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.     

Parallelism,parsimony, and the phytogeny of the lemuridae

In spite of the increasing popularity of cladistic methods in studies of primate systematics, few authors have investigated the effects of parallel evolution when such methods are applied to empirical data. To counter the effects of parallelism, cladistic techniques rely on the principle of evolutionary parsimony. When parsimony procedures are used to reconstruct the phylogeny of the Lemuridae, nine highly parsimonious phylogenies can be deduced. Further choice among these competing hypotheses of relationship is determined by the extent to which one embraces the parsimony principle. The phylogeny obtained by the most rigorous adherence to the parsimony principle is one which is wholly consistent with traditional evolutionary classifications of the Lemuridae. Moderate levels of parallelism can lead to the generation of several plausible, alternative phylogenetic hypotheses; less than 25% of the characters analyzed here need have evolved in parallel, yet they are largely responsible for the ambiguity of the nine different lemurid phylogenies. This suggests that phylogeny reconstructions based entirely on cladistic methods do not provide a suitable basis for the construction of classifications for groups such as the order Primates, where the degree of parallelism is likely to be quite high.     

PHYLOGENETIC ANALYSIS OF THE EVOLUTIONARY CORRELATION USING LIKELIHOOD

Many evolutionary processes can lead to a change in the correlation between continuous characters over time or on different branches of a phylogenetic tree. Shifts in genetic or functional constraint, in the selective regime, or in some combination thereof can influence both the evolution of continuous traits and their relation to each other. These changes can often be mapped on a phylogenetic tree to examine their influence on multivariate phenotypic diversification. We propose a new likelihood method to fit multiple evolutionary rate matrices (also called evolutionary variance–covariance matrices) to species data for two or more continuous characters and a phylogeny. The evolutionary rate matrix is a matrix containing the evolutionary rates for individual characters on its diagonal, and the covariances between characters (of which the evolutionary correlations are a function) elsewhere. To illustrate our approach, we apply the method to an empirical dataset consisting of two features of feeding morphology sampled from 28 centrarchid fish species, as well as to data generated via phylogenetic numerical simulations. We find that the method has appropriate type I error, power, and parameter estimation. The approach presented herein is the first to allow for the explicit testing of how and when the evolutionary covariances between characters have changed in the history of a group.     

A trait-based approach to determining principles of plant biogeography

Lineage-specific traits determine how plants interact with their surrounding environment. Unrelated species may evolve similar phenotypic characteristics to tolerate, persist in, and invade environments with certain characteristics, resulting in some traits becoming relatively more common in certain types of habitats. Analyses of these general patterns of geographical trait distribution have led to the proposal of general principles to explain how plants diversify in space over time. Trait–environment correlation analyses quantify to what extent unrelated lineages have similar evolutionary responses to a given type of habitat. In this synthesis, I give a short historical overview on trait–environment correlation analyses, from some key observations from classic naturalists to modern approaches using trait evolution models, large phylogenies, and massive data sets of traits and distributions. I discuss some limitations of modern approaches, including the need for more realistic models, the lack of data from tropical areas, and the necessary focus on trait scoring that goes beyond macromorphology. Overcoming these limitations will allow the field to explore new questions related to trait lability and niche evolution and to better identify generalities and exceptions in how plants diversify in space over time.     

Many‐to‐one form‐to‐function mapping weakens parallel morphological evolution

Evolutionary ecologists aim to explain and predict evolutionary change under different selective regimes. Theory suggests that such evolutionary prediction should be more difficult for biomechanical systems in which different trait combinations generate the same functional output: “many‐to‐one mapping.” Many‐to‐one mapping of phenotype to function enables multiple morphological solutions to meet the same adaptive challenges. Therefore, many‐to‐one mapping should undermine parallel morphological evolution, and hence evolutionary predictability, even when selection pressures are shared among populations. Studying 16 replicate pairs of lake‐ and stream‐adapted threespine stickleback (Gasterosteus aculeatus), we quantified three parts of the teleost feeding apparatus and used biomechanical models to calculate their expected functional outputs. The three feeding structures differed in their form‐to‐function relationship from one‐to‐one (lower jaw lever ratio) to increasingly many‐to‐one (buccal suction index, opercular 4‐bar linkage). We tested for (1) weaker linear correlations between phenotype and calculated function, and (2) less parallel evolution across lake‐stream pairs, in the many‐to‐one systems relative to the one‐to‐one system. We confirm both predictions, thus supporting the theoretical expectation that increasing many‐to‐one mapping undermines parallel evolution. Therefore, sole consideration of morphological variation within and among populations might not serve as a proxy for functional variation when multiple adaptive trait combinations exist.     

