Levels of genetic differentiation between populations can be highly variable across the genome, with divergent selection contributing to such heterogeneous genomic divergence. For example, loci under divergent selection and those tightly physically linked to them may exhibit stronger differentiation than neutral regions with weak or no linkage to such loci. Divergent selection can also increase genome-wide neutral differentiation by reducing gene flow (e.g. by causing ecological speciation), thus promoting divergence via the stochastic effects of genetic drift. These consequences of divergent selection are being reported in recently accumulating studies that identify: (i) ‘outlier loci’ with higher levels of divergence than expected under neutrality, and (ii) a positive association between the degree of adaptive phenotypic divergence and levels of molecular genetic differentiation across population pairs [‘isolation by adaptation’ (IBA)]. The latter pattern arises because as adaptive divergence increases, gene flow is reduced (thereby promoting drift) and genetic hitchhiking increased. Here, we review and integrate these previously disconnected concepts and literatures. We find that studies generally report 5–10% of loci to be outliers. These selected regions were often dispersed across the genome, commonly exhibited replicated divergence across different population pairs, and could sometimes be associated with specific ecological variables. IBA was not infrequently observed, even at neutral loci putatively unlinked to those under divergent selection. Overall, we conclude that divergent selection makes diverse contributions to heterogeneous genomic divergence. Nonetheless, the number, size, and distribution of genomic regions affected by selection varied substantially among studies, leading us to discuss the potential role of divergent selection in the growth of regions of differentiation (i.e. genomic islands of divergence), a topic in need of future investigation.

https://www.zoology.ubc.ca/~schluter/reprints/nosil%20et%20al%202009%20molec%20ecol%20-%20divergent%20selection%20and%20heterogeneous%20genomic%20divergence.pdf

Divergent selection and heterogeneous genomic divergence.

Landscape genetics has seen rapid growth in number of publications since the term was coined in 2003. An extensive literature search from 1998 to 2008 using keywords associated with landscape genetics yielded 655 articles encompassing a vast array of study organisms, study designs and methodology. These publications were screened to identify 174 studies that explicitly incorporated at least one landscape variable with genetic data. We systematically reviewed this set of papers to assess taxonomic and temporal trends in: (i) geographic regions studied; (ii) types of questions addressed; (iii) molecular markers used; (iv) statistical analyses used; and (v) types and nature of spatial data used. Overall, studies have occurred in geographic regions proximal to developed countries and more commonly in terrestrial vs. aquatic habitats. Questions most often focused on effects of barriers and/or landscape variables on gene flow. The most commonly used molecular markers were microsatellites and amplified fragment length polymorphism (AFLPs), with AFLPs used more frequently in plants than animals. Analysis methods were dominated by Mantel and assignment tests. We also assessed differences among journals to evaluate the uniformity of reporting and publication standards. Few studies presented an explicit study design or explicit descriptions of spatial extent. While some landscape variables such as topographic relief affected most species studied, effects were not universal, and some species appeared unaffected by the landscape. Effects of habitat fragmentation were mixed, with some species altering movement paths and others unaffected. Taken together, although some generalities emerged regarding effects of specific landscape variables, results varied, thereby reinforcing the need for species-specific work. We conclude by: highlighting gaps in knowledge and methodology, providing guidelines to authors and reviewers of landscape genetics studies, and suggesting promising future directions of inquiry.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1365-294X.2010.04691.x

Landscape genetics: where are we now?

