Examining the effects of environment, geography, and elevation on patterns of DNA methylation across populations of two widespread bumble bee species.

Understanding the myriad avenues through which spatial and environmental factors shape evolution is a major focus in biological research. From a molecular perspective, much work has been focused on genomic sequence variation, however, recently there has been increased interest in how epigenetic variation may be shaped by different variables across the landscape. DNA methylation has been of particular interest given that it is dynamic and can alter gene expression, potentially offering a path for a rapid response to environmental change. We utilized whole genome enzymatic methyl sequencing to evaluate the distribution of CpG methylation across the genome and to analyze patterns of spatial and environmental association in the methylomes of two broadly distributed montane bumble bees (Bombus vancouverensis Cresson and Bombus vosnesenskii Radoszkowski) across elevational gradients in the western US. Methylation patterns in both species are similar at the genomic scale with ∼1% of CpGs being methylated and most methylation being found in exons. At the landscape scale, neither species exhibited strong spatial or population structuring in patterns of methylation, although some weak relationships between methylation and distance or environmental variables were detected. Differential methylation analysis suggests a stronger environment association in B. vancouverensis given the larger number of differentially methylated CpG's compared to B. vosnesenskii. We also observed only a handful of genes with both differentially methylated CpGs and previously detected environmentally associated outlier SNPs. Overall results reveal a weak but present pattern in variation in methylation over the landscape in both species.

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.

Patterns of Genetic Diversity and Gene Flow Associated With an Aridity Gradient in Populations of Common Mole-rats, Cryptomys hottentotus hottentotus.

Genetic adaptation is the change of a population toward a phenotype that best fits the present ecological conditions of the environment it inhabits. As environmental conditions change, allele frequencies shift, resulting in different populations of the same species possessing genetic variation and divergent phenotypes. Cooperatively breeding common mole-rats (Cryptomys hottentotus hottentotus) inhabit environments along an aridity gradient in South Africa, which provides an opportunity for local genetic adaptations to occur. Using one mitochondrial gene (cytochrome b) and 3,540 SNP loci across the whole genome, we determined the phylogenetic relationship, population structure and genetic diversity of five populations of C. h. hottentotus located along an aridity gradient. Mitochondrial data identified population-specific clades that were less distinct in the two mesic populations, potentially indicating historical or recent gene flow, or the retention of ancestral haplotypes. Arid and semi-arid populations formed a distinct cluster from the non-arid populations. Genetic diversity and gene flow were higher in arid-dwelling individuals, suggesting greater connectivity and interactions between colonies in arid regions in comparison to mesic ones. Using an Aridity Index, we determined that isolation by environment, rather than isolation by geographical distance, best explains the genetic distance between the populations. Further analyses using target loci may determine if there are differing underlying genetic adaptations among populations of C. h. hottentotus. These analyses could help unravel population differences in response to environmental factors within a subspecies of bathyergid mole-rat and determine the adaptive capacity of this small nonmigratory subterranean rodent species in response to aridification in the face of climate change.

A genome-guided strategy for climate resilience in American chestnut restoration populations.

American chestnut (Castanea dentata) is a deciduous tree species of eastern North America that was decimated by the introduction of the chestnut blight fungus (Cryphonectria parasitica) in the early 20th century. Although millions of American chestnuts survive as root collar sprouts, these trees rarely reproduce. Thus, the species is considered functionally extinct. American chestnuts with improved blight resistance have been developed through interspecific hybridization followed by conspecific backcrossing, and by genetic engineering. Incorporating adaptive genomic diversity into these backcross families and transgenic lines is important for restoring the species across broad climatic gradients. To develop sampling recommendations for ex situ conservation of wild adaptive genetic variation, we coupled whole-genome resequencing of 384 stump sprouts with genotype-environment association analyses and found that the species range can be subdivided into three seed zones characterized by relatively homogeneous adaptive allele frequencies. We estimated that 21 to 29 trees per seed zone will need to be conserved to capture most extant adaptive diversity. We also resequenced the genomes of 269 backcross trees to understand the extent to which the breeding program has already captured wild adaptive diversity, and to estimate optimal reintroduction sites for specific families on the basis of their adaptive portfolio and future climate projections. Taken together, these results inform the development of an ex situ germplasm conservation and breeding plan to target blight-resistant breeding populations to specific environments and provides a blueprint for developing restoration plans for other imperiled tree species.

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.

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

BACKGROUND: Climate 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. RESULTS: Using 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. CONCLUSIONS: We 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.

Hybridization mediated range expansion and climate change resilience in two keystone tree species of boreal forests.

