Genetic Variance-covariance Matrices Research Articles

Interspecific selection in a diverse mycorrhizal symbiosis.

Coevolution describes evolutionary change in which two or more interacting species reciprocally drive each other's evolution, potentially resulting in trait diversification and ecological speciation. Much progress has been made in analysis of its dynamics and consequences, but relatively little is understood about how coevolution works in multispecies interactions, i.e., those with diverse suites of species on one or both sides of an interaction. Interactions among plant hosts and their mutualistic ectomycorrhizal fungi (ECM) may provide an ecologically unique arena to examine the nature of selection in multispecies interactions. Using native genotypes of Monterey pine (Pinus radiata), we performed a common garden experiment at a field site that contains native stands to investigate selection from ECM fungi on pine traits. We planted seedlings from all five native populations, as well as inter-population crosses to represent intermediate phenotypes/genotypes, and measured seedling traits and ECM fungal traits to evaluate the potential for evolution in the symbiosis. We then combined field estimates of selection gradients with estimates of heritability and genetic variance-covariance matrices for multiple traits of the mutualism to determine which fungal traits drive plant fitness variation. We found evidence that certain fungal operational taxonomic units, families and species-level morphological traits by which ECM fungi acquire and transport nutrients exert selection on plant traits related to growth and allocation patterns. This work represents the first field-based, community-level study measuring multispecific coevolutionary selection in nutritional symbioses.

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.

Two common, often coexisting grassland plant species differ in their evolutionary potential in response to experimental drought.

For terrestrial plant communities, the increase in frequency and intensity of drought events is considered as one of the most severe consequences of climate change. While single-species studies demonstrate that drought can lead to relatively rapid adaptive genetic changes, the evolutionary potential and constraints to selection need to be assessed in comparative approaches to draw more general conclusions. In a greenhouse experiment, we compare the phenotypic response and evolutionary potential of two co-occurring grassland plant species, Bromus erectus and Trifolium pratense, in two environments differing in water availability. We quantified variation in functional traits and reproductive fitness in response to drought and compared multivariate genetic variance-covariance matrices and predicted evolutionary responses between species. Species showed different drought adaptation strategies, reflected in both their species-specific phenotypic plasticity and predicted responses to selection indicating contrasting evolutionary potential under drought. In T. pratense we found evidence for stronger genetic constraints under drought compared to more favourable conditions, and for some traits plastic and predicted evolutionary responses to drought had opposing directions, likely limiting the potential for adaptive change. Our study contributes to a more detailed understanding of the evolutionary potential of species with different adaptive strategies in response to climate change and may help to inform future scenarios for semi-natural grassland ecosystems.

Genetic architecture of multiple mutualisms and mating system in Turnera ulmifolia.

Plants often associate with multiple arthropod mutualists. These partners provide important services to their hosts, but multiple interactions can constrain a plant's ability to respond to complex, multivariate selection. Here, we quantified patterns of genetic variance and covariance among rewards for pollination, biotic defence and seed dispersal mutualisms in multiple populations of Turnera ulmifolia to better understand how the genetic architecture of multiple mutualisms might influence their evolution. We phenotyped plants cultivated from 17 Jamaican populations for several mutualism and mating system-related traits. We then fit genetic variance-covariance (G) matrices for the island metapopulation and the five largest individual populations. At the metapopulation level, we observed significant positive genetic correlations among stigma-anther separation, floral nectar production and extrafloral nectar production. These correlations have the potential to significantly constrain or facilitate the evolution of multiple mutualisms in T.ulmifolia and suggest that pollination, seed dispersal and defence mutualisms do not evolve independently. In particular, we found that positive genetic correlations between floral and extrafloral nectar production may help explain their stable coexistence in the face of physiological trade-offs and negative interactions between pollinators and ant bodyguards. Locally, we found only small differences in G among our T.ulmifolia populations, suggesting that geographic variation in G may not shape the evolution of multiple mutualisms.

