Evolutionary and Ecological Processes Determining the Properties of the Matrix

AbstractThe G matrix is the matrix of additive genetic variances and covariances for a vector of phenotypes. Here we apply the classical theory for the balance among selection, genetic drift, and mutations to find the contributions to G from each locus for populations at stasis. The fitness is approximated by a linear function of phenotypes, with coefficients affected by environmental fluctuations. We show that the G matrix can be decomposed into four additive components generated by selection, drift, mutations, and environmental fluctuations. Selection is on average counteracted by the other three processes included in Fisher's concept of deterioration of the environment, generating considerable changes in mean phenotypes. The theory illustrates that neither Fisher's fundamental theorem nor Lande's classical gradient formula is sufficient for assessing adaptive changes through time unless the deteriorations are corrected for. This applies for populations at stasis, but also for populations that are subject to long-term evolutionary changes. The theory also indicates several possible comparative studies for investigations of deteriorating effects. Our analyses also suggest that the factor loadings to the eigenvector of the G matrix with the lowest eigenvalue will rather accurately indicate the relative contributions from different phenotype components to fitness. This is information notoriously difficult to obtain in natural populations.

Predictability, an Orrery, and a Speciation Machine: Quest for a Standard Model of Speciation

Accurate predictions are commonly taken as a hallmark of strong scientific understanding. Yet, we do not seem capable today of making many accurate predictions about biological speciation. Why? What limits predictability in general, what exactly is the function and value of predictions, and how might we go about predicting new species? Inspired by an orrery used to explain solar eclipses, we address these questions with a thought experiment in which we conceive an evolutionary speciation machine generating new species. This experiment highlights complexity, chance, and speciation pluralism as the three fundamental challenges for predicting speciation. It also illustrates the methodological value of predictions in testing and improving conceptual models. We then outline how we might move from the hypothetical speciation machine to a predictive standard model of speciation. Operationalizing, testing, and refining this model will require a concerted shift to large-scale, integrative, and interdisciplinary efforts across the tree of life. This endeavor, paired with technological advances, may reveal apparently stochastic processes to be deterministic, and promises to expand the breadth and depth of our understanding of speciation and more generally, of evolution.

The fitness consequences of wildlife conservation translocations: a meta-analysis

Conservation translocation is a common strategy to offset mounting rates of population declines through the transfer of captive- or wild-origin organisms into areas where conspecific populations are imperilled or completely extirpated. Translocations that supplement existing populations are referred to as reinforcements and can be conducted using captive-origin animals [ex situ reinforcement (ESR)] or wild-origin animals without any captive ancestry [in situ reinforcement (ISR)]. These programs have been criticized for low success rates and husbandry practices that produce individuals with genetic and performance deficits, but the post-release performance of captive-origin or wild-origin translocated groups has not been systematically reviewed to quantify success relative to wild-resident control groups. To assess the disparity in post-release performance of translocated organisms relative to wild-resident conspecifics and examine the association of performance disparity with organismal and methodological factors across studies, we conducted a systematic review and meta-analysis of 821 performance comparisons from 171 studies representing nine animal classes (101 species). We found that translocated organisms have 64% decreased odds of out-performing their wild-resident counterparts, supporting claims of systemic issues hampering conservation translocations. To help identify translocation practices that could maximize program success in the future, we further quantified the impact of broad organismal and methodological factors on the disparity between translocated and wild-resident conspecific performance. Pre-release animal enrichment significantly reduced performance disparities, whereas our results suggest no overall effects of taxonomic group, sex, captive generation time, or the type of fitness surrogate measured. This work is the most comprehensive systematic review to date of animal conservation translocations in which wild conspecifics were used as comparators, thereby facilitating an evaluation of the overall impact of this conservation strategy and identifying specific actions to increase success. Our review highlights the need for conservation managers to include both sympatric and allopatric wild-reference groups to ensure the post-release performance of translocated animals can be evaluated. Further, our analyses identify pre-release animal enrichment as a particular strategy for improving the outcomes of animal conservation translocations, and demonstrate how meta-analysis can be used to identify implementation choices that maximize translocated animal contributions to recipient population growth and viability.

