The potential for evolutionary rescue in an Arctic seashore plant threatened by climate change.

The impacts of climate change may be particularly severe for geographically isolated populations, which must adjust through plastic responses or evolve. Here, we study an endangered Arctic plant, Primula nutans ssp. finmarchica, confined to Fennoscandian seashores and showing indications of maladaptation to warming climate. We evaluate the potential of these populations to evolve to facilitate survival in the rapidly warming Arctic (i.e. evolutionary rescue) by utilizing manual crossing experiments in a nested half-sibling breeding design. We estimate G-matrices, evolvability and genetic constraints in traits with potentially conflicting selection pressures. To explicitly evaluate the potential for climate change adaptation, we infer the expected time to evolve from a northern to a southern phenotype under different selection scenarios, using demographic and climatic data to relate expected evolutionary rates to projected rates of climate change. Our results indicate that, given the nearly 10-fold greater evolvability of vegetative than of floral traits, adaptation in these traits may take place nearly in concert with changing climate, given effective climate mitigation. However, the comparatively slow expected evolutionary modification of floral traits may hamper the evolution of floral traits to track climate-induced changes in pollination environment, compromising sexual reproduction and thus reducing the likelihood of evolutionary rescue.

Disentangling variational bias: the roles of development, mutation, and selection.

The extraordinary diversity and adaptive fit of organisms to their environment depends fundamentally on the availability of variation. While most population genetic frameworks assume that random mutations produce isotropic phenotypic variation, the distribution of variation available to natural selection is more restricted, as the distribution of phenotypic variation is affected by a range of factors in developmental systems. Here, we revisit the concept of developmental bias - the observation that the generation of phenotypic variation is biased due to the structure, character, composition, or dynamics of the developmental system - and argue that a more rigorous investigation into the role of developmental bias in the genotype-to-phenotype map will produce fundamental insights into evolutionary processes, with potentially important consequences on the relation between micro- and macro-evolution. We discuss the hierarchical relationships between different types of variational biases, including mutation bias and developmental bias, and their roles in shaping the realized phenotypic space. Furthermore, we highlight the challenges in studying variational bias and propose potential approaches to identify developmental bias using modern tools.

Effect of the Seed Bank on Evolutionary Rescue in Small Populations: Univariate and Multivariate Demogenetic Dynamics.

AbstractUnder global change, the impact of seed banks on evolutionary rescue is uncertain. They buffer plant populations from demographic and genetic stochasticity but extend generation time and can become a reservoir of maladapted alleles. We built analytical and individual-based models to predict the effect of seed banks on the persistence of small annual plant populations facing an abrupt or sustained directional change in uni- or multivariate trait optima. Demogenetic dynamics predict that under most scenarios seed banks increase the lag yet enhance persistence to 200-250 years by absorbing demographic losses. Simulations indicate that the seed bank has a minimal impact on the genetic skew, although we suggest that this result could depend on the fitness component under selection. Our multivariate model reveals that by enlarging and reshaping the G matrix, seed banks can diminish the impact of mutational correlation and even accelerate adaptation under antagonistic pleiotropy relative to populations without a bank. We illustrate how the magnitude of optimum fluctuations, type and degree of optimum change, selection strength, and vital rates are weights that tip the scales determining persistence. Finally, our work highlights that migration from the past is not maladaptative when optimum fluctuations are large enough to create stepping stones to the new optimum.

Evolution of "invasion syndrome" in invasive goldenrod is not constrained by genetic trade-offs.

A suite of plant traits is thought to make weed populations highly invasive, including vigorous growth and reproduction, superior competitive ability, and high dispersal ability. Using a breeding design and a common garden experiment, we tested whether such an "invasion syndrome" has evolved in an invasive range of Solidago altissima, and whether the evolution is likely to be genetically constrained. We found an overall shift in invasive phenotypes between native North American and invasive Japanese populations. The invasive populations were taller and produced more leaves, suggesting a superior ability to exploit limited resources. The populations also produced more allelopathic compounds that can suppress competitor growth. Finally, invasive populations produced more seeds, which are smaller and are released from a greater height, indicating a potential for superior dispersal ability than the native populations. Quantitative genetics analyses found a large amount of additive genetic variation in most focal traits across native and invasive populations, with no systematic differences in its magnitude between the ranges. Genetic covariances among three traits representing invasion strategies (leaf mass, polyacetylene concentration and seed size) were small. The R metric, which measures the effect of genetic covariances on the rate of adaptation, indicated that the covariance neither constrains nor accelerates concerted evolution of these traits. The results suggest that the invasion syndrome in S. altissima has evolved in the novel range due to ample additive genetic variation, and relatively free from genetic trade-offs.