A conserved genetic architecture among populations of the maize progenitor, teosinte, was radically altered by domestication

Very little is known about how domestication was constrained by the quantitative genetic architecture of crop progenitors and how quantitative genetic architecture was altered by domestication. Yang et al. [C. J. Yang et al., Proc. Natl. Acad. Sci. U.S.A. 116, 5643-5652 (2019)] drew multiple conclusions about how genetic architecture influenced and was altered by maize domestication based on one sympatric pair of teosinte and maize populations. To test the generality of their conclusions, we assayed the structure of genetic variances, genetic correlations among traits, strength of selection during domestication, and diversity in genetic architecture within teosinte and maize. Our results confirm that additive genetic variance is decreased, while dominance genetic variance is increased, during maize domestication. The genetic correlations are moderately conserved among traits between teosinte and maize, while the genetic variance-covariance matrices (G-matrices) of teosinte and maize are quite different, primarily due to changes in the submatrix for reproductive traits. The inferred long-term selection intensities during domestication were weak, and the neutral hypothesis was rejected for reproductive and environmental response traits, suggesting that they were targets of selection during domestication. The G-matrix of teosinte imposed considerable constraint on selection during the early domestication process, and constraint increased further along the domestication trajectory. Finally, we assayed variation among populations and observed that genetic architecture is generally conserved among populations within teosinte and maize but is radically different between teosinte and maize. While selection drove changes in essentially all traits between teosinte and maize, selection explains little of the difference in domestication traits among populations within teosinte or maize.

Introduced populations of ragweed show as much evolutionary potential as native populations.

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.

The genetic architecture of teosinte catalyzed and constrained maize domestication

The process of evolution under domestication has been studied using phylogenetics, population genetics-genomics, quantitative trait locus (QTL) mapping, gene expression assays, and archaeology. Here, we apply an evolutionary quantitative genetic approach to understand the constraints imposed by the genetic architecture of trait variation in teosinte, the wild ancestor of maize, and the consequences of domestication on genetic architecture. Using modern teosinte and maize landrace populations as proxies for the ancestor and domesticate, respectively, we estimated heritabilities, additive and dominance genetic variances, genetic-by-environment variances, genetic correlations, and genetic covariances for 18 domestication-related traits using realized genomic relationships estimated from genome-wide markers. We found a reduction in heritabilities across most traits, and the reduction is stronger in reproductive traits (size and numbers of grains and ears) than vegetative traits. We observed larger depletion in additive genetic variance than dominance genetic variance. Selection intensities during domestication were weak for all traits, with reproductive traits showing the highest values. For 17 of 18 traits, neutral divergence is rejected, suggesting they were targets of selection during domestication. Yield (total grain weight) per plant is the sole trait that selection does not appear to have improved in maize relative to teosinte. From a multivariate evolution perspective, we identified a strong, nonneutral divergence between teosinte and maize landrace genetic variance-covariance matrices (G-matrices). While the structure of G-matrix in teosinte posed considerable genetic constraint on early domestication, the maize landrace G-matrix indicates that the degree of constraint is more unfavorable for further evolution along the same trajectory.

Structure and stability of genetic variance-covariance matrices: A Bayesian sparse factor analysis of transcriptional variation in the three-spined stickleback.

The genetic variance-covariance matrix (G) is a quantity of central importance in evolutionary biology due to its influence on the rate and direction of multivariate evolution. However, the predictive power of empirically estimated G-matrices is limited for two reasons. First, phenotypes are high-dimensional, whereas traditional statistical methods are tuned to estimate and analyse low-dimensional matrices. Second, the stability of G to environmental effects and over time remains poorly understood. Using Bayesian sparse factor analysis (BSFG) designed to estimate high-dimensional G-matrices, we analysed levels variation and covariation in 10,527 expressed genes in a large (n=563) half-sib breeding design of three-spined sticklebacks subject to two temperature treatments. We found significant differences in the structure of G between the treatments: heritabilities and evolvabilities were higher in the warm than in the low-temperature treatment, suggesting more and faster opportunity to evolve in warm (stressful) conditions. Furthermore, comparison of G and its phenotypic equivalent P revealed the latter is a poor substitute of the former. Most strikingly, the results suggest that the expected impact of G on evolvability-as well as the similarity among G-matrices-may depend strongly on the number of traits included into analyses. In our results, the inclusion of only few traits in the analyses leads to underestimation in the differences between the G-matrices and their predicted impacts on evolution. While the results highlight the challenges involved in estimating G, they also illustrate that by enabling the estimation of large G-matrices, the BSFG method can improve predicted evolutionary responses to selection.