Evolutionary models of extended phenotypes

A variety of theoretical models incorporate phenotypes expressed in the external environment, but a core question is whether such traits generate dynamics that alter evolution. This has proven to be a challenging and controversial proposition. However, several recent modelling frameworks provide insight: indirect genetic effect (IGE) models, niche construction models, and evolutionary feedback models. These distinct approaches converge upon the observation that gene action at a distance generates feedback that expands the range of trait values and evolutionary rates that we should expect to observe in empirical studies. Such conceptual replication provides solid evidence that traits with extended effects have important evolutionary consequences, but more empirical work is needed to evaluate the predictive power of different modelling approaches.     

Journeys through discrete‐character morphospace: synthesizing phylogeny,tempo, and disparity

Palaeontologists have long employed discrete categorical data to capture morphological variation in fossil species, using the resulting character–taxon matrices to measure evolutionary tempo, infer phylogenies and capture morphological disparity. However, to date these have been seen as separate approaches despite a common goal of understanding morphological evolution over deep time. Here I argue that there are clear advantages to considering these three lines of enquiry in a single space: the phylomorphospace. Conceptually these high‐dimensional spaces capture how a phylogenetic tree explores morphospace and allow us to consider important process questions around evolutionary rates, constraints, convergence and directional trends. Currently the literature contains fundamentally different approaches used to generate such spaces, with no direct comparison between them, despite the differing evolutionary histories they imply. Here I directly compare five different phylomorphospace approaches, three with direct literature equivalents and two that are novel. I use a single empirical case study of coelurosaurian theropod dinosaurs (152 taxa, 853 characters) to show that under many analyses the literature‐derived approaches tend to reflect introduced phylogenetic (rather than the intended morphological) signal. The two novel approaches, which produce limited ancestral state estimates prior to ordination, are able to minimize this phylogenetic signal and thus exhibit more realistic amounts of phylogenetic signal, rate heterogeneity, and convergent evolution.     

Multivariate Phenotypic Divergence Due to the Fixation of Beneficial Mutations in Experimentally Evolved Lineages of a Filamentous Fungus

The potential for evolutionary change is limited by the availability of genetic variation. Mutations are the ultimate source of new alleles, yet there have been few experimental investigations of the role of novel mutations in multivariate phenotypic evolution. Here, we evaluated the degree of multivariate phenotypic divergence observed in a long-term evolution experiment whereby replicate lineages of the filamentous fungus Aspergillus nidulans were derived from a single genotype and allowed to fix novel (beneficial) mutations while maintained at two different population sizes. We asked three fundamental questions regarding phenotypic divergence following approximately 800 generations of adaptation: (1) whether divergence was limited by mutational supply, (2) whether divergence proceeded in relatively many (few) multivariate directions, and (3) to what degree phenotypic divergence scaled with changes in fitness (i.e. adaptation). We found no evidence that mutational supply limited phenotypic divergence. Divergence also occurred in all possible phenotypic directions, implying that pleiotropy was either weak or sufficiently variable among new mutations so as not to constrain the direction of multivariate evolution. The degree of total phenotypic divergence from the common ancestor was positively correlated with the extent of adaptation. These results are discussed in the context of the evolution of complex phenotypes through the input of adaptive mutations.     

A note on simulating null distributions for G matrix comparisons

Genetic variances and covariances, summarized in G matrices, are key determinants of the course of adaptive evolution. Consequently, understanding how G matrices vary among populations is critical to answering a variety of questions in evolutionary biology. A method has recently been proposed for generating null distributions of statistics pertaining to differences in G matrices among populations. The general approach facilitated by this method is likely to prove to be very important in studies of the evolution of G . We have identified an issue in the method that will cause it to create null distributions of differences in G matrices that are likely to be far too narrow. The issue arises from the fact that the method as currently used generates null distributions of statistics pertaining to differences in G matrices across populations by simulating breeding value vectors based on G matrices estimated from data, randomizing these vectors across populations, and then calculating null values of statistics from G matrices that are calculated directly from the variances and covariances among randomized vectors. This calculation treats breeding values as quantities that are directly measurable, instead of predicted from G matrices that are themselves estimated from patterns of covariance among kin. The existing method thus neglects a major source of uncertainty in G matrices, which renders it anti‐conservative. We first suggest a correction to the method. We then apply the original and modified methods to a very simple instructive scenario. Finally, we demonstrate the use of both methods in the analysis of a real data set.     

