Genotype-environment associations to reveal the molecular basis of environmental adaptation

Evidence of landscape-driven repeated adaptation among 13 Eucalyptus species

Local adaptation is the biological process by which native populations become more fit. Intraspecific patterns of local adaptation occur through shifts in allele frequency within or near genes and may occur similarly across species. Identifying repeated adaptation across species increases statistical power to determine causal genes driving adaptation and reveals insights into the nature of evolution. These types of insights could have theoretical and applied applications, particularly as the climate continues to change. We interrogate repeated molecular adaptation across 13 eucalypt species. In total, we found 38 candidate genes with shared putatively adaptive signals in as many as 12 species. This suite of genes contains important functions, including myeloblastosis (MYB) proteins, acyl-CoA dehydrogenases, and Leucine-rich kinases. Species with restricted and widespread geographical distributions shared putative patterns of adaptation, and phylogenetic closeness did not increase patterns of repeated adaptation compared to geographic overlap. This work provides further evidence that repeated adaptation can occur among orthologs, which may play a consistent role in local adaptation.

Environmental data provide marginal benefit for predicting climate adaptation

Climate change poses a major challenge for both natural and cultivated species. Genomic tools are increasingly used in both conservation and breeding to identify adaptive loci that can be used to guide management in future climates. Here, we study the utility of climate and genomic data for identifying promising alleles using common gardens of a large, geographically diverse sample of traditional maize varieties to evaluate multiple approaches. First, we used genotype data to predict environmental characteristics of germplasm collections to identify varieties that may be pre-adapted to target environments. Second, we used environmental GWAS (envGWAS) to identify loci associated with historical divergence along climatic gradients. Finally, we compared the value of environmental data and envGWAS-prioritized loci to genomic data for prioritizing traditional varieties. We find that maize yield traits are best predicted by genome-wide relatedness and population structure, and that incorporating envGWAS-identified variants or environment-of-origin data provide little additional predictive information. While our results suggest that environmental data provide limited benefit in predicting fitness-related phenotypes, environmental GWAS is nonetheless a potentially powerful approach to identify individual novel loci associated with adaptation, especially when coupled with high density genotyping.

Agroecological genomics and participatory science: optimizing crop mixtures for agricultural diversification

Crops based on mixtures of species or genotypes support yield stability by providing multiple ecosystem services. However, the genetic, molecular, and evolutionary dynamics underlying co-adaptation within such mixtures must be understood to optimize beneficial plant-plant interactions. We therefore propose agroecological genomics as an integrated quantitative and population genetics approach that can be combined with cutting-edge omics methods and participatory science. This strategy embraces the heterogeneity of agroecosystems derived from interactions between biotic and physical environmental components such as climate, crop management, and socio-cultural factors by exploiting decentralized research. The integration of such results will reveal the whole-genome patterns of co-adaptation in crop mixtures, leading to greater knowledge of the key traits that drive adaptation as well as to the development of innovative tools for mixed-crop breeding.

Maladaptation in cereal crop landraces following a soot-producing climate catastrophe

Aerosol-producing catastrophes like nuclear war or asteroid strikes, though rare, pose serious risks to human survival. The injected aerosols would reduce solar radiation, lower temperatures, and alter precipitation, impacting crop productivity, including for locally adapted traditional crop varieties, i.e. landraces. We assess post-catastrophic climate effects on crops with extensive landrace cultivation, barley, maize, rice, and sorghum, under climate scenarios that differ in the quantity of soot injection. Using a crop growth model, we estimate environmental stress gradients and together with genomic markers apply gradient forest offset methods to predict post-catastrophic maladaptation in landraces over time. We find landraces are most maladapted where soot-induced climate shifts were strongest. Validating our approach, gradient forest models successfully capture a signal of maize landrace adaptation in common gardens across Mexico. We further use our gradient forest models to identify landrace varieties best matched to specific post-catastrophic conditions, indicating potential substitutions for agricultural resilience. The best substituted varieties require long migration distances, often across country borders, though countries with more climatic diversity have better within-country substitutions. Our findings highlight that a soot-producing catastrophe would drive global maladaptation in landraces and suggest current adaptive diversity is insufficient for agricultural resilience.

Leveraging Automated Machine Learning for Environmental Data-Driven Genetic Analysis and Genomic Prediction in Maize Hybrids

Genotype, environment, and genotype-by-environment (G×E) interactions play a critical role in shaping crop phenotypes. Here, a large-scale, multi-environment hybrid maize dataset is used to construct and validate an automated machine learning framework that integrates environmental and genomic data for improved accuracy and efficiency in genetic analyses and genomic predictions. Dimensionality-reduced environmental parameters (RD_EPs) aligned with developmental stages are applied to establish linear relationships between RD_EPs and traits to assess the influence of environment on phenotype. Genome-wide association study identifies 539 phenotypic plasticity trait-associated markers (PP-TAMs), 223 environmental stability TAMs (Main-TAMs), and 92 G×E-TAMs, revealing distinct genetic bases for PP and G×E interactions. Training genomic prediction models with both TAMs and RD_EPs increase prediction accuracy by 14.02% to 28.42% over that of genome-wide marker approaches. These results demonstrate the potential of utilizing environmental data for improving genetic analysis and genomic selection, offering a scalable approach for developing climate-adaptive maize varieties.