The Sweet Passion Fruit (Passiflora alata) Crop: Genetic and Phenotypic Parameter Estimates and QTL Mapping for Fruit Traits

Despite their economic importance, some tropical crop species are largely neglected when it comes to conducting genetic studies characterizing target traits for breeding. Herein, genetic and phenotypic parameters as well quantitative trait loci (QTL) are described for the first time in a full-sib progeny of sweet passion fruit (Passiflora alata). A hundred F1 individuals were evaluated in two locations for seven fruit traits: diameter of fruit (DF, in mm), length of fruit (LF, in mm), weight of fruit (WF, in g), thickness of fruit skin (TS, in mm), weight of fruit skin (WS, in g), weight of fruit pulp (WP, in g) and soluble solids (SS, in °Brix). Mixed models fitted complex, unstructured genetic variance-covariance matrices for all traits in phenotypic analysis. Because of important genetic correlations among skin and pulp traits, multiplicative index selection to select the most promising individuals was successfully applied. A previously reported integrated map supported composite interval mapping (CIM) analyses. In total, we found 22 QTLs mapped in seven out of nine linkage groups. Heritabilities (from 59.8 % to 82.7 %) and proportion of phenotypic variance explained by the QTLs (from 42.0 % to 64.3 %) were comparable for each trait. Principal component analysis on TS, WS and WP showed that the first two principal components (PCs) accounted for 93.6 % of the total variability. CIM analyses on these two PCs revealed five putative QTLs controlling variation for these three traits simultaneously. Thus, genetic improvement for sweet passion fruit should be based on correlations between traits and QTL-related information can be a useful tool.

Quantitative Genetic Architecture at Latitudinal Range Boundaries: Reduced Variation but Higher Trait Independence.

Species distribution limits are hypothesized to be caused by small population size and limited genetic variation in ecologically relevant traits, but earlier studies have not evaluated genetic variation in multivariate phenotypes. We asked whether populations at the latitudinal edges of the distribution have altered quantitative genetic architecture of ecologically relevant traits compared with midlatitude populations. We calculated measures of evolutionary potential in nine Arabidopsis lyrata populations spanning the latitudinal range of the species in eastern and midwestern North America. Environments at the latitudinal extremes have reduced water availability, and therefore plants were assessed under wet and dry treatments. We estimated genetic variance-covariance (G-) matrices for 10 traits related to size, development, and water balance. Populations at southern and northern distribution edges had reduced levels of genetic variation across traits, but their G-matrices were more spherical; G-matrix orientation was unrelated to latitude. As a consequence, the predicted short-term response to selection was at least as strong in edge populations as in central populations. These results are consistent with genetic drift eroding variation and reducing the effectiveness of correlational selection at distribution margins. We conclude that genetic variation of isolated traits poorly predicts the capacity to evolve in response to multivariate selection and that the response to selection may frequently be greater than expected at species distribution margins because of genetic drift.

Estimating sampling error of evolutionary statistics based on genetic covariance matrices using maximum likelihood.

We explore the estimation of uncertainty in evolutionary parameters using a recently devised approach for resampling entire additive genetic variance-covariance matrices (G). Large-sample theory shows that maximum-likelihood estimates (including restricted maximum likelihood, REML) asymptotically have a multivariate normal distribution, with covariance matrix derived from the inverse of the information matrix, and mean equal to the estimated G. This suggests that sampling estimates of G from this distribution can be used to assess the variability of estimates of G, and of functions of G. We refer to this as the REML-MVN method. This has been implemented in the mixed-model program WOMBAT. Estimates of sampling variances from REML-MVN were compared to those from the parametric bootstrap and from a Bayesian Markov chain Monte Carlo (MCMC) approach (implemented in the R package MCMCglmm). We apply each approach to evolvability statistics previously estimated for a large, 20-dimensional data set for Drosophila wings. REML-MVN and MCMC sampling variances are close to those estimated with the parametric bootstrap. Both slightly underestimate the error in the best-estimated aspects of the G matrix. REML analysis supports the previous conclusion that the G matrix for this population is full rank. REML-MVN is computationally very efficient, making it an attractive alternative to both data resampling and MCMC approaches to assessing confidence in parameters of evolutionary interest.