Designing More Informative Multiple-Driver Experiments

For decades, multiple-driver/stressor research has examined interactions among drivers that will undergo large changes in the future: temperature, pH, nutrients, oxygen, pathogens, and more. However, the most commonly used experimental designs-present-versus-future and ANOVA-fail to contribute to general understanding or predictive power. Linking experimental design to process-based mathematical models would help us predict how ecosystems will behave in novel environmental conditions. We review a range of experimental designs and assess the best experimental path toward a predictive ecology. Full factorial response surface, fractional factorial, quadratic response surface, custom, space-filling, and especially optimal and sequential/adaptive designs can help us achieve more valuable scientific goals. Experiments using these designs are challenging to perform with long-lived organisms or at the community and ecosystem levels. But they remain our most promising path toward linking experiments and theory in multiple-driver research and making accurate, useful predictions.

Comparing the Predicted versus Realized Rate of Adaptation of Chamaecrista fasciculata to Climate Change

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.

Hidden genetic variation in plasticity provides the potential for rapid adaptation to novel environments

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.

Rapid, nonparallel genomic evolution of Brassica rapa (field mustard) under experimental drought

While we know that climate change can potentially cause rapid phenotypic evolution, our understanding of the genetic basis and degree of genetic parallelism of rapid evolutionary responses to climate change is limited. In this study, we combined the resurrection approach with an evolve-and-resequence design to examine genome-wide evolutionary changes following drought. We exposed genetically similar replicate populations of the annual plant Brassica rapa derived from a field population in southern California to four generations of experimental drought or watered conditions in a greenhouse. Genome-wide sequencing of ancestral and descendant population pools identified hundreds of SNPs that showed evidence of rapidly evolving in response to drought. Several of these were in stress response genes, and two were identified in a prior study of drought response in this species. However, almost all genetic changes were unique among experimental populations, indicating that the evolutionary changes were largely nonparallel, despite the fact that genetically similar replicates of the same founder population had experienced controlled and consistent selection regimes. This nonparallelism of evolution at the genetic level is potentially because of polygenetic adaptation allowing for multiple different genetic routes to similar phenotypic outcomes. Our findings help to elucidate the relationship between rapid phenotypic and genomic evolution and shed light on the degree of parallelism and predictability of genomic evolution to environmental change.

Using landscape genomics to assess local adaptation and genomic vulnerability of a perennial herb Tetrastigma hemsleyanum (Vitaceae) in subtropical China

Understanding adaptive genetic variation of plant populations and their vulnerabilities to climate change are critical to preserve biodiversity and subsequent management interventions. To this end, landscape genomics may represent a cost-efficient approach for investigating molecular signatures underlying local adaptation. Tetrastigma hemsleyanum is, in its native habitat, a widespread perennial herb of warm-temperate evergreen forest in subtropical China. Its ecological and medicinal values constitute a significant revenue for local human populations and ecosystem. Using 30,252 single nucleotide polymorphisms (SNPs) derived from reduced-representation genome sequencing in 156 samples from 24 sites, we conducted a landscape genomics study of the T. hemsleyanum to elucidate its genomic variation across multiple climate gradients and genomic vulnerability to future climate change. Multivariate methods identified that climatic variation explained more genomic variation than that of geographical distance, which implied that local adaptation to heterogeneous environment might represent an important source of genomic variation. Among these climate variables, winter precipitation was the strongest predictor of the contemporary genetic structure. F _ST outlier tests and environment association analysis totally identified 275 candidate adaptive SNPs along the genetic and environmental gradients. SNP annotations of these putatively adaptive loci uncovered gene functions associated with modulating flowering time and regulating plant response to abiotic stresses, which have implications for breeding and other special agricultural aims on the basis of these selection signatures. Critically, modelling revealed that the high genomic vulnerability of our focal species via a mismatch between current and future genotype-environment relationships located in central-northern region of the T. hemsleyanum's range, where populations require proactive management efforts such as assistant adaptation to cope with ongoing climate change. Taken together, our results provide robust evidence of local climate adaption for T. hemsleyanum and further deepen our understanding of adaptation basis of herbs in subtropical China.

