Bayesian estimation of recent migration rates after a spatial expansion

Unravelling the mystery of endemic versus translocated populations of the endangered Australian lungfish (Neoceratodus forsteri)

The Australian lungfish is a primitive and endangered representative of the subclass Dipnoi. The distribution of this species is limited to south-east Queensland, with some populations considered endemic and others possibly descending from translocations in the late nineteenth century shortly after European discovery. Attempts to resolve the historical distribution of this species have met with conflicting results based on descriptive genetic studies. Understanding if all populations are endemic or some are the result of, or influenced by, translocation events, has implications for conservation management. In this work, we analysed the genetic variation at three types of markers (mtDNA genomes, 11 STRs and 5196 nuclear SNPs) using the approximate Bayesian computation (ABC) algorithm to compare several demographic models. We postulated different contributions of Mary River and Burnett River gene pools into the Brisbane River and North Pine River populations, related to documented translocation events. We ran the analysis for each marker type separately, and we also estimated the posterior probabilities of the models combining the markers. Nuclear SNPs have the highest power to correctly identify the true model among the simulated datasets (where the model was known), but different marker types typically provided similar answers. The most supported demographic model able to explain the real dataset implies that an endemic gene pool is still present in the Brisbane and North Pine Rivers and coexists with the gene pools derived from past documented translocation events. These results support the view that ABC modelling can be useful to reconstruct complex historical translocation events with contemporary implications, and will inform ongoing conservation efforts for the endangered and iconic Australian lungfish.

Approximate Bayesian computational methods to estimate the strength of divergent selection in population genomics models

Statistical estimation of parameters in large models of evolutionary processes is often too computationally inefficient to pursue using exact model likelihoods, even with single-nucleotide polymorphism (SNP) data, which offers a way to reduce the size of genetic data while retaining relevant information. Approximate Bayesian Computation (ABC) to perform statistical inference about parameters of large models takes the advantage of simulations to bypass direct evaluation of model likelihoods. We develop a mechanistic model to simulate forward-in-time divergent selection with variable migration rates, modes of reproduction (sexual, asexual), length and number of migration-selection cycles. We investigate the computational feasibility of ABC to perform statistical inference and study the quality of estimates on the position of loci under selection and the strength of selection. To expand the parameter space of positions under selection, we enhance the model by implementing an outlier scan on summarized observed data. We evaluate the usefulness of summary statistics well-known to capture the strength of selection, and assess their informativeness under divergent selection. We also evaluate the effect of genetic drift with respect to an idealized deterministic model with single-locus selection. We discuss the role of the recombination rate as a confounding factor in estimating the strength of divergent selection, and emphasize its importance in break down of linkage disequilibrium (LD). We answer the question for which part of the parameter space of the model we recover strong signal for estimating the selection, and determine whether population differentiation-based summary statistics or LD-based summary statistics perform well in estimating selection.

Extensive introgression at late stages of species formation: Insights from grasshopper hybrid zones

The process of species formation is characterised by the accumulation of multiple reproductive barriers. The evolution of hybrid male sterility, or Haldane's rule, typically characterises later stages of species formation, when reproductive isolation is strongest. Yet, understanding how quickly reproductive barriers evolve and their consequences for maintaining genetic boundaries between emerging species remains a challenging task because it requires studying taxa that hybridise in nature. Here, we address these questions using the meadow grasshopper Pseudochorthippus parallelus, where populations that show multiple reproductive barriers, including hybrid male sterility, hybridise in two natural hybrid zones. Using mitochondrial data, we infer that such populations have diverged some 100,000 years ago, at the beginning of the last glacial cycle in Europe. Nuclear data shows that contractions at multiple glacial refugia, and post-glacial expansions have facilitated genetic differentiation between lineages that today interact in hybrid zones. We find extensive introgression throughout the sampled species range, irrespective of current strength of reproductive isolation. Populations exhibiting hybrid male sterility in two hybrid zones show repeatable patterns of genomic differentiation, consistent with shared genomic constraints affecting ancestral divergence or with the role of those regions in reproductive isolation. Together, our results suggest that reproductive barriers that characterise late stages of species formation can evolve relatively quickly, particularly when associated with strong demographic changes. Moreover, we show that such barriers persist in the face of extensive gene flow, allowing future studies to identify associated genomic regions.

Demographic inferences after a range expansion can be biased: the test case of the blacktip reef shark (Carcharhinus melanopterus)

The evolutionary history of species is a dynamic process as they modify, expand, and contract their spatial distributions over time. Range expansions (REs) occur through a series of founder events that are followed by migration among neighboring demes. The process usually results in structured metapopulations and leaves a distinct signature in the genetic variability of species. Explicitly modeling the consequences of complex demographic events such as REs is computationally very intensive. Here we propose an an alternative approach that requires less computational effort than a comprehensive RE model, but that can recover the demography of species undergoing a RE, by combining spatially explicit modelling with simplified but realistic metapopulation models. We examine the demographic and colonization history of Carcharhinus melanopterus, an abundant reef-associated shark, as a test case. We first used a population genomics approach to statistically confirm the occurrence of a RE in C. melanopterus, and identify its origin in the Indo-Australian Archipelago. Spatial genetic modelling identified two waves of stepping-stone colonization: an eastward wave moving through the Pacific and a westward one moving through the Indian Ocean. We show that metapopulation models best describe the demographic history of this species and that not accounting for this may lead to incorrectly interpreting the observed genetic variation as signals of widespread population bottlenecks. Our study highlights insights that can be gained about demography by coupling metapopulation models with spatial modeling and underscores the need for cautious interpretation of population genetic data when advancing conservation priorities.

