Identifying adaptive genetic divergence among populations from genome scans

The identification of signatures of natural selection in genomic surveys has become an area of intense research, stimulated by the increasing ease with which genetic markers can be typed. Loci identified as subject to selection may be functionally important, and hence (weak) candidates for involvement in disease causation. They can also be useful in determining the adaptive differentiation of populations, and exploring hypotheses about speciation. Adaptive differentiation has traditionally been identified from differences in allele frequencies among different populations, summarised by an estimate of F-ST. Low outliers relative to an appropriate neutral population-genetics model indicate loci subject to balancing selection, whereas high outliers suggest adaptive (directional) selection. However, the problem of identifying statistically significant departures from neutrality is complicated by confounding effects on the distribution of F-ST estimates, and current methods have not yet been tested in large-scale simulation experiments. Here, we simulate data from a structured population at many unlinked, diallelic loci that are predominantly neutral but with some loci subject to adaptive or balancing selection. We develop a hierarchical-Bayesian method, implemented via Markov chain Monte Carlo (MCMC), and assess its performance in distinguishing the loci simulated under selection from the neutral loci. We also compare this performance with that of a frequentist method, based on moment-based estimates of F-ST. We find that both methods can identify loci subject to adaptive selection when the selection coefficient is at least five times the migration rate. Neither method could reliably distinguish loci under balancing selection in our simulations, even when the selection coefficient is twenty times the migration rate.

Genetic analysis of complex demographic scenarios: Spatially expanding populations of the cane toad, Bufo marinus

Inferring the spatial expansion dynamics of invading species from molecular data is notoriously difficult due to the complexity of the processes involved. For these demographic scenarios, genetic data obtained from highly variable markers may be profitably combined with specific sampling schemes and information from other sources using a Bayesian approach. The geographic range of the introduced toad Bufo marinus is still expanding in eastern and northern Australia, in each case from isolates established around 1960. A large amount of demographic and historical information is available on both expansion areas. In each area, samples were collected along a transect representing populations of different ages and genotyped at 10 microsatellite loci. Five demographic models of expansion, differing in the dispersal pattern for migrants and founders and in the number of founders, were considered. Because the demographic history is complex, we used an approximate Bayesian method, based on a rejection-regression algorithm. to formally test the relative likelihoods of the five models of expansion and to infer demographic parameters. A stepwise migration-foundation model with founder events was statistically better supported than other four models in both expansion areas. Posterior distributions supported different dynamics of expansion in the studied areas. Populations in the eastern expansion area have a lower stable effective population size and have been founded by a smaller number of individuals than those in the northern expansion area. Once demographically stabilized, populations exchange a substantial number of effective migrants per generation in both expansion areas, and such exchanges are larger in northern than in eastern Australia. The effective number of migrants appears to be considerably lower than that of founders in both expansion areas. We found our inferences to be relatively robust to various assumptions on marker. demographic, and historical features. The method presented here is the only robust, model-based method available so far, which allows inferring complex population dynamics over a short time scale. It also provides the basis for investigating the interplay between population dynamics, drift, and selection in invasive species.

Comparative evaluation of a new effective population size estimator based on approximate Bayesian computation

We describe and evaluate a new estimator of the effective population size (N-e), a critical parameter in evolutionary and conservation biology. This new "SummStat" N-e. estimator is based upon the use of summary statistics in an approximate Bayesian computation framework to infer N-e. Simulations of a Wright-Fisher population with known N-e show that the SummStat estimator is useful across a realistic range of individuals and loci sampled, generations between samples, and N-e values. We also address the paucity of information about the relative performance of N-e estimators by comparing the SUMMStat estimator to two recently developed likelihood-based estimators and a traditional moment-based estimator. The SummStat estimator is the least biased of the four estimators compared. In 32 of 36 parameter combinations investigated rising initial allele frequencies drawn from a Dirichlet distribution, it has the lowest bias. The relative mean square error (RMSE) of the SummStat estimator was generally intermediate to the others. All of the estimators had RMSE > 1 when small samples (n = 20, five loci) were collected a generation apart. In contrast, when samples were separated by three or more generations and Ne less than or equal to 50, the SummStat and likelihood-based estimators all had greatly reduced RMSE. Under the conditions simulated, SummStat confidence intervals were more conservative than the likelihood-based estimators and more likely to include true N-e. The greatest strength of the SummStat estimator is its flexible structure. This flexibility allows it to incorporate any, potentially informative summary statistic from Population genetic data.