An exploration into the conversion of dominance to additive genetic variance in contrasting environments

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.

What determines the evolutionary trajectories of wild plant species? Approaches to the study of quantitative fitness-related traits

Wild plant species provide excellent examples of qualitative traits that evolve in response to environmental challenges (e.g., flower color, heavy metal tolerance, cyanogenesis, and male sterility). In addition to such discrete characters, a dazzling array of continuously distributed, quantitative traits are expressed at every phase of the life cycle. These traits are known or suspected to have evolved by natural selection because they are heritable, differ among populations or closely related taxa occupying distinct habitats, and have individual phenotypes associated with survival and reproductive success. This special issue [American Journal of Botany 109(11)] focuses on the tools and approaches for detecting or inferring the ecological and genetic factors contributing to changes in genetically based variation of quantitative traits within or among populations, or causing their divergence among taxa. The assembled articles use one or more of three primary approaches to detect the process or outcome of natural selection on morphological, life history, reproductive, chemical, and physiological quantitative traits: the analysis of phenotypic or artificially imposed selection to detect direct and indirect selection on traits whose function is well-understood; common garden experiments, including reciprocal transplants and "resurrection" experiments; and quantitative genetic analyses designed to detect and to estimate the environmental and genetic sources of phenotypic variation or to forecast short-term evolutionary change. Together, these articles examine and reveal the adaptive capacity of quantitative traits and the genetically based constraints that may limit their directional evolutionary change, thereby informing and testing inferences, hypotheses, and predictions concerning the evolutionary trajectories of wild plant species.

A method to predict the response to directional selection using a Kalman filter

Predicting evolution remains challenging. The field of quantitative genetics provides predictions for the response to directional selection through the breeder's equation, but these predictions can have errors. The sources of these errors include omission of traits under selection, inaccurate estimates of genetic variance, and nonlinearities in the relationship between genetic and phenotypic variation. Previous research showed that the expected value of these prediction errors is often not zero, so predictions are systematically biased. Here, we propose that this bias, rather than being a nuisance, can be used to improve the predictions. We use this to develop a method to predict evolution, which is built on three key innovations. First, the method predicts change as the breeder's equation plus a bias term. Second, the method combines information from the breeder's equation and from the record of past changes in the mean to predict change using a Kalman filter. Third, the parameters of the filter are fitted in each generation using a learning algorithm on the record of past changes. We compare the method to the breeder's equation in two artificial selection experiments, one using the wing of the fruit fly and another using simulations that include a complex mapping of genotypes to phenotypes. The proposed method outperforms the breeder's equation, particularly when traits under selection are omitted from the analysis, when data are noisy, and when additive genetic variance is estimated inaccurately or not estimated at all. The proposed method is easy to apply, requiring only the trait means over past generations.

Loss-of-heterozygosity facilitates a fitness valley crossing in experimentally evolved multicellular yeast

Determining how adaptive possibilities do or do not become evolutionary realities is central to understanding the tempo and mode of evolutionary change. Some of the simplest evolutionary landscapes arise from underdominance at a single locus where the fitness valley consists of only one less-fit genotype. Despite their potential for rapid evolutionary change, few such examples have been investigated. We capitalized on an experimental system in which a significant evolutionary shift, the transition from uni-to-multicellularity, was observed in asexual diploid populations of Saccharomyces cerevisiae experimentally selected for increased settling rates. The multicellular phenotype results from recessive single-locus mutations that undergo loss-of-heterozygosity (LOH) events. By reconstructing the necessary heterozygous intermediate steps, we found that the evolution of multicellularity involves a decrease in size during the first steps. Heterozygous genotypes are 20% smaller in size than genotypes with functional alleles. Nevertheless, populations of heterozygotes give rise to multicellular genotypes more readily than unicellular genotypes with two functional alleles, by rapid LOH events. LOH drives adaptation that may enable rapid evolution in diploid yeast. Together these results show discordance between the phenotypic and genotypic multicellular transition. The evolutionary path to multicellularity, and the adaptive benefits of increased size, requires initial size reductions.