The phenome-wide distribution of genetic variance.

A general observation emerging from estimates of additive genetic variance in sets of functionally or developmentally related traits is that much of the genetic variance is restricted to few trait combinations as a consequence of genetic covariance among traits. While this biased distribution of genetic variance among functionally related traits is now well documented, how it translates to the broader phenome and therefore any trait combination under selection in a given environment is unknown. We show that 8,750 gene expression traits measured in adult male Drosophila serrata exhibit widespread genetic covariance among random sets of five traits, implying that pleiotropy is common. Ultimately, to understand the phenome-wide distribution of genetic variance, very large additive genetic variance-covariance matrices (G) are required to be estimated. We draw upon recent advances in matrix theory for completing high-dimensional matrices to estimate the 8,750-trait G and show that large numbers of gene expression traits genetically covary as a consequence of a single genetic factor. Using gene ontology term enrichment analysis, we show that the major axis of genetic variance among expression traits successfully identified genetic covariance among genes involved in multiple modes of transcriptional regulation. Our approach provides a practical empirical framework for the genetic analysis of high-dimensional phenome-wide trait sets and for the investigation of the extent of high-dimensional genetic constraint.

Spatially varying selection shapes life history clines among populations of Drosophila melanogaster from sub-Saharan Africa.

Clines in life history traits, presumably driven by spatially varying selection, are widespread. Major latitudinal clines have been observed, for example, in Drosophila melanogaster, an ancestrally tropical insect from Africa that has colonized temperate habitats on multiple continents. Yet, how geographic factors other than latitude, such as altitude or longitude, affect life history in this species remains poorly understood. Moreover, most previous work has been performed on derived European, American and Australian populations, but whether life history also varies predictably with geography in the ancestral Afro-tropical range has not been investigated systematically. Here, we have examined life history variation among populations of D.melanogaster from sub-Saharan Africa. Viability and reproductive diapause did not vary with geography, but body size increased with altitude, latitude and longitude. Early fecundity covaried positively with altitude and latitude, whereas lifespan showed the opposite trend. Examination of genetic variance-covariance matrices revealed geographic differentiation also in trade-off structure, and QST -FST analysis showed that life history differentiation among populations is likely shaped by selection. Together, our results suggest that geographic and/or climatic factors drive adaptive phenotypic differentiation among ancestral African populations and confirm the widely held notion that latitude and altitude represent parallel gradients.

Interspecific Divergence of Transcription Networks along Lines of Genetic Variance in Drosophila: Dimensionality, Evolvability, and Constraint

Change in gene expression is a major facilitator of phenotypic evolution. Understanding the evolutionary potential of gene expression requires taking into account complex systems of regulatory networks, the structure of which could potentially bias evolutionary trajectories. We analyzed the evolutionary potential and divergence of multigene expression in three well-characterized signaling pathways in Drosophila, the mitogen-activated protein kinase (MapK), the Toll, and the insulin receptor/Foxo (InR/Foxo or InR/TOR) pathways in a multivariate quantitative genetic framework. Gene expression data from a natural population of D. melanogaster were used to estimate the genetic variance-covariance matrices (G) for each network. Although most genes within each pathway exhibited significant genetic variance, the number of independent dimensions of multivariate genetic variance was fewer than the number of genes analyzed. However, for expression, the reduction in dimensionality was not as large as seen for other trait types such as morphology. We then tested whether gene expression divergence between D. melanogaster and an additional six species of the Drosophila genus was biased along the major axes of standing variation observed in D. melanogaster. In many cases, divergence was restricted to directions of phenotypic space harboring above average levels of genetic variance in D. melanogaster, indicating that genetic covariances between genes within pathways have biased interspecific divergence. We tested whether co-expression of genes in both sexes has also biased the pattern of divergence. Including cross-sex genetic covariances increased the degree to which divergence was biased along major axes of genetic variance, suggesting that the co-expression of genes in males and females can generate further constraints on divergence across the Drosophila phylogeny. In contrast to patterns seen for morphological traits in vertebrates, transcriptional constraints do not appear to break down as divergence time between species increases, instead they persist over tens of millions of years of divergence.