Environmental genomic selection to leverage polygenic local adaptation in barley landraces

Climate change is threatening agricultural production across the globe. Germplasm collections provide an opportunity to explore where variation exists with important crop species. Genome environment association (GEA) is a standard approach for investigating the genetic basis of adaptation to natural environments. While these analyses provide insight into local adaptation, they have not been widely adopted in breeding or conservation programs. This may be attributable to the difficulty in identifying the best individuals for transplantation/relocation in conservation efforts or identification of the best parents in breeding programs. To explore the potential utility in future breeding programs, we used the cereal crop - barley (Hordeum vulgare L.) due to its wide adaptability to different environments and agroecologies, ranging from marginal and low input fields to high-productive farms. Here, we conduct environmental genomic selection (EGS) on 753 landrace barley accessions using a mini-core of 31 landrace accessions and a de-novo core of 100 as the training populations. Since local adaptation to the environment is polygenic, a whole-genome approach is likely to be more accurate for selection. Here we show how an integrative approach coupling environmental genomic selection and species distribution modelling can help identify key parents for adaptation to specific environmental variables.

Analysis of allohexaploid wheatgrass genome reveals its Y haplome origin in Triticeae and high-altitude adaptation

Phylogenetic origin of the Y haplome present in allopolyploid Triticeae species remains unknown. Here, we report the 10.47 Gb chromosome-scale genome of allohexaploid Elymus nutans (StStYYHH). Phylogenomic analyses reveal that the Y haplome is sister to the clade comprising V and Jv haplomes from Dasypyrum and Thinopyum. In addition, H haplome from the Hordeum-like ancestor, St haplome from the Pseudoroegneria-like ancestor and Y haplome are placed in the successively diverged clades. Resequencing data reveal the allopolyploid origins with St, Y, and H haplome combinations in Elymus. Population genomic analyses indicate that E. nutans has expanded from medium to high/low-altitude regions. Phenotype/environmental association analyses identify MAPKKK18 promoter mutations reducing its expression, aiding UV-B adaptation in high-altitude populations. These findings enhance understanding of allopolyploid evolution and aid in breeding forage and cereal crops through intergeneric hybridization within Triticeae.

Utilization of natural alleles and haplotypes of Ctb1 for rice cold adaptability

Cold stress during the booting stage of rice (Oryza sativa) significantly reduces yields, particularly in temperate and high-altitude regions. This study investigates the Ctb1 gene, critical for booting-stage cold tolerance, to improve breeding of resilient rice varieties. Re-sequencing the Ctb1 promoter in 202 accessions identified six Insertions and/or deletions (InDels) and four Single nucleotide polymorphisms (SNPs), with an InDel at -1,302 bp significantly boosting Ctb1 expression and cold tolerance. Accessions carrying this InDel (Haplotype I) exhibited the highest tolerance. Near-isogenic lines (NIL-Ctb1HapI) introduced Haplotype I into the cold-sensitive Huazhan (HZ) variety, resulting in a 5.9-fold increase in Ctb1 expression, higher seedling survival, improved pollen fertility, a 64.2 % increase in seed setting rate, and a 12 g per plant yield boost under cold stress. These findings confirm the critical role of the -1,302 InDel in cold tolerance and establish NIL-Ctb1HapI as a valuable breeding tool for cold-resilient rice.

Population genomics of Marchantia polymorpha subsp. ruderalis reveals evidence of climate adaptation

Sexual reproduction results in the development of haploid and diploid cell states during the life cycle. In bryophytes, the dominant multicellular haploid phase produces motile sperm that swim through water to the egg to effect fertilization from which a relatively small diploid phase develops. In angiosperms, the reduced multicellular haploid phase produces non-motile sperm that is delivered to the egg through a pollen tube to effect fertilization from which the dominant diploid phase develops. These different life cycle characteristics are likely to impact the distribution of genetic variation among populations. However, little is known about the distribution of genetic variation among wild populations of bryophytes. To investigate how genetic variation is distributed among populations of a bryophyte and to establish the foundation for population genetics research in bryophytes, we described the genetic diversity of collections of Marchantia polymorpha subsp. ruderalis, a cosmopolitan ruderal liverwort. We identified 78 genetically unique (non-clonal) from a total of 209 sequenced accessions collected from 37 sites in Europe and Japan. There was no detectable population structure among European populations but significant genetic differentiation between Japanese and European populations. By associating genetic variation across the genome with global climate data, we showed that temperature and precipitation influence the frequency of potentially adaptive alleles. This collection establishes the core of an experimental platform that exploits natural genetic variation to answer diverse questions in biology.

The Marchantia polymorpha pangenome reveals ancient mechanisms of plant adaptation to the environment

Plant adaptation to terrestrial life started 450 million years ago and has played a major role in the evolution of life on Earth. The genetic mechanisms allowing this adaptation to a diversity of terrestrial constraints have been mostly studied by focusing on flowering plants. Here, we gathered a collection of 133 accessions of the model bryophyte Marchantia polymorpha and studied its intraspecific diversity using selection signature analyses, a genome-environment association study and a pangenome. We identified adaptive features, such as peroxidases or nucleotide-binding and leucine-rich repeats (NLRs), also observed in flowering plants, likely inherited from the first land plants. The M. polymorpha pangenome also harbors lineage-specific accessory genes absent from seed plants. We conclude that different land plant lineages still share many elements from the genetic toolkit evolved by their most recent common ancestor to adapt to the terrestrial habitat, refined by lineage-specific polymorphisms and gene family evolution.