Climate Adaptation, Drought Susceptibility, and Genomic-Informed Predictions of Future Climate Refugia for the Australian Forest Tree Eucalyptus globulus

Understanding the capacity of forest tree species to adapt to climate change is of increasing importance for managing forest genetic resources. Through a genomics approach, we modelled spatial variation in climate adaptation within the Australian temperate forest tree Eucalyptus globulus, identified putative climate drivers of this genomic variation, and predicted locations of future climate refugia and populations at-risk of future maladaptation. Using 812,158 SNPs across 130 individuals from 30 populations (i.e., localities) spanning the species’ natural range, a gradientForest algorithm found 1177 SNPs associated with locality variation in home-site climate (climate-SNPs), putatively linking them to climate adaptation. Very few climate-SNPs were associated with population-level variation in drought susceptibility, signalling the multi-faceted nature and complexity of climate adaptation. Redundancy analysis (RDA) showed 24% of the climate-SNP variation could be explained by annual precipitation, isothermality, and maximum temperature of the warmest month. Spatial predictions of the RDA climate vectors associated with climate-SNPs allowed mapping of genomically informed climate selective surfaces across the species’ range under contemporary and projected future climates. These surfaces suggest over 50% of the current distribution of E. globulus will be outside the modelled adaptive range by 2070 and at risk of climate maladaptation. Such surfaces present a new integrated approach for natural resource managers to capture adaptive genetic variation and plan translocations in the face of climate change.

On the capacity for rapid adaptation and plastic responses to herbivory and intraspecific competition in insular populations of Plectritis congesta

A capacity for rapid adaptation should enhance the persistence of populations subject to temporal and spatial heterogeneity in natural selection, but examples from nature remain scarce. Plectritis congesta (Caprifoliaceae) is a winter annual that exhibits local adaptation to browsing by ungulates and hypothesized to show context‐dependent trade‐offs in traits affecting success in competition versus resistance or tolerance to browsing. We grew P. congesta from 44 insular populations historically exposed or naïve to ungulates in common gardens to (1) quantify genetic, plastic and competitive effects on phenotype; (2) estimate a capacity for rapid adaptation (evolvability); and (3) test whether traits favoured by selection with ungulates present were selected against in their absence. Plants from browsed populations bolted and flowered later, had smaller inflorescences, were less fecund and half as tall as plants from naïve populations on average, replicating patterns in nature. Estimated evolvabilities (3–36%) and narrow‐sense heritabilities (h2; 0.13–0.32) imply that differences in trait values as large as reported here can arise in 2–18 generations in an average population. Phenotypic plasticity was substantial, varied by browsing history and fruit phenotype and increased with competition. Fecundity increased with plasticity in flowering height given competition (0.47 ± 0.02 florets/cm, β ± se), but 23–77% faster in naïve plants bearing winged fruits (0.53 ± 0.04) than exposed‐wingless plants (0.43 ± 0.03) or exposed‐winged and naïve‐wingless plants (0.30 ± 0.03, each case). Our results support the hypothesis that context‐dependent variation in natural selection in P. congesta populations has conferred a substantial capacity for adaptation in response to selection in traits affecting success in competition versus resistance or tolerance to browsing in the absence versus presence of ungulates, respectively. Theory suggests that conserving adaptive capacity in P. congesta will require land managers to maintain spatial heterogeneity in natural selection, prevent local extinctions and maintain gene flow.

Evolution of Thermal Sensitivity in Changing and Variable Climates

Evolutionary adaptation to temperature and climate depends on both the extent to which organisms experience spatial and temporal environmental variation (exposure) and how responsive they are to the environmental variation (sensitivity). Theoretical models and experiments suggesting substantial potential for thermal adaptation have largely omitted realistic environmental variation. Environmental variation can drive fluctuations in selection that slow adaptive evolution. We review how carefully filtering environmental conditions based on how organisms experience their environment and further considering organismal sensitivity can improve predictions of thermal adaptation. We contrast taxa differing in exposure and sensitivity. Plasticity can increase the rate of evolutionary adaptation in taxa exposed to pronounced environmental variation. However, forms of plasticity that severely limit exposure, such as behavioral thermoregulation and phenological shifts, can hinder thermal adaptation. Despite examples of rapid thermal adaptation, experimental studies often reveal evolutionary constraints. Further investigating these constraints and issues of timescale and thermal history are needed to predict evolutionary adaptation and, consequently, population persistence in changing and variable environments.