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

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

Measuring and comparing evolvability and constraint in multivariate characters

The Lande equation forms the basis for our understanding of the short-term evolution of quantitative traits in a multivariate context. It predicts the response to selection as the product of an additive genetic variance matrix and a selection gradient. The selection gradient approximates the force and direction of selection, and the genetic variance matrix quantifies the role of the genetic system in evolution. Attempts to understand the evolutionary significance of the genetic variance matrix are hampered by the fact that the majority of the methods used to characterize and compare variance matrices have not been derived in an explicit theoretical context. We use the Lande equation to derive new measures of the ability of a variance matrix to allow or constrain evolution in any direction in phenotype space. Evolvability captures the ability of a population to evolve in the direction of selection when stabilizing selection is absent. Conditional evolvability captures the ability of a population to respond to directional selection in the presence of stabilizing selection on other trait combinations. We then derive measures of character autonomy and integration from these evolvabilities. We study the properties of these measures and show how they can be used to interpret and compare variance matrices. As an illustration, we show that divergence of wing shape in the dipteran family Drosophilidae has proceeded in directions that have relatively high evolvabilities.     

Resurrecting complexity: the interplay of plasticity and rapid evolution in the multiple trait response to strong changes in predation pressure in the water flea Daphnia magna

A resurrection ecology reconstruction of 14 morphological, life history and behavioural traits revealed that a natural Daphnia magna population rapidly tracked changes in fish predation by integrating phenotypic plasticity and widespread evolutionary changes both in mean trait values and in trait plasticity. Increased fish predation mainly generated rapid adaptive evolution of plasticity (especially in the presence of maladaptive ancestral plasticity) resulting in an important change in the magnitude and direction of the multivariate reaction norm. Subsequent relaxation of the fish predation pressure resulted in reversed phenotypic plasticity and mainly caused evolution of the trait means towards the ancestral pre‐fish means. Relaxation from fish predation did, however, not result in a complete reversal to the ancestral fishless multivariate phenotype. Our study emphasises that the study population rapidly tracked environmental changes through a mosaic of plasticity, evolution of trait means and evolution of plasticity to generate integrated phenotypic changes in multiple traits.     

Correlation analysis of dissimilarity matrices

Distance-based methods have been a valuable tool for ecologists for decades. Indirectly, distance-based ordination and cluster analysis, in particular, have been widely practiced as they allow the visualization of a multivariate data set in a few dimensions. The explicitly distance-based Mantel test and multiple regression on distance matrices (MRM) add hypothesis testing to the toolbox. One concern for ecologists wishing to use these methods lies in deciding whether to combine data vectors into a compound multivariate dissimilarity to analyze them individually. For Euclidean distances on scaled data, the correlation of a pair of multivariate distance matrices can be calculated from the correlations between the two sets of individual distance matrices if one set is orthogonal, demonstrating a clear link between individual and compound distances. The choice between Mantel and MRM should be driven by ecological hypotheses rather than mathematical concerns. The relationship between individual and compound distance matrices also provides a means for calculating the maximum possible value of the Mantel statistic, which can be considerably less than 1 for a given analysis. These relationships are demonstrated with simulated data. Although these mathematical relationships are only strictly true for Euclidean distances when one set of variables is orthogonal, simulations show that they are approximately true for weakly correlated variables and Bray–Curtis dissimilarities.     

Similarity in G matrix structure among natural populations of Arabidopsis lyrata

Understanding the stability of the G matrix in natural populations is fundamental for predicting evolutionary trajectories; yet, the extent of its spatial variation and how this impacts responses to selection remain open questions. With a nested paternal half‐sib crossing design and plants grown in a field experiment, we examined differences in the genetic architecture of flowering time, floral display, and plant size among four Scandinavian populations of Arabidopsis lyrata. Using a multivariate Bayesian framework, we compared the size, shape, and orientation of G matrices and assessed their potential to facilitate or constrain trait evolution. Flowering time, floral display and rosette size varied among populations and significant additive genetic variation within populations indicated potential to evolve in response to selection. Yet, some characters, including flowering start and number of flowers, may not evolve independently because of genetic correlations. Using a multivariate framework, we found few differences in the genetic architecture of traits among populations. G matrices varied mostly in size rather than shape or orientation. Differences in multivariate responses to selection predicted from differences in G were small, suggesting overall matrix similarity and shared constraints to trait evolution among populations.     

Correlated evolution of multivariate traits: detecting co-divergence across multiple dimensions

Tests of correlated evolution typically treat phenotypic characters as univariate variables, even though different trait attributes may contribute to their association with other traits. In this study, patterns of character covariation among species are analysed in a multivariate framework to test for both correlated rates and directions of evolutionary change in traits forming the genitalic complex of male grasshoppers. Although the covariation structure differs among traits, and among the constituent species of two grasshopper clades, significant co-divergence was detected among the most closely interacting genitalic traits (i.e. intromittent characters) in both clades. Co-divergence across shape space is not accompanied by similar rates of evolution among species, although the intromittent characters tend to show accelerated evolution (relative to nonintromittent characters). Differences in the evolutionary trajectories among traits may relate to their varied roles during mating. The study emphasizes the importance of a multivariate framework for detecting macroevolutionary patterns of correlated change.     

