Landscape Genomic Analyses Research Articles

Beyond cyanogenesis: Temperature gradients drive environmental adaptation in North American white clover (Trifolium repens L.).

Species that repeatedly evolve phenotypic clines across environmental gradients have been highlighted as ideal systems for characterizing the genomic basis of local environmental adaptation. However, few studies have assessed the importance of observed phenotypic clines for local adaptation: conspicuous traits that vary clinally may not necessarily be the most critical in determining local fitness. The present study was designed to fill this gap, using a plant species characterized by repeatedly evolved adaptive phenotypic clines. White clover is naturally polymorphic for its chemical defence cyanogenesis (HCN release with tissue damage); climate-associated cyanogenesis clines have evolved throughout its native and introduced range worldwide. We performed landscape genomic analyses on 415 wild genotypes from 43 locations spanning much of the North American species range to assess the relative importance of cyanogenesis loci vs. other genomic factors in local climatic adaptation. We find clear evidence of local adaptation, with temperature-related climatic variables best describing genome-wide differentiation between sampling locations. The same climatic variables are also strongly correlated with cyanogenesis frequencies and gene copy number variations (CNVs) at cyanogenesis loci. However, landscape genomic analyses indicate no significant contribution of cyanogenesis loci to local adaptation. Instead, several genomic regions containing promising candidate genes for plant response to seasonal cues are identified - some of which are shared with previously identified QTLs for locally adaptive fitness traits in North American white clover. Our findings suggest that local adaptation in white clover is likely determined primarily by genes controlling the timing of growth and flowering in response to local seasonal cues. More generally, this work suggests that caution is warranted when considering the importance of conspicuous phenotypic clines as primary determinants of local adaptation.

Integrating very high resolution environmental proxies in genotype-environment association studies.

Landscape genomic analyses associating genetic variation with environmental variables are powerful tools for studying molecular signatures of species' local adaptation and for detecting candidate genes under selection. The development of landscape genomics over the past decade has been spurred by improvements in resolutions of genomic and environmental datasets, allegedly increasing the power to identify putative genes underlying local adaptation in non-model organisms. Although these associations have been successfully applied to numerous species across a diverse array of taxa, the spatial scale of environmental predictor variables has been largely overlooked, potentially limiting conclusions to be reached with these methods. To address this knowledge gap, we systematically evaluated performances of genotype-environment association (GEA) models using predictor variables at multiple spatial resolutions. Specifically, we used multivariate redundancy analyses to associate whole-genome sequence data from the plant Arabis alpina L. collected across four neighboring valleys in the western Swiss Alps, with very high-resolution topographic variables derived from digital elevation models of grain sizes between 0.5 m and 16 m. These comparisons highlight the sensitivity of landscape genomic models to spatial resolution, where the optimal grain sizes were specific to variable type, terrain characteristics, and study extent. To assist in selecting variables at appropriate spatial resolutions, we demonstrate a practical approach to produce, select, and integrate multiscale variables into GEA models. After generalizing fine-grained variables to multiple spatial resolutions, a forward selection procedure is applied to retain only the most relevant variables for a particular context. Depending on the spatial resolution, the relevance for topographic variables in GEA studies calls for integrating multiple spatial scales into landscape genomic models. By carefully considering spatial resolutions, candidate genes under selection by a more realistic range of pressures can be detected for downstream analyses, with important applied implications for experimental research and conservation management of natural populations.

Selection signatures and landscape genomics analysis to reveal climate adaptation of goat breeds