Behavioral syndromes as evolutionary constraints

Behaviors are commonly correlated between individuals in so-called syndromes. Between-individual correlations of phenotypic traits can change the trajectories of evolutionary responses available to populations and even prevent evolutionary change if underpinned by genetic correlations. Whether behavioral syndromes also influence the course of evolution in this manner remains unknown. Here, we provide the first test of the degree to which evolutionary responses might be affected by behavioral syndrome structure. This test, based on a meta-analysis of additive genetic variance–covariance matrices, shows that behavioral syndromes constrain potential evolutionary responses by an average of 33%. For comparison, correlations between life-history or between morphological traits suggest constraints of 13–18%. This finding demonstrates that behavioral syndromes might substantially constrain the evolutionary trajectories available to populations, prompts novel future directions for the study of behavioral syndromes, emphasizes the importance of viewing syndrome research from an evolutionary perspective, and provides a bridge between syndrome research and theoretical quantitative genetics.

There is more than one way to skin a G matrix

Because of its importance in directing evolutionary trajectories, there has been considerable interest in comparing variation among genetic variance-covariance (G) matrices. Numerous statistical approaches have been suggested but no general analysis of the relationship among these methods has previously been published. In this study, we used data from a half-sib experiment and simulations to explore the results of applying eight tests (T method, modified Mantel test, Bartlett's test, Flury hierarchy, jackknife-manova, jackknife-eigenvalue test, random skewers, selection skewers). Whereas a randomization approach produced acceptable estimates, those from a bootstrap were typically unacceptable and we recommend randomization as the preferred method. All methods except the jackknife-eigenvalue test gave similar results although a fine-scale analysis suggested that the former group can be subdivided into two or possibly three groups, hierarchical tests, skewers and the rest (jackknife-manova, modified Mantel, T method, probably Bartlett's). An advantage of the jackknife methods is that they permit tests of association with other factors, such as in this case, temperature and sex. We recommend applying all the tests described in this article, with the exception of the T method, and provide R functions for this purpose.

Genetic architecture of fruit quality traits in Malus x domestica (Borkh.) compared between own-rooted seedlings and vegetative propagules on ‘M. 9’ rootstock

The comparative performance of vegetative propagules (VP) grafted on ‘M. 9’ rootstock and own-rooted (OR) seedlings of the same genotypes was investigated for apple [Malus x domestica (Borkh.)] fruit quality traits using offspring from 22 control-pollinated families. Fifteen fruit quality traits including fruit size and shape, skin colour, russet and sensory eating characteristics were evaluated for individual seedlings. Estimates of genetic variance–covariance matrices (G) were obtained and compared between the two treatments (OR versus VP) using Mantel’s test and regression approaches. Empirical estimates of correlations, at the individual-tree and family-mean levels, between the two treatments were compared with those obtained from deterministic simulations. Estimates of narrow-sense heritability (h2) were higher in the VP population than in the OR population for 11 of the 15 traits. The estimated G matrices were significantly different between the two treatments. Simulation study showed that with an increase in the total genetic variance, the correspondence between the two treatments at the family-mean level improved dramatically for different family sizes. As the ratio of dominance/additive variance increased from 0 to 1, the family-mean correlations between the two treatments decreased for all family sizes. The average estimated correlations between the two treatments at the family-mean level were higher than at the individual-tree level (0.78 and 0.42, respectively). These observed correlations were very similar to our theoretical expectations. In the light of these results, caution is required when comparing apple seedlings tested on their own roots with those tested on ‘M. 9’ rootstock, as potential cultivar breeding parents.