Analysis of the R1b-DF27 haplogroup shows that a large fraction of Iberian Y-chromosome lineages originated recently in situ

Haplogroup R1b-M269 comprises most Western European Y chromosomes; of its main branches, R1b-DF27 is by far the least known, and it appears to be highly prevalent only in Iberia. We have genotyped 1072 R1b-DF27 chromosomes for six additional SNPs and 17 Y-STRs in population samples from Spain, Portugal and France in order to further characterize this lineage and, in particular, to ascertain the time and place where it originated, as well as its subsequent dynamics. We found that R1b-DF27 is present in frequencies ~40% in Iberian populations and up to 70% in Basques, but it drops quickly to 6–20% in France. Overall, the age of R1b-DF27 is estimated at ~4,200 years ago, at the transition between the Neolithic and the Bronze Age, when the Y chromosome landscape of W Europe was thoroughly remodeled. In spite of its high frequency in Basques, Y-STR internal diversity of R1b-DF27 is lower there, and results in more recent age estimates; NE Iberia is the most likely place of origin of DF27. Subhaplogroup frequencies within R1b-DF27 are geographically structured, and show domains that are reminiscent of the pre-Roman Celtic/Iberian division, or of the medieval Christian kingdoms.

Early mutation bursts in colorectal tumors

Tumor growth is an evolutionary process involving accumulation of mutations, copy number alterations, and cancer stem cell (CSC) division and differentiation. As direct observation of this process is impossible, inference regarding when mutations occur and how stem cells divide is difficult. However, this ancestral information is encoded within the tumor itself, in the form of intratumoral heterogeneity of the tumor cell genomes. Here we present a framework that allows simulation of these processes and estimation of mutation rates at the various stages of tumor development and CSC division patterns for single-gland sequencing data from colorectal tumors. We parameterize the mutation rate and the CSC division pattern, and successfully retrieve their posterior distributions based on DNA sequence level data. Our approach exploits Approximate Bayesian Computation (ABC), a method that is becoming widely-used for problems of ancestral inference.

Revealing patterns of cultural transmission from frequency data: equilibrium and non-equilibrium assumptions

A long tradition of cultural evolutionary studies has developed a rich repertoire of mathematical models of social learning. Early studies have laid the foundation of more recent endeavours to infer patterns of cultural transmission from observed frequencies of a variety of cultural data, from decorative motifs on potsherds to baby names and musical preferences. While this wide range of applications provides an opportunity for the development of generalisable analytical workflows, archaeological data present new questions and challenges that require further methodological and theoretical discussion. Here we examine the decorative motifs of Neolithic pottery from an archaeological assemblage in Western Germany, and argue that the widely used (and relatively undiscussed) assumption that observed frequencies are the result of a system in equilibrium conditions is unwarranted, and can lead to incorrect conclusions. We analyse our data with a simulation-based inferential framework that can overcome some of the intrinsic limitations in archaeological data, as well as handle both equilibrium conditions and instances where the mode of cultural transmission is time-variant. Results suggest that none of the models examined can produce the observed pattern under equilibrium conditions, and suggest. instead temporal shifts in the patterns of cultural transmission.

On the role played by the carrying capacity and the ancestral population size during a range expansion

Most species are structured and various population genetics models have been proposed to investigate their history. For mathematical tractability, most of these models make the simplifying assumption of equilibrium. Here we focus on the properties of a nonequilibrium spatial explicit model, range expansions (REs). Despite their abundance, many details of their genetic consequences need yet to be fully investigated. The model we studied is characterized by four main parameters: the effective population size of each deme (N), the migration rate per generation per deme (m), the time of the expansion (T_exp) and the effective size of the deme from which the expansion started (N_anc). By means of extensive coalescent simulations, we focused on two aspects of range expansions for fixed Nm: (1) the separate influence of N and m and (2) the role of N_anc. We compared our results with an equilibrium stepping stone model and found two main features typical of REs: an excess of rare variants for larger N and a complex interaction between N, T_exp and N_anc in shaping the degree of population differentiation (which depends only on Nm in the stepping stone model). Finally, we developed an approximate Bayesian computation approach to jointly estimate N and m and to infer N_anc. When applied to pseudo-observed data sets, we could correctly recover both N and m (but not N_anc), provided a large number of demes were sampled. These findings highlight how it will be possible to estimate the dispersal rate in nonequilibrium metapopulations by using population genetics approaches.