Maladaptation in cereal crop landraces following a soot-producing climate catastrophe

Aerosol-producing global catastrophes such as nuclear war, super-volcano eruption, or asteroid strike, although rare, pose a serious threat to human survival. Light-absorbing aerosols would sharply reduce temperature and solar radiation reaching the earth's surface, decreasing crop productivity including for locally adapted traditional crop varieties, i.e. landraces. Here, we test post-catastrophic climate impacts on barley, maize, rice, and sorghum, four crops with extensive landrace cultivation, under a range of nuclear war scenarios that differ in the amount of black carbon aerosol (soot) injected into the climate model. We used a crop growth model to estimate gradients of environmental stressors that drive local adaptation. We then fit genotype environment associations using high density genomic markers with gradient forest offset (GF offset) methods and predicted maladaptation through time. As a validation, we found that our GF models successfully predicted local adaptation of maize landraces in multiple common gardens across Mexico. We found strong concordance between GF offset and disruptions in climate, and landraces of all tested crop species were predicted to be the most maladapted across space and time where soot-induced climate change was the greatest. We further used our GF models to identify landrace varieties best matched to specific post-catastrophic conditions, indicating potential substitutions for agricultural resilience. We found the best landrace genotype was often far away or in another nation, though countries with more climatic diversity had better within-country substitutions. Our results highlight that a soot-producing catastrophe would result in the global maladaptation of landraces and suggest that current landrace adaptive diversity is insufficient for agricultural resilience in the case of the soot scenarios with the greatest change to climate.

Agricultural landscape genomics to increase crop resilience

Populations are continually adapting to their environment. Knowledge of which populations and individuals harbor unique and agriculturally useful variations has the potential to accelerate crop adaptation to the increasingly challenging environments predicted for the coming century. Landscape genomics, which identifies associations between environmental and genomic variation, provides a means for obtaining this knowledge. However, despite extensive efforts to assemble and characterize ex situ collections of crops and their wild relatives, gaps remain in the genomic and environmental datasets needed to robustly implement this approach. This article outlines the history of landscape genomics, which, to date, has mainly been used in conservation and evolutionary studies, provides an overview of crops and wild relative collections that have the necessary data for implementation and identifies areas where new data generation is needed. We find that 60% of the crops covered by the International Treaty on Plant Genetic Resources for Food and Agriculture lack the data necessary to conduct this kind of analysis, necessitating identification of crops in need of more collections, sequencing, or phenotyping. By highlighting these aspects, we aim to help develop agricultural landscape genomics as a sub-discipline that brings together evolutionary genetics, landscape ecology, and plant breeding, ultimately enhancing the development of resilient and adaptable crops for future environmental challenges.

Locally Specific Genome-Wide Signatures of Adaptation to Environmental Variation at High Resolution in an Alpine Plant

Microevolutionary processes shape adaptive responses to heterogeneous environments, where these effects vary both among and within species. However, it remains largely unknown to which degree signatures of adaptation to environmental drivers can be detected based on the choice of spatial scale and genomic marker. We studied signatures of local adaptation across two levels of spatial extents, investigating complementary types of genomic variants-single-nucleotide polymorphisms (SNPs) and polymorphic transposable elements (TEs)-in populations of the alpine model plant species Arabis alpina . We coupled environmental factors, derived from remote sensed digital elevation models (DEMs) at very high resolution (0.5 m), with whole-genome sequencing data of 304 individuals across four populations. By comparing putatively adaptive loci detected between each local population versus a regional assessment including all populations simultaneously, we demonstrate that responses of A. alpina to similar amounts of abiotic variation are largely governed by local evolutionary processes. Furthermore, we find minimally overlapping signatures of local adaptation between SNPs and polymorphic TEs. Notably, functional annotations of candidate genes for adaptation revealed several symbiosis-related genes associated with the abiotic factors studied, which could represent selective pressures from biotic agents. Our results highlight the importance of considering different spatial extents and types of genomic polymorphisms when searching for signatures of adaptation to environmental variation. Such insights provide key information on microevolutionary processes and could guide management decisions to mitigate negative impacts of climate change on alpine plant populations.

Experimental validation of genome-environment associations in Arabidopsis

Identifying the genetic basis of local adaptation is a key goal in evolutionary biology. Allele frequency clines along environmental gradients, known as genotype-environment associations (GEA), are often used to detect potential loci causing local adaptation, but GEA are rarely followed by experimental validation. Here, we tested loci identified in three different moisture-related GEA studies on Arabidopsis. We studied 44 GEA-identified genes using t-DNA knockout lines under drought and tested for effects on flowering time, an adaptive trait, and genotype-by-environment (GxE) interactions on performance and fitness. We found that wrky38 mutants had significant GxE effects for fitness; lsd1 plants had a significant GxE effect for flowering time, while 11 genes showed flowering time effects with no drought interaction. However, most GEA candidates did not exhibit GxE. In the follow-up experiments we found wrky38 caused decreased stomatal conductance and specific leaf area under drought, indicating potentially adaptive drought avoidance. Additionally, we found that GEA identified natural putative LoF variants of WRKY38 associated with dry environments, as well as alleles associated with variation in LSD1 expression. While only a few GEA putative drought-adapted genes were validated for GxE interactions for fitness under drought, we likely overlooked some genes because experiments might not well represent natural environments and t-DNA insertions might not well represent natural alleles. Nevertheless, GEAs apparently identified some genes contributing to local adaptation. GEA and follow-up experiments are straightforward to implement in model systems and thus demonstrate prospects for GEA discovery of new local adaptations.