Environmental effects on genetic variance are likely to constrain adaptation in novel environments.

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.

Evolutionary potential under heat and drought stress at the southern range edge of North American Arabidopsis lyrata.

The warm edges of species' distributions are vulnerable to global warming. Evidence is the recent range retraction from there found in many species. It is unclear why populations cannot easily adapt to warmer, drier, or combined hot and dry conditions and locally persist. Here, we assessed the ability to adapt to these stressors in the temperate species Arabidopsis lyrata. We grew plants from replicate seed families of a central population with high genetic diversity under a temperature and precipitation regime typical of the low-latitude margin or under hotter and/or drier conditions within naturally occurring amplitudes. We then estimated genetic variance-covariance (G-) matrices of traits depicting growth and allocation as well as selection vectors to compare the predicted adaptation potential under the different climate-stress regimes. We found that the sum of genetic variances and genetic correlations were not significantly different under stress as compared to benign conditions. However, under drought and heat drought, the predicted ability to adapt was severely constrained due to strong selection and selection pointing in a direction with less multivariate genetic variation. The much-reduced ability to adapt to dry and hot-dry conditions is likely to reduce the persistence of populations at the low-latitude margin of the species' distribution and contribute to the local extinction of the species under further warming.

Divergence in evolutionary potential of life history traits among wild populations is predicted by differences in climatic conditions.

Short-term adaptive evolution represents one of the primary mechanisms allowing species to persist in the face of global change. Predicting the adaptive response at the species level requires reliable estimates of the evolutionary potential of traits involved in adaptive responses, as well as understanding how evolutionary potential varies across a species' range. Theory suggests that spatial variation in the fitness landscape due to environmental variation will directly impact the evolutionary potential of traits. However, empirical evidence on the link between environmental variation and evolutionary potential across a species range in the wild is lacking. In this study, we estimate multivariate evolutionary potential (via the genetic variance-covariance matrix, or G-matrix) for six morphological and life history traits in 10 wild populations of great tits (Parus major) distributed across Europe. The G-matrix significantly varies in size, shape, and orientation across populations for both types of traits. For life history traits, the differences in G-matrix are larger when populations are more distant in their climatic niche. This suggests that local climates contribute to shaping the evolutionary potential of phenotypic traits that are strongly related to fitness. However, we found no difference in the overall evolutionary potential (i.e., G-matrix size) between populations closer to the core or the edge of the distribution area. This large-scale comparison of G-matrices across wild populations emphasizes that integrating variation in multivariate evolutionary potential is important to understand and predict species' adaptive responses to new selective pressures.

Improving Genomic Predictions in Multi-Breed Cattle Populations: A Comparative Analysis of BayesR and GBLUP Models.

Numerous studies have shown that combining populations from similar or closely related genetic breeds improves the accuracy of genomic predictions (GP). Extensive experimentation with diverse Bayesian and genomic best linear unbiased prediction (GBLUP) models have been developed to explore multi-breed genomic selection (GS) in livestock, ultimately establishing them as successful approaches for predicting genomic estimated breeding value (GEBV). This study aimed to assess the effectiveness of using BayesR and GBLUP models with linkage disequilibrium (LD)-weighted genomic relationship matrices (GRMs) for genomic prediction in three different beef cattle breeds to identify the best approach for enhancing the accuracy of multi-breed genomic selection in beef cattle. Additionally, a comparison was conducted to evaluate the predictive precision of different marker densities and genetic correlations among the three breeds of beef cattle. The GRM between Yunling cattle (YL) and other breeds demonstrated modest affinity and highlighted a notable genetic concordance of 0.87 between Chinese Wagyu (WG) and Huaxi (HX) cattle. In the within-breed GS, BayesR demonstrated an advantage over GBLUP. The prediction accuracies for HX cattle using the BayesR model were 0.52 with BovineHD BeadChip data (HD) and 0.46 with whole-genome sequencing data (WGS). In comparison to the GBLUP model, the accuracy increased by 26.8% for HD data and 9.5% for WGS data. For WG and YL, BayesR doubled the within-breed prediction accuracy to 14.3% from 7.1%, outperforming GBLUP across both HD and WGS datasets. Moreover, analyzing multiple breeds using genomic selection showed that BayesR consistently outperformed GBLUP in terms of predictive accuracy, especially when using WGS. For instance, in a mixed reference population of HX and WG, BayesR achieved a significant accuracy of 0.53 using WGS for HX, which was a substantial enhancement over the accuracies obtained with GBLUP models. The research further highlights the benefit of including various breeds in the reference group, leading to enhanced accuracy in predictions and emphasizing the importance of comprehensive genomic selection methods. Our research findings indicate that BayesR exhibits superior performance compared to GBLUP in multi-breed genomic prediction accuracy, achieving a maximum improvement of 33.3%, especially in genetically diverse breeds. The improvement can be attributed to the effective utilization of higher single nucleotide polymorphism (SNP) marker density by BayesR, resulting in enhanced prediction accuracy. This evidence conclusively demonstrates the significant impact of BayesR on enhancing genomic predictions in diverse cattle populations, underscoring the crucial role of genetic relatedness in selection methodologies. In parallel, subsequent studies should focus on refining GRM and exploring alternative models for GP.