Differential influences of local subpopulations on regional diversity and differentiation for greater sage-grouse (Centrocercus urophasianus)

The distribution of spatial genetic variation across a region can shape evolutionary dynamics and impact population persistence. Local population dynamics and among-population dispersal rates are strong drivers of this spatial genetic variation, yet for many species we lack a clear understanding of how these population processes interact in space to shape within-species genetic variation. Here, we used extensive genetic and demographic data from 10 subpopulations of greater sage-grouse to parameterize a simulated approximate Bayesian computation (ABC) model and (i) test for regional differences in population density and dispersal rates for greater sage-grouse subpopulations in Wyoming, and (ii) quantify how these differences impact subpopulation regional influence on genetic variation. We found a close match between observed and simulated data under our parameterized model and strong variation in density and dispersal rates across Wyoming. Sensitivity analyses suggested that changes in dispersal (via landscape resistance) had a greater influence on regional differentiation, whereas changes in density had a greater influence on mean diversity across all subpopulations. Local subpopulations, however, varied in their regional influence on genetic variation. Decreases in the size and dispersal rates of central populations with low overall and net immigration (i.e. population sources) had the greatest negative impact on genetic variation. Overall, our results provide insight into the interactions among demography, dispersal and genetic variation and highlight the potential of ABC to disentangle the complexity of regional population dynamics and project the genetic impact of changing conditions.

Recombination hotspots: Models and tools for detection

Recombination hotspots are the regions within the genome where the rate, and the frequency of recombination are optimum with a size varying from 1 to 2kb. The recombination event is mediated by the double-stranded break formation, guided by the combined enzymatic action of DNA topoisomerase and Spo 11 endonuclease. These regions are distributed non-uniformly throughout the human genome and cause distortions in the genetic map. Numerous lines of evidence suggest that the number of hotspots known in humans has increased manifold in recent years. A few facts about the hotspot evolutions were also put forward, indicating the differences in the hotspot position between chimpanzees and humans. In mice, recombination hot spots were found to be clustered within the major histocompatibility complex (MHC) region. Several models, that help explain meiotic recombination has been proposed. Moreover, scientists also developed some computational tools to locate the hotspot position and estimate their recombination rate in humans is of great interest to population and medical geneticists. Here we reviewed the molecular mechanisms, models and in silico prediction techniques of hot spot residues.

Using ABC and microsatellite data to detect multiple introductions of invasive species from a single source

The introduction of invasive species to new locations (that is, biological invasions) can have major impact on biodiversity, agriculture and public health. As such, determining the routes and modality of introductions with genetic data has become a fundamental goal in molecular ecology. To assist with this goal, new statistical methods and frameworks have been developed, such as approximate Bayesian computation (ABC) for inferring invasion history. Here, we present a model of invasion accounting for multiple introductions from a single source (MISS), a heretofore largely unexplored model. We simulate microsatellite data to evaluate the power of ABC to distinguish between single and multiple introductions from the same source, under a range of demographic parameters. We also apply ABC to microsatellite data from three invasions of bumblebee in New Zealand. In addition, we assess the performance of several methods of summary statistics selection. Our simulated results suggested good ability to distinguish between one- and two-wave models over much but not all of the parameter space tested, independent of summary statistics used. Globally, parameter estimation was good except for bottleneck timing. For one of the bumblebee species, we clearly rejected the MISS model, while for the other two we found inconclusive results. Since a second wave may provide genetic reinforcement to initial colonists, help relieve inbreeding among founders, or increase the hazard of the invasion, its detection may be crucial for managing invasions; we suggest that the MISS model could be considered as a potential model in future theoretical and empirical studies of invasions.

Continental-scale footprint of balancing and positive selection in a small rodent (Microtus arvalis)

Genetic adaptation to different environmental conditions is expected to lead to large differences between populations at selected loci, thus providing a signature of positive selection. Whereas balancing selection can maintain polymorphisms over long evolutionary periods and even geographic scale, thus leads to low levels of divergence between populations at selected loci. However, little is known about the relative importance of these two selective forces in shaping genomic diversity, partly due to difficulties in recognizing balancing selection in species showing low levels of differentiation. Here we address this problem by studying genomic diversity in the European common vole (Microtus arvalis) presenting high levels of differentiation between populations (average F_ST = 0.31). We studied 3,839 Amplified Fragment Length Polymorphism (AFLP) markers genotyped in 444 individuals from 21 populations distributed across the European continent and hence over different environmental conditions. Our statistical approach to detect markers under selection is based on a Bayesian method specifically developed for AFLP markers, which treats AFLPs as a nearly codominant marker system, and therefore has increased power to detect selection. The high number of screened populations allowed us to detect the signature of balancing selection across a large geographic area. We detected 33 markers potentially under balancing selection, hence strong evidence of stabilizing selection in 21 populations across Europe. However, our analyses identified four-times more markers (138) being under positive selection, and geographical patterns suggest that some of these markers are probably associated with alpine regions, which seem to have environmental conditions that favour adaptation. We conclude that despite favourable conditions in this study for the detection of balancing selection, this evolutionary force seems to play a relatively minor role in shaping the genomic diversity of the common vole, which is more influenced by positive selection and neutral processes like drift and demographic history.