The microbiome extends host evolutionary potential

The microbiome shapes many host traits, yet the biology of microbiomes challenges traditional evolutionary models. Here, we illustrate how integrating the microbiome into quantitative genetics can help untangle complexities of host-microbiome evolution. We describe two general ways in which the microbiome may affect host evolutionary potential: by shifting the mean host phenotype and by changing the variance in host phenotype in the population. We synthesize the literature across diverse taxa and discuss how these scenarios could shape the host response to selection. We conclude by outlining key avenues of research to improve our understanding of the complex interplay between hosts and microbiomes.

Quantitative assessment of observed versus predicted responses to selection

Although artificial-selection experiments seem well suited to testing our ability to predict evolution, the correspondence between predicted and observed responses is often ambiguous due to the lack of uncertainty estimates. We present equations for assessing prediction error in direct and indirect responses to selection that integrate uncertainty in genetic parameters used for prediction and sampling effects during selection. Using these, we analyzed a selection experiment on floral traits replicated in two taxa of the Dalechampia scandens (Euphorbiaceae) species complex for which G-matrices were obtained from a diallel breeding design. After four episodes of bidirectional selection, direct and indirect responses remained within wide prediction intervals, but appeared different from the predictions. Combined analyses with structural-equation models confirmed that responses were asymmetrical and lower than predicted in both species. We show that genetic drift is likely to be a dominant source of uncertainty in typically-dimensioned selection experiments in plants and a major obstacle to predicting short-term evolutionary trajectories.

Back to the future: Reintegrating biology to understand how past eco-evolutionary change can predict future outcomes

During the last few decades, biologists have made remarkable progress in understanding the fundamental processes that shape life. But despite the unprecedented level of knowledge now available, large gaps still remain in our understanding of the complex interplay of eco-evolutionary mechanisms across scales of life. Rapidly changing environments on Earth provide a pressing need to understand the potential implications of eco-evolutionary dynamics, which can be achieved by improving existing eco-evolutionary models and fostering convergence among the sub-fields of biology. We propose a new, data-driven approach that harnesses our knowledge of the functioning of biological systems to expand current conceptual frameworks and develop corresponding models that can more accurately represent and predict future eco-evolutionary outcomes. We suggest a roadmap toward achieving this goal. This long-term vision will move biology in a direction that can wield these predictive models for scientific applications that benefit humanity and increase the resilience of natural biological systems. We identify short, medium, and long-term key objectives to connect our current state of knowledge to this long-term vision, iteratively progressing across three stages: 1) utilizing knowledge of biological systems to better inform eco-evolutionary models, 2) generating models with more accurate predictions, and 3) applying predictive models to benefit the biosphere. Within each stage, we outline avenues of investigation and scientific applications related to the timescales over which evolution occurs, the parameter space of eco-evolutionary processes, and the dynamic interactions between these mechanisms. The ability to accurately model, monitor, and anticipate eco-evolutionary changes would be transformational to humanity's interaction with the global environment, providing novel tools to benefit human health, protect the natural world, and manage our planet's biosphere.

Nothing in Evolution Makes Sense Except in the Light of Biology

A key question in biology is the predictability of the evolutionary process. If we can correctly predict the outcome of evolution, we may be better equipped to anticipate and manage species' adaptation to climate change, habitat loss, invasive species, or emerging infectious diseases, as well as improve our basic understanding of the history of life on Earth. In the present article, we ask the questions when, why, and if the outcome of future evolution is predictable. We first define predictable and then discuss two conflicting views: that evolution is inherently unpredictable and that evolution is predictable given the ability to collect the right data. We identify factors that generate unpredictability, the data that might be required to make predictions at some level of precision or at a specific timescale, and the intellectual and translational value of understanding when prediction is or is not possible.