Genomics-Driven Monitoring of Fraxinus latifolia (Oregon Ash) to Inform Conservation and EAB-Resistance Breeding

Understanding the evolutionary processes underlying range-wide genomic variation is critical to designing effective conservation and restoration strategies. Evaluating the influence of connectivity, demographic change and environmental adaptation for threatened species can be invaluable to proactive conservation of evolutionary potential. In this study, we assessed genomic variation across the range of Fraxinus latifolia, a foundational riparian tree native to western North America recently exposed to the invasive emerald ash borer (Agrilus planipennis; EAB). Over 1000 individuals from 61 populations were sequenced using reduced representation (ddRAD-seq) across the species' range. Strong population structure was evident along a latitudinal gradient, with population connectivity largely maintained along central valley river systems, and a centre of genetic diversity coinciding with major river systems central to the species' range. Despite evidence of connectivity, estimates of nucleotide diversity and effective population size were low across all populations, suggesting the patchy distribution of F. latifolia populations may impact its long-term evolutionary potential. Range-wide estimates of genomic offset, which indicate genomic change required to adjust to future climate projections, were greatest in the eastern and lowest in the southern portions of the species' range, suggesting the regional distribution of genomic variation may impact evolutionary potential longer-term. To preserve evolutionary capacity across populations needed for the development of breeding and restoration programmes, prioritising conservation of range-wide genomic diversity will provide a foundation for long-term species management.

Biochemical, photosynthetic and metabolomics insights of single and combined effects of salinity, heat, cold and drought in Arabidopsis

Ensuring food security is one of the main challenges related to a growing global population under climate change conditions. The increasing soil salinity levels, drought, heatwaves, and late chilling severely threaten crops and often co-occur in field conditions. This work aims to provide deeper insight into the impact of single vs. combined abiotic stresses at the growth, biochemical and photosynthetic levels in Arabidopsis thaliana (L.). Reduced QY max was recorded in salinity-stressed plants, NPQ increased in heat and salinity single and combined stresses, and qP decreased in combined stresses. MDA and H2O2 content were consistently altered under all stress conditions, but higher values were recorded under salinity alone and in combination. Salinity alone and in stress combinations (especially with cold) provided a stronger hierarchical effect. Despite glycine and GABA osmolytes not significantly changing, proline highlighted the hierarchically stronger impact of salinity, while glycine-betaine was decreased under drought combinations. Untargeted metabolomics pointed out distinct metabolic reprogramming triggered by the different stress conditions, alone or in combination. Pathway analysis revealed that abiotic stresses significantly affected hormones, amino acids and derivates, and secondary metabolites. Flavonoids accumulated under drought (alone and combined with heat and cold stresses), while N-containing compounds decreased under all combined stresses. Looking at the interactions across the parameters investigated, antagonistic, additive, or synergistic effects could be observed depending on the biochemical process considered. Notwithstanding, these results contribute to delving into the impact of various stress combinations, hierarchically highlighting the stress-specific effects and pointing out different combinations.

Geography, altitude, agriculture, and hypoxia

Reduced oxygen availability (hypoxia) represents a key plant abiotic stress in natural and agricultural systems, but conversely it is also an important component of normal growth and development. We review recent advances that demonstrate how genetic adaptations associated with hypoxia impact the known plant oxygen-sensing mechanism through the PLANT CYSTEINE OXIDASE N-degron pathway. Only 3 protein substrates of this pathway have been identified, and all adaptations identified to date are associated with the most important of these, the group VII ETHYLENE RESPONSE FACTOR transcription factors. We discuss how geography, altitude, and agriculture have all shaped molecular responses to hypoxia and how these responses have emerged at different taxonomic levels through the evolution of land plants. Understanding how ecological and agricultural genetic variation acts positively to enhance hypoxia tolerance will provide novel tools and concepts to improve the performance of crops in the face of increasing extreme flooding events.

Environmental genome-wide association studies across precipitation regimes reveal that the E3 ubiquitin ligase MBR1 regulates plant adaptation to rainy environments

In an era characterized by rapidly changing and less-predictable weather conditions fueled by the climate crisis, understanding the mechanisms underlying local adaptation in plants is of paramount importance for the conservation of species. As the frequency and intensity of extreme precipitation events increase, so are the flooding events resulting from soil water saturation. The subsequent onset of hypoxic stress is one of the leading causes of crop damage and yield loss. By combining genomics and remote sensing data, it is now possible to probe natural plant populations that have evolved in different rainfall regimes and look for molecular adaptation to hypoxia. Here, using an environmental genome-wide association study (eGWAS) of 934 non-redundant georeferenced Arabidopsis ecotypes, we have identified functional variants of the gene MED25 BINDING RING-H2 PROTEIN 1 (MBR1). This gene encodes a ubiquitin-protein ligase that regulates MEDIATOR25 (MED25), part of a multiprotein complex that interacts with transcription factors that act as key drivers of the hypoxic response in Arabidopsis, namely the RELATED TO AP2 proteins RAP2.2 and RAP2.12. Through experimental validation, we show that natural variants of MBR1 have different effects on the stability of MED25 and, in turn, on hypoxia tolerance. This study also highlights the pivotal role of the MBR1/MED25 module in establishing a comprehensive hypoxic response. Our findings show that molecular candidates for plant environmental adaptation can be effectively mined from large datasets. This thus supports the need for integration of forward and reverse genetics with robust molecular physiology validation of outcomes.