The evolution of genetic covariance and modularity as a result of multigenerational environmental fluctuation.

The genetic covariance between traits can affect the evolution of a population through selection, drift, and migration. Conversely, research has demonstrated the reciprocal effect of evolutionary processes on changing genetic covariances, in part through mutational covariance, correlational selection, and plasticity. In this article, we propose that correlated changes in selective optima over generations can cause the evolution of genetic covariance and the G-matrix in such a way that the population can, in the future, evolve faster. We use individual-based simulations of populations exposed to three types of changing environments that differ in the correlation of the change between selective pressures. Our simulation experiments demonstrate that selection pressures for different traits changing in a correlated pattern over generations can lead to stronger trait correlations compared to the case with independently changing selective optima. Our findings show that correlated selective pressures result in significantly higher genetic trait covariance and that pleiotropy accounts for the majority of the difference in covariance between treatments. We also observe that the mutational variance evolves according to the environment that the populations were exposed to. Moreover, we show that clustered patterns of changes in selection can allow the evolution of genetic modularity. We show that the pattern of change in the selective environment affects the pace at which fitness evolves, with populations experiencing correlated change in optima having on average higher mean fitness than those experiencing uncorrelated environment change.

Drift on holey landscapes as a dominant evolutionary process.

An organism's phenotype has been shaped by evolution but the specific processes have to be indirectly inferred for most species. For example, correlations among traits imply the historical action of correlated selection and, more generally, the expression and distribution of traits is expected to be reflective of the adaptive landscapes that have shaped a population. However, our expectations about how quantitative traits-like most behaviors, physiological processes, and life-history traits-should be distributed under different evolutionary processes are not clear. Here, we show that genetic variation in quantitative traits is not distributed as would be expected under dominant evolutionary models. Instead, we found that genetic variation in quantitative traits across six phyla and 60 species (including both Plantae and Animalia) is consistent with evolution across high-dimensional "holey landscapes." This suggests that the leading conceptualizations and modeling of the evolution of trait integration fail to capture how phenotypes are shaped and that traits are integrated in a manner contrary to predictions of dominant evolutionary theory. Our results demonstrate that our understanding of how evolution has shaped phenotypes remains incomplete and these results provide a starting point for reassessing the relevance of existing evolutionary models.

Does the definition of a novel environment affect the ability to detect cryptic genetic variation?

Anthropogenic change exposes populations to environments that have been rare or entirely absent from their evolutionary past. Such novel environments are hypothesized to release cryptic genetic variation, a hidden store of variance that can fuel evolution. However, support for this hypothesis is mixed. One possible reason is a lack of clarity in what is meant by 'novel environment', an umbrella term encompassing conditions with potentially contrasting effects on the exposure or concealment of cryptic variation. Here, we use a meta-analysis approach to investigate changes in the total genetic variance of multivariate traits in ancestral versus novel environments. To determine whether the definition of a novel environment could explain the mixed support for a release of cryptic genetic variation, we compared absolute novel environments, those not represented in a population's evolutionary past, to extreme novel environments, those involving frequency or magnitude changes to environments present in a population's ancestry. Despite sufficient statistical power, we detected no broad-scale pattern of increased genetic variance in novel environments, and finding the type of novel environment did not explain any significant variation in effect sizes. When effect sizes were partitioned by experimental design, we found increased genetic variation in studies based on broad-sense measures of variance, and decreased variation in narrow-sense studies, in support of previous research. Therefore, the source of genetic variance, not the definition of a novel environment, was key to understanding environment-dependant genetic variation, highlighting non-additive genetic variance as an important component of cryptic genetic variation and avenue for future research.