Goats have achieved global prominence as essential livestock since their initial domestication, primarily owing to their remarkable adaptability to diverse environmental and production systems. Differential selection pressures influenced by climate have led to variations in their physical attributes, leaving genetic imprints within the genomes of goat breeds raised in diverse agroecological settings. In light of this, our study pursued a comprehensive analysis, merging environmental data with single nucleotide polymorphism (SNP) variations, to unearth indications of selection shaped by climate-mediated forces in goats. Through the examination of 43,300 SNPs from 51 indigenous goat breeds adapting to different climatic conditions using four analytical methods: latent factor mixed models (LFMM), F-statistics (Fst), Extended haplotype homozygosity across populations (XPEHH), and spatial analysis method (SAM), A total of 74 genes were revealed to display clear signs of selection, which are believed to be influenced by climatic conditions. Among these genes, 32 were consistently identified by at least two of the applied methods, and three genes (DENND1A, PLCB1, and ITPR2) were confirmed by all four approaches. Moreover, our investigation yielded 148 Gene Ontology (GO) terms based on these 74 genes, underlining pivotal biological pathways crucial for environmental adaptation. These pathways encompass functions like vascular smooth muscle contraction, cellular response to heat, GTPase regulator activity, rhythmic processes, and responses to temperature stimuli. Of significance, GO terms about endocrine regulation and energy metabolic responses, key for local adaptation were also uncovered, including biological processes, such as cell differentiation, regulation of peptide hormone secretion, and lipid metabolism. These findings contribute to our knowledge of the genetic structure of climate-triggered adaptation across the goat genome and have practical implications for marker-assisted breeding in goats.

Genomic insights into local adaptation and vulnerability of Quercus longinux to climate change

BackgroundClimate change is expected to alter the factors that drive changes in adaptive variation. This is especially true for species with long life spans and limited dispersal capabilities. Rapid climate changes may disrupt the migration of beneficial genetic variations, making it challenging for them to keep up with changing environments. Understanding adaptive genetic variations in tree species is crucial for conservation and effective forest management. Our study used landscape genomic analyses and phenotypic traits from a thorough sampling across the entire range of Quercus longinux, an oak species native to Taiwan, to investigate the signals of adaptation within this species.ResultsUsing ecological data, phenotypic traits, and 1,933 single-nucleotide polymorphisms (SNPs) from 205 individuals, we classified three genetic groups, which were also phenotypically and ecologically divergent. Thirty-five genes related to drought and freeze resistance displayed signatures of natural selection. The adaptive variation was driven by diverse environmental pressures such as low spring precipitation, low annual temperature, and soil grid sizes. Using linear-regression-based methods, we identified isolation by environment (IBE) as the optimal model for adaptive SNPs. Redundancy analysis (RDA) further revealed a substantial joint influence of demography, geology, and environments, suggesting a covariation between environmental gradients and colonization history. Lastly, we utilized adaptive signals to estimate the genetic offset for each individual under diverse climate change scenarios. The required genetic changes and migration distance are larger in severe climates. Our prediction also reveals potential threats to edge populations in northern and southeastern Taiwan due to escalating temperatures and precipitation reallocation.ConclusionsWe demonstrate the intricate influence of ecological heterogeneity on genetic and phenotypic adaptation of an oak species. The adaptation is also driven by some rarely studied environmental factors, including wind speed and soil features. Furthermore, the genetic offset analysis predicted that the edge populations of Q. longinux in lower elevations might face higher risks of local extinctions under climate change.

Individual-based landscape genomics for conservation: An analysis pipeline.

Landscape genomics can harness environmental and genetic data to inform conservation decisions by providing essential insights into how landscapes shape biodiversity. The massive increase in genetic data afforded by the genomic era provides exceptional resolution for answering critical conservation genetics questions. The accessibility of genomic data for non-model systems has also enabled a shift away from population-based sampling to individual-based sampling, which now provides accurate and robust estimates of genetic variation that can be used to examine the spatial structure of genomic diversity, population connectivity and the nature of environmental adaptation. Nevertheless, the adoption of individual-based sampling in conservation genetics has been slowed due, in large part, to concerns over how to apply methods developed for population-based sampling to individual-based sampling schemes. Here, we discuss the benefits of individual-based sampling for conservation and describe how landscape genomic methods, paired with individual-based sampling, can answer fundamental conservation questions. We have curated key landscape genomic methods into a user-friendly, open-source workflow, which we provide as a new R package, A Landscape Genomics Analysis Toolkit in R (algatr). The algatr package includes novel added functionality for all of the included methods and extensive vignettes designed with the primary goal of making landscape genomic approaches more accessible and explicitly applicable to conservation biology.

Conservation genomics of an endangered montane amphibian reveals low population structure, low genomic diversity and selection pressure from disease.