The trichome pattern diversity of Cardamine shares genetic mechanisms with Arabidopsis but differs in environmental drivers

Natural variation in trichome pattern (amount and distribution) is prominent among populations of many angiosperms. However, the degree of parallelism in the genetic mechanisms underlying this diversity and its environmental drivers in different species remain unclear. To address these questions, we analyzed the genomic and environmental bases of leaf trichome pattern diversity in Cardamine hirsuta, a relative of Arabidopsis (Arabidopsis thaliana). We characterized 123 wild accessions for their genomic diversity, leaf trichome patterns at different temperatures, and environmental adjustments. Nucleotide diversities and biogeographical distribution models identified two major genetic lineages with distinct demographic and adaptive histories. Additionally, C. hirsuta showed substantial variation in trichome pattern and plasticity to temperature. Trichome amount in C. hirsuta correlated positively with spring precipitation but negatively with temperature, which is opposite to climatic patterns in A. thaliana. Contrastingly, genetic analysis of C. hirsuta glabrous accessions indicated that, like for A. thaliana, glabrousness is caused by null mutations in ChGLABRA1 (ChGL1). Phenotypic genome-wide association studies (GWAS) further identified a ChGL1 haplogroup associated with low trichome density and ChGL1 expression. Therefore, a ChGL1 series of null and partial loss-of-function alleles accounts for the parallel evolution of leaf trichome pattern in C. hirsuta and A. thaliana. Finally, GWAS also detected other candidate genes (e.g. ChETC3, ChCLE17) that might affect trichome pattern. Accordingly, the evolution of this trait in C. hirsuta and A. thaliana shows partially conserved genetic mechanisms but is likely involved in adaptation to different environments.

The structure of the environment influences the patterns and genetics of local adaptation

Environmental heterogeneity can lead to spatially varying selection, which can, in turn, lead to local adaptation. Population genetic models have shown that the pattern of environmental variation in space can strongly influence the evolution of local adaptation. In particular, when environmental variation is highly autocorrelated in space local adaptation will more readily evolve. However, there have been few attempts to test this prediction empirically or characterize the consequences it would have for the genetic architecture underlying local adaptation. In this study, I analyze a large-scale provenance trial conducted on lodgepole pine and find suggestive evidence that spatial autocorrelation in environmental variation is related to the strength of local adaptation that has evolved in that species. Motivated by those results, I use simulations to model local adaptation to different spatial patterns of environmental variation. The simulations confirm that local adaptation is expected to increase with the degree of spatial autocorrelation in the selective environment, but also show that highly heterogeneous environments are more likely to exhibit high variation in local adaptation, a result not previously described. I find that the spatial pattern of environmental variation influences the genetic architectures underlying local adaptation. In highly autocorrelated environments, the genetic architecture of local adaptation tends to be composed of high-frequency alleles with small phenotypic effects. In weakly autocorrelated environments, locally adaptive alleles may have larger phenotypic effects but are present at lower frequencies across species' ranges and experience more evolutionary turnover. Overall, this work emphasizes the profound importance that the spatial pattern of selection can have on the evolution of local adaptation and how spatial autocorrelation should be considered when formulating hypotheses in ecological and genetic studies.

The genomics and physiology of abiotic stressors associated with global elevational gradients in Arabidopsis thaliana

Phenotypic and genomic diversity in Arabidopsis thaliana may be associated with adaptation along its wide elevational range, but it is unclear whether elevational clines are consistent among different mountain ranges. We took a multi-regional view of selection associated with elevation. In a diverse panel of ecotypes, we measured plant traits under alpine stressors (low CO2 partial pressure, high light, and night freezing) and conducted genome-wide association studies. We found evidence of contrasting locally adaptive regional clines. Western Mediterranean ecotypes showed low water use efficiency (WUE)/early flowering at low elevations to high WUE/late flowering at high elevations. Central Asian ecotypes showed the opposite pattern. We mapped different candidate genes for each region, and some quantitative trait loci (QTL) showed elevational and climatic clines likely maintained by selection. Consistent with regional heterogeneity, trait and QTL clines were evident at regional scales (c. 2000 km) but disappeared globally. Antioxidants and pigmentation rarely showed elevational clines. High elevation east African ecotypes might have higher antioxidant activity under night freezing. Physiological and genomic elevational clines in different regions can be unique, underlining the complexity of local adaptation in widely distributed species, while hindering global trait-environment or genome-environment associations. To tackle the mechanisms of range-wide local adaptation, regional approaches are thus warranted.

Restricted Dispersal and Phenotypic Response to Water Depth in a Foundation Seagrass

Species conservation and management benefit from precise understanding of natural patterns of dispersal and genetic variation. Using recent advances in indirect genetic methods applied to both adult plants and dispersed seeds, we find that the mean seed dispersal in a threatened marine foundation plant (the eelgrass Zostera marina) is approximately 100-200 m. This distance is surprisingly more similar to that of wind-dispersed terrestrial seeds (~10s to 100s of meters) than the passive dispersal of marine propagules via currents (~10s to 100s of kilometres). Because nearshore marine plants like Zostera are commonly distributed across strong selective gradients driven by bathymetry (depth) even within these restricted spatial scales, seeds are capable of dispersing to novel water depths and experiencing profound shifts in light availability, temperature and wave exposure. We documented strong phenotypic variation and genome-wide differentiation among plants separated by approximately the spatial scale of mean realised dispersal. This result suggests genetic isolation by environment in response to depth-related environmental gradients as one plausible explanation for this pattern. The ratio of effective to census size (or Ne/Nc) approximated 0.1%, indicating that a fraction of existing plants provides the genetic variation to allow adaptation to environmental change. Our results suggest that successful conservation of seagrass meadows that can adapt to microspatial and temporal variation in environmental conditions will be low without direct and persistent intervention using large numbers of individuals or a targeted selection of genotypes.