Current global climate change is expected to affect biodiversity negatively at all scales leading to mass biodiversity loss. Many studies have shown that the distribution of allele frequencies across a species' range is often influenced by specific genetic loci associated with local environmental variables. This association reflects local adaptation and allele changes at those loci could thereby contribute to the evolutionary response to climate change. However, predicting how species will adapt to climate change from this type of data alone remains challenging. In the present study, we combined exome capture sequences and environmental niche reconstruction, to test multiple methods for assessing local adaptation and climate resilience in two widely distributed conifers, Norway spruce and Siberian spruce. Both species are keystone species of the boreal forest and share a vast hybrid zone. We show that local adaptation in conifers can be detected through allele frequency variation, population-level ecological preferences, and historical niche movement. Moreover, we integrated genetic and ecological information into genetic offset predictive models to show that hybridization plays a central role in expanding the niche breadth of the two conifer species and may help both species to cope better with future changing climates. This joint genetic and ecological analysis also identified spruce populations that are at risk under current climate change.

Genomic architecture controls multivariate adaptation to climate change.

As climate change advances, environmental gradients may decouple, generating novel multivariate environments that stress wild populations. A commonly invoked mechanism of evolutionary rescue is adaptive gene flow tracking climate shifts, but gene flow from populations inhabiting similar conditions on one environmental axis could cause maladaptive introgression when populations are adapted to different environmental variables that do not shift together. Genomic architecture can play an important role in determining the effectiveness and relative magnitudes of adaptive gene flow and in situ adaptation. This may have direct consequences for how species respond to climate change but is often overlooked. Here, we simulated microevolutionary responses to environmental change under scenarios defined by variation in the polygenicity, linkage, and genetic redundancy of two independent traits, one of which is adapted to a gradient that shifts under climate change. We used these simulations to examine how genomic architecture influences evolutionary outcomes under climate change. We found that climate-tracking (up-gradient) gene flow, though present in all scenarios, was strongly constrained under scenarios of lower linkage and higher polygenicity and redundancy, suggesting in situ adaptation as the predominant mechanism of evolutionary rescue under these conditions. We also found that high polygenicity caused increased maladaptation and demographic decline, a concerning result given that many climate-adapted traits may be polygenic. Finally, in scenarios with high redundancy, we observed increased adaptive capacity. This finding adds to the growing recognition of the importance of redundancy in mediating in situ adaptive capacity and suggests opportunities for better understanding the climatic vulnerability of real populations.

Genomic evidence for climate-linked diversity loss and increased vulnerability of wild barley spanning 28 years of climate warming.

The information on how plant populations respond genetically to climate warming is scarce. Here, landscape genomic and machine learning approaches were integrated to assess genetic response of 10 wild barley (Hordeum vulgare ssp. spontaneum; WB) populations in the past and future, using whole genomic sequencing (WGS) data. The WB populations were sampled in 1980 and again in 2008. Phylogeny of accessions was roughly in conformity with sampling sites, which accompanied by admixture/introgressions. The 28-y climate warming resulted in decreased genetic diversity, increased selection pressure, and an increase in deleterious single nucleotide polymorphism (dSNP) numbers, heterozygous deleterious and total deleterious burdens for WB. Genome-environment associations identified some candidate genes belonging to peroxidase family (HORVU2Hr1G057450, HORVU4Hr1G052060 and HORVU4Hr1G057210) and heat shock protein 70 family (HORVU2Hr1G112630). The gene HORVU2Hr1G120170 identified by selective sweep analysis was under strong selection during the climate warming of the 28-y, and its derived haplotypes were fixed by WB when faced with the 28-y increasingly severe environment. Temperature variables were found to be more important than precipitation variables in influencing genomic variation, with an eco-physiological index gdd5 (growing degree-days at the baseline threshold temperature of 5 °C) being the most important determinant. Gradient forest modelling revealed higher predicted genomic vulnerability in Sede Boqer under future climate scenarios at 2041-2070 and 2071-2100. Additionally, estimates of effective population size (Ne) tracing back to 250 years indicated a forward decline in all populations over time. Our assessment about past genetic response and future vulnerability of WB under climate warming is crucial for informing conservation efforts for wild cereals and rational use strategies.

Environment predicts the maintenance of reproductive isolation in a mosaic hybrid zone of rubber rabbitbrush.

