INTEGRATING LANDSCAPE GENOMICS AND SPATIALLY EXPLICIT APPROACHES TO DETECT LOCI UNDER SELECTION IN CLINAL POPULATIONS

Circadian rhythm and redox homeostasis candidate genes showed association with shallow elevation in Norway spruce

The analysis of genetic variation underlying local adaptation in natural populations, together with the response to different external stimuli, is currently a hot topic in forest sciences, with the aim of identifying genetic markers controlling key phenotypic traits of interest for their inclusion in restoration and breeding programs. In Europe, one of the main tree species is Norway spruce (Picea abies (L.) H.Karst.). Using the MassARRAY® platform, 568 trees from North Rhine-Westphalia (Germany) were genotyped with 94 single nucleotide polymorphisms (SNPs) related to circadian and growth rhythms, and to stress response. The association analysis of the selected markers with health status and elevation was performed using three different methods, and those identified by at least two of these were considered as high confidence associated SNPs. While just five markers showed a weak association with health condition, 32 SNPs were correlated with elevation, six of which were considered as high confidence associated SNPs, as indicated by at least two different association methods. Among these genes, thioredoxin and pseudo response regulator 1 (PRR1) are involved in redox homeostasis and ROS detoxification, APETALA2-like 3 (AP2L3), a transcription factor, is involved in seasonal apical growth, and a RPS2-like is a disease resistance gene. The function of some of these genes in controlling light-dependent reactions and metabolic processes suggests signatures of adaptation to local photoperiod and the synchronization of the circadian rhythm. This work provides new insights into the genetic basis of local adaptation over a shallow elevation gradient in Norway spruce.

Epigenetic architecture of Pseudotaxus chienii: Revealing the synergistic effects of climate and soil variables

Whether conifers can withstand environmental changes especially temperature fluctuations has been controversial. Epigenetic analysis may provide new perspectives for solving the issue. Pseudotaxus chienii is an endangered gymnosperm species endemic to China. In this study, we have examined the genetic and epigenetic variations in its natural populations aiming to disentangle the synergistic effects of climate and soil on its population (epi)genetic differentiation by using amplified fragment length polymorphism (AFLP) and methylation-sensitive AFLP (MSAP) techniques. We identified 23 AFLP and 26, 7, and 5 MSAP outliers in P. chienii. Twenty-one of the putative adaptive AFLP loci were found associated with climate and/or soil variables including precipitation, temperature, K, Fe, Zn, and Cu, whereas 21, 7, and 4 MSAP outliers were significantly related to precipitation of wettest month (Bio13), precipitation driest of month (Bio14), percent tree cover (PTC), and soil Fe, Mn, and Cu compositions. Total precipitation and precipitation in the driest seasons were the most influential factors for genetic and epigenetic variation, respectively. In addition, a high full-methylation level and a strong correlation between genetic and epigenetic variation were detected in P. chienii. Climate is found of greater importance than soil in shaping adaptive (epi)genetic differentiation, and the synergistic effects of climate and climate-soil variables were also observed. The identified climate and soil variables should be considered when applying ex situ conservation.

Multi-locus genomic signatures of local adaptation to snow across the landscape in California populations of a willow leaf beetle

Organisms living in mountains contend with extreme climatic conditions, including short growing seasons and long winters with extensive snow cover. Anthropogenic climate change is driving unprecedented, rapid warming of montane regions across the globe, resulting in reduced winter snowpack. Loss of snow as a thermal buffer may have serious consequences for animals overwintering in soil, yet little is known about how variability in snowpack acts as a selective agent in montane ecosystems. Here, we examine genomic variation in California populations of the leaf beetle Chrysomela aeneicollis, an emerging natural model system for understanding how organisms respond to climate change. We used a genotype-environment association approach to identify genomic signatures of local adaptation to microclimate in populations from three montane regions with variable snowpack and a coastal region with no snow. We found that both winter-associated environmental variation and geographical distance contribute to overall genomic variation across the landscape. We identified non-synonymous variation in novel candidate loci associated with cytoskeletal function, ion transport and membrane stability, cellular processes associated with cold tolerance in other insects. These findings provide intriguing evidence that variation in snowpack imposes selective gradients in montane ecosystems.

Genome-phenotype-environment associations identify signatures of selection in a panmictic population of threespine stickleback

Adaptive genetic divergence occurs when selection imposed by the environment causes the genomic component of the phenotype to differentiate. However, genomic signatures of natural selection are usually identified without information on which trait is responding to selection by which selective agent(s). Here, we integrate whole-genome sequencing with phenomics and measures of putative selective agents to assess the extent of adaptive divergence in threespine stickleback occupying the highly heterogeneous lake Mývatn, NE Iceland. We find negligible genome wide divergence, yet multiple traits (body size, gill raker structure and defence traits) were divergent along known ecological gradients (temperature, predatory bird densities and water depth). SNP based heritability of all measured traits was high (h²  = 0.42-0.65), indicating adaptive potential for all traits. Environment-association analyses further identified thousands of loci putatively involved in selection, related to genes linked to, for instance, neuron development and protein phosphorylation. Finally, we found that loci linked to water depth were concurrently associated with pelvic spine length variation - supporting the conclusion that divergence in pelvic spine length occurred in the face of gene flow. Our results suggest that whilst there is substantial genetic variation in the traits measured, phenotypic divergence of Mývatn stickleback is mostly weakly associated with environmental gradients, potentially as a result of substantial gene flow. Our study illustrates the value of integrative studies that combine genomic assays of multivariate trait variation with landscape genomics.