Integrating evolutionary genomics of forest trees to inform future tree breeding amid rapid climate change

Global climate change is leading to rapid and drastic shifts in environmental conditions, posing threats to biodiversity and nearly all life forms worldwide. Forest trees serve as foundational components of terrestrial ecosystems and play a crucial and leading role in combating and mitigating the adverse effects of extreme climate events, despite their own vulnerability to these threats. Therefore, understanding and monitoring how natural forests respond to rapid climate change is a key priority for biodiversity conservation. Recent progress in evolutionary genomics, driven primarily by cutting-edge multi-omics technologies, offers powerful new tools to address several key issues. These include precise delineation of species and evolutionary units, inference of past evolutionary histories and demographic fluctuations, identification of environmentally adaptive variants, and measurement of genetic load levels. As the urgency to deal with more extreme environmental stresses grows, understanding the genomics of evolutionary history, local adaptation, future responses to climate change, and conservation and restoration of natural forest trees will be critical for research at the nexus of global change, population genomics, and conservation biology. In this review, we explore the application of evolutionary genomics to assess the effects of global climate change using multi-omics approaches and discuss the outlook for breeding of climate-adapted trees.

Genomic variation of European beech reveals signals of local adaptation despite high levels of phenotypic plasticity

Local adaptation is key for ecotypic differentiation and species evolution. Understanding underlying genomic patterns can allow the prediction of future maladaptation and ecosystem stability. Here, we report the whole-genome resequencing of 874 individuals from 100 range-wide populations of European beech (Fagus sylvatica L.), an important forest tree species in Europe. We show that genetic variation closely mirrors geography with a clear pattern of isolation-by-distance. Genome-wide analyses for genotype-environment associations (GEAs) identify relatively few potentially adaptive variants after correcting for an overwhelming signal of statistically significant but non-causal GEAs. We characterize the single high confidence genomic region and pinpoint a candidate gene possibly involved in winter temperature adaptation via modulation of spring phenology. Surprisingly, allelic variation at this locus does not result in any apparent fitness differences in a common garden. More generally, reciprocal transplant experiments across large climate distances suggest extensive phenotypic plasticity. Nevertheless, we find indications of polygenic adaptation which may be essential in natural ecosystems. This polygenic signal exhibits broad- and fine-scale variation across the landscape, highlighting the relevance of spatial resolution. In summary, our results emphasize the importance, but also exemplify the complexity, of employing natural genetic variation for forest conservation under climate change.

A telomere-to-telomere Eucalyptus regnans genome: unveiling haplotype variance in structure and genes within one of the world's tallest trees

BACKGROUND

Eucalyptus regnans (Mountain Ash) is an Australian native giant tree species which form forests that are among the highest known carbon-dense biomasses in the world. To enhance genomic studies in this ecologically important species, we assembled a high-quality, mostly telomere-to-telomere complete, chromosome-level, haplotype-resolved reference genome. We sampled a single tree, the Centurion, which is currently a contender for the world's tallest flowering plant.

RESULTS

Using long-read sequencing data (PacBio HiFi, Oxford Nanopore ultra-long reads) and chromosome conformation capture data (Hi-C), we assembled the most contiguous and complete Eucalyptus reference genome to date. For each haplotype, we observed contig N50s exceeding 36 Mbp, scaffold N50s exceeding 43 Mbp, and genome BUSCO completeness exceeding 99%. The assembled genome revealed extensive structural variations between the two haplotypes, consisting mostly of insertions, deletions, duplications and translocations. Analysis of gene content revealed haplotype-specific genes, which were enriched in functional categories related to transcription, energy production and conservation. Additionally, many genes reside within structurally rearranged regions, particularly duplications, suggesting that haplotype-specific variation may contribute to environmental adaptation in the species.

CONCLUSIONS

Our study provides a foundation for future research into E. regnans environmental adaptation, and the high-quality genome will be a powerful resource for conservation of carbon-dense giant tree forests.

Genomic and Epigenomic Influences on Resilience across Scales: Lessons from the Responses of Fish to Environmental Stressors

Understanding the factors that influence the resilience of biological systems to environmental change is a pressing concern in the face of increasing human impacts on ecosystems and the organisms that inhabit them. However, most considerations of biological resilience have focused at the community and ecosystem levels, whereas here we discuss how including consideration of processes occurring at lower levels of biological organization may provide insights into factors that influence resilience at higher levels. Specifically, we explore how processes at the genomic and epigenomic levels may cascade up to influence resilience at higher levels. We ask how the concepts of "resistance," or the capacity of a system to minimize change in response to a disturbance, and "recovery," or the ability of a system to return to its original state following a disturbance and avoid tipping points and resulting regime shifts, map to these lower levels of biological organization. Overall, we suggest that substantial changes at these lower levels may be required to support resilience at higher levels, using selected examples of genomic and epigenomic responses of fish to climate-change-related stressors such as high temperature and hypoxia at the levels of the genome, epigenome, and organism.