Wildlife diseases are a major global threat to biodiversity. Boreal toads (Anaxyrus [Bufo] boreas) are a state-endangered species in the southern Rocky Mountains of Colorado and New Mexico, and a species of concern in Wyoming, largely due to lethal skin infections caused by the amphibian chytrid fungus Batrachochytrium dendrobatidis (Bd). We performed conservation and landscape genomic analyses using single nucleotide polymorphisms from double-digest, restriction site-associated DNA sequencing in combination with the development of the first boreal toad (and first North American toad) reference genome to investigate population structure, genomic diversity, landscape connectivity and adaptive divergence. Genomic diversity (π = 0.00034-0.00040) and effective population sizes (Ne = 8.9-38.4) were low, likely due to post-Pleistocene founder effects and Bd-related population crashes over the last three decades. Population structure was also low, likely due to formerly high connectivity among a higher density of geographically proximate populations. Boreal toad gene flow was facilitated by low precipitation, cold minimum temperatures, less tree canopy, low heat load and less urbanization. We found >8X more putatively adaptive loci related to Bd intensity than to all other environmental factors combined, and evidence for genes under selection related to immune response, heart development and regulation and skin function. These data suggest boreal toads in habitats with Bd have experienced stronger selection pressure from disease than from other, broad-scale environmental variations. These findings can be used by managers to conserve and recover the species through actions including reintroduction and supplementation of populations that have declined due to Bd.

Landscape genomics analysis provides insights into future climate change-driven risk in rhesus macaque

Climate change significantly affects the suitability of wildlife habitats. Thus, understanding how animals adapt ecologically and genetically to climate change is important for targeted species protection. Rhesus macaques (Macaca mulatta) are widely distributed and multi-climatically adapted primates. This study explored how rhesus macaques adapt to climate change by integrating ecological and genetic methods and applying species distribution models (SDMs) and a gradient forest (GF) model. The findings suggested that temperature seasonality primarily affects habitat suitability and indicated that climate change will have a dramatic impact on macaque populations in the future. We also applied genotype-environment association (GEA) analyses and selection signature analyses to identify genes associated with climate change and provide possible explanations for the adaptation of rhesus macaques to climatic environments. The population genomics analyses suggested that the Taihang population has the highest genomic vulnerability with inbreeding and low heterozygosity. Combined with the higher ecological vulnerability, additional conservation strategies are required for this population under higher risk of climate change. Our work measured the impact of climate change and enabled the identification of populations that exhibit high vulnerability to severe climate change. Such information is useful for selecting populations of rhesus macaques as subject of long-term monitoring or evolutionary rescue under future climate change.

Isolation by resistance explains genetic diversity in the Arremon brushfinches of northern Mesoamerica.

Genetic differentiation between and within natural populations is the result of the joint effects of neutral and adaptative processes. In addition, the spatial arrangement of the landscape promotes connectivity or creates barriers to gene flow, directly affecting speciation processes. In this study, we carried out a landscape genomics analysis using NextRAD data from a montane forest specialist bird complex, the Mesoamerican Chestnut-capped/Green-striped Brushfinch of the genus Arremon. Specifically, we examined population genomic structure using different assignment methods and genomic differentiation and diversity, and we tested alternative genetic isolation hypotheses at the individual level (e.g., isolation by barrier, IBB; isolation by environment, IBE; isolation by resistance, IBR). We found well-delimited genomic structuring (K = 5) across Mesoamerican montane forests in the studied group. Individual-level genetic distances among major montane ranges were mainly explained by IBR hypotheses in this sedentary Neotropical taxon. Our results uncover genetic distances/differentiation and patterns of gene flow in allopatric species that support the role of tropical mountains as spatial landscape drivers of biodiversity. IBR clearly supports a pattern of conserved niche-tracking of suitable habitat conditions and topographic complexity throughout glacial-interglacial dynamics.

Demographic and spatially-explicit landscape genomic analyses in a tropical oak reveal the impacts of late Quaternary climate change on Andean montane forests.

