Combining demography and genetic analysis to assess the population structure of an amphibian in a human-dominated landscape

In this article, we applied demographic and genetic approaches to assess how landscape features influence dispersal patterns and genetic structure of the common frog Rana temporaria in a landscape where anthropogenic perturbations are pervasive (urbanization and roads). We used a combination of GIS methods that integrate radiotracking and landscape configuration data, and simulation techniques in order to estimate the potential dispersal area around breeding patches. Additionally, genetic data provided indirect measures of dispersal and allowed to characterise the spatial genetic structure of ponds and the patterns of gene flow across the landscape. Although demographic simulations predicted six distinct groups of habitat patches within which movement can occur, genetic analyses suggested a different configuration. More precisely, BAPS5 spatial clustering method with ponds as the analysis unit detected five spatial clusters. Individual-based analyses were not able to detect significant genetic structure. We argue that (1) taking into account that each individual breeds in specific breeding patch allowed for better explanation of population functioning, (2) the discrepancy between direct (radiotracking) and indirect (genetic) estimates of subpopulations (breeding patches) is due to a recent landscape fragmentation (e.g. traffic increase). We discuss the future of this population in the face of increasing landscape fragmentation, focusing on the need for combining demographic and genetic approaches when evaluating the conservation status of population subjected to rapid landscape changes.     

Quantifying population structure using the F-model

We review a model-based approach to estimate local population F(ST) 's that is based on the multinomial-Dirichlet distribution, the so-called F-model. As opposed to the standard method of estimating a single F(ST) value, this approach takes into account the fact that in most if not all realistic situations, local populations differ in their effective sizes and migration rates. Therefore, the use of this approach can help better describe the genetic structure of populations. Despite this obvious advantage, this method has remained largely underutilized by molecular ecologists. Thus, the objective of this review is to foster its use for studying the genetic structure of metapopulations. We present the derivation of the Bayesian formulation for the estimation of population-specific F(ST) 's based on the multinomial-Dirichlet distribution. We describe several recent applications of the F-model and present the results of a small simulation study that explains how the F-model can help better describe the genetic structure of populations.     

Identifying the environmental factors that determine the genetic structure of populations

The study of population genetic structure is a fundamental problem in population biology because it helps us obtain a deeper understanding of the evolutionary process. One of the issues most assiduously studied in this context is the assessment of the relative importance of environmental factors (geographic distance, language, temperature, altitude, etc.) on the genetic structure of populations. The most widely used method to address this question is the multivariate Mantel test, a nonparametric method that calculates a correlation coefficient between a dependent matrix of pairwise population genetic distances and one or more independent matrices of environmental differences. Here we present a hierarchical Bayesian method that estimates F(ST) values for each local population and relates them to environmental factors using a generalized linear model. The method is demonstrated by applying it to two data sets, a data set for a population of the argan tree and a human data set comprising 51 populations distributed worldwide. We also carry out a simulation study to investigate the performance of the method and find that it can correctly identify the factors that play a role in the structuring of genetic diversity under a wide range of scenarios.     

Making inferences about speciation using sophisticated statistical genetics methods: look before you leap

Understanding speciation is a fundamental aim of evolutionary biology and a very challenging one. Speciation can be viewed as the dynamics of genetic differentiation between populations resulting in substantial reproductive isolation (Gavrilets 2003). It was generally accepted that very small levels of migration prevent genetic differentiation among populations and, therefore, speciation. However, recent theoretical work showed that sympatric speciation is possible (Gavrilets 2003). Nevertheless, providing empirical evidence that gene flow occurred during speciation is challenging because several gene flow scenarios can explain observed patterns of genetic differentiation. Positive migration rate estimates alone do not prove ongoing gene flow during divergence. We also need to know whether migration took place before, during or after speciation. There is no statistical method specifically aimed at estimating gene flow timing, but several studies used the isolation with migration model (Hey & Nielsen 2004, 2007; Hey 2010b) to estimate this parameter and make inferences about speciation scenarios. It is tempting to use statistical methods to estimate important evolutionary parameters even if they do not appear explicitly in the inference model. Nevertheless, before doing so, we need to determine whether they can provide reliable results. In this issue of Molecular Ecology, Strasburg and Rieseberg (2011) present a simulation study that examines the degree to which gene flow timing estimates obtained from IMa2 (Hey 2010b) can be used to make inferences about speciation mode. Their results are sobering; gene flow timing estimates obtained in this way are not reliable and cannot be used to unequivocally establish if gene flow was ongoing during speciation.     

Computer simulations: tools for population and evolutionary genetics

Computer simulations are excellent tools for understanding the evolutionary and genetic consequences of complex processes whose interactions cannot be analytically predicted. Simulations have traditionally been used in population genetics by a fairly small community with programming expertise, but the recent availability of dozens of sophisticated, customizable software packages for simulation now makes simulation an accessible option for researchers in many fields. The in silico genetic data produced by simulations, along with greater availability of population-genomics data, are transforming genetic epidemiology, anthropology, evolutionary and population genetics and conservation. In this Review of the state-of-the-art of simulation software, we identify applications of simulations, evaluate simulator capabilities, provide a guide for their use and summarize future directions.     

