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Adaptive plasticity allows populations to cope with environmental variation but is expected to fail as conditions become unfamiliar. In novel conditions, populations may instead rely on rapid adaptation to increase fitness and avoid extinction. Adaptation should be fastest when both plasticity and selection occur in directions of the multivariate phenotype that contain abundant genetic variation. However, tests of this prediction from field experiments are rare. Here, we quantify how additive genetic variance in a multivariate phenotype changes across an elevational gradient, and test whether plasticity and selection align with genetic variation. We do so using two closely related, but ecologically distinct, sister species of Sicilian daisy (Senecio, Asteraceae) adapted to high and low elevations on Mt. Etna. Using a quantitative genetic breeding design, we generated and then reciprocally planted c. 19,000 seeds of both species, across an elevational gradient spanning each species' native elevation, and then quantified mortality and five leaf traits of emergent seedlings. We found that genetic variance in leaf traits changed more across elevations than between species. The high-elevation species at novel lower elevations showed changes in the distribution of genetic variance among the leaf traits, which reduced the amount of genetic variance in the directions of selection and the native phenotype. By contrast, the low-elevation species mainly showed changes in the amount of genetic variance at the novel high elevation, and genetic variance was concentrated in the direction of the native phenotype. For both species, leaf trait plasticity across elevations was in a direction of the multivariate phenotype that contained a moderate amount of genetic variance. Together, these data suggest that where plasticity is adaptive, selection on genetic variance for an initially plastic response could promote adaptation. However, large environmental effects on genetic variance are likely to reduce adaptive potential in novel environments.

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Species distribution models (SDMs) are often built upon the "niche conservatism" assumption, such that they ignore the possibility of "evolutionary rescue" and may underestimate species' future range limits under climate change. We select aphids and ladybirds as model species and develop an eco-evolutionary model to explore evolutionary rescue in a predator-prey system under climate change. We model the adaptive change of species' thermal performances, accounting for biotic interactions. Our study suggests that, without considering evolutionary adaptation, the warming climate will result in a reduction in aphid populations and the extinction of ladybirds in large parts of the United States. However, when incorporating evolutionary adaptation into the model, aphids can adapt to climate change, whereas ladybirds demonstrate geographic variation in their evolutionary rescue potential. Specifically, ladybirds in southern regions are more likely to be rescued than those in the north. In certain northern regions, ladybirds do not avoid extinction due to severe warming trends and seasonality of the climate. While higher warming trends do prompt stronger evolutionary changes in phenotype, they also lead to reduced aphid population abundance such that ecology constrains ladybird population growth. Higher seasonality induces an ecological effect by limiting the length of reproductive season, thereby reducing the capacity for evolutionary rescue. Together, these findings reveal the complex interplay between ecological and evolutionary dynamics in the context of evolutionary adaptation to climate change.

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The direction of research in population genetics theory is currently, and correctly, retrospective, that is directed toward the past. What events in the past have led to the presently observed genetic constitution of a population? This direction is inspired, first, by the large volumes of genomic data now available and, second, by the success of the classical prospective theory in validating the Darwinian theory in terms of Mendelian genetics. However, the prospective theory should not be forgotten, and in that theory, perhaps the most interesting and certainly the most controversial, is Fisher's so-called "Fundamental Theorem of Natural Selection." This article describes the history and the current status of that theorem.

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Dispersal affects evolutionary processes by changing population size and genetic composition, influencing the viability and persistence of populations. Investigating which mechanisms underlie variation in dispersal phenotypes and whether populations harbour adaptive potential for dispersal is crucial to understanding the eco-evolutionary dynamics of this important trait. Here, we investigate the genetic architecture of dispersal among successfully recruited individuals in an insular metapopulation of house sparrows. We use an extensive long-term individual-based ecological data set and high-density single-nucleotide polymorphism (SNP) genotypes for over 2500 individuals. We conducted a genome-wide association study (GWAS), and found a relationship between dispersal probability and a SNP located near genes known to regulate circadian rhythm, glycogenesis and exercise performance, among other functions. However, this SNP only explained 3.8% of variance, suggesting that dispersal is a polygenic trait. We then used an animal model to estimate heritable genetic variation (σA 2 ), which composes 10% of the total variation in dispersal probability. Finally, we investigated differences in σA 2 across populations occupying ecologically relevant habitat types (farm vs. non-farm) using a genetic groups animal model. We found different adaptive potentials across habitats, with higher mean breeding value, σA 2 , and heritability for the habitat presenting lower dispersal rates, suggesting also different roles of environmental variation. Our results suggest a complex genetic architecture of dispersal and demonstrate that adaptive potential may be environment dependent in key eco-evolutionary traits. The eco-evolutionary implications of such environment dependence and consequent spatial variation are likely to become ever more important with the increased fragmentation and loss of suitable habitats for many natural populations.