Widely distributed plants of western North America experience divergent selection across environmental gradients, have complex histories shaped by biogeographic barriers and distributional shifts and often illustrate continuums of reproductive isolation. Rubber rabbitbrush (Ericameria nauseosa) is a foundational shrub species that occurs across diverse environments of western North America. Its remarkable phenotypic diversity is currently ascribed to two subspecies-Ericameria nauseosa nauseosa and Ericameria nauseosa consimilis-and 22 named varieties. To understand how genetic variation is partitioned across subspecies, varieties, and environments, we used high throughput sequencing of reduced representation libraries. We found clear evidence for divergence between the two subspecies, despite largely sympatric distributions. Numerous locations exhibiting admixed ancestry were not geographically localized but were widely distributed across a mosaic hybrid zone. The occurrence of hybrid and subspecific ancestries was strongly predicted by environmental variables as well as the proximity to major ecotones between ecoregions. Although this repeatability illustrates the importance of environmental factors in shaping reproductive isolation, variability in the prevalence of hybridization also indicates these factors likely differ across ecological contexts. There was mixed evidence for the evolutionary cohesiveness of varieties, but several genetically distinct and narrow endemic varieties exhibited admixed subspecific ancestries, hinting at the possibility for transgressive hybridization to contribute to phenotypic novelty and the colonization of new environments in E. nauseosa.

The WZA: A window-based method for characterizing genotype-environment associations.

Genotype-environment association (GEA) studies have the potential to identify the genetic basis of local adaptation in natural populations. Specifically, GEA approaches look for a correlation between allele frequencies and putatively selective features of the environment. Genetic markers with extreme evidence of correlation with the environment are presumed to be tagging the location of alleles that contribute to local adaptation. In this study, we propose a new method for GEA studies called the Weighted-Z Analysis (WZA) that combines information from closely linked sites into analysis windows in a way that was inspired by methods for calculating FST . Performing GEA methods in analysis windows has the advantage that it takes advantage of the increased linkage disequilibrium expected surrounding sites subject to local adaptation. We analyse simulations modelling local adaptation to heterogeneous environments to compare the WZA with existing methods. In the majority of cases we tested, the WZA either outperformed single-SNP (single nucleotide polymorphism)-based approaches or performed similarly. In particular, the WZA outperformed individual SNP approaches when a small number of individuals or demes were sampled. Particularly troubling, we found that some GEA methods exhibit very high false positive rates. We applied the WZA to previously published data from lodgepole pine and identified candidate loci that were identified in the original study alongside numerous loci that were not found in the original study.

slimr: An R package for tailor-made integrations of data in population genomic simulations over space and time.

Software for realistically simulating complex population genomic processes is revolutionizing our understanding of evolutionary processes, and providing novel opportunities for integrating empirical data with simulations. However, the integration between standalone simulation software and R is currently not well developed. Here, we present slimr, an R package designed to create a seamless link between standalone software SLiM >3.0, one of the most powerful population genomic simulation frameworks, and the R development environment, with its powerful data manipulation and analysis tools. We show how slimr facilitates smooth integration between genetic data, ecological data and simulation in a single environment. The package enables pipelines that begin with data reading, cleaning and manipulation, proceed to constructing empirically based parameters and initial conditions for simulations, then to running numerical simulations and finally to retrieving simulation results in a format suitable for comparisons with empirical data - aided by advanced analysis and visualization tools provided by R. We demonstrate the use of slimr with an example from our own work on the landscape population genomics of desert mammals, highlighting the advantage of having a single integrated tool for both data analysis and simulation. slimr makes the powerful simulation ability of SLiM directly accessible to R users, allowing integrated simulation projects that incorporate empirical data without the need to switch between software environments. This should provide more opportunities for evolutionary biologists and ecologists to use realistic simulations to better understand the interplay between ecological and evolutionary processes.

Multiple genomic solutions for local adaptation in two closely related species (sheep and goats) facing the same climatic constraints.

The question of how local adaptation takes place remains a fundamental question in evolutionary biology. The variation of allele frequencies in genes under selection over environmental gradients remains mainly theoretical and its empirical assessment would help understanding how adaptation happens over environmental clines. To bring new insights to this issue we set up a broad framework which aimed to compare the adaptive trajectories over environmental clines in two domesticated mammal species co-distributed in diversified landscapes. We sequenced the genomes of 160 sheep and 161 goats extensively managed along environmental gradients, including temperature, rainfall, seasonality and altitude, to identify genes and biological processes shaping local adaptation. Allele frequencies at putatively adaptive loci were rarely found to vary gradually along environmental gradients, but rather displayed a discontinuous shift at the extremities of environmental clines. Of the 430 candidate adaptive genes identified, only 6 were orthologous between sheep and goats and those responded differently to environmental pressures, suggesting different putative mechanisms involved in local adaptation in these two closely related species. Interestingly, the genomes of the 2 species were impacted differently by the environment, genes related to signatures of selection were most related to altitude, slope and rainfall seasonality for sheep, and summer temperature and spring rainfall for goats. The diversity of candidate adaptive pathways may result from a high number of biological functions involved in the adaptations to multiple eco-climatic gradients, and a differential role of climatic drivers on the two species, despite their co-distribution along the same environmental gradients. This study describes empirical examples of clinal variation in putatively adaptive alleles with different patterns in allele frequency distributions over continuous environmental gradients, thus showing the diversity of genetic responses in adaptive landscapes and opening new horizons for understanding genomics of adaptation in mammalian species and beyond.