Structure of the G-matrix in relation to phenotypic contributions to fitness

Classical theory in population genetics includes derivation of the stationary distribution of allele frequencies under balance between selection, genetic drift, and mutation. Here we investigate the simplest generalization of these single locus models to quantitative genetics with many loci, assuming simple additive effects on a set of phenotypes and a linear approximation to the fitness function. Genetic effects and pleiotropy are simulated by a prescribed stochastic model. Our goal is to analyze the structure of the G-matrix at stasis when the model is not very close to being neutral. The smallest eigenvalue of the G-matrix is practically zero by Fisher's fundamental theorem for natural selection and the fitness function is approximately a linear function of the corresponding eigenvector. Evolution of genetic trade-offs is closely linked to the fitness function. If a single locus never codes for more than two traits, then additive genetic covariance between two phenotype components always has the opposite sign of the product of their coefficients in the fitness function under no mutation, a pattern that is likely to occur frequently also in more complex models. In our major examples only 1-2 percent of the loci are over-dominant for fitness, but they still account for practically all dominance variance in fitness as well as all contributions to the G-matrix. These analyses show that the structure of the G-matrix reveals important information about the contribution of different traits to fitness.

Immigration counter-acts local micro-evolution of a major fitness component: Migration-selection balance in free-living song sparrows

Ongoing adaptive evolution, and resulting “evolutionary rescue” of declining populations, requires additive genetic variation in fitness. Such variation can be increased by gene flow resulting from immigration, potentially facilitating evolution. But, gene flow could in fact constrain rather than facilitate local adaptive evolution if immigrants have low additive genetic values for local fitness. Local migration‐selection balance and micro‐evolutionary stasis could then result. However, key quantitative genetic effects of natural immigration, comprising the degrees to which gene flow increases the total local additive genetic variance yet counteracts local adaptive evolutionary change, have not been explicitly quantified in wild populations. Key implications of gene flow for population and evolutionary dynamics consequently remain unclear. Our quantitative genetic analyses of long‐term data from free‐living song sparrows (Melospiza melodia) show that mean breeding value for local juvenile survival to adulthood, a major component of fitness, increased across cohorts more than expected solely due to drift. Such micro‐evolutionary change should be expected given nonzero additive genetic variance and consistent directional selection. However, this evolutionary increase was counteracted by negative additive genetic effects of recent immigrants, which increased total additive genetic variance but prevented a net directional evolutionary increase in total additive genetic value. These analyses imply an approximate quantitative genetic migration‐selection balance in a major fitness component, and hence demonstrate a key mechanism by which substantial additive genetic variation can be maintained yet decoupled from local adaptive evolutionary change.

Estimating the capacity of Chamaecrista fasciculata for adaptation to change in precipitation

Adaptation through natural selection may be the only means by which small and fragmented plant populations will persist through present day environmental change. A population's additive genetic variance for fitness (V_A (W)) represents its immediate capacity to adapt to the environment in which it exists. We evaluated this property for a population of the annual legume Chamaecrista fasciculata through a quantitative genetic experiment in the tallgrass prairie region of the Midwestern United States, where changing climate is predicted to include more variability in rainfall. To reduce incident rainfall, relative to controls receiving ambient rain, we deployed rain exclusion shelters. We found significant V_A (W) in both treatments. We also detected a significant genotype-by-treatment interaction for fitness, which suggests that the genetic basis of the response to natural selection will differ depending on precipitation. For the trait-specific leaf area, we detected maladaptive phenotypic plasticity and an interaction between genotype and environment. Selection for thicker leaves was detected with increased precipitation. These results indicate capacity of this population of C. fasciculata to adapt in situ to environmental change.