Fitting models of continuous trait evolution to incompletely sampled comparative data using approximate Bayesian computation

In recent years, a suite of methods has been developed to fit multiple rate models to phylogenetic comparative data. However, most methods have limited utility at broad phylogenetic scales because they typically require complete sampling of both the tree and the associated phenotypic data. Here, we develop and implement a new, tree-based method called MECCA (Modeling Evolution of Continuous Characters using ABC) that uses a hybrid likelihood/approximate Bayesian computation (ABC)-Markov-Chain Monte Carlo approach to simultaneously infer rates of diversification and trait evolution from incompletely sampled phylogenies and trait data. We demonstrate via simulation that MECCA has considerable power to choose among single versus multiple evolutionary rate models, and thus can be used to test hypotheses about changes in the rate of trait evolution across an incomplete tree of life. We finally apply MECCA to an empirical example of body size evolution in carnivores, and show that there is no evidence for an elevated rate of body size evolution in the pinnipeds relative to terrestrial carnivores. ABC approaches can provide a useful alternative set of tools for future macroevolutionary studies where likelihood-dependent approaches are lacking.     

Parallel Genomic Changes Drive Repeated Evolution of Placentas in Live-Bearing Fish

The evolutionary origin of complex organs challenges empirical study because most organs evolved hundreds of millions of years ago. The placenta of live-bearing fish in the family Poeciliidae represents a unique opportunity to study the evolutionary origin of complex organs, because in this family a placenta evolved at least nine times independently. It is currently unknown whether this repeated evolution is accompanied by similar, repeated, genomic changes in placental species. Here, we compare whole genomes of 26 poeciliid species representing six out of nine independent origins of placentation. Evolutionary rate analysis revealed that the evolution of the placenta coincides with convergent shifts in the evolutionary rate of 78 protein-coding genes, mainly observed in transporter- and vesicle-located genes. Furthermore, differences in sequence conservation showed that placental evolution coincided with similar changes in 76 noncoding regulatory elements, occurring primarily around genes that regulate development. The unexpected high occurrence of GATA simple repeats in the regulatory elements suggests an important function for GATA repeats in developmental gene regulation. The distinction in molecular evolution observed, with protein-coding parallel changes more often found in metabolic and structural pathways, compared with regulatory change more frequently found in developmental pathways, offers a compelling model for complex trait evolution in general: changing the regulation of otherwise highly conserved developmental genes may allow for the evolution of complex traits.     

Sexual dimorphism modifies habitat‐associated divergence: Evidence from beach and creek breeding sockeye salmon

Studies of parallel or convergent evolution (the repeated, independent evolution of similar traits in similar habitats) rarely explicitly quantify the extent of parallelism (i.e. variation in the direction and/or magnitude of divergence) between the sexes; instead, they often investigate both sexes together or exclude one sex. However, differences in male and female patterns of divergence could contribute to overall variation in the extent of parallelism among ecotype pairs, especially in sexually dimorphic traits. Failing to properly attribute such variation could lead to underestimates of the importance of environmental variation in shaping phenotypes. We investigate the extent of parallelism in the body shape of male and female beach and creek spawning sockeye salmon (Oncorhynchus nerka) from two lake systems in western Alaska that were colonized independently after the last ice age. Although both sexes showed some degree of parallelism, patterns of beach‐creek body shape divergence vary between the sexes and between lake systems. Phenotypic change vector analyses revealed highly parallel aspects of divergence between males from different lake systems (males from beaches had deeper bodies than males from creeks) but weaker parallelism in females and high parallelism between the sexes in one lake system but not the other. Body shape also had population‐specific components, which were mostly, but not entirely, explained by environmental variation in the form of creek depth. Our results highlight the importance of explicitly considering the extent of parallelism between the sexes and environmental variation among sites within habitat types.     

Genes,culture, and the human niche: An overview

The sharp distinction between biological traits and culturally based traits, which had long been standard in evolutionary approaches to behavior, was blurred in the early 1980s by mathematical models that allowed a co‐dependent evolution of genetic transmission and cultural information. Niche‐construction theory has since added another contrast to standard evolutionary theory, in that it views niche construction as a cause of evolutionary change rather than simply a product of selection. While offering a new understanding of the coevolution of genes, culture, and human behavior, niche‐construction models also invoke multivariate causality, which require multiple time series to resolve. The empirical challenge lies in obtaining time‐series data on causal pathways involved in the coevolution of genes, culture, and behavior. This is a significant issue in archeology, where time series are often sparse and causal behaviors are represented only by proxies in the material record.