Using landscape genomics to assess local adaptation and genomic vulnerability of a perennial herb Tetrastigma hemsleyanum (Vitaceae) in subtropical China

Understanding adaptive genetic variation of plant populations and their vulnerabilities to climate change are critical to preserve biodiversity and subsequent management interventions. To this end, landscape genomics may represent a cost-efficient approach for investigating molecular signatures underlying local adaptation. Tetrastigma hemsleyanum is, in its native habitat, a widespread perennial herb of warm-temperate evergreen forest in subtropical China. Its ecological and medicinal values constitute a significant revenue for local human populations and ecosystem. Using 30,252 single nucleotide polymorphisms (SNPs) derived from reduced-representation genome sequencing in 156 samples from 24 sites, we conducted a landscape genomics study of the T. hemsleyanum to elucidate its genomic variation across multiple climate gradients and genomic vulnerability to future climate change. Multivariate methods identified that climatic variation explained more genomic variation than that of geographical distance, which implied that local adaptation to heterogeneous environment might represent an important source of genomic variation. Among these climate variables, winter precipitation was the strongest predictor of the contemporary genetic structure. F _ST outlier tests and environment association analysis totally identified 275 candidate adaptive SNPs along the genetic and environmental gradients. SNP annotations of these putatively adaptive loci uncovered gene functions associated with modulating flowering time and regulating plant response to abiotic stresses, which have implications for breeding and other special agricultural aims on the basis of these selection signatures. Critically, modelling revealed that the high genomic vulnerability of our focal species via a mismatch between current and future genotype-environment relationships located in central-northern region of the T. hemsleyanum's range, where populations require proactive management efforts such as assistant adaptation to cope with ongoing climate change. Taken together, our results provide robust evidence of local climate adaption for T. hemsleyanum and further deepen our understanding of adaptation basis of herbs in subtropical China.

Genotype‐environment associations across spatial scales reveal the importance of putative adaptive genetic variation in divergence

Identifying areas of high evolutionary potential is a judicious strategy for developing conservation priorities in the face of environmental change. For wide‐ranging species occupying heterogeneous environments, the evolutionary forces that shape distinct populations can vary spatially. Here, we investigate patterns of genomic variation and genotype–environment associations in the hermit thrush (Catharus guttatus), a North American songbird, at broad (across the breeding range) and narrow spatial scales (at a hybrid zone). We begin by building a genoscape or map of genetic variation across the breeding range and find five distinct genetic clusters within the species, with the greatest variation occurring in the western portion of the range. Genotype–environment association analyses indicate higher allelic turnover in the west than in the east, with measures of temperature surfacing as key predictors of putative adaptive genomic variation rangewide. Since broad patterns detected across a species' range represent the aggregate of many locally adapted populations, we investigate whether our broadscale analysis is consistent with a finer scale analysis. We find that top rangewide temperature‐associated loci vary in their clinal patterns (e.g., steep clines vs. fixed allele frequencies) across a hybrid zone in British Columbia, suggesting that the environmental predictors and the associated candidate loci identified in the rangewide analysis are of variable importance in this particular region. However, two candidate loci exhibit strong concordance with the temperature gradient in British Columbia, suggesting a potential role for temperature‐related barriers to gene flow and/or temperature‐driven ecological selection in maintaining putative local adaptation. This study demonstrates how patterns identified at the broad (macrogeographic) scale can be validated by investigating genotype–environment correlations at the local (microgeographic) scale. Furthermore, our results highlight the importance of considering the spatial distribution of putative adaptive variation when assessing population‐level sensitivity to climate change and other stressors.

Host, microbiome, and complex space: applying population and landscape genetic approaches to gut microbiome research in wild populations

In recent years, emerging sequencing technologies and computational tools have driven a tidal wave of research on host-associated microbiomes, particularly the gut microbiome. These studies demonstrate numerous connections between the gut microbiome and vital host functions, primarily in humans, model organisms, and domestic animals. As the adaptive importance of the gut microbiome becomes clearer, interest in studying the gut microbiomes of wild populations has increased, in part due to the potential for discovering conservation applications. The study of wildlife gut microbiomes holds many new challenges and opportunities due to the complex genetic, spatial, and environmental structure of wild host populations, and the potential for these factors to interact with the microbiome. The emerging picture of adaptive coevolution in host-microbiome relationships highlights the importance of understanding microbiome variation in the context of host population genetics and landscape heterogeneity across a wide range of host populations. We propose a conceptual framework for understanding wildlife gut microbiomes in relation to landscape variables and host population genetics, including the potential of approaches derived from landscape genetics. We use this framework to review current research, synthesize important trends, highlight implications for conservation, and recommend future directions for research. Specifically, we focus on how spatial structure and environmental variation interact with host population genetics and microbiome variation in natural populations, and what we can learn from how these patterns of covariation differ depending on host ecological and evolutionary traits.

Temperature heterogeneity correlates with intraspecific variation in physiological flexibility in a small endotherm

Phenotypic flexibility allows individuals to reversibly modify trait values and theory predicts an individual's relative degree of flexibility positively correlates with the environmental heterogeneity it experiences. We test this prediction by integrating surveys of population genetic and physiological variation with thermal acclimation experiments and indices of environmental heterogeneity in the Dark-eyed Junco (Junco hyemalis) and its congeners. We combine field measures of thermogenic capacity for 335 individuals, 22,006 single nucleotide polymorphisms genotyped in 181 individuals, and laboratory acclimations replicated on five populations. We show that Junco populations: (1) differ in their thermogenic responses to temperature variation in the field; (2) harbor allelic variation that also correlates with temperature heterogeneity; and (3) exhibit intra-specific variation in thermogenic flexibility in the laboratory that correlates with the heterogeneity of their native thermal environment. These results provide comprehensive support that phenotypic flexibility corresponds with environmental heterogeneity and highlight its importance for coping with environmental change.