Tropical Andes are one of the most important biodiversity hotspots on earth, yet our understanding on how their biotas have responded to Quaternary climatic oscillations is extraordinarily limited and the alternative models proposed to explain their demographic dynamics have been seldom formally evaluated. Here, we test the hypothesis that the interplay between the spatial configuration of geographical barriers to dispersal and elevational displacements driven by Quaternary cooling-warming cycles have shaped the demographic trajectories of montane oak forests (Quercus humboldtii) from the Colombian Andes. Specifically, we integrate genomic data and environmental niche modelling at fine temporal resolution to test competing spatially-explicit demographic and coalescent models, including scenarios considering (i) isotropic gene flow through the landscape, (ii) the hypothetical impact of contemporary barriers to dispersal (i.e., inter-Andean valleys), and (ii) distributional shifts of montane oak forests from the Last Glacial Maximum to present. Although our data revealed a marked genetic fragmentation of montane oak forests, statistical support for isolation-with-migration models indicate that geographically separated populations from the different Andean Cordilleras regularly exchange gene flow. Accordingly, spatiotemporally-explicit demographic analyses supported a model of flickering connectivity, with scenarios considering isotropic gene flow or currently unsuitable habitats as persistent barriers to dispersal providing a comparatively worse fit to empirical genomic data. Overall, these results emphasize the role of landscape heterogeneity on shaping spatial patterns of genomic variation in montane oak forests, rejecting the hypothesis of genetic continuity and supporting a significant impact of Quaternary climatic oscillations on their demographic trajectories.

Genomic analyses point to a low evolutionary potential of prospective source populations for assisted migration in a forest herb.

Climate change is increasingly impacting temperate forest ecosystems and many forest herbs might be unable to track the changing climate due to dispersal limitation. Forest herbs with a low adaptive capacity may therefore benefit from conservation strategies that mitigate dispersal limitation and evolutionary constraints, such as assisted migration. However, assisted migration strategies rarely consider evolutionary constraints of potential source populations that may jeopardize their success. In cases where climate adaptation is overshadowed by competing evolutionary processes, assisted migration is unlikely to support adaptation to future climates. Using a combination of population and landscape genomic analyses, we disentangled local adaptation drivers and quantified the adaptability and vulnerability to climate change of the self-incompatible deciduous forest herb Primula elatior. Southern populations displayed a sharp genetic turnover and a considerable amount of local adaptation under diversifying selection was discovered. However, most of the outlier loci could not be linked to climate variables (71%) and were likely related to other local adaptation drivers, such as photoperiodism. Furthermore, specific adaptations to climate extremes, such as drought stress, could not be detected. This is in line with the typical occurrence of forest herbs in buffered climatic conditions, which can be expected to reduce selection pressures imposed by climate. Finally, populations in the south of the distribution area had increased sensitivity to climate change due to a reduced adaptive capacity and a moderate genetic offset, while central European populations were sensitive due to a high genetic offset. We conclude that assisted migration from southern source populations could bear significant risk due to nonclimatic maladaptation and a low adaptive capacity. Regional admixture and restoration of ecological connectivity to increase the adaptive capacity, and assisted range expansion to suitable habitat in the north might be more appropriate mitigation strategies.

Landscape genomics of the American lobster (Homarus americanus).

In marine species experiencing intense fishing pressures, knowledge of genetic structure and local adaptation represent a critical information to assist sustainable management. In this study, we performed a landscape genomics analysis in the American lobster to investigate the issues pertaining to the consequences of making use of putative adaptive loci to reliably infer population structure and thus more rigorously delineating biological management units in marine exploited species. Toward this end, we genotyped 14,893 single nucleotide polymorphism (SNPs) in 4190 lobsters sampled across 96 sampling sites distributed along 1000 km in the northwest Atlantic in both Canada and the USA. As typical for most marine species, we observed a weak, albeit highly significant genetic structure. We also found that adaptive genetic variation allows detecting fine-scale population structure not resolved by neutral genetic variation alone. Using the recent genome assembly of the American lobster, we were able to map and annotate several SNPs located in functional genes potentially implicated in adaptive processes such as thermal stress response, salinity tolerance and growth metabolism pathways. Taken together, our study indicates that weak population structure in high gene flow systems can be resolved at various spatial scales, and that putatively adaptive genetic variation can substantially enhance the delineation of biological management units of marine exploited species.

Habitat-linked genetic variation supports microgeographic adaptive divergence in an island-endemic bird species.