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AbstractFisher's fundamental theorem of natural selection (FTNS) can be used in a quantitative genetics framework to predict the rate of adaptation in populations. Here, we estimated the capacity for a wild population of the annual legume Chamaecrista fasciculata to adapt to future environments and compared predicted and realized rates of adaptation. We planted pedigreed seeds from one population into three prairie reconstructions along an east-to-west decreasing precipitation gradient. The FTNS predicted adaptation at all sites, but we found a response to selection that was smaller at the home and westernmost sites and maladaptive at the middle site because of changes in the selective environment between generations. However, mean fitness of the progeny generation at the home and westernmost sites exceeded population replacement, which suggests that the environment was sufficiently favorable to promote population persistence. More studies employing the FTNS are needed to clarify the degree to which predictions of the rate of adaptation are realized and its utility in the conservation of populations at risk of extinction from climate change.

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OBJECTIVE: This study compared five distinct sets of biological pathways and associated genes related to semen volume (VOL), number of sperm (NS), and sperm motility (MOT) in the Thai multibreed dairy population. METHODS: The phenotypic data included 13,533 VOL records, 12,773 NS records, and 12,660 MOT records from 131 bulls. The genotypic data consisted of 76,519 imputed and actual single nucleotide polymorphisms (SNPs) from 72 animals. The SNP additive genetic variances for VOL, NS, and MOT were estimated for SNP windows of one SNP (SW1), ten SNP (SW10), 30 SNP (SW30), 50 SNP (SW50), and 100 SNP (SW100) using a single-step genomic best linear unbiased prediction approach. The fixed effects in the model were contemporary group, ejaculate order, bull age, ambient temperature, and heterosis. The random effects accounted for animal additive genetic effects, permanent environment effects, and residual. The SNPs explaining at least 0.001% of the additive genetic variance in SW1, 0.01% in SW10, 0.03% in SW30, 0.05% in SW50, and 0.1% in SW100 were selected for gene identification through the NCBI database. The pathway analysis utilized genes associated with the identified SNP windows. RESULTS: Comparison of overlapping and non-overlapping SNP windows revealed notable differences among the identified pathways and genes associated with the studied traits. Overlapping windows consistently yielded a larger number of shared biological pathways and genes than non-overlapping windows. In particular, overlapping SW30 and SW50 identified the largest number of shared pathways and genes in the Thai multibreed dairy population. CONCLUSION: This study yielded valuable insights into the genetic architecture of VOL, NS, and MOT. It also highlighted the importance of assessing overlapping and non-overlapping SNP windows of various sizes for their effectiveness to identify shared pathways and genes influencing multiple traits.