EST-SSR-based landscape genetics of Pseudotaxus chienii, a tertiary relict conifer endemic to China

Pseudotaxus chienii, belonging to the monotypic genus Pseudotaxus (Taxaceae), is a relict conifer endemic to China. Its populations are usually small and patchily distributed, having a low capacity of natural regeneration. To gain a clearer understanding of how landscape variables affect the local adaptation of P. chienii, we applied EST-SSR markers in conjunction with landscape genetics methods: (a) to examine the population genetic pattern and spatial genetic structure; (b) to perform genome scan and selection scan to identify outlier loci and the associated landscape variables; and (c) to model the ecological niche under climate change. As a result, P. chienii was found to have a moderate level of genetic variation and a high level of genetic differentiation. Its populations displayed a significant positive relationship between the genetic and geographical distance (i.e., "isolation by distance" pattern) and a strong fine-scale spatial genetic structure within 2 km. A putatively adaptive locus EMS6 (functionally annotated to cellulose synthase A catalytic subunit 7) was identified, which was found significantly associated with soil Cu, K, and Pb content and the combined effects of temperature and precipitation. Moreover, P. chienii was predicted to experience significant range contractions in future climate change scenarios. Our results highlight the potential of specific soil metal content and climate variables as the driving force of adaptive genetic differentiation in P. chienii. The data would also be useful to develop a conservation action plan for P. chienii.

Demographic history and local adaptation of Myripnois dioica (Asteraceae) provide insight on plant evolution in northern China flora

The flora of northern China forms the main part of the Sino-Japanese floristic region and is located in a south-north vegetative transect in East Asia. Phylogeographic studies have demonstrated that an arid belt in this region has promoted divergence of plants in East Asia. However, little is known about how plants that are restricted to the arid belt of flora in northern China respond to climatic oscillation and environmental change. Here, we used genomic-level data of Myripnois dioica across its distribution as a representative of northern China flora to reconstruct plant demographic history, examine local adaptation related to environmental disequilibrium, and investigate the factors related to effective population size change. Our results indicate M. dioica originated from the northern area and expanded to the southern area, with the Taihang Mountains serving as a physical barrier promoting population divergence. Genome-wide evidence found strong correlation between genomic variation and environmental factors, specifically signatures associated with local adaptation to drought stress in heterogeneous environments. Multiple linear regression analyses revealed joint effects of population age, mean temperature of coldest quarter, and precipitation of wettest month on effective population size (Ne). Our current study uses M. dioica as a case for providing new insights into the evolutionary history and local adaptation of northern China flora and provides qualitative strategies for plant conservation.

An evaluation of sequencing coverage and genotyping strategies to assess neutral and adaptive diversity

Whole genome sequences (WGS) greatly increase our ability to precisely infer population genetic parameters, demographic processes, and selection signatures. However, WGS may still be not affordable for a representative number of individuals/populations. In this context, our goal was to assess the efficiency of several SNP genotyping strategies by testing their ability to accurately estimate parameters describing neutral diversity and to detect signatures of selection. We analysed 110 WGS at 12× coverage for four different species, i.e., sheep, goats and their wild counterparts. From these data we generated 946 data sets corresponding to random panels of 1K to 5M variants, commercial SNP chips and exome capture, for sample sizes of five to 48 individuals. We also extracted low-coverage genome resequencing of 1×, 2× and 5× by randomly subsampling reads from the 12× resequencing data. Globally, 5K to 10K random variants were enough for an accurate estimation of genome diversity. Conversely, commercial panels and exome capture displayed strong ascertainment biases. Besides the characterization of neutral diversity, the detection of the signature of selection and the accurate estimation of linkage disequilibrium (LD) required high-density panels of at least 1M variants. Finally, genotype likelihoods increased the quality of variant calling from low coverage resequencing but proportions of incorrect genotypes remained substantial, especially for heterozygote sites. Whole genome resequencing coverage of at least 5× appeared to be necessary for accurate assessment of genomic variations. These results have implications for studies seeking to deploy low-density SNP collections or genome scans across genetically diverse populations/species showing similar genetic characteristics and patterns of LD decay for a wide variety of purposes.

The role of dispersal, selection, and timing of sampling on the false discovery rate of loci under selection during geographic range expansion

Identifying adaptive loci is important to understand the evolutionary potential of species undergoing range expansion. However, in expanding populations, spatial demographic processes such as allele surfing can create spatial patterns of neutral genetic variation that appear similar to those generated through adaptive processes. As a result, the false discovery rate of adaptive loci may be inflated in landscape genomic analyses. Here, we take a simulation modelling approach to investigate how range expansion affects our ability to correctly distinguish between neutral and adaptive genetic variation, using the mountain pine beetle outbreak system as a motivating example. We simulated the demographic and population genetic dynamics of populations undergoing range expansion using an individual-based genetic model CDMetaPOP. We investigated how the false discovery rate of adaptive loci is affected by (i) dispersal capacity, (ii) timing of sampling, and (iii) the strength of selection on an adaptive reference locus. We found that a combination of weak dispersal, weak selection, and early sampling presents the greatest risk of misidentifying loci under selection. Expanding populations present unique challenges to the reliable identification of adaptive loci. We demonstrate that there is a need for further methodological development to account for directional demographic processes in landscape genomics.

