SIMULATION‐BASED LIKELIHOOD APPROACH FOR EVOLUTIONARY MODELS OF PHENOTYPIC TRAITS ON PHYLOGENY

Phylogenetic comparative methods (PCMs) have been used to test evolutionary hypotheses at phenotypic levels. The evolutionary modes commonly included in PCMs are Brownian motion (genetic drift) and the Ornstein–Uhlenbeck process (stabilizing selection), whose likelihood functions are mathematically tractable. More complicated models of evolutionary modes, such as branch‐specific directional selection, have not been used because calculations of likelihood and parameter estimates in the maximum‐likelihood framework are not straightforward. To solve this problem, we introduced a population genetics framework into a PCM, and here, we present a flexible and comprehensive framework for estimating evolutionary parameters through simulation‐based likelihood computations. The method does not require analytic likelihood computations, and evolutionary models can be used as long as simulation is possible. Our approach has many advantages: it incorporates different evolutionary modes for phenotypes into phylogeny, it takes intraspecific variation into account, it evaluates full likelihood instead of using summary statistics, and it can be used to estimate ancestral traits. We present a successful application of the method to the evolution of brain size in primates. Our method can be easily implemented in more computationally effective frameworks such as approximate Bayesian computation (ABC), which will enhance the use of computationally intensive methods in the study of phenotypic evolution.     

Tree‐sequence recording in SLiM opens new horizons for forward‐time simulation of whole genomes

There is an increasing demand for evolutionary models to incorporate relatively realistic dynamics, ranging from selection at many genomic sites to complex demography, population structure, and ecological interactions. Such models can generally be implemented as individual‐based forward simulations, but the large computational overhead of these models often makes simulation of whole chromosome sequences in large populations infeasible. This situation presents an important obstacle to the field that requires conceptual advances to overcome. The recently developed tree‐sequence recording method (Kelleher, Thornton, Ashander, & Ralph, 2018), which stores the genealogical history of all genomes in the simulated population, could provide such an advance. This method has several benefits: (1) it allows neutral mutations to be omitted entirely from forward‐time simulations and added later, thereby dramatically improving computational efficiency; (2) it allows neutral burn‐in to be constructed extremely efficiently after the fact, using “recapitation”; (3) it allows direct examination and analysis of the genealogical trees along the genome; and (4) it provides a compact representation of a population's genealogy that can be analysed in Python using the msprime package. We have implemented the tree‐sequence recording method in SLiM 3 (a free, open‐source evolutionary simulation software package) and extended it to allow the recording of non‐neutral mutations, greatly broadening the utility of this method. To demonstrate the versatility and performance of this approach, we showcase several practical applications that would have been beyond the reach of previously existing methods, opening up new horizons for the modelling and exploration of evolutionary processes.     

Simulating and detecting autocorrelation of molecular evolutionary rates among lineages

Evolutionary timescales can be estimated from genetic data using phylogenetic methods based on the molecular clock. To account for molecular rate variation among lineages, a number of relaxed‐clock models have been developed. Some of these models assume that rates vary among lineages in an autocorrelated manner, so that closely related species share similar rates. In contrast, uncorrelated relaxed clocks allow all of the branch‐specific rates to be drawn from a single distribution, without assuming any correlation between rates along neighbouring branches. There is uncertainty about which of these two classes of relaxed‐clock models are more appropriate for biological data. We present an R package, NELSI, that allows the evolution of DNA sequences to be simulated according to a range of clock models. Using data generated by this package, we assessed the ability of two Bayesian phylogenetic methods to distinguish among different relaxed‐clock models and to quantify rate variation among lineages. The results of our analyses show that rate autocorrelation is typically difficult to detect, even when there is complete taxon sampling. This provides a potential explanation for past failures to detect rate autocorrelation in a range of data sets.     

tipdatingbeast: an r package to assist the implementation of phylogenetic tip‐dating tests using beast