Comparing phylogeographic hypotheses by simulating DNA sequences under a spatially explicit model of coalescence

Computer simulations of genetic data are increasingly used to investigate the impact of complex historical scenarios on patterns of genetic variation. Yet, in most empirical studies, relatively large portions of species ranges are often treated as panmictic populations, ignoring the underlying spatial context. In some cases, however, a more accurate spatial model is required. We use a spatially explicit model of coalescence (easily constructed by overlaying a two-dimensional grid on maps displaying an estimate of past and current species ranges) to evaluate the potential of several summary statistics to differentiate three typical phylogeographic scenarios. We first explore the variation of each summary statistic within the boundaries of each phylogeographic scenario, and identify those that appear most promising for a comparison of historical scenarios and/or to infer historical parameters. We then combine a selected set of summary statistics in a single chi-square statistic and evaluate whether it can be used to differentiate past geographic fragmentation or range expansion from a simple scenario of isolation by distance. We also investigate the benefits of using a spatially explicit model by comparing its performance to alternative models that are less spatially explicit (lower geographic resolution). The results identify conditions in which each summary statistic is useful to infer the evolution of a species range, and allow us to validate our spatially explicit model of coalescence and our procedure to compare simulated and observed sequence data. We also provide a detailed description of the spatially explicit model of coalescence used, which is currently lacking.

An approximate Bayesian computation approach to parameter estimation in a stochastic stage-structured population model

Complex population processes may require equally complex models, which can lead to analytically intractable estimation problems. Approximate Bayesian computation (ABC) is a computational tool for parameter estimation in situations where likelihoods cannot be computed. Instead of using likelihoods, ABC methods quantify the similarities between an observed data set and repeated simulations from a model. A practical obstacle to implementing an ABC algorithm is selecting summary statistics and distance metrics that accurately capture the main features of the data. We demonstrate the application of a sequential Monte Carlo ABC sampler (ABC SMC) to parameter estimation of a general stochastic stage-structured population model with ongoing reproduction and heterogeneity in development and mortality. Individual variation in demographic traits has considerable consequences for population dynamics in many systems, but including it in a population model by explicitly allowing stage durations to follow a realistic distribution creates a complex model. We applied the ABC SMC to fit the model to a simulated representative data set with known underlying parameters to evaluate the performance of the algorithm. We also introduced a systematic method for selecting summary statistics and distance metrics, using simulated data and receiver operating characteristic (ROC) curves from classification theory. Evaluations suggest that the approach is promising for model inference in our example of realistic stage-structured population models.

Amount of information needed for model choice in Approximate Bayesian Computation

Approximate Bayesian Computation (ABC) has become a popular technique in evolutionary genetics for elucidating population structure and history due to its flexibility. The statistical inference framework has benefited from significant progress in recent years. In population genetics, however, its outcome depends heavily on the amount of information in the dataset, whether that be the level of genetic variation or the number of samples and loci. Here we look at the power to reject a simple constant population size coalescent model in favor of a bottleneck model in datasets of varying quality. Not only is this power dependent on the number of samples and loci, but it also depends strongly on the level of nucleotide diversity in the observed dataset. Whilst overall model choice in an ABC setting is fairly powerful and quite conservative with regard to false positives, detecting weaker bottlenecks is problematic in smaller or less genetically diverse datasets and limits the inferences possible in non-model organism where the amount of information regarding the two models is often limited. Our results show it is important to consider these limitations when performing an ABC analysis and that studies should perform simulations based on the size and nature of the dataset in order to fully assess the power of the study.

Can we continue to neglect genomic variation in introgression rates when inferring the history of speciation? A case study in a Mytilus hybrid zone

The use of molecular data to reconstruct the history of divergence and gene flow between populations of closely related taxa represents a challenging problem. It has been proposed that the long-standing debate about the geography of speciation can be resolved by comparing the likelihoods of a model of isolation with migration and a model of secondary contact. However, data are commonly only fit to a model of isolation with migration and rarely tested against the secondary contact alternative. Furthermore, most demographic inference methods have neglected variation in introgression rates and assume that the gene flow parameter (Nm) is similar among loci. Here, we show that neglecting this source of variation can give misleading results. We analysed DNA sequences sampled from populations of the marine mussels, Mytilus edulis and M. galloprovincialis, across a well-studied mosaic hybrid zone in Europe and evaluated various scenarios of speciation, with or without variation in introgression rates, using an Approximate Bayesian Computation (ABC) approach. Models with heterogeneous gene flow across loci always outperformed models assuming equal migration rates irrespective of the history of gene flow being considered. By incorporating this heterogeneity, the best-supported scenario was a long period of allopatric isolation during the first three-quarters of the time since divergence followed by secondary contact and introgression during the last quarter. By contrast, constraining migration to be homogeneous failed to discriminate among any of the different models of gene flow tested. Our simulations thus provide statistical support for the secondary contact scenario in the European Mytilus hybrid zone that the standard coalescent approach failed to confirm. Our results demonstrate that genomic variation in introgression rates can have profound impacts on the biological conclusions drawn from inference methods and needs to be incorporated in future studies.