Climate adaptive loci revealed by seascape genomics correlate with phenotypic variation in heat tolerance of the coral Acropora millepora

One of the main challenges in coral reef conservation and restoration is the identification of coral populations resilient under global warming. Seascape genomics is a powerful tool to uncover genetic markers potentially involved in heat tolerance among large populations without prior information on phenotypes. Here, we aimed to provide first insights on the role of candidate heat associated loci identified using seascape genomics in driving the phenotypic response of Acropora millepora from New Caledonia to thermal stress. We subjected 7 colonies to a long-term ex-situ heat stress assay (4 °C above the maximum monthly mean) and investigated their physiological response along with their Symbiodiniaceae communities and genotypes. Despite sharing similar thermal histories and associated symbionts, these conspecific individuals differed greatly in their tolerance to heat stress. More importantly, the clustering of individuals based on their genotype at heat-associated loci matched the phenotypic variation in heat tolerance. Colonies that sustained on average lower mortality, higher Symbiodiniaceae/chlorophyll concentrations and photosynthetic efficiency under prolonged heat stress were also the closest based on their genotypes, although the low sample size prevented testing loci predictive accuracy. Together these preliminary results support the relevance of coupling seascape genomics and long-term heat stress experiments in the future, to evaluate the effect size of candidate heat associated loci and pave the way for genomic predictive models of corals heat tolerance.

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.

Estimating scale-specific and localized spatial patterns in allele frequency

Characterizing spatial patterns in allele frequencies is fundamental to evolutionary biology because these patterns contain evidence of underlying processes. However, the spatial scales at which gene flow, changing selection, and drift act are often unknown. Many of these processes can operate inconsistently across space, causing nonstationary patterns. We present a wavelet approach to characterize spatial pattern in allele frequency that helps solve these problems. We show how our approach can characterize spatial patterns in relatedness at multiple spatial scales, i.e. a multilocus wavelet genetic dissimilarity. We also develop wavelet tests of spatial differentiation in allele frequency and quantitative trait loci (QTL). With simulation, we illustrate these methods under different scenarios. We also apply our approach to natural populations of Arabidopsis thaliana to characterize population structure and identify locally adapted loci across scales. We find, for example, that Arabidopsis flowering time QTL show significantly elevated genetic differentiation at 300-1,300 km scales. Wavelet transforms of allele frequencies offer a flexible way to reveal geographic patterns and underlying evolutionary processes.

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.

Elevated [CO2] and temperature augment gas exchange and shift the fitness landscape in a montane forb

Climate change is simultaneously increasing carbon dioxide concentrations ([CO2]) and temperature. These factors could interact to influence plant physiology and performance. Alternatively, increased [CO2] may offset costs associated with elevated temperatures. Furthermore, the interaction between elevated temperature and [CO2] may differentially affect populations from along an elevational gradient and disrupt local adaptation. We conducted a multifactorial growth chamber experiment to examine the interactive effects of temperature and [CO2] on fitness and ecophysiology of diverse accessions of Boechera stricta (Brassicaceae) sourced from a broad elevational gradient in Colorado. We tested whether increased [CO2] would enhance photosynthesis across accessions, and whether warmer conditions would depress the fitness of high-elevation accessions owing to steep reductions in temperature with increasing elevation in this system. Elevational clines in [CO2] are not as evident, making it challenging to predict how locally adapted ecotypes will respond to elevated [CO2]. This experiment revealed that elevated [CO2] increased photosynthesis and intrinsic water use efficiency across all accessions. However, these instantaneous responses to treatments did not translate to changes in fitness. Instead, increased temperatures reduced the probability of reproduction for all accessions. Elevated [CO2] and increased temperatures interacted to shift the adaptive landscape, favoring lower elevation accessions for the probability of survival and fecundity. Our results suggest that elevated temperatures and [CO2] associated with climate change could have severe negative consequences, especially for high-elevation populations.

Current status of community resources and priorities for weed genomics research

Weeds are attractive models for basic and applied research due to their impacts on agricultural systems and capacity to swiftly adapt in response to anthropogenic selection pressures. Currently, a lack of genomic information precludes research to elucidate the genetic basis of rapid adaptation for important traits like herbicide resistance and stress tolerance and the effect of evolutionary mechanisms on wild populations. The International Weed Genomics Consortium is a collaborative group of scientists focused on developing genomic resources to impact research into sustainable, effective weed control methods and to provide insights about stress tolerance and adaptation to assist crop breeding.

Mining of QTLs for Spring Bread Wheat Spike Productivity by Comparing Spring Wheat Cultivars Released in Different Decades of the Last Century

Genome-wide association studies (GWAS) are among the genetic tools for the mining of genomic loci associated with useful agronomic traits. The study enabled us to find new genetic markers associated with grain yield as well as quality. The sample under study consisted of spring wheat cultivars developed in different decades of the last century. A panel of 186 accessions was evaluated at VIR's experiment station in Pushkin across a 3-year period of field trials. In total, 24 SNPs associated with six productivity characteristics were revealed. Along with detecting significant markers for each year of the field study, meta-analyses were conducted. Loci associated with useful yield-related agronomic characteristics were detected on chromosomes 4A, 5A, 6A, 6B, and 7B. In addition to previously described regions, novel loci associated with grain yield and quality were identified during the study. We presume that the utilization of contrast cultivars which originated in different breeding periods allowed us to identify new markers associated with useful agronomic characteristics.