Molecular tip dating of phylogenetic trees is a growing discipline that uses DNA sequences sampled at different points in time to coestimate the timing of evolutionary events with rates of molecular evolution. In this context, beast , a program for Bayesian analysis of molecular sequences, is the most widely used phylogenetic tool. Here, we introduce tipdatingbeast , an r package built to assist the implementation of various phylogenetic tip‐dating tests using beast . tipdatingbeast currently contains two main functions. The first one allows preparing date‐randomization analyses, which assess the temporal signal of a data set. The second function allows performing leave‐one‐out analyses, which test for the consistency between independent calibration sequences and allow pinpointing those leading to potential bias. We apply those functions to an empirical data set and supply practical guidance for results interpretation.     

The genome‐centric concept: resynthesis of evolutionary theory

Modern biology has been heavily influenced by the gene‐centric concept. Paradoxically, this very concept – on which bioresearch is based – is challenged by the success of gene‐based research in terms of explaining evolutionary theory. To overcome this major roadblock, it is essential to establish new theories, to not only solve the key puzzles presented by the gene‐centric concept, but also to provide a conceptual framework that allows the field to grow. This paper discusses a number of paradoxes and illustrates how they can be addressed by the genome‐centric concept in order to further resynthesize evolutionary theory. In particular, methodological breakthroughs that analyze genome evolution are discussed. The multiple interactions among different levels of a complex system provide the key to understanding the relationship between self‐organization and natural selection. Darwinian natural selection applies to the biological level due to its unique genetic and heterogeneous features, but does not simply or directly apply to either the lower non‐living level or higher intellectual society level. At the complex bio‐system level, the genome context (the entire package of genes and their genomic physical relationship or genomic topology), not the individual genes, defines the system and serves as the principle selection platform for evolution.     

BBMV: an R package for the estimation of macroevolutionary landscapes

The distribution of traits along phylogenies bears signatures of how ecological and evolutionary processes have interacted to influence phenotypic evolution, which can be deciphered using macroevolutionary models. BBMV implements a model for the evolution of continuous characters on phylogenies that generalizes existing ones, like Brownian motion and the Ornstein‐Uhlenbeck model. In this model quantitative characters evolve under both random diffusion and a deterministic force that can be of any possible shape and strength. The model can be used to infer evolutionary scenarios that remained inaccessible so far, like directional trends, disruptive selection, and even bounded evolution. With this new tool at hand, researchers will be able to test complex hypothesis‐driven scenarios regarding trait evolution, but they will also have the possibility to estimate the shape of the adaptive landscapes in which traits evolved. Ultimately, this will provide a way to infer how ecological processes have influenced phenotypic evolution over long timescales. The BBMV package is implemented in the R statistical language and is freely available on the CRAN repository < https://CRAN.R‐project.org/package=BBMV >. All source code can also be found on < https://github.com/fcboucher/BBMV >, along with a detailed tutorial.     

driftsel: an R package for detecting signals of natural selection in quantitative traits

Approaches and tools to differentiate between natural selection and genetic drift as causes of population differentiation are of frequent demand in evolutionary biology. Based on the approach of Ovaskainen et al. (2011), we have developed an R package (driftsel ) that can be used to differentiate between stabilizing selection, diversifying selection and random genetic drift as causes of population differentiation in quantitative traits when neutral marker and quantitative genetic data are available. Apart from illustrating the use of this method and the interpretation of results using simulated data, we apply the package on data from three‐spined sticklebacks (Gasterosteus aculeatus) to highlight its virtues. driftsel can also be used to perform usual quantitative genetic analyses in common‐garden study designs.     