Landscape genetic structure of Scirpus mariqueter reveals a putatively adaptive differentiation under strong gene flow in estuaries

Estuarine organisms grow in highly heterogeneous habitats, and their genetic differentiation is driven by selective and neutral processes as well as population colonization history. However, the relative importance of the processes that underlie genetic structure is still puzzling. Scirpus mariqueter is a perennial grass almost limited in the Changjiang River estuary and its adjacent Qiantang River estuary. Here, using amplified fragment length polymorphism (AFLP), a moderate-high level of genetic differentiation among populations (range F ST: 0.0310-0.3325) was showed despite large ongoing dispersal. FLOCK assigned all individuals to 13 clusters and revealed a complex genetic structure. Some genetic clusters were limited in peripheries compared with very mixing constitution in center populations, suggesting local adaptation was more likely to occur in peripheral populations. 21 candidate outliers under positive selection were detected, and further, the differentiation patterns correlated with geographic distance, salinity difference, and colonization history were analyzed with or without the outliers. Combined results of AMOVA and IBD based on different dataset, it was found that the effects of geographic distance and population colonization history on isolation seemed to be promoted by divergent selection. However, none-liner IBE pattern indicates the effects of salinity were overwhelmed by spatial distance or other ecological processes in certain areas and also suggests that salinity was not the only selective factor driving population differentiation. These results together indicate that geographic distance, salinity difference, and colonization history co-contributed in shaping the genetic structure of S. mariqueter and that their relative importance was correlated with spatial scale and environment gradient.

The Effects of Epistasis and Pleiotropy on Genome-Wide Scans for Adaptive Outlier Loci

With the advent of next-generation sequencing approaches, the search for individual loci underlying local adaptation has become a major enterprise in evolutionary biology. One promising method to identify such loci is to examine genome-wide patterns of differentiation, using an FST-outlier approach. The effects of pleiotropy and epistasis on this approach are not yet known. Here, we model 2 populations of a sexually reproducing, diploid organism with 2 quantitative traits, one of which is involved in local adaptation. We consider genetic architectures with and without pleiotropy and epistasis. We also model neutral marker loci on an explicit genetic map as the 2 populations diverge and apply FST outlier approaches to determine the extent to which quantitative trait loci (QTL) are detectable. Our results show, under a wide range of conditions, that only a small number of QTL are typically responsible for most of the trait divergence between populations, even when inheritance is highly polygenic. We find that the loci making the largest contributions to trait divergence tend to be detectable outliers. These loci also make the largest contributions to within-population genetic variance. The addition of pleiotropy reduces the extent to which quantitative traits can evolve independently but does not reduce the efficacy of outlier scans. The addition of epistasis, however, reduces the mean FST values for causative QTL, making these loci more difficult, but not impossible, to detect in outlier scans.

Genome-wide signatures of local adaptation among seven stoneflies species along a nationwide latitudinal gradient in Japan

Background

Environmental heterogeneity continuously produces a selective pressure that results in genomic variation among organisms; understanding this relationship remains a challenge in evolutionary biology. Here, we evaluated the degree of genome-environmental association of seven stonefly species across a wide geographic area in Japan and additionally identified putative environmental drivers and their effect on co-existing multiple stonefly species. Double-digest restriction-associated DNA (ddRAD) libraries were independently sequenced for 219 individuals from 23 sites across four geographical regions along a nationwide latitudinal gradient in Japan.

Results

A total of 4251 candidate single nucleotide polymorphisms (SNPs) strongly associated with local adaptation were discovered using Latent mixed models; of these, 294 SNPs showed strong correlation with environmental variables, specifically precipitation and altitude, using distance-based redundancy analysis. Genome–genome comparison among the seven species revealed a high sequence similarity of candidate SNPs within a geographical region, suggesting the occurrence of a parallel evolution process.

Conclusions

Our results revealed genomic signatures of local adaptation and their influence on multiple, co-occurring species. These results can be potentially applied for future studies on river management and climatic stressor impacts.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5453-3) contains supplementary material, which is available to authorized users.

Genome-wide signals of drift and local adaptation during rapid lineage divergence in a songbird

The formation of independent evolutionary lineages involves neutral and selective factors, and understanding their relative roles in population divergence is a fundamental goal of speciation research. Correlations between allele frequencies and environmental variability can reveal the role of selection, yet the relative contribution of drift can be difficult to establish. Recently diversified taxa like the Oregon junco (Aves, Passerellidae, Junco hyemalis oreganus) of western North America provide ideal scenarios to apply genetic-environment association analyses (GEA) while controlling for population structure. Analysis of genome-wide SNP loci revealed marked genetic structure consisting of differentiated populations in isolated, dry southern mountain ranges, and less divergent, recently expanded populations in humid northern latitudes. We used correlations between genomic and environmental variance to test for three specific modes of evolutionary divergence: (a) drift in geographic isolation, (b) differentiation along continuous selective gradients and (c) isolation-by-adaptation. We found evidence of strong drift in southern mountains, but also signals of local adaptation driven by temperature, precipitation, elevation and vegetation, especially when controlling for population history. We identified numerous variants under selection scattered across the genome, suggesting that local adaptation can promote rapid differentiation when acting over multiple independent loci.

