Evolutionary potential in the wild: more than meets the eye

The genus Aquilegia consists of 60–70 perennial plant species widely distributed throughout the northern hemisphere. Its flowers have a delicate and ornamental appearance that makes them a favourite of gardeners. In this genus, adaptive radiations for both floral and vegetative traits have occurred. These adaptive radiations, and the key phylogenetic placement of Aquilegia between Arabidopsis and rice, make this genus a ‘model system’ for plant evolution ( Kramer 2009 ). In this issue, Castellanos et al. (2011) use a marker‐based method to infer heritability for floral and vegetative traits in two Aquilegia species. Layered on top of this are estimates of the strength of natural selection. This novel joint estimation of heritability and selection in the wild showed that vegetative traits, compared to floral traits, have the highest evolutionarily potential. Evolutionary potential is the most important quantity to measure in wild populations. It combines inheritance and strength of selection and predicts the potential for populations to adapt to changing environments. The combination of molecular techniques with species in natural environments makes this work a model for molecular ecological investigations.     

Adaptive landscape genetics: pitfalls and benefits

Landscape genetics offers a promising framework for assessing the interactions between the environment and adaptive genetic variation in natural populations. A recent workshop held at the University of Neuchatel brought together leading experts in this field to address current insights and future research directions in adaptive landscape genetics. Considerable amounts of genetic and/or environmental data can now be collected, but the forthcoming challenge is to do more with such manna. This requires a markedly better understanding of the genetic variation that is adaptive and prompts for advances in information management together with the development of a balance between theory and data. Moreover, showing the links between landscapes and adaptive genetic variation will ultimately move the field beyond association studies.     

Pervasive selection or is it…? why are FST outliers sometimes so frequent?

It is now common for population geneticists to estimate F_ST for a large number of loci across the genome, before testing for selected loci as being outliers to the F_ST distribution. One surprising result of such F_ST scans is the often high proportion (>1% and sometimes >10%) of outliers detected, and this is often interpreted as evidence for pervasive local adaptation. In this issue of Molecular Ecolog, Fourcade et al. ( 2013 ) observe that a particularly high rate of F_ST outliers has often been found in river organisms, such as fishes or damselflies, despite there being no obvious reason why selection should affect a larger proportion of the genomes of these organisms. Using computer simulations, Fourcade et al. ( 2013 ) show that the strong correlation in co‐ancestry produced in long one‐dimensional landscapes (such as rivers, valleys, peninsulas, oceanic ridges or coastlines) greatly increases the neutral variance in F_ST, especially when the landscape is further reticulated into fractal networks. As a consequence, outlier tests have a high rate of false positives, unless this correlation can be taken into account. Fourcade et al.'s study highlights an extreme case of the general problem, first noticed by Robertson ( 1975a , b ) and Nei & Maruyama ( 1975 ), that correlated co‐ancestry inflates the neutral variance in F_ST when compared to its expectation under an island model of population structure. Similar warnings about the validity of outlier tests have appeared regularly since then but have not been widely cited in the recent genomics literature. We further emphasize that F_ST outliers can arise in many different ways and that outlier tests are not designed for situations where the genetic architecture of local adaptation involves many loci.     

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.