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Monitoring the genetic variance of traits is a key priority to ensure the sustainability of breeding programmes in populations under directional selection, since directional selection can decrease genetic variation over time. Studies monitoring changes in genetic variation have typically used long-term data from small experimental populations selected for a handful of traits. Here, we used a large dataset from a commercial breeding line spread over a period of twenty-three years. A total of 2,059,869 records and 2,062,112 animals in the pedigree were used for the estimations of variance components for the traits: body weight (BWT; 2,059,869 records) and hen-housed egg production (HHP; 45,939 records). Data were analysed with three estimation approaches: sliding overlapping windows, under frequentist (restricted maximum likelihood (REML)) and Bayesian (Gibbs sampling) methods; expected variances using coefficients of the full relationship matrix; and a "double trait covariances" analysis by computing correlations and covariances between the same trait in two distinct consecutive windows. The genetic variance showed marginal fluctuations in its estimation over time. Whereas genetic, maternal permanent environmental, and residual variances were similar for BWT in both the REML and Gibbs methods, variance components when using the Gibbs method for HHP were smaller than the variances estimated when using REML. Large data amounts were needed to estimate variance components and detect their changes. For Gibbs (REML), the changes in genetic variance from 1999-2001 to 2020-2022 were 82.29 to 93.75 (82.84 to 93.68) for BWT and 76.68 to 95.67 (98.42 to 109.04) for HHP. Heritability presented a similar pattern as the genetic variance estimation, changing from 0.32 to 0.36 (0.32 to 0.36) for BWT and 0.16 to 0.15 (0.21 to 0.18) for HHP. On the whole, genetic parameters tended slightly to increase over time. The expected variance estimates were lower than the estimates when using overlapping windows. That indicates the low effect of the drift-selection process on the genetic variance, or likely, the presence of genetic variation sources compensating for the loss. Double trait covariance analysis confirmed the maintenance of variances over time, presenting genetic correlations >0.86 for BWT and >0.82 for HHP. Monitoring genetic variance in broiler breeding programmes is important to sustain genetic progress. Although the genetic variances of both traits fluctuated over time, in some windows, particularly between 2003 and 2020, increasing trends were observed, which warrants further research on the impact of other factors, such as novel mutations, operating on the dynamics of genetic variance.

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Additive genetic variance, VA, is the key parameter for predicting adaptive and neutral phenotypic evolution. Changes in demography (e.g. increased close-relative inbreeding) can alter VA, but how they do so depends on the (typically unknown) gene action and allele frequencies across many loci. For example, VA increases proportionally with the inbreeding coefficient when allelic effects are additive, but smaller (or larger) increases can occur when allele frequencies are unequal at causal loci with dominance effects. Here, we describe an experimental approach to assess the potential for dominance effects to deflate VA under inbreeding. Applying a powerful paired pedigree design in Drosophila serrata, we measured 11 wing traits on half-sibling families bred via either random or sibling mating, differing only in homozygosity (not allele frequency). Despite close inbreeding and substantial power to detect small VA, we detected no deviation from the expected additive effect of inbreeding on genetic (co)variances. Our results suggest the average dominance coefficient is very small relative to the additive effect, or that allele frequencies are relatively equal at loci affecting wing traits. We outline the further opportunities for this paired pedigree approach to reveal the characteristics of VA, providing insight into historical selection and future evolutionary potential.

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AbstractGenetic correlations concentrate genetic variation in certain directions of the multivariate phenotype. Adaptation and, under some models, plasticity is expected to occur in the direction of the phenotype containing the greatest amount of genetic variation (gmax). However, this may hinge on environmental heterogeneity, which can affect patterns of genetic variation. I use experimental evolution to test whether plasticity and phenotypic evolution follow gmax during adaptation to environments that varied in environmental heterogeneity. For >25 generations, Drosophila melanogaster populations were exposed to six homogeneous or spatially and temporally heterogeneous treatments involving hot (25°C) and cold (16°C) temperatures. Five wing traits were assayed in both temperatures. Wing morphology diverged between populations evolving in homogeneous hot and cold temperatures in a direction of the phenotype containing a large proportion of genetic variance and that aligned closely with gmax at 16°C but not at 25°C. Spatial heterogeneity produced an intermediate phenotype, which was associated with similar genetic variance across assay temperatures compared with all other treatments. Surprisingly, plasticity across assay temperatures was in a different direction to phenotypic evolution and aligned better with maternal variance than gmax. Together, these results provide experimental evidence for evolution along genetic lines of least resistance in homogeneous environments but no support for predicting plastic responses from the orientation of genetic variation. These results also suggest that spatial heterogeneity could maintain genetic variation that increases the stability of genetic variance across environments.