The holocentric chromosome microevolution: From phylogeographic patterns to genomic associations with environmental gradients.

Geographic isolation and chromosome evolution are two of the major drivers of diversification in eukaryotes in general, and specifically, in plants. On one hand, range shifts induced by Pleistocene glacial oscillations deeply shaped the evolutionary trajectories of species in the Northern Hemisphere. On the other hand, karyotype variability within species or species complexes may have adaptive potential as different karyotypes may represent different recombination rates and linkage groups that may be associated with locally adapted genes or supergenes. Organisms with holocentric chromosomes are ideal to study the link between local adaptation and chromosome evolution, due to their high cytogenetic variability, especially when it seems to be related to environmental variation. Here, we integrate the study of the phylogeography, chromosomal evolution and ecological requirements of a plant species complex distributed in the Western Euro-Mediterranean region (Carex gr. laevigata, Cyperaceae). We aim to clarify the relative influence of these factors on population differentiation and ultimately on speciation. We obtained a well-resolved RADseq phylogeny that sheds light on the phylogeographic patterns of molecular and chromosome number variation, which are compatible with south-to-north postglacial migration. In addition, landscape genomics analyses identified candidate loci for local adaptation, and also strong significant associations between the karyotype and the environment. We conclude that karyotype distribution in C. gr. laevigata has been constrained by both range shift dynamics and local adaptation. Our study demonstrates that chromosome evolution may be responsible, at least partially, for microevolutionary patterns of population differentiation and adaptation in Carex.

Population genomic analyses reveal population structure and major hubs of invasive Anopheles stephensi in the Horn of Africa.

Anopheles stephensi invasion in the Horn of Africa (HoA) poses a substantial risk of increased malaria disease burden in the region. An understanding of the history of introduction(s), establishment(s) and potential A. stephensi sources in the HoA is needed to predict future expansions and establish where they may be effectively controlled. To this end, we take a landscape genomic approach to assess A. stephensi origins and spread throughout the HoA, information essential for vector control. Specifically, we assayed 2070 genome-wide single nucleotide polymorphisms across 214 samples spanning 13 populations of A. stephensi from Ethiopia and Somaliland collected in 2018 and 2020, respectively. Principal component and genetic ancestry analyses revealed clustering that followed an isolation-by-distance pattern, with genetic divergence among the Ethiopian samples significantly correlating with geographical distance. Additionally, genetic relatedness was observed between the northeastern and east central Ethiopian A. stephensi populations and the Somaliland A. stephensi populations. These results reveal population differentiation and genetic connectivity within HoA A. stephensi populations. Furthermore, based on genetic network analysis, we uncovered that Dire Dawa, the site of a spring 2022 malaria outbreak, was one of the major hubs from which sequential founder events occurred in the rest of the eastern Ethiopian region. These findings can be useful for the selection of sites for heightened control to prevent future malaria outbreaks. Finally, we did not detect significant genotype-environmental associations, potentially due to the recency of their colonization and/or other anthropogenic factors leading to the initial spread and establishment of A. stephensi. Our study highlights how coupling genomic data at landscape levels can shed light into even ongoing invasions.

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.

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.

Whole-genome sequencing reveals fine-scale environment-associated divergence near the range limits of a temperate reef fish.

Environmental variation is increasingly recognized as an important driver of diversity in marine species despite the lack of physical barriers to dispersal and the presence of pelagic stages in many taxa. A robust understanding of the genomic and ecological processes involved in structuring populations is lacking for most marine species, often hindering management and conservation action. Cunner (Tautogolabrus adspersus) is a temperate reef fish with both pelagic early life-history stages and strong site-associated homing as adults; the species is also of interest for use as a cleaner fish in salmonid aquaculture in Atlantic Canada. We aimed to characterize genomic and geographic differentiation of cunner in the Northwest Atlantic. To achieve this, a chromosome-level genome assembly for cunner was produced and used to characterize spatial population structure throughout Atlantic Canada using whole-genome sequencing. The genome assembly spanned 0.72 Gbp and 24 chromosomes; whole-genome sequencing of 803 individuals from 20 locations from Newfoundland to New Jersey identified approximately 11 million genetic variants. Principal component analysis revealed four regional Atlantic Canadian groups. Pairwise FST and selection scans revealed signals of differentiation and selection at discrete genomic regions, including adjacent peaks on chromosome 10 across multiple pairwise comparisons (i.e. FST 0.5-0.75). Redundancy analysis suggested association of environmental variables related to benthic temperature and oxygen range with genomic structure. Results suggest regional scale diversity in this temperate reef fish and can directly inform the collection and translocation of cunner for aquaculture applications and the conservation of wild populations throughout the Northwest Atlantic.