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Preface to the special issue: advances in the analysis of spatial genetic data

From its origins in the early 1990s, the field of molecular ecology has progressed at an exhilarating pace. Advances in molecular techniques motivated its initial emergence and have provided ever-larger genetic data sets since, but these would be of little consequence without the rigorous hypothesis-testing framework provided by statistical population genetics. Indeed, this combination of increasingly sophisticated data and continually improving analytical tools now mean that the field routinely examines questions that were impossible a decade before, such as identifying selected genes in the wild or reconstructing the population dynamics of expanding populations. One persistent challenge in the analysis of population genetic data is the need to account for the spatial arrangement of samples and populations. Even if we are interested in studying a single and apparently fairly isolated population, we need to take into account the fact that processes, such as spatial population expansion and sporadic migration may have influenced its genetic composition. It is also possible that what looks like a homogeneous population exhibits cryptic population structure because of spatial discontinuities in environmental factors. The importance of all these issues was recognized early on by the founders of the field of population genetics. For example, Sewall Wright (1931) conceived the island model to study the effect of spatial subdivision on evolutionary processes. He also considered scenarios with a continuous spatial distribution in his isolation by distance model (Wright 1943). Fisher (1937) studied the spatial spread of a beneficial mutation using his wave of advance model, and Haldane (1948) made inferences about the intensity of selection in a population of Peromyscus polionotus based on a genetic cline model. The important effect of spatial heterogeneity in environmental variables and the potential effect of barriers to dispersal were recognized by Sokal, Smouse and Barbujani much before the emergence of the field of landscape genetics. They used spatial autocorrelation analysis (Sokal & Wartenberg 1983; Barbujani 1987) and the Mantel test (Smouse et al. 1986) to determine the factors underlying the observed population genetic structure. As processor power increased, more computationally intensive statistical approaches, such as Markov chain Monte Carlo (MCMC) and importance sampling (IS), became available and put maximum likelihood (ML) and Bayesian approaches at the forefront of population genetics data analysis (for a review, see Beaumont & Rannala 2004). An important advantage of these methods is that they are based on probabilistic models and can therefore account for the stochastic nature of population genetic processes. Model-based statistical methods thus became a hallmark of genetic data analysis; a very welcome development given that many important molecular ecology problems are not amenable to experimental manipulation. Instead, we are limited to the observation of data that have arisen from the natural dynamics of the system (c.f. MacNeil 2008). This has important implications for the way in which we carry out molecular ecology research, the most important of which is that this research falls outside the epistemological tradition that has dominated science since Popper (1959) introduced the idea that scientific hypotheses can only be disproved. Under Popperian testing, scientific ideas are falsified by carrying out controlled experiments where subjects are randomly assigned to control and treatment groups so that postexperiment differences between groups are directly attributable to the effect of experimental treatment (MacNeil 2008; Beaumont et al. 2010). Fisher’s (1925) null hypothesis testing relies on this framework and provides a direct link between statistical and Popperian testing. However, most molecular ecology studies do not fit within this framework. Instead, the natural dynamics of the system are influenced by a multitude of different factors (mutation, selection, demographic history, etc.) and together, these simply give rise to the data. It is thus impossible to control for the effect of a specific factorthe best one can do is to account for it using model-based inference (i.e. ML or Bayesian approaches). Many, if not most of the statistical approaches discussed in this special issue are strongly grounded in these ideas. The objective of this special issue is to present an overview of the state of the art in the analysis of spatial genetic data. However, we do not aim at presenting an exhaustive list of the latest developments. Instead, we have chosen to include a series of review of methods that make inferences about genetic drift, migration and