Animal movement tools (amt): R package for managing tracking data and conducting habitat selection analyses

Advances in tracking technology have led to an exponential increase in animal location data, greatly enhancing our ability to address interesting questions in movement ecology, but also presenting new challenges related to data management and analysis. Step‐selection functions (SSFs) are commonly used to link environmental covariates to animal location data collected at fine temporal resolution. SSFs are estimated by comparing observed steps connecting successive animal locations to random steps, using a likelihood equivalent of a Cox proportional hazards model. By using common statistical distributions to model step length and turn angle distributions, and including habitat‐ and movement‐related covariates (functions of distances between points, angular deviations), it is possible to make inference regarding habitat selection and movement processes or to control one process while investigating the other. The fitted model can also be used to estimate utilization distributions and mechanistic home ranges. Here, we present the R package amt (animal movement tools) that allows users to fit SSFs to data and to simulate space use of animals from fitted models. The amt package also provides tools for managing telemetry data. Using fisher (Pekania pennanti) data as a case study, we illustrate a four‐step approach to the analysis of animal movement data, consisting of data management, exploratory data analysis, fitting of models, and simulating from fitted models.     

learnPopGen: An R package for population genetic simulation and numerical analysis

Here, I briefly present a new R package called learnPopGen that has been designed primarily for the purposes of teaching evolutionary biology, population genetics, and evolutionary theory. Functions of the package can be used to conduct simulations and numerical analyses of a wide range of evolutionary phenomena that would typically be covered in advanced undergraduate through graduate‐level curricula in population genetics or evolution. For instance, learnPopGen functions can be used to visualize gene frequency changes through time under multiple deterministic and stochastic processes, to compute and animate the changes in phenotypic trait values or distributions under natural selection, to numerically analyze and graph the outcome of simple game theory models, and to plot coalescence within a population experiencing genetic drift, along with a number of other things. Functions have been designed to be maximally didactic and frequently employ compelling animated visualizations. Furthermore, it is straightforward to export plots and animations from R in the form of flat or animated graphics, or as videos. For maximum flexibility, students working with the package can run functions directly in R; however, instructors may choose to guide students less adept in the R environment to one of various web interfaces that I have built for a number of the functions of the package and that are already available online.     

蕨类植物rbcL基因正选择和负选择位点的鉴定

基于分支模型、位点模型及分支-位点模型对蕨类的rbcL基因所受到的选择压力进行了分析.结果显示:分支模型下检测到大部分分支处于负选择,仅4个分支处于正选择压力下,并且仅2分支具有统计上的显著性;在位点模型下,通过比较模型M1a/M2a和M7/M8,在氨基酸水平上模型M2a和模型M8均鉴定出98L位点被正选择;在模型M8下,鉴定负向选择位点共228个,占总序列的83.82%,从而揭示出负选择对rbcL基因的进化起着非常重要的作用;在分支位点-模型c下鉴定出262A被正选择.98L、262A位点分别位于rbcL羧基末端α/β桶结构域的第3和第8个α螺旋上.蕨类通过该结构域的适应性进化,适应白垩纪被子植物兴起而引发的陆地生态系统改变,研究结果为以后实验分析提供了首选位点.     

Selection experiments and the study of phenotypic plasticity1

Abstract Laboratory selection experiments are powerful tools for establishing evolutionary potentials. Such experiments provide two types of information, knowledge about genetic architecture and insight into evolutionary dynamics. They can be roughly classified into two types: (1) artificial selection in which the experimenter selects on a focal trait or trait index, and (2) quasi‐natural selection in which the experimenter establishes a set of environmental conditions and then allows the population to evolve. Both approaches have been used in the study of phenotypic plasticity. Artificial selection experiments have taken various forms including: selection directly on a reaction norm, selection on a trait in multiple environments, and selection on a trait in a single environment. In the latter experiments, evolution of phenotypic plasticity is investigated as a correlated response. Quasi‐natural selection experiments have examined the effects of both spatial and temporal variation. I describe how to carry out such experiments, summarize past efforts, and suggest further avenues of research.     