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Understanding heritability patterns in functionally relevant traits is a cornerstone for evaluating their evolutionary potential and their role in local adaptation. In this study, we investigated patterns of heritability in the head shape of the Hungarian meadow viper (Vipera ursinii rakosiensis). To this end, we used geometric morphometric data from 12 families composed of 8 mothers, 6 fathers and 221 offspring, bred in captivity at the Hungarian Meadow Viper Conservation Centre (Hungary). We separately evaluated maternal and paternal contributions to the offspring phenotype, in addition to additive genetic effects, all determined using a mixed animal model. Our results indicate a strong genetic and maternal contribution to head shape variations. In contrast, the paternal effects-which are rarely evaluated in wild-ranging species-as well as residual environmental variance, were minimal. Overall, our results indicate a high evolutionary potential for head shape in the Hungarian meadow viper, which suggests a strong contribution of this ecologically important trait in shaping the ability of this endangered species to adapt to changing conditions and/or habitats. Furthermore, our results suggest that maternal phenotypes should be carefully considered when designing captive breeding parental pairs for reinforcing the adaptive capacity of threatened populations, whereas the paternal phenotypes seem less relevant.

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Rapid environmental change is forcing populations into environments where plasticity will no longer maintain fitness. When populations are exposed to novel environments, evolutionary theory predicts that genetic variation in fitness will increase and should be associated with genetic differences in plasticity. If true, then genetic variation in plasticity can increase adaptive potential in novel environments, and population persistence via evolutionary rescue is more likely. To test whether genetic variation in fitness increases in novel environments and is associated with plasticity, we transplanted 8149 clones of 314 genotypes of a Sicilian daisy (Senecio chrysanthemifolius) within and outside its native range, and quantified genetic variation in fitness, and plasticity in leaf traits and gene expression. Although mean fitness declined by 87% in the novel environment, genetic variance in fitness increased threefold and was correlated with plasticity in leaf traits. High fitness genotypes showed greater plasticity in gene expression, but lower plasticity in most leaf traits. Interestingly, genotypes with the highest fitness in the novel environment had the lowest fitness at the native site. These results suggest that standing genetic variation in plasticity could help populations to persist and adapt to novel environments, despite remaining hidden in native environments.

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PREMISE: The evolutionary response of a trait to environmental change depends upon the level of additive genetic variance. It has been long argued that sustained selection will tend to deplete additive genetic variance as favored alleles approach fixation. Non-additive genetic variance, due to interactions among alleles within and between loci, does not immediately contribute to an evolutionary response. However, shifts in the allele frequencies within and between interacting loci may convert non-additive variance into additive variance. Here we consider the possibility that an environmental shift may alter allelic interactions in ways that convert dominance into additive genetic variance. METHODS: We grew a pedigreed population of Brassica rapa in greenhouse and field conditions. The field conditions mimicked agricultural conditions from which the base population was drawn, while the greenhouse featured benign conditions. We used Bayesian models to estimate the additive, dominance, and maternal components of quantitative genetic variance. We also estimated genetic correlations across environments using parental breeding values. RESULTS: Although the additive genetic variance was elevated in the greenhouse condition, no consistent pattens emerged that would indicate a conversion of dominance variance. The unusually low genetic variance and broad confidence intervals for the variance estimates obtained through this analysis preclude definitive interpretations. CONCLUSIONS: Further studies are needed to determine whether between-environment changes in additive genetic variance can be traced to conversion of dominance variance.

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Phenotypic plasticity and adaptive evolution enable population persistence in response to global change. However, there are few experiments that test how these processes interact within and across generations, especially in marine species with broad distributions experiencing spatially and temporally variable temperature and pCO2. We employed a quantitative genetics experiment with the purple sea urchin, Strongylocentrotus purpuratus, to decompose family-level variation in transgenerational and developmental plastic responses to ecologically relevant temperature and pCO2. Adults were conditioned to controlled non-upwelling (high temperature, low pCO2) or upwelling (low temperature, high pCO2) conditions. Embryos were reared in either the same conditions as their parents or the crossed environment, and morphological aspects of larval body size were quantified. We find evidence of family-level phenotypic plasticity in response to different developmental environments. Among developmental environments, there was substantial additive genetic variance for one body size metric when larvae developed under upwelling conditions, although this differed based on parental environment. Furthermore, cross-environment correlations indicate significant variance for genotype-by-environment interactive effects. Therefore, genetic variation for plasticity is evident in early stages of S. purpuratus, emphasizing the importance of adaptive evolution and phenotypic plasticity in organismal responses to global change.

