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Understanding the effects of landscape heterogeneity on spatial genetic variation is a primary goal of landscape genetics. Ecological and geographic variables can contribute to genetic structure through geographic isolation, in which geographic barriers and distances restrict gene flow, and ecological isolation, in which gene flow among populations inhabiting different environments is limited by selection against dispersers moving between them. Although methods have been developed to study geographic isolation in detail, ecological isolation has received much less attention, partly because disentangling the effects of these mechanisms is inherently difficult. Here, I describe a novel approach for quantifying the effects of geographic and ecological isolation using multiple matrix regression with randomization. I explored the parameter space over which this method is effective using a series of individual-based simulations and found that it accurately describes the effects of geographic and ecological isolation over a wide range of conditions. I also applied this method to a set of real-world datasets to show that ecological isolation is an often overlooked but important contributor to patterns of spatial genetic variation and to demonstrate how this analysis can provide new insights into how landscapes contribute to the evolution of genetic variation in nature.

Relationships among multilocus genetic variation, geography, and environment can reveal how evolutionary processes affect genomes. We examined the evolution of an Australian bird, the eastern yellow robin Eopsaltria australis, using mitochondrial (mtDNA) and nuclear (nDNA) genetic markers, and bioclimatic variables. In southeastern Australia, two divergent mtDNA lineages occur east and west of the Great Dividing Range, perpendicular to latitudinal nDNA structure. We evaluated alternative scenarios to explain this striking discordance in landscape genetic patterning. Stochastic mtDNA lineage sorting can be rejected because the mtDNA lineages are essentially distinct geographically for > 1500 km. Vicariance is unlikely: the Great Dividing Range is neither a current barrier nor was it at the Last Glacial Maximum according to species distribution modeling; nuclear gene flow inferred from coalescent analysis affirms this. Female philopatry contradicts known female-biased dispersal. Contrasting mtDNA and nDNA demographies indicate their evolutionary histories are decoupled. Distance-based redundancy analysis, in which environmental temperatures explain mtDNA variance above that explained by geographic position and isolation-by-distance, favors a nonneutral explanation for mitochondrial phylogeographic patterning. Thus, observed mito-nuclear discordance accords with environmental selection on a female-linked trait, such as mtDNA, mtDNA–nDNA interactions or genes on W-chromosome, driving mitochondrial divergence in the presence of nuclear gene flow.

Tests of the genetic structure of empirical populations typically focus on the correlative relationships between population connectivity and geographic and/or environmental factors in landscape genetics. However, such tests may overlook or misidentify the impact of candidate factors on genetic structure, especially when connectivity patterns differ between past and present populations because of shifting environmental conditions over time. Here we account for the underlying demographic component of population connectivity associated with a temporarily dynamic landscape in tests of the factors structuring population genetic variation in an Australian lizard, Lerista lineopunctulata, from 24 nuclear loci. Correlative tests did not support significant effect from factors associated with a static contemporary landscape. However, spatially explicit demographic modeling of genetic differentiation shows that changes in environmental conditions (as estimated from paleoclimatic data) and corresponding distributional shifts from the past to present landscape significantly structures genetic variation. Results from model-based inference (i.e., from an integrative modeling approach that generates spatially explicit expectations that are tested with approximate Bayesian computation) contrasts with those from correlative analyses, highlighting the importance of expanding the landscape genetic perspective to tests the links between pattern and process, revealing how factors shape patterns of genetic variation within species.

A growing number of studies are examining the factors driving historical and contemporary evolution in wild populations. By combining surveys of genomic variation with a comprehensive assessment of environmental parameters, such studies can increase our understanding of the genomic and geographical extent of local adaptation in wild populations. We used a large-scale landscape genomics approach to examine adaptive and neutral differentiation across 54 North American populations of Atlantic salmon representing seven previously defined genetically distinct regional groups. Over 5500 genome-wide single nucleotide polymorphisms were genotyped in 641 individuals and 28 bulk assays of 25 pooled individuals each. Genome scans, linkage map, and 49 environmental variables were combined to conduct an innovative landscape genomic analysis. Our results provide valuable insight into the links between environmental variation and both neutral and potentially adaptive genetic divergence. In particular, we identified markers potentially under divergent selection, as well as associated selective environmental factors and biological functions with the observed adaptive divergence. Multivariate landscape genetic analysis revealed strong associations of both genetic and environmental structures. We found an enrichment of growth-related functions among outlier markers. Climate (temperature–precipitation) and geological characteristics were significantly associated with both potentially adaptive and neutral genetic divergence and should be considered as candidate loci involved in adaptation at the regional scale in Atlantic salmon. Hence, this study significantly contributes to the improvement of tools used in modern conservation and management schemes of Atlantic salmon wild populations.