Predicting the future.

Experiments on worms suggest that a statistical measure called the G matrix can accurately predict how phenotypes will adapt to a novel environment over multiple generations.

Selection and the direction of phenotypic evolution.

Predicting adaptive phenotypic evolution depends on invariable selection gradients and on the stability of the genetic covariances between the component traits of the multivariate phenotype. We describe the evolution of six traits of locomotion behavior and body size in the nematode Caenorhabditis elegans for 50 generations of adaptation to a novel environment. We show that the direction of adaptive multivariate phenotypic evolution can be predicted from the ancestral selection differentials, particularly when the traits were measured in the new environment. Interestingly, the evolution of individual traits does not always occur in the direction of selection, nor are trait responses to selection always homogeneous among replicate populations. These observations are explained because the phenotypic dimension with most of the ancestral standing genetic variation only partially aligns with the phenotypic dimension under directional selection. These findings validate selection theory and suggest that the direction of multivariate adaptive phenotypic evolution is predictable for tens of generations.

Morphology, evolution, and the whole organism imperative: Why evolutionary questions need multi-trait evolutionary quantitative genetics.

Since Washburn's New Physical Anthropology, researchers have sought to understand the complexities of morphological evolution among anatomical regions in human and non-human primates. Researchers continue, however, to preferentially use comparative and functional approaches to examine complex traits, but these methods cannot address questions about evolutionary process and often conflate function with fitness. Moreover, researchers also tend to examine anatomical elements in isolation, which implicitly assumes independent evolution among different body regions. In this paper, we argue that questions asked in primate evolution are best examined using multiple anatomical regions subjected to model-bound methods built from an understanding of evolutionary quantitative genetics. A nascent but expanding number of studies over the last two decades use this approach, examining morphological integration, evolvability, and selection modeling. To help readers learn how to use these methods, we review fundamentals of evolutionary processes within a quantitative genetic framework, explore the importance of neutral evolutionary theory, and explain the basics of evolutionary quantitative genetics, namely the calculation of evolutionary potential for multiple traits in response to selection. Leveraging these methods, we demonstrate their use to understand non-independence in possible evolutionary responses across the limbs, limb girdles, and basicranium of humans. Our results show that model-bound quantitative genetic methods can reveal unexpected genetic covariances among traits that create a novel but measurable understanding of evolutionary complexity among multiple traits. We advocate for evolutionary quantitative genetic methods to be a standard whenever appropriate to keep studies of primate morphological evolution relevant for the next seventy years and beyond.

Complexities of recapitulating polygenic effects in natural populations: replication of genetic effects on wing shape in artificially selected and wild-caught populations of Drosophila melanogaster.

Identifying the genetic architecture of complex traits is important to many geneticists, including those interested in human disease, plant and animal breeding, and evolutionary genetics. Advances in sequencing technology and statistical methods for genome-wide association studies have allowed for the identification of more variants with smaller effect sizes, however, many of these identified polymorphisms fail to be replicated in subsequent studies. In addition to sampling variation, this failure to replicate reflects the complexities introduced by factors including environmental variation, genetic background, and differences in allele frequencies among populations. Using Drosophila melanogaster wing shape, we ask if we can replicate allelic effects of polymorphisms first identified in a genome-wide association studies in three genes: dachsous, extra-macrochaete, and neuralized, using artificial selection in the lab, and bulk segregant mapping in natural populations. We demonstrate that multivariate wing shape changes associated with these genes are aligned with major axes of phenotypic and genetic variation in natural populations. Following seven generations of artificial selection along the dachsous shape change vector, we observe genetic differentiation of variants in dachsous and genomic regions containing other genes in the hippo signaling pathway. This suggests a shared direction of effects within a developmental network. We also performed artificial selection with the extra-macrochaete shape change vector, which is not a part of the hippo signaling network, but showed a largely shared direction of effects. The response to selection along the emc vector was similar to that of dachsous, suggesting that the available genetic diversity of a population, summarized by the genetic (co)variance matrix (G), influenced alleles captured by selection. Despite the success with artificial selection, bulk segregant analysis using natural populations did not detect these same variants, likely due to the contribution of environmental variation and low minor allele frequencies, coupled with small effect sizes of the contributing variants.

Experimental Evidence That Phenotypic Evolution but Not Plasticity Occurs along Genetic Lines of Least Resistance in Homogeneous Environments.

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.

Eco-Evolutionary Consequences of Dispersal Syndromes during Colonization in a Passerine Bird.