Genomic Prediction of (Mal)Adaptation Across Current and Future Climatic Landscapes

Signals of local adaptation have been found in many plants and animals, highlighting the heterogeneity in the distribution of adaptive genetic variation throughout species ranges. In the coming decades, global climate change is expected to induce shifts in the selective pressures that shape this adaptive variation. These changes in selective pressures will likely result in varying degrees of local climate maladaptation and spatial reshuffling of the underlying distributions of adaptive alleles. There is a growing interest in using population genomic data to help predict future disruptions to locally adaptive gene-environment associations. One motivation behind such work is to better understand how the effects of changing climate on populations’ short-term fitness could vary spatially across species ranges. Here we review the current use of genomic data to predict the disruption of local adaptation across current and future climates. After assessing goals and motivationsunderlying the approach, we review the main steps and associated statistical methods currently in use and explore our current understanding of the limits and future potential of using genomics to predict climate change (mal)adaptation.

Unidirectional response to bidirectional selection on body size II. Quantitative genetics

Anticipating the genetic and phenotypic changes induced by natural or artificial selection requires reliable estimates of trait evolvabilities (genetic variances and covariances). However, whether or not multivariate quantitative genetics models are able to predict precisely the evolution of traits of interest, especially fitness-related, life history traits, remains an open empirical question. Here, we assessed to what extent the response to bivariate artificial selection on both body size and maturity in the medaka Oryzias latipes, a model fish species, fits the theoretical predictions. Three lines (Large, Small, and Control lines) were differentially selected for body length at 75 days of age, conditional on maturity. As maturity and body size were phenotypically correlated, this selection procedure generated a bi-dimensional selection pattern on two life history traits. After removal of nonheritable trends and noise with a random effect ("animal") model, the observed selection response did not match the expected bidirectional response. For body size, Large and Control lines responded along selection gradients (larger body size and stasis, respectively), but, surprisingly, the Small did not evolve a smaller body length and remained identical to the Control line throughout the experiment. The magnitude of the empirical response was smaller than the theoretical prediction in both selected directions. For maturity, the response was opposite to the expectation (the Large line evolved late maturity compared to the Control line, while the Small line evolved early maturity, while the opposite pattern was predicted due to the strong positive genetic correlation between both traits). The mismatch between predicted and observed response was substantial and could not be explained by usual sources of uncertainties (including sampling effects, genetic drift, and error in G matrix estimates).

Senecio as a model system for integrating studies of genotype, phenotype and fitness

Two major developments have made it possible to use examples of ecological radiations as model systems to understand evolution and ecology. First, the integration of quantitative genetics with ecological experiments allows detailed connections to be made between genotype, phenotype, and fitness in the field. Second, dramatic advances in molecular genetics have created new possibilities for integrating field and laboratory experiments with detailed genetic sequencing. Combining these approaches allows evolutionary biologists to better study the interplay between genotype, phenotype, and fitness to explore a wide range of evolutionary processes. Here, we present the genus Senecio (Asteraceae) as an excellent system to integrate these developments, and to address fundamental questions in ecology and evolution. Senecio is one of the largest and most phenotypically diverse genera of flowering plants, containing species ranging from woody perennials to herbaceous annuals. These Senecio species exhibit many growth habits, life histories, and morphologies, and they occupy a multitude of environments. Common within the genus are species that have hybridized naturally, undergone polyploidization, and colonized diverse environments, often through rapid phenotypic divergence and adaptive radiation. These diverse experimental attributes make Senecio an attractive model system in which to address a broad range of questions in evolution and ecology.

Evolutionary genomics can improve prediction of species' responses to climate change