Detecting adaptive evolution based on association with ecological gradients: Orientation matters!

Population genetic signatures of local adaptation are frequently investigated by identifying loci with allele frequencies that exhibit high correlation with ecological variables. One difficulty with this approach is that ecological associations might be confounded by geographic variation at selectively neutral loci. Here, we consider populations that underwent spatial expansion from their original range, and for which geographical variation of adaptive allele frequency coincides with habitat gradients. Using range expansion simulations, we asked whether our ability to detect genomic regions involved in adaptation could be impacted by the orientation of the ecological gradients. For three ecological association methods tested, we found, counter-intuitively, fewer false-positive associations when ecological gradients aligned along the main axis of expansion than when they aligned along any other direction. This result has important consequences for the analysis of genomic data under non-equilibrium population genetic models. Alignment of gradients with expansion axes is likely to be common in scenarios in which expanding species track their ecological niche during climate change while adapting to changing environments at their rear edge.     

Energetic aspects of CO oxidation in desulfovibrio desulfuricans

In cell suspension of Desulfovibrio desulfuricans B-1388, oxidation of CO as the only energy source is associated with reduction of SO42-. After a 2-h incubation of cells in 8% CO, 81% of the gas is converted. Oxidation of 1 mole CO results in formation of 0.23 mole H2S. Intracellular ATP content increases from 2.5 (control) to 8.3 nmoles/mg (during CO conversion). Dinitrophenol inhibits sulfate reduction and CO oxidation. CO dehydrogenase was detected in cytoplasmic and membrane cell fractions (59 and 34%, respectively).     

An approximate Bayesian computation approach to overcome biases that arise when using amplified fragment length polymorphism markers to study population structure

There is great interest in using amplified fragment length polymorphism (AFLP) markers because they are inexpensive and easy to produce. It is, therefore, possible to generate a large number of markers that have a wide coverage of species genomes. Several statistical methods have been proposed to study the genetic structure using AFLPs but they assume Hardy-Weinberg equilibrium and do not estimate the inbreeding coefficient, F(IS). A Bayesian method has been proposed by Holsinger and colleagues that relaxes these simplifying assumptions but we have identified two sources of bias that can influence estimates based on these markers: (i) the use of a uniform prior on ancestral allele frequencies and (ii) the ascertainment bias of AFLP markers. We present a new Bayesian method that avoids these biases by using an implementation based on the approximate Bayesian computation (ABC) algorithm. This new method estimates population-specific F(IS) and F(ST) values and offers users the possibility of taking into account the criteria for selecting the markers that are used in the analyses. The software is available at our web site (http://www-leca.ujf-grenoble.fr/logiciels.htm). Finally, we provide advice on how to avoid the effects of ascertainment bias.     

Interactions between environmental factors can hide isolation by distance patterns: a case study of Ctenomys rionegrensis in Uruguay

Identifying the factors responsible for the structuring of genetic diversity is of fundamental importance for biodiversity conservation. However, arriving at such understanding is difficult owing to the many factors involved and the potential interactions between them. Here, we present an example of how such interactions can preclude us from arriving at a complete characterization of the demographic history and genetic structure of a species. Ctenomys rionegrensis is a species with restricted dispersal abilities and, as such, should exhibit an isolation by distance (IBD) pattern, which previous studies were unable to uncover. It was therefore concluded that this species underwent a recent population expansion. Using a novel hierarchical Bayesian method, we show that the inability to detect the IBD pattern is due to the interaction between elevation and geographical distance. We posit that populations in low areas suffer periodic floods that may reduce local population sizes, increasing genetic drift, a process that masks the effect of distance on genetic differentiation. Our results do not refute the possibility that the populations of C. rionegrensis underwent a recent population expansion but they indicate that an alternative scenario described by a metapopulation model at or near migration-drift equilibrium cannot be excluded either.     

Metapopulation genetic structure of two coexisting parasitoids of the Glanville fritillary butterfly

We investigated the metapopulation genetic structure of two specialist parasitoids, Cotesia melitaearum and Hyposoter horticola, attacking the Glanville fritillary butterfly (Melitaea cinxia) in the Åland Islands south-western Finland. The host butterfly persists as a classic metapopulation in a network of 4,000 small habitat patches within an area of 50 by 70 km . The two parasitoids are known to differ greatly in their population dynamics and spatial pattern of occupancy in local host populations. Analysis of genetic population structure using F_ST and clustering of multilocus genotypes revealed a distinct large-scale spatial structure in C. melitaearum but a very weak pattern in H. horticola. This result is consistent with the known difference in the dispersal range (much longer in H. horticola) and population size (much greater in H. horticola) of the two parasitoids.