Short-term consequences of reproductive mode variation on the genetic architecture of energy metabolism and life-history traits in the pea aphid

Cyclically parthenogenetic animals such as aphids are able alternating sexual and asexual reproduction during its life cycle, and represent good models for studying short-term evolutionary consequences of sex. In aphids, different morphs, whether sexual or asexual, winged or wingless, are produced in response to specific environmental cues. The production of these morphs could imply a differential energy investment between the two reproductive phases (i.e., sexual and asexual), which can also be interpreted in terms of changes in genetic variation and/or trade-offs between the associated traits. In this study we compared the G-matrices of energy metabolism, life-history traits and morph production in 10 clonal lineages (genotypes) of the pea aphid, Acyrthosiphon pisum, during both sexual and asexual phases. The heritabilities (broad-sense) were significant for almost all traits in both phases; however the only significant genetic correlation we found was a positive correlation between resting metabolic rate and production of winged parthenogenetic females during the asexual phase. These results suggest the pea aphid shows some lineage specialization in terms of energy costs, but a higher specialization in the production of the different morphs (e.g., winged parthenogenetic females). Moreover, the production of winged females during the asexual phase appears to be more costly than wingless females. Finally, the structures of genetic variance–covariance matrices differed between both phases. These differences were mainly due to the correlation between resting metabolic rate and winged parthenogenetic females in the asexual phase. This structural difference would be indicating that energy allocation rules changes between phases, emphasizing the dispersion role of asexual morphs.

GENETIC VARIATION AND COVARIATION IN FLORAL ALLOCATION OF TWO SPECIES OF SCHIEDEA WITH CONTRASTING LEVELS OF SEXUAL DIMORPHISM

The evolution of sexual dimorphism depends in part on the additive genetic variance-covariance matrices within females, within males, and across the sexes. We investigated quantitative genetics of floral biomass allocation in females and hermaphrodites of gynodioecious Schiedea adamantis (Caryophyllaceae). The G-matrices within females (G(f)), within hermaphrodites (G(m)), and between sexes (B) were compared to those for the closely related S. salicaria, which exhibits a lower frequency of females and less-pronounced sexual dimorphism. Additive genetic variation was detected in all measured traits in S. adamantis, with narrow-sense heritability from 0.34-1.0. Female allocation and floral size traits covaried more tightly than did those traits with allocation to stamens. Between-sex genetic correlations were all <1, indicating sex-specific expression of genes. Common principal-components analysis detected differences between G(f) and G(m) , suggesting potential for further independent evolution of the sexes. The two species of Schiedea differed in G(m) and especially so in G(f) , with S. adamantis showing greater genetic variation in capsule mass and tighter genetic covariation between female allocation traits and flower size in females. Despite greater sexual dimorphism in S. adamantis, genetic correlations between the two sexes (standardized elements of B) were similar to correlations between sexes in S. salicaria.

ACROSS-ENVIRONMENT GENETIC CORRELATIONS AND THE FREQUENCY OF SELECTIVE ENVIRONMENTS SHAPE THE EVOLUTIONARY DYNAMICS OF GROWTH RATE IN IMPATIENS CAPENSIS

Trade-offs can exist within and across environments, and constrain evolutionary trajectories. To examine the effects of competition and resource availability on trade-offs, we grew individuals of recombinant inbred lines of Impatiens capensis in a factorial combination of five densities with two light environments (full light and neutral shade) and used a Bayesian logistic growth analysis to estimate intrinsic growth rates. To estimate across-environment constraints, we developed a variance decomposition approach to principal components analysis, which accounted for sample size, model-fitting, and within-RIL variation prior to eigenanalysis. We detected negative across-environment genetic covariances in intrinsic growth rates, although only under full-light. To evaluate the potential importance of these covariances, we surveyed natural populations of I. capensis to measure the frequency of different density environments across space and time. We combined our empirical estimates of across-environment genetic variance-covariance matrices and frequency of selective environments with hypothetical (yet realistic) selection gradients to project evolutionary responses in multiple density environments. Selection in common environments can lead to correlated responses to selection in rare environments that oppose and counteract direct selection in those rare environments. Our results highlight the importance of considering both the frequency of selective environments and the across-environment genetic covariances in traits simultaneously.