Global climate change (GCC) increasingly threatens biodiversity through the loss of species, and the transformation of entire ecosystems. Many species are challenged by the pace of GCC because they might not be able to respond fast enough to changing biotic and abiotic conditions. Species can respond either by shifting their range, or by persisting in their local habitat. If populations persist, they can tolerate climatic changes through phenotypic plasticity, or genetically adapt to changing conditions depending on their genetic variability and census population size to allow for de novo mutations. Otherwise, populations will experience demographic collapses and species may go extinct. Current approaches to predicting species responses to GCC begin to combine ecological and evolutionary information for species distribution modelling. Including an evolutionary dimension will substantially improve species distribution projections which have not accounted for key processes such as dispersal, adaptive genetic change, demography, or species interactions. However, eco-evolutionary models require new data and methods for the estimation of a species' adaptive potential, which have so far only been available for a small number of model species. To represent global biodiversity, we need to devise large-scale data collection strategies to define the ecology and evolutionary potential of a broad range of species, especially of keystone species of ecosystems. We also need standardized and replicable modelling approaches that integrate these new data to account for eco-evolutionary processes when predicting the impact of GCC on species' survival. Here, we discuss different genomic approaches that can be used to investigate and predict species responses to GCC. This can serve as guidance for researchers looking for the appropriate experimental setup for their particular system. We furthermore highlight future directions for moving forward in the field and allocating available resources more effectively, to implement mitigation measures before species go extinct and ecosystems lose important functions.

Evolution in the Anthropocene: Informing Governance and Policy

The Anthropocene biosphere constitutes an unprecedented phase in the evolution of life on Earth with one species, humans, exerting extensive control. The increasing intensity of anthropogenic forces in the twenty-first century has widespread implications for attempts to govern both human-dominated ecosystems and the last remaining wild ecosystems. Here, we review how evolutionary biology can inform governance and policies in the Anthropocene, focusing on five governance challenges that span biodiversity, environmental management, food and other biomass production, and human health. The five challenges are: ( a) evolutionary feedbacks, ( b) maintaining resilience, ( c) alleviating constraints, ( d) coevolutionary disruption, and ( e) biotechnology. Strategies for governing these dynamics will themselves have to be coevolutionary, as eco-evolutionary and social dynamics change in response to each other.

Understanding Maladaptation by Uniting Ecological and Evolutionary Perspectives

Evolutionary biologists have long trained their sights on adaptation, focusing on the power of natural selection to produce relative fitness advantages while often ignoring changes in absolute fitness. Ecologists generally have taken a different tack, focusing on changes in abundance and ranges that reflect absolute fitness while often ignoring relative fitness. Uniting these perspectives, we articulate various causes of relative and absolute maladaptation and review numerous examples of their occurrence. This review indicates that maladaptation is reasonably common from both perspectives, yet often in contrasting ways. That is, maladaptation can appear strong from a relative fitness perspective, yet populations can be growing in abundance. Conversely, resident individuals can appear locally adapted (relative to nonresident individuals) yet be declining in abundance. Understanding and interpreting these disconnects between relative and absolute maladaptation, as well as the cases of agreement, is increasingly critical in the face of accelerating human-mediated environmental change. We therefore present a framework for studying maladaptation, focusing in particular on the relationship between absolute and relative fitness, thereby drawing together evolutionary and ecological perspectives. The unification of these ecological and evolutionary perspectives has the potential to bring together previously disjunct research areas while addressing key conceptual issues and specific practical problems.

Additive genetic variance for lifetime fitness and the capacity for adaptation in an annual plant

The immediate capacity for adaptation under current environmental conditions is directly proportional to the additive genetic variance for fitness, V_A (W). Mean absolute fitness, W ¯ , is predicted to change at the rate V A ( W ) W ¯ , according to Fisher's Fundamental Theorem of Natural Selection. Despite ample research evaluating degree of local adaptation, direct assessment of V_A (W) and the capacity for ongoing adaptation is exceedingly rare. We estimated V_A (W) and W ¯ in three pedigreed populations of annual Chamaecrista fasciculata, over three years in the wild. Contrasting with common expectations, we found significant V_A (W) in all populations and years, predicting increased mean fitness in subsequent generations (0.83 to 6.12 seeds per individual). Further, we detected two cases predicting "evolutionary rescue," where selection on standing V_A (W) was expected to increase fitness of declining populations ( W ¯ < 1.0) to levels consistent with population sustainability and growth. Within populations, inter-annual differences in genetic expression of fitness were striking. Significant genotype-by-year interactions reflected modest correlations between breeding values across years, indicating temporally variable selection at the genotypic level that could contribute to maintaining V_A (W). By directly estimating V_A (W) and total lifetime W ¯ , our study presents an experimental approach for studies of adaptive capacity in the wild.