We investigated the potential mechanisms driving habitat‐linked genetic divergence within a bird species endemic to a single 250‐km2 island. The island scrub‐jay (Aphelocoma insularis) exhibits microgeographic divergence in bill morphology across pine–oak ecotones on Santa Cruz Island, California (USA), similar to adaptive differences described in mainland congeners over much larger geographic scales. To test whether individuals exhibit genetic differentiation related to habitat type and divergence in bill length, we genotyped over 3000 single nucleotide polymorphisms in 123 adult island scrub‐jay males from across Santa Cruz Island using restriction site‐associated DNA sequencing. Neutral landscape genomic analyses revealed that genome‐wide genetic differentiation was primarily related to geographic distance and differences in habitat composition. We also found 168 putatively adaptive loci associated with habitat type using multivariate redundancy analysis while controlling for spatial effects. Finally, two genome‐wide association analyses revealed a polygenic basis to variation in bill length with multiple loci detected in or near genes known to affect bill morphology in other birds. Our findings support the hypothesis that divergent selection at microgeographic scales can cause adaptive divergence in the presence of ongoing gene flow.

Redundancy analysis: A Swiss Army Knife for landscape genomics

Abstract Landscape genomics identifies how spatial and environmental factors structure the amount and distribution of genetic variation among populations. Landscape genomic analyses have been applied across diverse taxonomic groups and ecological settings, and are increasingly used to analyse datasets composed of large numbers of genomic markers and multiple environmental predictors. It is in this context that multivariate methods show their strengths. Redundancy analysis (RDA) is a constrained ordination that, in a landscape genomics framework, models linear relationships among environment predictors and genomic variation, effectively identifying covarying allele frequencies associated with the multivariate environment. RDA can be used at both individual and population levels, can include covariates to account for confounding factors and can be used to directly infer genotype–environment associations on the landscape. The modelling of both multivariate response and explanatory variables allows RDA to accommodate the genomic and environmental complexity found in nature, producing a powerful and efficient tool for landscape genomics. In this review, we outline the diverse uses of RDA in landscape genomics, including variable selection, variance partitioning, genotype–environment associations, and the calculation of adaptive indices and genomic offset. To illustrate these applications, we use a published dataset for lodgepole pine that includes genomic, phenotypic and environmental data. We provide an introduction to the statistical basis of RDA, a tutorial on its use and interpretation in landscape genomics applications, discuss limitations and provide guidelines to avoid misuse. This review and associated tutorial provide a comprehensive resource to the landscape genomics community to improve understanding of RDA as a modelling framework, and encourage the appropriate use of RDA across diverse landscape genomics applications. RDA is truly a Swiss Army Knife for landscape genomics: a multipurpose, adaptable and versatile approach to identifying, evaluating and forecasting relationships between genetic and environmental variation.

Population Genetic Diversity and Structure of an Endangered Salicaceae Species in Northeast China: Chosenia arbutifolia (Pall.) A. Skv.

Chosenia arbutifolia (Pall.) A. Skv. is a unique and endangered species belonging to the Salicaceae family. It has great potential for ornamental and industrial use. However, human interference has led to a decrease in and fragmentation of its natural populations in the past two decades. To effectively evaluate, utilize, and conserve available resources, the genetic diversity and population structure of C. arbutifolia were analyzed in this study. A total of 142 individuals from ten provenances were sampled and sequenced. Moderate diversity was detected among these, with a mean expected heterozygosity and Shannon’s Wiener index of 0.3505 and 0.5258, respectively. The inbreeding coefficient was negative, indicating a significant excess of heterozygotes. The fixation index varied from 0.0068 to 0.3063, showing a varied genetic differentiation between populations. Analysis of molecular variance demonstrated that differentiation accounted for 82.23% of the total variation among individuals, while the remaining 17.77% variation was between populations. Furthermore, the results of population structure analysis indicated that the 142 individuals originated from three primitive groups. To provide genetic information and help design conservation and management strategies, landscape genomics analysis was performed by investigating loci associated with environmental variables. Eighteen SNP markers were associated with altitude and annual average temperature, of which five were ascribed with specific functions. In conclusion, the current study furthers the understanding of C. arbutifolia genetic architecture and provides insights for germplasm protection.