Landscape genomics provides evidence of climate-associated genetic variation in Mexican populations of Quercus rugosa

Local adaptation is a critical evolutionary process that allows plants to grow better in their local compared to non-native habitat and results in species-wide geographic patterns of adaptive genetic variation. For forest tree species with a long generation time, this spatial genetic heterogeneity can shape the ability of trees to respond to rapid climate change. Here, we identify genomic variation that may confer local environmental adaptations and then predict the extent of adaptive mismatch under future climate as a tool for forest restoration or management of the widely distributed high-elevation oak species Quercus rugosa in Mexico. Using genotyping by sequencing, we identified 5,354 single nucleotide polymorphisms (SNPs) genotyped from 103 individuals across 17 sites in the Trans-Mexican Volcanic Belt, and, after controlling for neutral genetic structure, we detected 74 F ST outlier SNPs and 97 SNPs associated with climate variation. Then, we deployed a nonlinear multivariate model, Gradient Forests, to map turnover in allele frequencies along environmental gradients and predict areas most sensitive to climate change. We found that spatial patterns of genetic variation were most strongly associated with precipitation seasonality and geographic distance. We identified regions of contemporary genetic and climatic similarities and predicted regions where future populations of Q. rugosa might be at risk due to high expected rate of climate change. Our findings provide preliminary details for future management strategies of Q. rugosa in Mexico and also illustrate how a landscape genomic approach can provide a useful tool for conservation and resource management strategies.

Navigating the Interface Between Landscape Genetics and Landscape Genomics

As next-generation sequencing data become increasingly available for non-model organisms, a shift has occurred in the focus of studies of the geographic distribution of genetic variation. Whereas landscape genetics studies primarily focus on testing the effects of landscape variables on gene flow and genetic population structure, landscape genomics studies focus on detecting candidate genes under selection that indicate possible local adaptation. Navigating the transition between landscape genomics and landscape genetics can be challenging. The number of molecular markers analyzed has shifted from what used to be a few dozen loci to thousands of loci and even full genomes. Although genome scale data can be separated into sets of neutral loci for analyses of gene flow and population structure and putative loci under selection for inference of local adaptation, there are inherent differences in the questions that are addressed in the two study frameworks. We discuss these differences and their implications for study design, marker choice and downstream analysis methods. Similar to the rapid proliferation of analysis methods in the early development of landscape genetics, new analytical methods for detection of selection in landscape genomics studies are burgeoning. We focus on genome scan methods for detection of selection, and in particular, outlier differentiation methods and genetic-environment association tests because they are the most widely used. Use of genome scan methods requires an understanding of the potential mismatches between the biology of a species and assumptions inherent in analytical methods used, which can lead to high false positive rates of detected loci under selection. Key to choosing appropriate genome scan methods is an understanding of the underlying demographic structure of study populations, and such data can be obtained using neutral loci from the generated genome-wide data or prior knowledge of a species' phylogeographic history. To this end, we summarize recent simulation studies that test the power and accuracy of genome scan methods under a variety of demographic scenarios and sampling designs. We conclude with a discussion of additional considerations for future method development, and a summary of methods that show promise for landscape genomics studies but are not yet widely used.

Ten Years of Landscape Genomics: Challenges and Opportunities

Landscape genomics is a relatively new discipline that aims to reveal the relationship between adaptive genetic imprints in genomes and environmental heterogeneity among natural populations. Although the interest in landscape genomics has increased since this term was coined, studies on this topic remain scarce. Landscape genomics has become a powerful method to scan and determine the genes responsible for the complex adaptive evolution of species at population (mostly) and individual (more rarely) level. This review outlines the sampling strategies, molecular marker types and research categories in 37 articles published during the first 10 years of this field (i.e., 2007-2016). We also address major challenges and future directions for landscape genomics. This review aims to promote interest in conducting additional studies in landscape genomics.

Union of phylogeography and landscape genetics

Phylogeography and landscape genetics have arisen within the past 30 y. Phylogeography is said to be the bridge between population genetics and systematics, and landscape genetics the bridge between landscape ecology and population genetics. Both fields can be considered as simply the amalgamation of classic biogeography with genetics and genomics; however, they differ in the temporal, spatial, and organismal scales addressed and the methodology used. I begin by briefly summarizing the history and purview of each field and suggest that, even though landscape genetics is a younger field (coined in 2003) than phylogeography (coined in 1987), early studies by Dobzhansky on the "microgeographic races" of Linanthus parryae in the Mojave Desert of California and Drosophila pseudoobscura across the western United States presaged the fields by over 40 y. Recent advances in theory, models, and methods have allowed researchers to better synthesize ecological and evolutionary processes in their quest to answer some of the most basic questions in biology. I highlight a few of these novel studies and emphasize three major areas ripe for investigation using spatially explicit genomic-scale data: the biogeography of speciation, lineage divergence and species delimitation, and understanding adaptation through time and space. Examples of areas in need of study are highlighted, and I end by advocating a union of phylogeography and landscape genetics under the more general field: biogeography.

Simulating the spread of selection-driven genotypes using landscape resistance models for desert bighorn sheep

Landscape genetic studies based on neutral genetic markers have contributed to our understanding of the influence of landscape composition and configuration on gene flow and genetic variation. However, the potential for species to adapt to changing landscapes will depend on how natural selection influences adaptive genetic variation. We demonstrate how landscape resistance models can be combined with genetic simulations incorporating natural selection to explore how the spread of adaptive variation is affected by landscape characteristics, using desert bighorn sheep (Ovis canadensis nelsoni) in three differing regions of the southwestern United States as an example. We conducted genetic sampling and least-cost path modeling to optimize landscape resistance models independently for each region, and then simulated the spread of an adaptive allele favored by selection across each region. Optimized landscape resistance models differed between regions with respect to landscape variables included and their relationships to resistance, but the slope of terrain and the presence of water barriers and major roads had the greatest impacts on gene flow. Genetic simulations showed that differences among landscapes strongly influenced spread of adaptive genetic variation, with faster spread (1) in landscapes with more continuously distributed habitat and (2) when a pre-existing allele (i.e., standing genetic variation) rather than a novel allele (i.e., mutation) served as the source of adaptive genetic variation. The combination of landscape resistance models and genetic simulations has broad conservation applications and can facilitate comparisons of adaptive potential within and between landscapes.