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Successfully predicting adaptive phenotypic responses to environmental changes, and predicting resulting population outcomes, requires that additive genetic (co)variances underlying microevolutionary and plastic responses of key traits are adequately estimated on appropriate quantitative scales. Such estimation in turn requires that focal traits, and their underlying quantitative genetic architectures, are appropriately conceptualized. Here, we highlight that directly analyzing observed phenotypes as continuously distributed quantitative traits can potentially generate biased and misleading estimates of additive genetic variances and individual-by-environment and gene-by-environment interactions, and hence of forms of plasticity and genetic constraints, if in fact the underlying biology is best conceptualized as an environmentally sensitive threshold trait. We illustrate this scenario with particular reference to the key phenological trait of seasonal breeding date, which has become a focus for quantifying joint microevolutionary, plastic, and population responses to environmental change, but has also become a focus for highlighting that predicted adaptive outcomes are not always observed. Specifically, we use simple simulations to illustrate how potentially misleading inferences on magnitudes of additive genetic variance, and forms of environmental interactions, can arise by directly analyzing observed breeding dates if the transition to breeding in fact represents a threshold trait with latent-scale plasticity. We summarize how existing and new datasets could be (re)analyzed, potentially providing new insights into how critical microevolutionary and plastic phenological responses to environmental variation and change can arise and be constrained.

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Additive genetic variance in fitness is a prerequisite for adaptive evolution, as a trait must be genetically correlated with fitness to evolve. Despite its relevance, additive genetic variance in fitness has not often been estimated in nature. Here, we investigate additive genetic variance in lifetime and annual fitness components in common terns (Sterna hirundo). Using 28 years of data comprising approximately 6000 pedigreed individuals, we find that additive genetic variances in the zero-inflated and Poisson components of lifetime fitness were effectively zero but estimated with high uncertainty. Similarly, additive genetic variances in adult annual reproductive success and survival did not differ from zero but were again associated with high uncertainty. Simulations suggested that we would be able to detect additive genetic variances as low as 0.05 for the zero-inflated component of fitness but not for the Poisson component, for which adequate statistical power would require approximately two more decades (four tern generations) of data collection. As such, our study suggests heritable variance in common tern fitness to be rather low if not zero, shows how studying the quantitative genetics of fitness in natural populations remains challenging, and highlights the importance of maintaining long-term individual-based studies of natural populations.

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Human impacts on the natural world often generate environmental trends that can have detrimental effects on distributions of phenotypic traits. We do not have a good understanding of how deteriorating environments might impact evolutionary trajectories across multiple generations, even though effects of environmental trends are often significant in the statistical quantitative genetic analyses of phenotypic trait data that are used to estimate additive genetic (co)variances. These environmental trends capture reaction norms, where the same (average) genotype expresses different phenotypic trait values in different environments. Not incorporated into the predictive models typically parameterised from statistical analyses to predict evolution, such as the breeder's equation. We describe how these environmental effects can be incorporated into multi-generational, evolutionarily explicit, structured population models before exploring how these effects can influence evolutionary dynamics. The paper is primarily a description of the modelling approach, but we also show how incorporation into models of the types of environmental trends that human activity has generated can have considerable impacts on the evolutionary dynamics that are predicted.

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Genetic variance is not equal for all multivariate combinations of traits. This inequality, in which some combinations of traits have abundant genetic variation while others have very little, biases the rate and direction of multivariate phenotypic evolution. However, we still understand little about what causes genetic variance to differ among trait combinations. Here, we investigate the relative roles of mutation and selection in determining the genetic variance of multivariate phenotypes. We accumulated mutations in an outbred population of Drosophila serrata and analyzed wing shape and size traits for over 35,000 flies to simultaneously estimate the additive genetic and additive mutational (co)variances. This experimental design allowed us to gain insight into the phenotypic effects of mutation as they arise and come under selection in naturally outbred populations. Multivariate phenotypes associated with more (less) genetic variance were also associated with more (less) mutational variance, suggesting that differences in mutational input contribute to differences in genetic variance. However, mutational correlations between traits were stronger than genetic correlations, and most mutational variance was associated with only one multivariate trait combination, while genetic variance was relatively more equal across multivariate traits. Therefore, selection is implicated in breaking down trait covariance and resulting in a different pattern of genetic variance among multivariate combinations of traits than that predicted by mutation and drift. Overall, while low mutational input might slow evolution of some multivariate phenotypes, stabilizing selection appears to reduce the strength of evolutionary bias introduced by pleiotropic mutation.