Genetic differentiation can be highly variable across the genome. For example, loci under divergent selection and those tightly linked to them may exhibit elevated differentiation compared to neutral regions. These represent “outlier loci” whose differentiation exceeds neutral expectations. Adaptive divergence can also increase genome-wide differentiation by promoting general barriers to neutral gene flow, thereby facilitating genomic divergence via genetic drift. This latter process can yield a positive correlation between adaptive phenotypic divergence and neutral genetic differentiation (described here as “isolation-by-adaptation”). Here, we examine both these processes by combining an AFLP genome scan of two host plant ecotypes of Timema cristinae walking-sticks with existing data on adaptive phenotypic divergence and ecological speciation in these insects. We found that about 8% of loci are outliers in multiple population comparisons. Replicated comparisons between population-pairs using the same versus different host species revealed that 1‐2% of loci are subject to host-related selection specifically. Locus-specific analyses revealed that up to 10% of putatively neutral (nonoutlier) AFLP loci exhibit significant isolation-by-adaptation. Our results suggest that selection may affect differentiation directly, via linkage, or by facilitating genetic drift. They thus illustrate the varied and sometimes nonintuitive contributions of selection to heterogeneous genomic differentiation.

/pdf/heterogeneous-genomic-differentiation-between-walking-stick-3yob1o80rx.pdf

Heterogeneous genomic differentiation between walking-stick ecotypes: "isolation by adaptation" and multiple roles for divergent selection.

The joint analysis of spatial and genetic data is rapidly becoming the norm in population genetics. More and more studies explicitly describe and quantify the spatial organization of genetic variation and try to relate it to underlying ecological processes. As it has become increasingly difficult to keep abreast with the latest methodological developments, we review the statistical toolbox available to analyse population genetic data in a spatially explicit framework. We mostly focus on statistical concepts but also discuss practical aspects of the analytical methods, highlighting not only the potential of various approaches but also methodological pitfalls.

Statistical methods in spatial genetics.

The goal of landscape genetics is to detect and explain landscape effects on genetic diversity and structure. Despite the increasing popularity of landscape genetic approaches, the statistical methods for linking genetic and landscape data remain largely untested. This lack of method evaluation makes it difficult to compare studies utilizing different statistics, and compromises the future development and application of the field. To investigate the suitability and comparability of various statistical approaches used in landscape genetics, we simulated data sets corresponding to five landscape-genetic scenarios. We then analyzed these data with eleven methods, and compared the methods based on their statistical power, type-1 error rates, and their overall ability to lead researchers to accurate conclusions about landscape-genetic relationships. Results suggest that some of the most commonly applied techniques (e.g. Mantel and partial Mantel tests) have high type-1 error rates, and that multivariate, non-linear methods are better suited for landscape genetic data analysis. Furthermore, different methods generally show only moderate levels of agreement. Thus, analyzing a data set with only one method could yield method-dependent results, potentially leading to erroneous conclusions. Based on these findings, we give recommendations for choosing optimal combinations of statistical methods, and identify future research needs for landscape genetic data analyses.

/pdf/statistical-approaches-in-landscape-genetics-an-evaluation-54amw86u8q.pdf

Statistical approaches in landscape genetics: an evaluation of methods for linking landscape and genetic data

Although the mechanisms controlling gene flow among populations are particularly important for evolutionary processes, they are still poorly understood, especially in the case of large carnivoran mammals with extensive continuous distributions. We studied the question of factors affecting population genetic structure in the grey wolf, Canis lupus, one of the most mobile terrestrial carnivores. We analysed variability in mitochondrial DNA and 14 microsatellite loci for a sample of 643 individuals from 59 localities representing most of the continuous wolf range in Eastern Europe. We tested an array of geographical, historical and ecological factors to check whether they may explain genetic differentiation among local wolf populations. We showed that wolf populations in Eastern Europe displayed nonrandom spatial genetic structure in the absence of obvious physical barriers to movement. Neither topographic barriers nor past fragmentation could explain spatial genetic structure. However, we found that the genetic differentiation among local populations was correlated with climate, habitat types, and wolf diet composition. This result shows that ecological processes may strongly influence the amount of gene flow among populations. We suggest natal-habitat-biased dispersal as an underlying mechanism linking population ecology with population genetic structure.

Krystyna Stachura

Papers

Ecological factors influence population genetic structure of European grey wolves.