strength and direction of selection gradients. Separate analyses of environmental variables showed that high temperatures and heavy rainfall caused stronger positive linear selection on morphological traits. Weaker linear selection was also measured in more extensively cultivated areas. Both strength and shape of nonlinear quadratic and correlational components of selection were modified by environmental variables. Considering all environmental variables revealed that precipitation since hatching affected patterns of linear selection on traits, while temperatures since hatching shaped nonlinear selection patterns. Our study underlines the importance of quantifying linear and nonlinear natural selection under various environmental conditions and how the evolutionary response of traits may be affected by ongoing human-induced environmental changes.     

Testing for biases in selection on avian reproductive traits and partitioning direct and indirect selection using quantitative genetic models

Key life history traits such as breeding time and clutch size are frequently both heritable and under directional selection, yet many studies fail to document microevolutionary responses. One general explanation is that selection estimates are biased by the omission of correlated traits that have causal effects on fitness, but few valid tests of this exist. Here, we show, using a quantitative genetic framework and six decades of life‐history data on two free‐living populations of great tits Parus major, that selection estimates for egg‐laying date and clutch size are relatively unbiased. Predicted responses to selection based on the Robertson–Price Identity were similar to those based on the multivariate breeder's equation (MVBE), indicating that unmeasured covarying traits were not missing from the analysis. Changing patterns of phenotypic selection on these traits (for laying date, linked to climate change) therefore reflect changing selection on breeding values, and genetic constraints appear not to limit their independent evolution. Quantitative genetic analysis of correlational data from pedigreed populations can be a valuable complement to experimental approaches to help identify whether apparent associations between traits and fitness are biased by missing traits, and to parse the roles of direct versus indirect selection across a range of environments.     

Evolutionary potential of a widespread clonal grass under changing climate

Adaptive responses are probably the most effective long‐term responses of populations to climate change, but they require sufficient evolutionary potential upon which selection can act. This requires high genetic variance for the traits under selection and low antagonizing genetic covariances between the different traits. Evolutionary potential estimates are still scarce for long‐lived, clonal plants, although these species are predicted to dominate the landscape with climate change. We studied the evolutionary potential of a perennial grass, Festuca rubra, in western Norway, in two controlled environments corresponding to extreme environments in natural populations: cold–dry and warm–wet, the latter being consistent with the climatic predictions for the country. We estimated genetic variances, covariances, selection gradients and response to selection for a wide range of growth, resource acquisition and physiological traits, and compared their estimates between the environments. We showed that the evolutionary potential of F. rubra is high in both environments, and genetic covariances define one main direction along which selection can act with relatively few constraints to selection. The observed response to selection at present is not sufficient to produce genotypes adapted to the predicted climate change under a simple, space for time substitution model. However, the current populations contain genotypes which are pre‐adapted to the new climate, especially for growth and resource acquisition traits. Overall, these results suggest that the present populations of the long‐lived clonal plant may have sufficient evolutionary potential to withstand long‐term climate changes through adaptive responses.