Using Landscape Genetics Simulations for Planting Blister Rust Resistant Whitebark Pine in the US Northern Rocky Mountains

Recent population declines to the high elevation western North America foundation species whitebark pine, have been driven by the synergistic effects of the invasive blister rust pathogen, mountain pine beetle (MPB), fire exclusion, and climate change. This has led to consideration for listing whitebark pine (WBP) as a threatened or endangered species under the Endangered Species Act, which has intensified interest in developing management strategies for maintaining and restoring the species. An important, but poorly studied, aspect of WBP restoration is the spatial variation in adaptive genetic variation and the potential of blister rust resistant strains to maintain viable populations in the future. Here, we present a simulation modeling framework to improve understanding of the long-term genetic consequences of the blister rust pathogen, the evolution of rust resistance, and scenarios of planting rust resistant genotypes of whitebark pine. We combine climate niche modeling and eco-evolutionary landscape genetics modeling to evaluate the effects of different scenarios of planting rust-resistant genotypes and impacts of wind field direction on patterns of gene flow. Planting scenarios showed different levels for local extirpation of WBP and increased population-wide blister rust resistance, suggesting that the spatial arrangement and choice of planting locations can greatly affect survival rates of whitebark pine. This study presents a preliminary, but potentially important, framework for facilitating the conservation of whitebark pine.

High performance computation of landscape genomic models including local indicators of spatial association

With the increasing availability of both molecular and topo-climatic data, the main challenges facing landscape genomics - that is the combination of landscape ecology with population genomics - include processing large numbers of models and distinguishing between selection and demographic processes (e.g. population structure). Several methods address the latter, either by estimating a null model of population history or by simultaneously inferring environmental and demographic effects. Here we present samβada, an approach designed to study signatures of local adaptation, with special emphasis on high performance computing of large-scale genetic and environmental data sets. samβada identifies candidate loci using genotype-environment associations while also incorporating multivariate analyses to assess the effect of many environmental predictor variables. This enables the inclusion of explanatory variables representing population structure into the models to lower the occurrences of spurious genotype-environment associations. In addition, samβada calculates local indicators of spatial association for candidate loci to provide information on whether similar genotypes tend to cluster in space, which constitutes a useful indication of the possible kinship between individuals. To test the usefulness of this approach, we carried out a simulation study and analysed a data set from Ugandan cattle to detect signatures of local adaptation with samβada, bayenv, lfmm and an F_ST outlier method (FDIST approach in arlequin) and compare their results. samβada - an open source software for Windows, Linux and Mac OS X available at http://lasig.epfl.ch/sambada - outperforms other approaches and better suits whole-genome sequence data processing.

Finding the Genomic Basis of Local Adaptation: Pitfalls, Practical Solutions, and Future Directions

Uncovering the genetic and evolutionary basis of local adaptation is a major focus of evolutionary biology. The recent development of cost-effective methods for obtaining high-quality genome-scale data makes it possible to identify some of the loci responsible for adaptive differences among populations. Two basic approaches for identifying putatively locally adaptive loci have been developed and are broadly used: one that identifies loci with unusually high genetic differentiation among populations (differentiation outlier methods) and one that searches for correlations between local population allele frequencies and local environments (genetic-environment association methods). Here, we review the promises and challenges of these genome scan methods, including correcting for the confounding influence of a species' demographic history, biases caused by missing aspects of the genome, matching scales of environmental data with population structure, and other statistical considerations. In each case, we make suggestions for best practices for maximizing the accuracy and efficiency of genome scans to detect the underlying genetic basis of local adaptation. With attention to their current limitations, genome scan methods can be an important tool in finding the genetic basis of adaptive evolutionary change.

Navigating the pitfalls and promise of landscape genetics

The field of landscape genetics has been evolving rapidly since its emergence in the early 2000s. New applications, techniques and criticisms of techniques appear like clockwork with each new journal issue. The developments are an encouraging, and at times bewildering, sign of progress in an exciting new field of study. However, we suggest that the rapid expansion of landscape genetics has belied important flaws in the development of the field, and we add an air of caution to this breakneck pace of expansion. Specifically, landscape genetic studies often lose sight of the fundamental principles and complex consequences of gene flow, instead favouring simplistic interpretations and broad inferences not necessarily warranted by the data. Here, we describe common pitfalls that characterize such studies, and provide practical guidance to improve landscape genetic investigation, with careful consideration of inferential limits, scale, replication, and the ecological and evolutionary context of spatial genetic patterns. Ultimately, the utility of landscape genetics will depend on translating the relationship between gene flow and landscape features into an understanding of long-term population outcomes. We hope the perspective presented here will steer landscape genetics down a more scientifically sound and productive path, garnering a field that is as informative in the future as it is popular now.