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Invasive species are a global economic and ecological problem. They also offer an opportunity to understand evolutionary processes in a colonizing context. The impacts of evolutionary factors, such as genetic variation, on the invasion process are increasingly appreciated, but there remain gaps in the empirical literature. The adaptive potential of populations can be quantified using genetic variance-covariance matrices (G), which encapsulate the heritable genetic variance in a population. Here, we use a multivariate Bayesian approach to assess the adaptive potential of invasive populations of ragweed (Ambrosia artemisiifolia), a serious allergen and agricultural weed. We compared several aspects of genetic architecture and the structure of G matrices between three native and three introduced populations, based on phenotypic data collected in a field common garden experiment. We found moderate differences in the quantitative genetic architecture among populations, but we did not find that introduced populations suffer from a limited adaptive potential or increased genetic constraint compared with native populations. Ragweed has an annual life history, is an obligate outcrosser, and produces very large numbers of seeds and pollen grains. These characteristics, combined with the significant additive genetic variance documented here, suggest ragweed will be able to respond quickly to selection pressures in both its native and introduced ranges.

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Pesticides are often toxic to nontarget organisms, especially to those living in rivers that drain agricultural land. The brown trout (Salmo trutta) is a keystone species in many such rivers, and natural populations have hence been chronically exposed to pesticides over multiple generations. The introduction of pesticides decades ago could have induced evolutionary responses within these populations. Such a response would be predicted to reduce the toxicity over time but also deplete any additive genetic variance for the tolerance to the pesticides. If so, populations are now expected to differ in their susceptibility and in the variance for the tolerance depending on the pesticides they have been exposed to. We sampled breeders from seven natural populations that differ in their habitats and that show significant genetic differentiation. We stripped them for their gametes and produced 118 families by in vitro fertilization. We then raised 20 embryos per family singly in experimentally controlled conditions and exposed them to one of two ecologically relevant concentrations of either the herbicide S-metolachlor or the insecticide diazinon. Both pesticides affected embryo and larval development at all concentrations. We found no statistically significant additive genetic variance for tolerance to these stressors within or between populations. Tolerance to the pesticides could also not be linked to variation in carotenoid content of the eggs. However, pesticide tolerance was linked to egg size, with smaller eggs being more tolerant to the pesticides than larger eggs. We conclude that an evolutionary response to these pesticides is currently unlikely and that (a) continuous selection in the past has either depleted genetic variance in all the populations we studied or (b) that exposure to the pesticides never induced an evolutionary response. The observed toxicity selects against large eggs that are typically spawned by larger and older females.

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Cultivated bread wheat (Triticum aestivum L.) is an allohexaploid species resulting from the natural hybridization and chromosome doubling of allotetraploid durum wheat (T. turgidum) and a diploid goatgrass Aegilops tauschii Coss (Ae. tauschii). Synthetic hexaploid wheat (SHW) was developed through the interspecific hybridization of Ae. tauschii and T. turgidum, and then crossed to T. aestivum to produce synthetic hexaploid wheat derivatives (SHWDs). Owing to this founding variability, one may infer that the genetic variances of native wild populations vs improved wheat may vary due to their differential origin and evolutionary history. In this study, we partitioned the additive variance of SHW and SHWD with respect to their breed origin by fitting a hierarchical Bayesian model with heterogeneous covariance structure for breeding values to estimate variance components for each breed category, and segregation variance. Two data sets were used to test the proposed hierarchical Bayesian model, one from a multi-year multi-location field trial of SHWD and the other comprising the two species of SHW. For the SHWD, the Bayesian estimates of additive variances of grain yield from each breed category were similar for T. turgidum and Ae. tauschii, but smaller for T. aestivum. Segregation variances between Ae. tauschii-T. aestivum and T. turgidum-T. aestivum populations explained a sizable proportion of the phenotypic variance. Bayesian additive variance components and the Best Linear Unbiased Predictors (BLUPs) estimated by two well-known software programs were similar for multi-breed origin and for the sum of the breeding values by origin for both data sets. Our results support the suitability of models with heterogeneous additive genetic variances to predict breeding values in wheat crosses with variable ploidy levels.

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