Alternative forms for genomic clines

Understanding factors regulating hybrid fitness and gene exchange is a major research challenge for evolutionary biology. Genomic cline analysis has been used to evaluate alternative patterns of introgression, but only two models have been used widely and the approach has generally lacked a hypothesis testing framework for distinguishing effects of selection and drift. I propose two alternative cline models, implement multivariate outlier detection to identify markers associated with hybrid fitness, and simulate hybrid zone dynamics to evaluate the signatures of different modes of selection. Analysis of simulated data shows that previous approaches are prone to false positives (multinomial regression) or relatively insensitive to outlier loci affected by selection (Barton's concordance). The new, theory‐based logit‐logistic cline model is generally best at detecting loci affecting hybrid fitness. Although some generalizations can be made about different modes of selection, there is no one‐to‐one correspondence between pattern and process. These new methods will enhance our ability to extract important information about the genetics of reproductive isolation and hybrid fitness. However, much remains to be done to relate statistical patterns to particular evolutionary processes. The methods described here are implemented in a freely available package “HIest” for the R statistical software (CRAN; http://cran.r-project.org/ ).     

THE CONTRIBUTION OF MATERNAL EFFECTS TO SELECTION RESPONSE: AN EMPIRICAL TEST OF COMPETING MODELS

Maternal effects can dramatically influence the evolutionary process, in some cases facilitating and in others hindering adaptive evolution. Maternal effects have been incorporated into quantitative genetic models using two theoretical frameworks: the variance‐components approach, which partitions variance into direct and maternal components, and the trait‐based approach, which assumes that maternal effects are mediated by specific maternal traits. Here, we demonstrate parallels between these models and test their ability to predict evolutionary change. First, we show that the two approaches predict equivalent responses to selection in the absence of maternal effects mediated by traits that are themselves maternally influenced. We also introduce a correction factor that may be applied when such cascading maternal effects are present. Second, we use several maternal effect models, as well as the standard breeder's equation, to predict evolution in response to artificial selection on flowering time in American bellflower, Campanulastrum americanum. Models that included maternal effects made much more accurate predictions of selection response than the breeder's equation. Maternal effect models differed somewhat in their fit, with a version of the trait‐based model providing the best fit. We recommend fitting such trait‐based models when possible and appropriate to make the most accurate evolutionary predictions.     

Assessing variation in life‐history tactics within a population using mixture regression models: a practical guide for evolutionary ecologists

Mixed models are now well‐established methods in ecology and evolution because they allow accounting for and quantifying within‐ and between‐individual variation. However, the required normal distribution of the random effects can often be violated by the presence of clusters among subjects, which leads to multi‐modal distributions. In such cases, using what is known as mixture regression models might offer a more appropriate approach. These models are widely used in psychology, sociology, and medicine to describe the diversity of trajectories occurring within a population over time (e.g. psychological development, growth). In ecology and evolution, however, these models are seldom used even though understanding changes in individual trajectories is an active area of research in life‐history studies. Our aim is to demonstrate the value of using mixture models to describe variation in individual life‐history tactics within a population, and hence to promote the use of these models by ecologists and evolutionary ecologists. We first ran a set of simulations to determine whether and when a mixture model allows teasing apart latent clustering, and to contrast the precision and accuracy of estimates obtained from mixture models versus mixed models under a wide range of ecological contexts. We then used empirical data from long‐term studies of large mammals to illustrate the potential of using mixture models for assessing within‐population variation in life‐history tactics. Mixture models performed well in most cases, except for variables following a Bernoulli distribution and when sample size was small. The four selection criteria we evaluated [Akaike information criterion (AIC), Bayesian information criterion (BIC), and two bootstrap methods] performed similarly well, selecting the right number of clusters in most ecological situations. We then showed that the normality of random effects implicitly assumed by evolutionary ecologists when using mixed models was often violated in life‐history data. Mixed models were quite robust to this violation in the sense that fixed effects were unbiased at the population level. However, fixed effects at the cluster level and random effects were better estimated using mixture models. Our empirical analyses demonstrated that using mixture models facilitates the identification of the diversity of growth and reproductive tactics occurring within a population. Therefore, using this modelling framework allows testing for the presence of clusters and, when clusters occur, provides reliable estimates of fixed and random effects for each cluster of the population. In the presence or expectation of clusters, using mixture models offers a suitable extension of mixed models, particularly when evolutionary ecologists aim at identifying how ecological and evolutionary processes change within a population. Mixture regression models therefore provide a valuable addition to the statistical toolbox of evolutionary ecologists. As these models are complex and have their own limitations, we provide recommendations to guide future users.     