Controlling false discoveries in genome scans for selection

Population differentiation (PD) and ecological association (EA) tests have recently emerged as prominent statistical methods to investigate signatures of local adaptation using population genomic data. Based on statistical models, these genomewide testing procedures have attracted considerable attention as tools to identify loci potentially targeted by natural selection. An important issue with PD and EA tests is that incorrect model specification can generate large numbers of false-positive associations. Spurious association may indeed arise when shared demographic history, patterns of isolation by distance, cryptic relatedness or genetic background are ignored. Recent works on PD and EA tests have widely focused on improvements of test corrections for those confounding effects. Despite significant algorithmic improvements, there is still a number of open questions on how to check that false discoveries are under control and implement test corrections, or how to combine statistical tests from multiple genome scan methods. This tutorial study provides a detailed answer to these questions. It clarifies the relationships between traditional methods based on allele frequency differentiation and EA methods and provides a unified framework for their underlying statistical tests. We demonstrate how techniques developed in the area of genomewide association studies, such as inflation factors and linear mixed models, benefit genome scan methods and provide guidelines for good practice while conducting statistical tests in landscape and population genomic applications. Finally, we highlight how the combination of several well-calibrated statistical tests can increase the power to reject neutrality, improving our ability to infer patterns of local adaptation in large population genomic data sets.

Detecting spatial genetic signatures of local adaptation in heterogeneous landscapes

The spatial structure of the environment (e.g. the configuration of habitat patches) may play an important role in determining the strength of local adaptation. However, previous studies of habitat heterogeneity and local adaptation have largely been limited to simple landscapes, which poorly represent the multiscale habitat structure common in nature. Here, we use simulations to pursue two goals: (i) we explore how landscape heterogeneity, dispersal ability and selection affect the strength of local adaptation, and (ii) we evaluate the performance of several genotype-environment association (GEA) methods for detecting loci involved in local adaptation. We found that the strength of local adaptation increased in spatially aggregated selection regimes, but remained strong in patchy landscapes when selection was moderate to strong. Weak selection resulted in weak local adaptation that was relatively unaffected by landscape heterogeneity. In general, the power of detection methods closely reflected levels of local adaptation. False-positive rates (FPRs), however, showed distinct differences across GEA methods based on levels of population structure. The univariate GEA approach had high FPRs (up to 55%) under limited dispersal scenarios, due to strong isolation by distance. By contrast, multivariate, ordination-based methods had uniformly low FPRs (0-2%), suggesting these approaches can effectively control for population structure. Specifically, constrained ordinations had the best balance of high detection and low FPRs and will be a useful addition to the GEA toolkit. Our results provide both theoretical and practical insights into the conditions that shape local adaptation and how these conditions impact our ability to detect selection.

Prospects and challenges for the conservation of farm animal genomic resources, 2015-2025

Livestock conservation practice is changing rapidly in light of policy developments, climate change and diversifying market demands. The last decade has seen a step change in technology and analytical approaches available to define, manage and conserve Farm Animal Genomic Resources (FAnGR). However, these rapid changes pose challenges for FAnGR conservation in terms of technological continuity, analytical capacity and integrative methodologies needed to fully exploit new, multidimensional data. The final conference of the ESF Genomic Resources program aimed to address these interdisciplinary problems in an attempt to contribute to the agenda for research and policy development directions during the coming decade. By 2020, according to the Convention on Biodiversity's Aichi Target 13, signatories should ensure that “…the genetic diversity of …farmed and domesticated animals and of wild relatives …is maintained, and strategies have been developed and implemented for minimizing genetic erosion and safeguarding their genetic diversity.” However, the real extent of genetic erosion is very difficult to measure using current data. Therefore, this challenging target demands better coverage, understanding and utilization of genomic and environmental data, the development of optimized ways to integrate these data with social and other sciences and policy analysis to enable more flexible, evidence-based models to underpin FAnGR conservation. At the conference, we attempted to identify the most important problems for effective livestock genomic resource conservation during the next decade. Twenty priority questions were identified that could be broadly categorized into challenges related to methodology, analytical approaches, data management and conservation. It should be acknowledged here that while the focus of our meeting was predominantly around genetics, genomics and animal science, many of the practical challenges facing conservation of genomic resources are societal in origin and are predicated on the value (e.g., socio-economic and cultural) of these resources to farmers, rural communities and society as a whole. The overall conclusion is that despite the fact that the livestock sector has been relatively well-organized in the application of genetic methodologies to date, there is still a large gap between the current state-of-the-art in the use of tools to characterize genomic resources and its application to many non-commercial and local breeds, hampering the consistent utilization of genetic and genomic data as indicators of genetic erosion and diversity. The livestock genomic sector therefore needs to make a concerted effort in the coming decade to enable to the democratization of the powerful tools that are now at its disposal, and to ensure that they are applied in the context of breed conservation as well as development.

The population genomic signature of environmental selection in the widespread insect-pollinated tree species Frangula alnus at different geographical scales

The evaluation of the molecular signatures of selection in species lacking an available closely related reference genome remains challenging, yet it may provide valuable fundamental insights into the capacity of populations to respond to environmental cues. We screened 25 native populations of the tree species Frangula alnus subsp. alnus (Rhamnaceae), covering three different geographical scales, for 183 annotated single-nucleotide polymorphisms (SNPs). Standard population genomic outlier screens were combined with individual-based and multivariate landscape genomic approaches to examine the strength of selection relative to neutral processes in shaping genomic variation, and to identify the main environmental agents driving selection. Our results demonstrate a more distinct signature of selection with increasing geographical distance, as indicated by the proportion of SNPs (i) showing exceptional patterns of genetic diversity and differentiation (outliers) and (ii) associated with climate. Both temperature and precipitation have an important role as selective agents in shaping adaptive genomic differentiation in F. alnus subsp. alnus, although their relative importance differed among spatial scales. At the 'intermediate' and 'regional' scales, where limited genetic clustering and high population diversity were observed, some indications of natural selection may suggest a major role for gene flow in safeguarding adaptability. High genetic diversity at loci under selection in particular, indicated considerable adaptive potential, which may nevertheless be compromised by the combined effects of climate change and habitat fragmentation.