Back to BaySICS: a user-friendly program for Bayesian Statistical Inference from Coalescent Simulations

Inference of population demographic history has vastly improved in recent years due to a number of technological and theoretical advances including the use of ancient DNA. Approximate Bayesian computation (ABC) stands among the most promising methods due to its simple theoretical fundament and exceptional flexibility. However, limited availability of user-friendly programs that perform ABC analysis renders it difficult to implement, and hence programming skills are frequently required. In addition, there is limited availability of programs able to deal with heterochronous data. Here we present the software BaySICS: Bayesian Statistical Inference of Coalescent Simulations. BaySICS provides an integrated and user-friendly platform that performs ABC analyses by means of coalescent simulations from DNA sequence data. It estimates historical demographic population parameters and performs hypothesis testing by means of Bayes factors obtained from model comparisons. Although providing specific features that improve inference from datasets with heterochronous data, BaySICS also has several capabilities making it a suitable tool for analysing contemporary genetic datasets. Those capabilities include joint analysis of independent tables, a graphical interface and the implementation of Markov-chain Monte Carlo without likelihoods.

Origin and demographic history of the endemic Taiwan spruce (Picea morrisonicola)

Taiwan spruce (Picea morrisonicola) is a vulnerable conifer species endemic to the island of Taiwan. A warming climate and competition from subtropical tree species has limited the range of Taiwan spruce to the higher altitudes of the island. Using seeds sampled from an area in the central mountain range of Taiwan, 15 nuclear loci were sequenced in order to measure genetic variation and to assess the long-term genetic stability of the species. Genetic diversity is low and comparable to other spruce species with limited ranges such as Picea breweriana, Picea chihuahuana, and Picea schrenkiana. Importantly, analysis using approximate Bayesian computation (ABC) provides evidence for a drastic decline in the effective population size approximately 0.3–0.5 million years ago (mya). We used simulations to show that this is unlikely to be a false-positive result due to the limited sample used here. To investigate the phylogenetic origin of Taiwan spruce, additional sequencing was performed in the Chinese spruce Picea wilsonii and combined with previously published data for three other mainland China species, Picea purpurea, Picea likiangensis, and P. schrenkiana. Analysis of population structure revealed that P. morrisonicola clusters most closely with P. wilsonii, and coalescent analyses using the program MIMAR dated the split to 4–8 mya, coincidental to the formation of Taiwan. Considering the population decrease that occurred after the split, however, led to a much more recent origin.

Approximation Bayesian Computation

Approximation Bayesian computation [ABC] is an analysis approach that has arisen in response to the recent trend to collect data that is of a magnitude far higher than has been historically the case. This has led to many existing methods become intractable because of difficulties in calculating the likelihood function. ABC circumvents this issue by replacing calculation of the likelihood with a simulation step in which it is estimated in one way or another. In this review we give an overview of the ABC approach, giving examples of some of the more popular specific forms of ABC. We then discuss some of the areas of most active research and application in the field, specifically, choice of low-dimensional summaries of complex datasets and metrics for measuring similarity between observed and simulated data. Next, we consider the question of how to do model selection in an ABC context. Finally, we discuss an area of growing prominence in the ABC world, use of ABC methods in genetic pathway inference.

Crossing the species barrier: genomic hotspots of introgression between two highly divergent Ciona intestinalis species

Inferring a realistic demographic model from genetic data is an important challenge to gain insights into the historical events during the speciation process and to detect molecular signatures of selection along genomes. Recent advances in divergence population genetics have reported that speciation in face of gene flow occurred more frequently than theoretically expected, but the approaches used did not account for genome-wide heterogeneity (GWH) in introgression rates. Here, we investigate the impact of GWH on the inference of divergence with gene flow between two cryptic species of the marine model Ciona intestinalis by analyzing polymorphism and divergence patterns in 852 protein-coding sequence loci. These morphologically similar entities are highly diverged molecular-wise, but evidence of hybridization has been reported in both laboratory and field studies. We compare various speciation models and test for GWH under the approximate Bayesian computation framework. Our results demonstrate the presence of significant extents of gene flow resulting from a recent secondary contact after >3 My of divergence in isolation. The inferred rates of introgression are relatively low, highly variable across loci and mostly unidirectional, which is consistent with the idea that numerous genetic incompatibilities have accumulated over time throughout the genomes of these highly diverged species. A genomic map of the level of gene flow identified two hotspots of introgression, that is, large genome regions of unidirectional introgression. This study clarifies the history and degree of isolation of two cryptic and partially sympatric model species and provides a methodological framework to investigate GWH at various stages of speciation process.

Systems Modeling at Multiple Levels of Regulation: Linking Systems and Genetic Networks to Spatially Explicit Plant Populations

Selection and adaptation of individuals to their underlying environments are highly dynamical processes, encompassing interactions between the individual and its seasonally changing environment, synergistic or antagonistic interactions between individuals and interactions amongst the regulatory genes within the individual. Plants are useful organisms to study within systems modeling because their sedentary nature simplifies interactions between individuals and the environment, and many important plant processes such as germination or flowering are dependent on annual cycles which can be disrupted by climate behavior. Sedentism makes plants relevant candidates for spatially explicit modeling that is tied in with dynamical environments. We propose that in order to fully understand the complexities behind plant adaptation, a system that couples aspects from systems biology with population and landscape genetics is required. A suitable system could be represented by spatially explicit individual-based models where the virtual individuals are located within time-variable heterogeneous environments and contain mutable regulatory gene networks. These networks could directly interact with the environment, and should provide a useful approach to studying plant adaptation.