Uncovering the genetic basis of adaptation hinges on the ability to detect loci under selection. However, population genomics outlier approaches to detect selected loci may be inappropriate for clinal populations or those with unclear population structure because they require that individuals be clustered into populations. An alternate approach, landscape genomics, uses individual-based approaches to detect loci under selection and reveal potential environmental drivers of selection. We tested four landscape genomics methods on a simulated clinal population to determine their effectiveness at identifying a locus under varying selection strengths along an environmental gradient. We found all methods produced very low type I error rates across all selection strengths, but elevated type II error rates under \"weak\" selection. We then applied these methods to an AFLP genome scan of an alpine plant, Campanula barbata, and identified five highly supported candidate loci associated with precipitation variables. These loci also showed spatial autocorrelation and cline patterns indicative of selection along a precipitation gradient. Our results suggest that landscape genomics in combination with other spatial analyses provides a powerful approach for identifying loci potentially under selection and explaining spatially complex interactions between species and their environment.

Individuals are typically not randomly distributed in space; consequently ecological and evolutionary theory depends heavily on understanding the spatial structure of populations. The central challenge of landscape genetics is therefore to link spatial heterogeneity of environments to population genetic structure. Here, we employ multivariate spatial analyses to identify environmentally induced genetic structures in a single breeding population of 1174 great tits Parus major genotyped at 4701 single-nucleotide polymorphism (SNP) loci. Despite the small spatial scale of the study relative to natal dispersal, we found multiple axes of genetic structure. We built distance-based Moran's eigenvector maps to identify axes of pure spatial variation, which we used for spatial correction of regressions between SNPs and various external traits known to be related to fitness components (avian malaria infection risk, local density of conspecifics, oak tree density, and altitude). We found clear evidence of fine-scale genetic structure, with 21, seven, and nine significant SNPs, respectively, associated with infection risk by two species of avian malaria (Plasmodium circumflexum and P. relictum) and local conspecific density. Such fine-scale genetic structure relative to dispersal capabilities suggests ecological and evolutionary mechanisms maintain within-population genetic diversity in this population with the potential to drive microevolutionary change.

Spatio-temporal studies of hybrid zones provide an opportunity to test evolutionary hypotheses of hybrid zone maintenance and movement. We conducted a landscape genetics study on a classic hybrid zone of the south-eastern Australian frogs, Litoria ewingii and Litoria paraewingi. This hybrid zone has been comprehensively studied since the 1960s, providing the unique opportunity to directly assess changes in hybrid zone structure across time. We compared both mtDNA and male advertisement call data from two time periods (present and 1960s). Clinal analysis of the coincidence (same center) and concordance (same width) of these traits indicated that the center of the hybrid zone has shifted 1 km south over the last 40 years, although the width of the zone and the rate of introgression remained unchanged. The low frequency of hybrids, the strong concordance of clines within a time period, and the small but significant movement across the study period despite significant anthropogenic changes through the region, suggest the hybrid zone is a tension zone located within a low-density trough. Hybrid zone movement has not been considered common in the past but our findings highlight that it should be considered a crucial component to our understanding of evolution.

The main objective of this special section is not to review the broad field of landscape genetics, but to provide a glimpse of how the developing landscape genetics perspective has the potential to change the way we study evolution. Evolutionary landscape genetics is the study of how migration and population structure affects evolutionary processes. As a field it dates back to Sewall Wright and the origin of theoretical population genetics, but empirical tests of adaptive processes of evolution in natural landscapes have been rare. Now, with recent developments in technology, methodology, and modeling tools, we are poised to trace adaptive genetic variation across space and through time. Not only will we see more empirical tests of classical theory, we can expect to see new phenomena emerging, as we reveal complex interactions among evolutionary processes as they unfold in natural landscapes.

Evolutionary inferences are usually based on statistical models that compare mean genotypes or phenotypes (or their frequencies) among populations. An alternative is to use the full distribution of genotypes and phenotypes to infer the \"exchangeability\" of individuals among populations. We illustrate this approach by using discriminant functions on principal components to classify individuals among paired lake and stream populations of threespine stickleback in each of six independent watersheds. Classification based on neutral and nonneutral microsatellite markers was highest to the population of origin and next highest to populations in the same watershed. These patterns are consistent with the influence of historical contingency (separate colonization of each watershed) and subsequent gene flow (within but not between watersheds). In comparison to this low genetic exchangeability, ecological (diet) and morphological (trophic and armor traits) exchangeability was relatively high—particularly among populations from similar habitats. These patterns reflect the role of natural selection in driving parallel adaptive changes when independent populations colonize similar habitats. Importantly, however, substantial nonparallelism was also evident. Our results show that analyses based on exchangeability can confirm inferences based on statistical analyses of means or frequencies, while also refining insights into the drivers of—and constraints on—evolutionary diversification.

Molecular-genetic technology and statistical methods based on principles of population genetics provide valuable information to wildlife managers. Genetic data analyzed in a hierarchical, spatial context among individuals and among populations at micro- and macro-geographic scales has been widely used to provide information on the degree of population structure and to estimate rates of dispersal. Our goals were to (1) provide an overview of spatial statistics commonly used in empirical population genetics, and (2) introduce analytical designs that can be employed to extend hypothesis-testing capabilities by incorporating space-time interactions and by using information on habitat quality, distribution, and degree of connectivity. We show that genetics data can be used to quantify the degree of habitat permeability to dispersal and to qualify the negative consequences of habitat loss. We highlight empirical examples that use information on spatial genetic structure in areas of harvest derivation for admixed migratory species, wildlife disease, and habitat equivalency analysis.