Ecotypes of an ecologically dominant prairie grass (Andropogon gerardii) exhibit genetic divergence across the U.S. Midwest grasslands' environmental gradient

Big bluestem (Andropogon gerardii) is an ecologically dominant grass with wide distribution across the environmental gradient of U.S. Midwest grasslands. This system offers an ideal natural laboratory to study population divergence and adaptation in spatially varying climates. Objectives were to: (i) characterize neutral genetic diversity and structure within and among three regional ecotypes derived from 11 prairies across the U.S. Midwest environmental gradient, (ii) distinguish between the relative roles of isolation by distance (IBD) vs. isolation by environment (IBE) on ecotype divergence, (iii) identify outlier loci under selection and (iv) assess the association between outlier loci and climate. Using two primer sets, we genotyped 378 plants at 384 polymorphic AFLP loci across regional ecotypes from central and eastern Kansas and Illinois. Neighbour-joining tree and PCoA revealed strong genetic differentiation between Kansas and Illinois ecotypes, which was better explained by IBE than IBD. We found high genetic variability within prairies (80%) and even fragmented Illinois prairies, surprisingly, contained high within-prairie genetic diversity (92%). Using Bayenv2, 14 top-ranked outlier loci among ecotypes were associated with temperature and precipitation variables. Six of seven BayeScanFST outliers were in common with Bayenv2 outliers. High genetic diversity may enable big bluestem populations to better withstand changing climates; however, population divergence supports the use of local ecotypes in grassland restoration. Knowledge of genetic variation in this ecological dominant and other grassland species will be critical to understanding grassland response and restoration challenges in the face of a changing climate.

Spatial and temporal simulation of human evolution. Methods, frameworks and applications

Analyses of human evolution are fundamental to understand the current gradients of human diversity. In this concern, genetic samples collected from current populations together with archaeological data are the most important resources to study human evolution. However, they are often insufficient to properly evaluate a variety of evolutionary scenarios, leading to continuous debates and discussions. A commonly applied strategy consists of the use of computer simulations based on, as realistic as possible, evolutionary models, to evaluate alternative evolutionary scenarios through statistical correlations with the real data. Computer simulations can also be applied to estimate evolutionary parameters or to study the role of each parameter on the evolutionary process. Here we review the mainly used methods and evolutionary frameworks to perform realistic spatially explicit computer simulations of human evolution. Although we focus on human evolution, most of the methods and software we describe can also be used to study other species. We also describe the importance of considering spatially explicit models to better mimic human evolutionary scenarios based on a variety of phenomena such as range expansions, range shifts, range contractions, sex-biased dispersal, long-distance dispersal or admixtures of populations. We finally discuss future implementations to improve current spatially explicit simulations and their derived applications in human evolution.

Assessing the spatial dependence of adaptive loci in 43 European and Western Asian goat breeds using AFLP markers

BACKGROUND

During the past decades, neutral DNA markers have been extensively employed to study demography, population genetics and structure in livestock, but less interest has been devoted to the evaluation of livestock adaptive potential through the identification of genomic regions likely to be under natural selection.

METHODOLOGY/PRINCIPAL FINDINGS

Landscape genomics can greatly benefit the entire livestock system through the identification of genotypes better adapted to specific or extreme environmental conditions. Therefore we analyzed 101 AFLP markers in 43 European and Western Asian goat breeds both with Matsam software, based on a correlative approach (SAM), and with Mcheza and Bayescan, two FST based software able to detect markers carrying signatures of natural selection. Matsam identified four loci possibly under natural selection--also confirmed by FST-outlier methods--and significantly associated with environmental variables such as diurnal temperature range, frequency of precipitation, relative humidity and solar radiation.

CONCLUSIONS/SIGNIFICANCE

These results show that landscape genomics can provide useful information on the environmental factors affecting the adaptive potential of livestock living in specific climatic conditions. Besides adding conservation value to livestock genetic resources, this knowledge may lead to the development of novel molecular tools useful to preserve the adaptive potential of local breeds during genetic improvement programs, and to increase the adaptability of industrial breeds to changing environments.

Genomic signature of adaptation to climate in Medicago truncatula

Local adaptation and adaptive clines are pervasive in natural plant populations, yet the effects of these types of adaptation on genomic diversity are not well understood. With a data set of 202 accessions of Medicago truncatula genotyped at almost 2 million single nucleotide polymorphisms, we used mixed linear models to identify candidate loci responsible for adaptation to three climatic gradients-annual mean temperature (AMT), precipitation in the wettest month (PWM), and isothermality (ITH)-representing the major axes of climate variation across the species' range. Loci with the strongest association to these climate gradients tagged genome regions with high sequence similarity to genes with functional roles in thermal tolerance, drought tolerance, or resistance to herbivores of pathogens. Genotypes at these candidate loci also predicted the performance of an independent sample of plant accessions grown in climate-controlled conditions. Compared to a genome-wide sample of randomly drawn reference SNPs, candidates for two climate gradients, AMT and PWM, were significantly enriched for genic regions, and genome segments flanking genic AMT and PWM candidates harbored less nucleotide diversity, elevated differentiation between haplotypes carrying alternate alleles, and an overrepresentation of the most common haplotypes. These patterns of diversity are consistent with a history of soft selective sweeps acting on loci underlying adaptation to climate, but not with a history of long-term balancing selection.