Identifying adaptive loci can provide insight into the mechanisms underlying local adaptation. Genotype-environment association (GEA) methods, which identify these loci based on correlations between genetic and environmental data, are particularly promising. Univariate methods have dominated GEA, despite the high dimensional nature of genotype and environment. Multivariate methods, which analyse many loci simultaneously, may be better suited to these data as they consider how sets of markers covary in response to environment. These methods may also be more effective at detecting adaptive processes that result in weak, multilocus signatures. Here, we evaluate four multivariate methods and five univariate and differentiation-based approaches, using published simulations of multilocus selection. We found that Random Forest performed poorly for GEA. Univariate GEAs performed better, but had low detection rates for loci under weak selection. Constrained ordinations, particularly redundancy analysis (RDA), showed a superior combination of low false-positive and high true-positive rates across all levels of selection. These results were robust across the demographic histories, sampling designs, sample sizes and weak population structure tested here. The value of combining detections from different methods was variable and depended on the study goals and knowledge of the drivers of selection. Re-analysis of genomic data from grey wolves highlighted the unique, covarying sets of adaptive loci that could be identified using RDA. Although additional testing is needed, this study indicates that RDA is an effective means of detecting adaptation, including signatures of weak, multilocus selection, providing a powerful tool for investigating the genetic basis of local adaptation.

HOW TO CITE TSPACE ITEMSAlways cite the published version, so the author(s) will receive recognition through services that track citation counts, e.g. Scopus. If you need to cite the page number of the TSpace version (original manuscript or accepted manuscript) because you cannot access the published version, then cite the TSpace version in addition to the published version using the permanent URI (handle) found on the record page. Abstract. Species spatial distributions are the result of population demography, behavioral traits, and species interactions in spatially heterogeneous environmental conditions. Hence the composition of species assemblages is an integrative response variable, and its variability can be explained by the complex interplay among several structuring factors. The thorough analysis of spatial variation in species assemblages may help infer processes shaping ecological communities. We suggest that ecological studies would benefit from the combined use of the classical statistical models of community composition data, such as constrained or unconstrained multivariate analyses of site-by-species abundance tables, with rapidly emerging and diversifying methods of spatial pattern analysis. Doing so allows one to deal with spatially explicit ecological models of beta diversity in a biogeographic context through the multiscale analysis of spatial patterns in original species data tables, including spatial characterization of fitted or residual variation from environmental models. We summarize here the recent progress for specifying spatial features through spatial weighting matrices and spatial eigenfunctions in order to define spatially constrained or scale-explicit multivariate analyses. Through a worked example on tropical tree communities, we also show the potential of the overall approach to identify significant residual spatial patterns that could arise from the omission of important unmeasured explanatory variables or processes. REVIEWS

20Identifying adaptive loci can provide insight into the mechanisms underlying local adaptation. 21 Genotype-environment association (GEA) 40. CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

I nterdisciplinarity lies at the heart of landscape genetics, a field described as an "amalgamation of molecular population genetics and landscape ecology" (Manel et al. 2003). Storfer and colleagues (2007) proposed a more distinct definition of landscape genetics, stating that the field comprises "research that explicitly quantifies the effects of landscape composition, configuration and matrix quality on gene flow and spatial genetic variation. " In a broader sense, landscape genetics builds from those studies that combine population genetic data, adaptive or neutral, with data on landscape structure (Holderegger and Wagner 2006). The matrix in the quotation above defines the often-hostile space that separates the patches of a species' habitat in a given landscape (figure 1; Turner et al. 2001).The incorporation of the matrix into landscape genetics is a discriminating difference between landscape genetics and population genetics. At most, the latter includes the stretches of land between occupied habitat patches as a simple function of geographical distance; in contrast, in landscape genetics the matrix is seen as a major determinant of biological and ecological processes at the landscape level, and the different quantities and qualities of the areas that separate habitat patches are quite important. For instance, a strip of woodland might not hinder the movement of a ground-breeding bird found in open grasslands, but it could severely limit the migration of meadow butterflies or even form a complete barrier to the dispersal of meadow-plant seeds by wind. Landscape genetics does not possess its own conceptual methodological framework or its own analytical or statistical tool kit, but combines approaches and methods from landscape ecology, population genetics, and spatial statistics. We argue that landscape genetics is not a scientific discipline in itself but rather provides a perspective for examining spatio temporal processes such as habitat fragmentation (Fahrig 2003). The spatial scale and extent at which landscape genetic research occurs are predefined by the species-specific biological and ecological process under study, and by the spatial dimension at which operational practical measures can be taken. The "landscape" of landscape genetics therefore often consists of catchments, one or several valleys, hundreds of square kilometers of forest area, a part of a motorway and its hinterland, or an area of urban sprawl around a city center. A current question of landscape genetics: Inferring and testing landscape connectivityLandscape ecology and population genetics naturally converge in the exploration of how habitat loss and the spatial isolation or fragmentation of habitats affect the movement of species across landscapes. The constraints that landscape patterns and the matrix impose on dispersal-and thus on the distribution of animals, plants, and their genes in a land-

Species patchiness implies that nearby observations of species abundance tend to be similar or that individual conspecific organisms are more closely spaced than by random chance. This can be caused either by the positive spatial autocorrelation among the locations of individual organisms due to ecological spatial processes (e.g., species dispersal, competition for space and resources) or by spatial dependence due to (positive or negative) species responses to underlying environmental conditions. Both forms of spatial structure pose problems for statistical analysis, as spatial autocorrelation in the residuals violates the assumption of independent observations, while environmental heterogeneity restricts the comparability of replicates. In this paper, we discuss how spatial structure due to ecological spatial processes and spatial dependence affects spatial statistics, landscape metrics, and statistical modeling of the species-environment correlation. For instance, while spatial statistics can quantify spatial pattern due to an endogeneous spatial process, these methods are severely affected by landscape environmental heterogeneity. Therefore, statistical models of species response to the environment not only need to accommodate spatial structure, but need to distinguish between components due to exogeneous and endogeneous processes rather than discarding all spatial variance. To discriminate between different components of spatial structure, we suggest using (multivariate) spatial analysis of residuals or delineating the spatial realms of a stationary spatial process using boundary detection algorithms. We end by identifying conceptual and statistical challenges that need to be addressed for adequate spatial analysis of landscapes.