Sex-specific clines support incipient speciation in a common European mammal

Hybrid zones provide excellent opportunities to study processes and mechanisms underlying reproductive isolation and speciation. Here we investigated sex-specific clines of molecular markers in hybrid zones of morphologically cryptic yet genetically highly-diverged evolutionary lineages of the European common vole (Microtus arvalis). We analyzed the position and width of four secondary contact zones along three independent transects in the region of the Alps using maternally (mitochondrial DNA) and paternally (Y-chromosome) inherited genetic markers. Given male-biased dispersal in the common vole, a selectively neutral secondary contact would show broader paternal marker clines than maternal ones. In a selective case, for example, involving a form of Haldane's rule, Y-chromosomal clines would not be expected to be broader than maternal markers because they are transmitted by the heterogametic sex and thus gene flow would be restricted. Consistent with the selective case, paternal clines were significantly narrower or at most equal in width to maternal clines in all contact zones. In addition, analyses using maximum likelihood cline-fitting detected a shift of paternal relative to maternal clines in three of four contact zones. These patterns suggest that processes at the contact zones in the common vole are not selectively neutral, and that partial reproductive isolation is already established between these evolutionary lineages. We conclude that hybrid zone movement, sexual selection and/or genetic incompatibilities are likely associated with an unusual unidirectional manifestation of Haldane's rule in this common European mammal.

Inferring recent historic abundance from current genetic diversity

Recent historic abundance is an elusive parameter of great importance for conserving endangered species and understanding the pre-anthropogenic state of the biosphere. The number of studies that have used population genetic theory to estimate recent historic abundance from contemporary levels of genetic diversity has grown rapidly over the last two decades. Such assessments often yield unexpectedly large estimates of historic abundance. We review the underlying theory and common practices of estimating recent historic abundance from contemporary genetic diversity, and critically evaluate the potential issues at various estimation steps. A general issue of mismatched spatio-temporal scales between the estimation itself and the objective of the estimation emerged from our assessment; genetic diversity-based estimates of recent historic abundance represent long-term averages, whereas the objective typically is an estimate of recent abundance for a specific population. Currently, the most promising approach to estimate the difference between recent historic and contemporary abundance requires that genetic data be collected from samples of similar spatial and temporal duration. Novel genome-enabled inference methods may be able to utilize additional information of dense genome-wide distributions of markers, such as of identity-by-descent tracts, to infer recent historic abundance from contemporary samples only.

A novel approach for choosing summary statistics in approximate Bayesian computation

The choice of summary statistics is a crucial step in approximate Bayesian computation (ABC). Since statistics are often not sufficient, this choice involves a trade-off between loss of information and reduction of dimensionality. The latter may increase the efficiency of ABC. Here, we propose an approach for choosing summary statistics based on boosting, a technique from the machine-learning literature. We consider different types of boosting and compare them to partial least-squares regression as an alternative. To mitigate the lack of sufficiency, we also propose an approach for choosing summary statistics locally, in the putative neighborhood of the true parameter value. We study a demographic model motivated by the reintroduction of Alpine ibex (Capra ibex) into the Swiss Alps. The parameters of interest are the mean and standard deviation across microsatellites of the scaled ancestral mutation rate (θ(anc) = 4N(e)u) and the proportion of males obtaining access to matings per breeding season (ω). By simulation, we assess the properties of the posterior distribution obtained with the various methods. According to our criteria, ABC with summary statistics chosen locally via boosting with the L(2)-loss performs best. Applying that method to the ibex data, we estimate θ(anc)≈ 1.288 and find that most of the variation across loci of the ancestral mutation rate u is between 7.7 × 10(-4) and 3.5 × 10(-3) per locus per generation. The proportion of males with access to matings is estimated as ω≈ 0.21, which is in good agreement with recent independent estimates.

Patterns and processes of dispersal behaviour in arvicoline rodents

A good understanding of mammalian societies requires measuring patterns and comprehending processes of dispersal in each sex. We investigated dispersal behaviour in arvicoline rodents, a subfamily of mammals widespread in northern temperate environments and characterized by a multivoltine life cycle. In arvicoline rodents, variation in life history strategies occurs along a continuum from precocial to delayed maturation that reflects seasonal and ecological fluctuations. We compared dispersal across and within species focusing on the effects of external (condition-dependent) and internal (phenotype-dependent) factors. Our data revealed substantial, unexplained variation between species for dispersal distances and a strong variation within species for both dispersal distance and fraction. Some methodological aspects explained variation across studies, which cautions against comparisons that do not control for them. Overall, the species under consideration display frequent short-distance dispersal events and extremely flexible dispersal strategies, but they also have hitherto unexpected capacity to disperse long distances. Female arvicolines are predominantly philopatric relative to males, but we found no clear association between the mating system and the degree of sex bias in dispersal across species. Dispersal is a response to both various proximate and ultimate factors, including competition, inbreeding avoidance, mate searching and habitat quality. In particular, our review suggests that costs and benefits experienced during transience and settlement are prime determinants of condition dependence. Patterns of phenotype-dependent dispersal are idiosyncratic, except for a widespread association between an exploration/activity syndrome and natal dispersal. Consequences for population dynamics and genetic structures are discussed.