Estimation of population growth or decline in genetically monitored populations

This article introduces a new general method for genealogical inference that samples independent genealogical histories using importance sampling (IS) and then samples other parameters with Markov chain Monte Carlo (MCMC). It is then possible to more easily utilize the advantages of importance sampling in a fully Bayesian framework. The method is applied to the problem of estimating recent changes in effective population size from temporally spaced gene frequency data. The method gives the posterior distribution of effective population size at the time of the oldest sample and at the time of the most recent sample, assuming a model of exponential growth or decline during the interval. The effect of changes in number of alleles, number of loci, and sample size on the accuracy of the method is described using test simulations, and it is concluded that these have an approximately equivalent effect. The method is used on three example data sets and problems in interpreting the posterior densities are highlighted and discussed.

Population subdivision and molecular sequence variation: Theory and analysis of Drosophila ananassae data

Population subdivision complicates analysis of molecular variation. Even if neutrality is assumed, three evolutionary forces need to be considered: migration, mutation, and drift. Simplification can be achieved by assuming that the process of migration among and drift within subpopulations is occurring fast compared to Mutation and drift in the entire population. This allows a two-step approach in the analysis: (i) analysis of population subdivision and (ii) analysis of molecular variation in the migrant pool. We model population subdivision using an infinite island model, where we allow the migration/drift parameter Theta to vary among populations. Thus, central and peripheral populations can be differentiated. For inference of Theta, we use a coalescence approach, implemented via a Markov chain Monte Carlo (MCMC) integration method that allows estimation of allele frequencies in the migrant pool. The second step of this approach (analysis of molecular variation in the migrant pool) uses the estimated allele frequencies in the migrant pool for the study of molecular variation. We apply this method to a Drosophila ananassae sequence data set. We find little indication of isolation by distance, but large differences in the migration parameter among populations. The population as a whole seems to be expanding. A population from Bogor (Java, Indonesia) shows the highest variation and seems closest to the species center.

Heterogeneously catalyzed asymmetric hydrogenation of C = C bonds directed by surface-tethered chiral modifiers

Asymmetric hydrogenation of C=C bonds is of the highest importance in organic synthesis, and such reactions are currently carried out with organometallic homogeneous catalysts. Achieving heterogeneous metal-catalyzed hydrogenation, a highly desirable goal, necessitates forcing the crucial enantiodifferentiating step to take place at the metal surface. By synthesis and application of six chiral sulfide ligands that anchor robustly to Pd nanoparticles and resist displacement, we have for the first time accomplished heterogeneous enantioselective catalytic hydrogenation of isophorone. High resolution XPS data established that ligand adsorption from solution occurred exclusively on the Pd nanoparticles and not on the carbon support. All ligands contained a pyrrolidine nitrogen to enable their interaction with the isophorone substrate while the sulfide functionality provided the required interaction with the Pd surface. Enantioselective turnover numbers of up to similar to 100 product molecules per ligand molecule were found with a very large variation in asymmetric induction between ligands: observed enantiomeric excesses increased with increasing size of the alkyl group in the sulfide. This likely reflects varying degrees of ligand dispersion on the surface: bulky substituent groups hinder close approach of ligand molecules to each other, inhibiting close-packed island formation, favoring dispersion as separate molecules, and leading to effective asymmetric induction. Conversely, small substituents favor island formation leading to very low asymmetric induction. Enantioselective reaction most likely involves initial formation of an enamine or iminium species, confirmed by use of an analogous tertiary amine, which leads to racemic product. Ligand rigidity and resistance to self-assembled monolayer formation are important attributes that should be designed into improved chiral modifiers.

