COMPUTER PROGRAMS: onesamp: a program to estimate effective population size using approximate Bayesian computation

The estimation of effective population size from one sample of genotypes has been problematic because most estimators have been proven imprecise or biased. We developed a web-based program, onesamp that uses approximate Bayesian computation to estimate effective population size from a sample of microsatellite genotypes. onesamp requires an input file of sampled individuals' microsatellite genotypes along with information about several sampling and biological parameters. onesamp provides an estimate of effective population size, along with 95% credible limits. We illustrate the use of onesamp with an example data set from a re-introduced population of ibex Capra ibex.     

Disentangling the impact of demographic factors on population differentiation of an endangered freshwater crayfish (Austropotamobius pallipes) using population density and microsatellite data

1. Habitat fragmentation of stream ecosystems often results in decreased connectivity between populations and lower population sizes. Hence, understanding how habitat fragmentation affects genetic erosion is important for the preservation of freshwater biodiversity, in particular, as small populations suffer from loss of genetic diversity through genetic drift and loss of fitness because of inbreeding, increasing the risk of extinction. 2. Here, we assess the impact of demographic factors on population differentiation in the endangered freshwater crayfish Austropotamobius pallipes by analysing population genetic structure, estimating effective population sizes and comparing levels of polymorphism at five microsatellite loci with density estimates of 10 populations within a small French catchment that has become progressively confined to headwaters over the last six decades. 3. Levels of expected heterozygosity and allelic richness per population were relatively low (0.214–0.396 and 1.6–2.6, respectively). We found strong genetic differentiation between these geographically close populations (F_ST = 0.283), with weak statistical evidence for a pattern of isolation by distance. Estimates of effective population size were low (<150) in most populations, but potentially reached several thousands in three populations. 4. Population density and allelic richness were strongly positively correlated. A robust relationship between population density and heterozygosity values was also noted, but only after discarding two populations for which significant genetic signatures of a recent bottleneck were found; these two populations displayed high expected heterozygosity compared with a very low density. Populations with the highest densities of individuals had the highest effective population size estimates and vice versa. 5. Our results clearly show the importance of demographic factors and genetic drift on A. pallipes populations. Furthermore, analysis of genetic and population density data is a pragmatic and efficient approach to corroborate inferences from genetic data and can be particularly useful in the identification of populations experiencing a bottleneck and therefore in conservation genetics studies aiming at identifying priority populations for conservation.     

Estimating Effective Population Size from Temporally Spaced Samples with a Novel,Efficient Maximum-Likelihood Algorithm

The effective population size N_e is a key parameter in population genetics and evolutionary biology, as it quantifies the expected distribution of changes in allele frequency due to genetic drift. Several methods of estimating N_e have been described, the most direct of which uses allele frequencies measured at two or more time points. A new likelihood-based estimator NB^ for contemporary effective population size using temporal data is developed in this article. The existing likelihood methods are computationally intensive and unable to handle the case when the underlying N_e is large. This article tries to work around this problem by using a hidden Markov algorithm and applying continuous approximations to allele frequencies and transition probabilities. Extensive simulations are run to evaluate the performance of the proposed estimator NB^, and the results show that it is more accurate and has lower variance than previous methods. The new estimator also reduces the computational time by at least 1000-fold and relaxes the upper bound of N_e to several million, hence allowing the estimation of larger N_e. Finally, we demonstrate how this algorithm can cope with nonconstant N_e scenarios and be used as a likelihood-ratio test to test for the equality of N_e throughout the sampling horizon. An R package “NB” is now available for download to implement the method described in this article.     

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.     

Molecular and quantitative genetic differentiation across Europe in yellow dung flies

Relating geographic variation in quantitative traits to underlying population structure is crucial for understanding processes driving population differentiation, isolation and ultimately speciation. Our study represents a comprehensive population genetic survey of the yellow dung fly Scathophaga stercoraria, an important model organism for evolutionary and ecological studies, over a broad geographic scale across Europe (10 populations from the Swiss Alps to Iceland). We simultaneously assessed differentiation in five quantitative traits (body size, development time, growth rate, proportion of diapausing individuals and duration of diapause), to compare differentiation in neutral marker loci (F(ST)) to that of quantitative traits (Q(ST)). Despite long distances and uninhabitable areas between sampled populations, population structuring was very low but significant (F(ST) = 0.007, 13 microsatellite markers; F(ST) = 0.012, three allozyme markers; F(ST) = 0.007, markers combined). However, only two populations (Iceland and Sweden) showed significant allelic differentiation to all other populations. We estimated high levels of gene flow [effective number of migrants (Nm) = 6.2], there was no isolation by distance, and no indication of past genetic bottlenecks (i.e. founder events) and associated loss of genetic diversity in any northern or island population. In contrast to the low population structure, quantitative traits were strongly genetically differentiated among populations, following latitudinal clines, suggesting that selection is responsible for life history differentiation in yellow dung flies across Europe.