Molecular‐clock methods for estimating evolutionary rates and timescales

The molecular clock presents a means of estimating evolutionary rates and timescales using genetic data. These estimates can lead to important insights into evolutionary processes and mechanisms, as well as providing a framework for further biological analyses. To deal with rate variation among genes and among lineages, a diverse range of molecular‐clock methods have been developed. These methods have been implemented in various software packages and differ in their statistical properties, ability to handle different models of rate variation, capacity to incorporate various forms of calibrating information and tractability for analysing large data sets. Choosing a suitable molecular‐clock model can be a challenging exercise, but a number of model‐selection techniques are available. In this review, we describe the different forms of evolutionary rate heterogeneity and explain how they can be accommodated in molecular‐clock analyses. We provide an outline of the various clock methods and models that are available, including the strict clock, local clocks, discrete clocks and relaxed clocks. Techniques for calibration and clock‐model selection are also described, along with methods for handling multilocus data sets. We conclude our review with some comments about the future of molecular clocks.     

Eco‐evolutionary feedback promotes Red Queen dynamics and selects for sex in predator populations

Although numerous hypotheses exist to explain the overwhelming presence of sexual reproduction across the tree of life, we still cannot explain its prevalence when considering all inherent costs involved. The Red Queen hypothesis states that sex is maintained because it can create novel genotypes with a selective advantage. This occurs when the interactions between species induce frequent environmental change. Here, we investigate whether coevolution and eco‐evolutionary feedback dynamics in a predator‐prey system allows for indirect selection and maintenance of sexual reproduction in the predator. Combining models and chemostat experiments of a rotifer‐algae system we show a continuous feedback between population and trait change along with recurrent shifts from selection by predation and competition for a limited resource. We found that a high propensity for sex was indirectly selected and was maintained in rotifer populations within environments containing these eco‐evolutionary dynamics; whereas within environments under constant conditions, predators evolved rapidly to lower levels of sex. Thus, our results indicate that the influence of eco‐evolutionary feedback dynamics on the overall evolutionary change has been underestimated.     

Population and evolutionary dynamics in spatially structured seasonally varying environments

Increasingly imperative objectives in ecology are to understand and forecast population dynamic and evolutionary responses to seasonal environmental variation and change. Such population and evolutionary dynamics result from immediate and lagged responses of all key life‐history traits, and resulting demographic rates that affect population growth rate, to seasonal environmental conditions and population density. However, existing population dynamic and eco‐evolutionary theory and models have not yet fully encompassed within‐individual and among‐individual variation, covariation, structure and heterogeneity, and ongoing evolution, in a critical life‐history trait that allows individuals to respond to seasonal environmental conditions: seasonal migration. Meanwhile, empirical studies aided by new animal‐tracking technologies are increasingly demonstrating substantial within‐population variation in the occurrence and form of migration versus year‐round residence, generating diverse forms of ‘partial migration’ spanning diverse species, habitats and spatial scales. Such partially migratory systems form a continuum between the extreme scenarios of full migration and full year‐round residence, and are commonplace in nature. Here, we first review basic scenarios of partial migration and associated models designed to identify conditions that facilitate the maintenance of migratory polymorphism. We highlight that such models have been fundamental to the development of partial migration theory, but are spatially and demographically simplistic compared to the rich bodies of population dynamic theory and models that consider spatially structured populations with dispersal but no migration, or consider populations experiencing strong seasonality and full obligate migration. Second, to provide an overarching conceptual framework for spatio‐temporal population dynamics, we define a ‘partially migratory meta‐population’ system as a spatially structured set of locations that can be occupied by different sets of resident and migrant individuals in different seasons, and where locations that can support reproduction can also be linked by dispersal. We outline key forms of within‐individual and among‐individual variation and structure in migration that could arise within such systems and interact with variation in individual survival, reproduction and dispersal to create complex population dynamics and evolutionary responses across locations, seasons, years and generations. Third, we review approaches by which population dynamic and eco‐evolutionary models could be developed to test hypotheses regarding the dynamics and persistence of partially migratory meta‐populations given diverse forms of seasonal environmental variation and change, and to forecast system‐specific dynamics. To demonstrate one such approach, we use an evolutionary individual‐based model to illustrate that multiple forms of partial migration can readily co‐exist in a simple spatially structured landscape. Finally, we summarise recent empirical studies that demonstrate key components of demographic structure in partial migration, and demonstrate diverse associations with reproduction and survival. We thereby identify key theoretical and empirical knowledge gaps that remain, and consider multiple complementary approaches by which these gaps can be filled in order to elucidate population dynamic and eco‐evolutionary responses to spatio‐temporal seasonal environmental variation and change.     