Predictors of phylogeographic structure among codistributed taxa across the complex Australian monsoonal tropics.

Differences in the geographic scale and depth of phylogeographic structure across codistributed taxa can reveal how microevolutionary processes such as population isolation and persistence drive diversification. In turn, environmental heterogeneity, species' traits, and historical biogeographic barriers may influence the potential for isolation and persistence. Using extensive SNP data and a combination of population genetic summary statistics and landscape genomic analyses, we explored predictors of the scale and depth of phylogeographic structure in codistributed lizard taxa from the topographically and climatically complex monsoonal tropics (AMT) of Australia. We first resolved intraspecific lineages and then tested whether genetic divergence across space within lineages is related to isolation by distance, resistance and/or environment and whether these factors differ across genera or between rock-related versus habitat generalist taxa. We then tested whether microevolutionary processes within lineages explain differences in the geographic scale and depth of intraspecific phylogeographic lineages. The results indicated that landscape predictors of phylogeographic structure differ between taxa. Within lineages, there was prevalent isolation by distance, but the strength of isolation by distance is independent of the taxonomic family, habitat specialization, and climate. Isolation by environment is the strongest predictor of landscape-scale genetic divergence for all taxa, with both temperature and precipitation acting as limiting factors. The strength of isolation by distance does not predict the geographic scale of the phylogeographic structure. However, more localized lineages had higher mean individual heterozygosity and less negative Tajima's D. This result implies that finer-scale phylogeographic structuring within species is associated with larger and more stable populations and, hence, persistence.

The climatic and genetic heritage of Italian goat breeds with genomic SNP data

Local adaptation of animals to the environment can abruptly become a burden when faced with rapid climatic changes such as those foreseen for the Italian peninsula over the next 70 years. Our study investigates the genetic structure of the Italian goat populations and links it with the environment and how genetics might evolve over the next 50 years. We used one of the largest national datasets including > 1000 goats from 33 populations across the Italian peninsula collected by the Italian Goat Consortium and genotyped with over 50 k markers. Our results showed that Italian goats can be discriminated in three groups reflective of the Italian geography and its geo-political situation preceding the country unification around two centuries ago. We leveraged the remarkable genetic and geographical diversity of the Italian goat populations and performed landscape genomics analysis to disentangle the relationship between genotype and environment, finding 64 SNPs intercepting genomic regions linked to growth, circadian rhythm, fertility, and inflammatory response. Lastly, we calculated the hypothetical future genotypic frequencies of the most relevant SNPs identified through landscape genomics to evaluate their long-term effect on the genetic structure of the Italian goat populations. Our results provide an insight into the past and the future of the Italian local goat populations, helping the institutions in defining new conservation strategy plans that could preserve their diversity and their link to local realities challenged by climate change.

Landscape Genomics of a Widely Distributed Snake, Dolichophis caspius (Gmelin, 1789) across Eastern Europe and Western Asia.

Across the distribution of the Caspian whipsnake (Dolichophis caspius), populations have become increasingly disconnected due to habitat alteration. To understand population dynamics and this widespread but locally endangered snake’s adaptive potential, we investigated population structure, admixture, and effective migration patterns. We took a landscape-genomic approach to identify selected genotypes associated with environmental variables relevant to D. caspius. With double-digest restriction-site associated DNA (ddRAD) sequencing of 53 samples resulting in 17,518 single nucleotide polymorphisms (SNPs), we identified 8 clusters within D. caspius reflecting complex evolutionary patterns of the species. Estimated Effective Migration Surfaces (EEMS) revealed higher-than-average gene flow in most of the Balkan Peninsula and lower-than-average gene flow along the middle section of the Danube River. Landscape genomic analysis identified 751 selected genotypes correlated with 7 climatic variables. Isothermality correlated with the highest number of selected genotypes (478) located in 41 genes, followed by annual range (127) and annual mean temperature (87). We conclude that environmental variables, especially the day-to-night temperature oscillation in comparison to the summer-to-winter oscillation, may have an important role in the distribution and adaptation of D. caspius.

Genomic signatures of convergent adaptation to Alpine environments in three Brassicaceae species.