Adaptive divergence and speciation among sexual and pseudoviviparous populations of Festuca

Pseudovivipary is an environmentally induced flowering abnormality in which vegetative shoots replace seminiferous (sexual) inflorescences. Pseudovivipary is usually retained in transplantation experiments, indicating that the trait is not solely induced by the growing environment. Pseudovivipary is the defining characteristic of Festuca vivipara, and arguably the only feature separating this species from its closest seminiferous relative, Festuca ovina. We performed phylogenetic and population genetic analysis on sympatric F. ovina and F. vivipara samples to establish whether pseudovivipary is an adaptive trait that accurately defines the separation of genetically distinct Festuca species. Chloroplast and nuclear marker-based analyses revealed that variation at a geographical level can exceed that between F. vivipara and F. ovina. We deduced that F. vivipara is a recent species that frequently arises independently within F. ovina populations and has not accumulated significant genetic differentiation from its progenitor. We inferred local gene flow between the species. We identified one amplified fragment length polymorphism marker that may be linked to a pseudovivipary-related region of the genome, and several other markers provide evidence of regional local adaptation in Festuca populations. We conclude that F. vivipara can only be appropriately recognized as a morphologically and ecologically distinct species; it lacks genetic differentiation from its relatives. This is the first report of a ‘failure in normal flowering development’ that repeatedly appears to be adaptive, such that the trait responsible for species recognition constantly reappears on a local basis.

Plant-soil interactions and grassland diversity restoration

Restoration schemes aimed at enhancing plant species diversity of improved agricultural grassland have been a key feature of agri-environmental policy since the mid 1980s. Allied to this has been much research aimed at providing policy makers with guidelines on how best to manage grassland to restore botanical diversity. This research includes long-term studies of the consequences for grassland diversity of management techniques such as different hay cut dates, fertiliser additions, seed introductions and grazing regimes. Studies have also explored the role of introductions of Rhinanthus minor into species-poor swards to debilitate competitive grasses. While these studies have been successful in identifying some management features that control plant species diversity in agricultural grassland, they have taken a largely aboveground perspective on plant community dynamics.

Calibration and evaluation of individual-based models using Approximate Bayesian Computation

This paper investigates the feasibility of using approximate Bayesian computation (ABC) to calibrate and evaluate complex individual-based models (IBMs). As ABC evolves, various versions are emerging, but here we only explore the most accessible version, rejection-ABC. Rejection-ABC involves running models a large number of times, with parameters drawn randomly from their prior distributions, and then retaining the simulations closest to the observations. Although well-established in some fields, whether ABC will work with ecological IBMs is still uncertain. Rejection-ABC was applied to an existing 14-parameter earthworm energy budget IBM for which the available data consist of body mass growth and cocoon production in four experiments. ABC was able to narrow the posterior distributions of seven parameters, estimating credible intervals for each. ABC’s accepted values produced slightly better fits than literature values do. The accuracy of the analysis was assessed using cross-validation and coverage, currently the best available tests. Of the seven unnarrowed parameters, ABC revealed that three were correlated with other parameters, while the remaining four were found to be not estimable given the data available. It is often desirable to compare models to see whether all component modules are necessary. Here we used ABC model selection to compare the full model with a simplified version which removed the earthworm’s movement and much of the energy budget. We are able to show that inclusion of the energy budget is necessary for a good fit to the data. We show how our methodology can inform future modelling cycles, and briefly discuss how more advanced versions of ABC may be applicable to IBMs. We conclude that ABC has the potential to represent uncertainty in model structure, parameters and predictions, and to embed the often complex process of optimizing an IBM’s structure and parameters within an established statistical framework, thereby making the process more transparent and objective.