Evaluating character partitioning and molecular models in plastid phylogenomics at low taxonomic levels: A case study using Amphilophium (Bignonieae,Bignoniaceae)

The accurate analyses of massive amounts of data obtained through next‐generation sequencing depend on the selection of appropriate evolutionary models. Many plastid phylogenomic studies typically analyze plastome data as a single partition, or divided by a region, using a concatenate “supergene” approach. The effects of molecular evolutionary models and character partition strategies on plastome‐based phylogenies have generally been evaluated at higher taxonomic levels in green plants. Using plastome data from 32 species of Amphilophium, a genus of Neotropical lianas, we explored potential sources of topological incongruence with different plastid genome datasets and approaches. Specifically, we evaluated the effects of compositional heterogeneity, codon usage bias, positive selection, and incomplete lineage sorting as sources of systematic error (i.e., the recovery of well‐supported conflicting topologies). We compared different datasets (e.g., non‐coding regions, exons, and codon‐aligned and translated amino acids) using concatenated approaches under site‐heterogeneous and site‐homogeneous models, as well as multispecies coalescent (MSC) methods. We found incongruences in recovered phylogenetic relationships, which were mainly located in short internodes. The MSC and concatenated approaches recovered similar topologies. The analysis of GC content and codon usage bias indicated higher substitution rates and AT excess at the third codon positions, and we found evidence of positive selection in 3% of amino acid sites. There were no significant differences among species in site biochemical profiles. We argue that the selection of appropriate partition strategies and evolutionary models is important to increase accuracy in phylogenetic relationships, even when using plastome datasets, which is still the primarily used genome in plant phylogenetics.     

Cultural niche construction and human evolution

Organisms frequently choose, regulate, construct and destroy important components of their environments, in the process changing the selection pressures to which they and other organisms are exposed. We refer to these processes as niche construction. In humans, culture has greatly amplified our capacity for niche construction and our ability to modify selection pressures. We use gene‐culture coevolutionary models to explore the evolutionary consequences of culturally generated niche construction through human evolution. Our analysis suggests that where cultural traits are transmitted in an unbiased fashion from parent to offspring, cultural niche construction will have a similar effect to gene‐based niche construction. However, cultural transmission biases favouring particular cultural traits may either increase or reduce the range of parameter space over which niche construction has an impact, which means that niche construction with biased transmission will either have a much smaller or a much bigger effect than gene‐based niche construction. The analysis also reveals circumstances under which cultural transmission can overwhelm natural selection, accelerate the rate at which a favoured gene spreads, initiate novel evolutionary events and trigger hominid speciation. Because cultural processes typically operate faster than natural selection, cultural niche construction probably has more profound consequences than gene‐based niche construction, and is likely to have played an important role in human evolution.