It has long been discussed to what extent related species develop similar genetic mechanisms to adapt to similar environments. Most studies documenting such convergence have either used different lineages within species or surveyed only a limited portion of the genome. Here, we investigated whether similar or different sets of orthologous genes were involved in genetic adaptation of natural populations of three related plant species to similar environmental gradients in the Alps. We used whole‐genome pooled population sequencing to study genome‐wide SNP variation in 18 natural populations of three Brassicaceae (Arabis alpina, Arabidopsis halleri, and Cardamine resedifolia) from the Swiss Alps. We first de novo assembled draft reference genomes for all three species. We then ran population and landscape genomic analyses with ~3 million SNPs per species to look for shared genomic signatures of selection and adaptation in response to similar environmental gradients acting on these species. Genes with a signature of convergent adaptation were found at significantly higher numbers than expected by chance. The most closely related species pair showed the highest relative over‐representation of shared adaptation signatures. Moreover, the identified genes of convergent adaptation were enriched for nonsynonymous mutations, suggesting functional relevance of these genes, even though many of the identified candidate genes have hitherto unknown or poorly described functions based on comparison with Arabidopsis thaliana. We conclude that adaptation to heterogeneous Alpine environments in related species is partly driven by convergent evolution, but that most of the genomic signatures of adaptation remain species‐specific.

Genetic adaptation of Tibetan poplar (Populus szechuanica var. tibetica) to high altitudes on the Qinghai-Tibetan Plateau.

Plant adaptation to high altitudes has long been a substantial focus of ecological and evolutionary research. However, the genetic mechanisms underlying such adaptation remain poorly understood. Here, we address this issue by sampling, genotyping, and comparing populations of Tibetan poplar, Populus szechuanica var. tibetica, distributed from low (~2,000 m) to high altitudes (~3,000 m) of Sejila Mountain on the Qinghai–Tibet Plateau. Population structure analyses allow clear classification of two groups according to their altitudinal distributions. However, in contrast to the genetic variation within each population, differences between the two populations only explain a small portion of the total genetic variation (3.64%). We identified asymmetrical gene flow from high‐ to low‐altitude populations. Integrating population genomic and landscape genomic analyses, we detected two hotspot regions, one containing four genes associated with altitudinal variation, and the other containing ten genes associated with response to solar radiation. These genes participate in abiotic stress resistance and regulation of reproductive processes. Our results provide insight into the genetic mechanisms underlying high‐altitude adaptation in Tibetan poplar.

Contrasted patterns of local adaptation to climate change across the range of an evergreen oak, Quercus aquifolioides.

Long‐lived tree species are genetically differentiated and locally adapted with respect to fitness‐related traits, but the genetic basis of local adaptation remains largely unresolved. Recent advances in population genetics and landscape genomic analyses enable identification of putative adaptive loci and specific selective pressures acting on local adaptation. Here, we sampled 60 evergreen oak (Quercus aquifolioides) populations throughout the species' range and pool‐sequenced 587 individuals at drought‐stress candidate genes. We analyzed patterns of genetic diversity and differentiation for 381 single nucleotide polymorphisms (SNPs) from 65 candidate genes and eight microsatellites. Outlier loci were identified by genetic differentiation analysis and genome–environment associations. The response pattern of genetic variation to environmental gradient was assessed by linear isolation‐by‐distance/environment tests, redundancy analysis, and nonlinear methods. SNPs and microsatellites revealed two genetic lineages: Tibet and Hengduan Mountains–Western Sichuan Plateau (HDM‐WSP), with reduced genetic diversity in Tibet lineage. More outlier loci were detected in HDM‐WSP lineage than Tibet lineage. Among these, three SNPs in two genes responded to dry season precipitation in the HDM‐WSP lineage but not in Tibet. By contrast, genetic variation in the Tibet lineage was related to geographic distance instead of the environment. Furthermore, risk of nonadaptedness (RONA) analyses suggested HDM‐WSP lineage will have a better capacity to adapt in the predicted future climate compared with the Tibet lineage. We detected genetic imprints consistent with natural selection and molecular adaptation to drought on the Qinghai–Tibet Plateau (QTP) over a range of long‐lived and widely distributed oak species in a changing environment. Our results suggest that different within‐species adaptation processes occur in species occurring in heterogeneous environments.