AbstractIn most animal species, dispersing individuals possess phenotypic attributes that mitigate the costs of colonization and/or increase settlement success in new areas (dispersal syndromes). This phenotypic integration likely affects population dynamics and the direction of selection, but data are lacking for natural populations. Using an approach that combines population dynamics, quantitative genetics, and phenotypic selection analyses, we reveal the existence of dispersal syndromes in a pied flycatcher (Ficedula hypoleuca) population in the Netherlands: immigrants were larger, tended to have darker plumage, bred earlier, and produced larger clutches than local recruits, and some of these traits were genetically correlated. Over time, the phenotypic profile of the population gradually changed: each generation advanced arrival and breeding and exhibited longer wings as a result of direct and indirect selection on these correlated traits. Although phenotypic attributes of immigrants were favored by selection during the early phase of colonization, observed phenotypic changes were similar for immigrants and local recruits. We propose that immigrants facilitated initial population establishment but that temporal changes likely resulted from climate change-induced large-scale selection. This study highlights that newly established populations are of nonrandom composition and that phenotypic architecture affects evolutionary population trajectories.

Selection on an extreme-yet-conserved larval life-history strategy in a tapeworm.

Evolutionary stasis characterizes many phenotypes, even ones that seem suboptimal. Among tapeworms, Schistocephalus solidus and its relatives have some of the shortest developmental times in their first intermediate hosts, yet their development still seems excessively long considering they can grow faster, larger, and safer in the next hosts in their complex life cycles. I conducted 4 generations of selection on the developmental rate of S. solidus in its copepod first host, pushing a conserved-but-counterintuitive phenotype toward the limit of known tapeworm life-history strategies. Faster parasite development evolved and enabled earlier infectivity to the stickleback next host, but low heritability for infectivity moderated fitness gains. Fitness losses were more pronounced for slow-developing parasite families, irrespective of selection line, because directional selection released linked genetic variation for reduced infectivity to copepods, developmental stability, and fecundity. This deleterious variation is normally suppressed, implying development is canalized and thus under stabilizing selection. Nevertheless, faster development was not costly; fast-developing genotypes did not decrease copepod survival, even under host starvation, nor did they underperform in the next hosts, suggesting parasite stages in successive hosts are genetically decoupled. I speculate that, on longer time scales, the ultimate cost of abbreviated development is reduced size-dependent infectivity.

How development affects evolution.

Natural selection acts on developmentally constructed phenotypes, but how does development affect evolution? This question prompts a simultaneous consideration of development and evolution. However, there has been a lack of general mathematical frameworks mechanistically integrating the two, which may have inhibited progress on the question. Here, we use a new mathematical framework that mechanistically integrates development into evolution to analyse how development affects evolution. We show that, while selection pushes genotypic and phenotypic evolution up the fitness landscape, development determines the admissible evolutionary pathway, such that evolutionary outcomes occur at path peaks rather than landscape peaks. Changes in development can generate path peaks, triggering genotypic or phenotypic diversification, even on constant, single-peak landscapes. Phenotypic plasticity, niche construction, extra-genetic inheritance, and developmental bias alter the evolutionary path and hence the outcome. Thus, extra-genetic inheritance can have permanent evolutionary effects by changing the developmental constraints, even if extra-genetically acquired elements are not transmitted to future generations. Selective development, whereby phenotype construction points in the adaptive direction, may induce adaptive or maladaptive evolution depending on the developmental constraints. Moreover, developmental propagation of phenotypic effects over age enables the evolution of negative senescence. Overall, we find that development plays a major evolutionary role.

G-matrix stability in clinally diverging populations of an annual weed.

How phenotypic and genetic divergence among populations is influenced by the genetic architecture of those traits, and how microevolutionary changes in turn affect the within-population patterns of genetic variation, are of major interest to evolutionary biology. Work on Ipomoea hederacea, an annual vine, has found genetic clines in the means of a suite of ecologically important traits, including flowering time, growth rate, seed mass, and corolla width. Here we investigate the genetic (co)variances of these clinally varying traits in two northern range-edge and two central populations of I. hederacea to evaluate the influence of the genetic architecture on divergence across the range. We find (1) limited evidence for clear differentiation between Northern and Southern populations in the structure of G, suggesting overall stability of G across the range despite mean trait divergence and (2) that the axes of greatest variation (gmax) were unaligned with the axis of greatest multivariate divergence. Together these results indicate the role of the quantitative genetic architecture in constraining evolutionary response and divergence among populations across the geographic range.