Likelihood-based inferences under isolation by distance: two-dimensional habitats and confidence intervals

Likelihood-based methods of inference of population parameters from genetic data in structured populations have been implemented but still little tested in large networks of populations. In this work, a previous software implementation of inference in linear habitats is extended to two-dimensional habitats, and the coverage properties of confidence intervals are analyzed in both cases. Both standard likelihood and an efficient approximation are considered. The effects of misspecification of mutation model and dispersal distribution, and of spatial binning of samples, are considered. In the absence of model misspecification, the estimators have low bias, low mean square error, and the coverage properties of confidence intervals are consistent with theoretical expectations. Inferences of dispersal parameters and of the mutation rate are sensitive to misspecification or to approximations inherent to the coalescent algorithms used. In particular, coalescent approximations are not appropriate to infer the shape of the dispersal distribution. However, inferences of the neighborhood parameter (or of the product of population density and mean square dispersal rate) are generally robust with respect to complicating factors, such as misspecification of the mutation process and of the shape of the dispersal distribution, and with respect to spatial binning of samples. Likelihood inferences appear feasible in moderately sized networks of populations (up to 400 populations in this work), and they are more efficient than previous moment-based spatial regression method in realistic conditions.

Inferring the origin of populations introduced from a genetically structured native range by approximate Bayesian computation: case study of the invasive ladybird Harmonia axyridis

Correct identification of the source population of an invasive species is a prerequisite for testing hypotheses concerning the factors responsible for biological invasions. The native area of invasive species may be large, poorly known and/or genetically structured. Because the actual source population may not have been sampled, studies based on molecular markers may generate incorrect conclusions about the origin of introduced populations. In this study, we characterized the genetic structure of the invasive ladybird Harmonia axyridis in its native area using various population genetic statistics and methods. We found that native area of H. axyridis most probably consisted of two geographically distinct genetic clusters located in eastern and western Asia. We then performed approximate Bayesian computation (ABC) analyses on controlled simulated microsatellite data sets to evaluate (i) the risk of selecting incorrect introduction scenarios, including admixture between sources, when the populations of the native area are genetically structured and sampling is incomplete and (ii) the ability of ABC analysis to minimize such risks by explicitly including unsampled populations in the scenarios compared. Finally, we performed additional ABC analyses on real microsatellite data sets to retrace the origin of biocontrol and invasive populations of H. axyridis, taking into account the possibility that the structured native area may have been incompletely sampled. We found that the invasive population in eastern North America, which has served as the bridgehead for worldwide invasion by H. axyridis, was probably formed by an admixture between the eastern and western native clusters. This admixture may have facilitated adaptation of the bridgehead population.

Choice of summary statistic weights in approximate Bayesian computation

In this paper, we develop a Genetic Algorithm that can address the fundamental problem of how one should weight the summary statistics included in an approximate Bayesian computation analysis built around an accept/reject algorithm, and how one might choose the tolerance for that analysis. We then demonstrate that using weighted statistics, and a well-chosen tolerance, in such an approximate Bayesian computation approach can result in improved performance, when compared to unweighted analyses, using one example drawn purely from statistics and two drawn from the estimation of population genetics parameters.

High-throughput sequencing of complete human mtDNA genomes from the Caucasus and West Asia: high diversity and demographic inferences

To investigate the demographic history of human populations from the Caucasus and surrounding regions, we used high-throughput sequencing to generate 147 complete mtDNA genome sequences from random samples of individuals from three groups from the Caucasus (Armenians, Azeri and Georgians), and one group each from Iran and Turkey. Overall diversity is very high, with 144 different sequences that fall into 97 different haplogroups found among the 147 individuals. Bayesian skyline plots (BSPs) of population size change through time show a population expansion around 40-50 kya, followed by a constant population size, and then another expansion around 15-18 kya for the groups from the Caucasus and Iran. The BSP for Turkey differs the most from the others, with an increase from 35 to 50 kya followed by a prolonged period of constant population size, and no indication of a second period of growth. An approximate Bayesian computation approach was used to estimate divergence times between each pair of populations; the oldest divergence times were between Turkey and the other four groups from the South Caucasus and Iran (~400-600 generations), while the divergence time of the three Caucasus groups from each other was comparable to their divergence time from Iran (average of ~360 generations). These results illustrate the value of random sampling of complete mtDNA genome sequences that can be obtained with high-throughput sequencing platforms.

Enhanced AFLP genome scans detect local adaptation in high-altitude populations of a small rodent (Microtus arvalis)

Adaptation to adverse environmental conditions such as high altitude requires physiological and/or morphological changes. Genome scans provide a means to identify the genetic basis of such adaptations without previous knowledge about the particular genetic variants or traits under selection. In this study, we scanned 3027 amplified fragment length polymorphisms (AFLP) in four populations of the common vole Microtus arvalis for loci associated with local adaptation and high altitude. We investigated voles from two populations at high elevation (~2000 m a.s.l.) representing the upper limit of the altitudinal distribution of the species and two geographically close low-altitude populations (<600 m a.s.l.). Statistical analysis incorporated a new Bayesian F(ST) outlier approach specifically developed for AFLP markers, which considers the intensity of AFLP bands instead of mere presence/absence and allows to derive population-based estimates of allele frequencies and F(IS) values. Computer simulations showed that this approach increases the statistical power of the detection of AFLP markers under selection almost to the power of single nucleotide polymorphism (SNP) data without compromising specificity. Our enhanced genome scan resulted in 20 prime candidate markers for positive selection, which show mostly extremely high allele frequency differences between the low- and high-altitude populations. The comparison of global- and pairwise-enhanced genome scans demonstrated further that very strong selective signatures may also be associated with single populations suggesting the importance of local adaptation in alpine populations of common voles.

SPLATCHE2: a spatially explicit simulation framework for complex demography, genetic admixture and recombination

SUMMARY

SPLATCHE2 is a program to simulate the demography of populations and the resulting molecular diversity for a wide range of evolutionary scenarios. The spatially explicit simulation framework can account for environmental heterogeneity and fluctuations, and it can manage multiple population sources. A coalescent-based approach is used to generate genetic markers mostly used in population genetics studies (DNA sequences, SNPs, STRs or RFLPs). Various combinations of independent, fully or partially linked genetic markers can be produced under a recombination model based on the ancestral recombination graph. Competition between two populations (or species) can also be simulated with user-defined levels of admixture between the two populations. SPLATCHE2 may be used to generate the expected genetic diversity under complex demographic scenarios and can thus serve to test null hypotheses. For model parameter estimation, SPLATCHE2 can easily be integrated into an Approximate Bayesian Computation (ABC) framework.

AVAILABILITY AND IMPLEMENTATION

SPLATCHE2 is a C++ program compiled for Windows and Linux platforms. It is freely available at www.splatche.com, together with its related documentation and example data.

CONTACT

mathias.currat@unige.ch

Genomic scans support repetitive continental colonization events during the rapid radiation of voles (Rodentia: Microtus): the utility of AFLPs versus mitochondrial and nuclear sequence markers

Single locus studies might not resolve phylogenetic relationships and the evolutionary history of taxa. The analysis of multiple markers promises higher resolution, and congruence among loci may indicate that the phylogenies represent the underlying species history. Here, we examine the utility of a genome-wide approach based on amplified fragment length polymorphisms (AFLP) and several DNA sequence markers in resolving phylogenetic signals in the rapidly radiating rodent genus Microtus which produced about 70 vole species within the last 1.2-2 myr. The current Holarctic distribution of Microtus is assumed to have resulted from three independent colonization events out of Asia to North America, Europe, and northern Asia without subsequent colonization, which would have led to deep splits between species from different continents. We investigated this hypothesis of three single colonization events by reconstructing the phylogenetic relationships among species from all three continents based on data from the first exon of the nuclear arginine vasopressin receptor 1a gene (EXON1), an adjacent noncoding region and the mitochondrial cytochrome b gene. The phylogenetic patterns obtained from these sequence markers are contrasted to genome-wide data on more than 1800 amplified fragment length polymorphisms (AFLP) analyzed for the same samples. Our results show that the single sequence markers partially resolve the phylogenetic relationships within Microtus, but with some incongruence mostly between EXON1 and the other loci. However, deeper nodes of the radiation are only weakly supported and neither the combination of the markers nor additional nuclear sequences improved the resolution significantly. AFLPs provided much stronger support for major continent-specific clades, and show also that reciprocal monophyly of American and European voles is incomplete. Our results demonstrate that Microtus voles colonized the American and European continents each repeatedly in several independent events on similar colonization routes during their radiation. More generally, this study supports the suitability of AFLPs as an alternative to sequence markers to resolve the evolutionary history of rapidly radiating taxa.

ABC as a flexible framework to estimate demography over space and time: some cons, many pros

The analysis of genetic variation to estimate demographic and historical parameters and to quantitatively compare alternative scenarios recently gained a powerful and flexible approach: the Approximate Bayesian Computation (ABC). The likelihood functions does not need to be theoretically specified, but posterior distributions can be approximated by simulation even assuming very complex population models including both natural and human-induced processes. Prior information can be easily incorporated and the quality of the results can be analysed with rather limited additional effort. ABC is not a statistical analysis per se, but rather a statistical framework and any specific application is a sort of hybrid between a simulation and a data-analysis study. Complete software packages performing the necessary steps under a set of models and for specific genetic markers are already available, but the flexibility of the method is better exploited combining different programs. Many questions relevant in ecology can be addressed using ABC, but adequate amount of time should be dedicated to decide among alternative options and to evaluate the results. In this paper we will describe and critically comment on the different steps of an ABC analysis, analyse some of the published applications of ABC and provide user guidelines.