Genotype-by-environment interactions and local adaptation shape selection in the US National Chip Processing Trial

KEY MESSAGE

We find evidence of selection for local adaptation and extensive genotype-by-environment interaction in the potato National Chip Processing Trial (NCPT). We present a novel method for dissecting the interplay between selection, local adaptation and environmental response in plant breeding schemes. Balancing local adaptation and the desire for widely adapted cultivars is challenging for plant breeders and makes genotype-by-environment interactions (GxE) an important target of selection. Selecting for GxE requires plant breeders to evaluate plants across multiple environments. One way breeders have accomplished this is to test advanced materials across many locations. Public potato breeders test advanced breeding material in the National Chip Processing Trial (NCPT), a public-private partnership where breeders from ten institutions submit advanced chip lines to be evaluated in up to ten locations across the country. These clones are genotyped and phenotyped for important agronomic traits. We used these data to interrogate the NCPT for GxE. Further, because breeders submitting clones to the NCPT select in a relatively small geographic range for the first 3 years of selection, we examined these data for evidence of incidental selection for local adaptation, and the alleles underlying it, using an environmental genome-wide association study (envGWAS). We found genomic regions associated with continuous environmental variables and discrete breeding programs, as well as regions of the genome potentially underlying GxE for yield.

Climate biogeography of Arabidopsis thaliana: linking distribution models and individual variation

AIM

Patterns of individual variation are key to testing hypotheses about the mechanisms underlying biogeographic patterns. If species distributions are determined by environmental constraints, then populations near range margins may have reduced performance and be adapted to harsher environments. Model organisms are potentially important systems for biogeographical studies, given the available range-wide natural history collections, and the importance of providing biogeographical context to their genetic and phenotypic diversity.

LOCATION

Global.

TAXON

Arabidopsis thaliana ("Arabidopsis").

METHODS

We fit occurrence records to climate data, and then projected the distribution of Arabidopsis under last glacial maximum, current, and future climates. We confronted model predictions with individual performance measured on 2,194 herbarium specimens, and we asked whether predicted suitability was associated with life-history and genomic variation measured on ~900 natural accessions.

RESULTS

The most important climate variables constraining the Arabidopsis distribution were winter cold in northern and high elevation regions and summer heat in southern regions. Herbarium specimens from regions with lower habitat suitability in both northern and southern regions were smaller, supporting the hypothesis that the distribution of Arabidopsis is constrained by climate-associated factors. Climate anomalies partly explained interannual variation in herbarium specimen size, but these did not closely correspond to local limiting factors identified in the distribution model. Late-flowering genotypes were absent from the lowest suitability regions, suggesting slower life histories are only viable closer to the center of the realized niche. We identified glacial refugia farther north than previously recognized, as well as refugia concordant with previous population genetic findings. Lower latitude populations, known to be genetically distinct, are most threatened by future climate change. The recently colonized range of Arabidopsis was well-predicted by our native-range model applied to certain regions but not others, suggesting it has colonized novel climates.

MAIN CONCLUSIONS

Integration of distribution models with performance data from vast natural history collections is a route forward for testing biogeographical hypotheses about species distributions and their relationship with evolutionary fitness across large scales.

The drivers of intraspecific trait variation and their implications for future tree productivity and survival

Forests are facing unprecedented levels of stress from pest and disease outbreaks, disturbance, fragmentation, development, and a changing climate. These selective agents act to alter forest composition from regional to cellular levels. Thus, a central challenge for understanding how forests will be impacted by future change is how to integrate across scales of biology. Phenotype, or an observable trait, is the product of an individual's genes (G) and the environment in which an organism lives (E). To date, researchers have detailed how environment drives variation in tree phenotypes over long time periods (e.g., long-term ecological research sites [LTERs]) and across large spatial scales (e.g., flux network). In parallel, researchers have discovered the genes and pathways that govern phenotypes, finding high degrees of genetic control and signatures of local adaptation in many plant traits. However, the research in these two areas remain largely independent of each other, hindering our ability to generate accurate predictions of plant response to environment, an increasingly urgent need given threats to forest systems. I present the importance of both genes and environment in determining tree responses to climate stress. I highlight why the difference between G versus E in driving variation is critical for our understanding of climate responses, then propose means of accelerating research that examines G and E simultaneously by leveraging existing long-term, large-scale phenotypic data sets from ecological networks and adding newly affordable sequence (-omics) data to both drill down to find the genes and alleles influencing phenotypes and scale up to find how patterns of demography and local adaptation may influence future response to change.

Evidence that variation in root anatomy contributes to local adaptation in Mexican native maize

Mexican native maize (Zea mays ssp. mays) is adapted to a wide range of climatic and edaphic conditions. Here, we focus specifically on the potential role of root anatomical variation in this adaptation. Given the investment required to characterize root anatomy, we present a machine-learning approach using environmental descriptors to project trait variation from a relatively small training panel onto a larger panel of genotyped and georeferenced Mexican maize accessions. The resulting models defined potential biologically relevant clines across a complex environment that we used subsequently for genotype-environment association. We found evidence of systematic variation in maize root anatomy across Mexico, notably a prevalence of trait combinations favoring a reduction in axial hydraulic conductance in varieties sourced from cooler, drier highland areas. We discuss our results in the context of previously described water-banking strategies and present candidate genes that are associated with both root anatomical and environmental variation. Our strategy is a refinement of standard environmental genome-wide association analysis that is applicable whenever a training set of georeferenced phenotypic data is available.

Strategies for breeding crops for future environments

The green revolution successfully increased agricultural output in the early 1960s by relying primarily on three pillars: plant breeding, irrigation, and chemical fertilization. Today, the need to reduce the use of chemical fertilizers, water scarcity, and future environmental changes, together with a growing population, requires innovative strategies to adapt to a new context and prevent food shortages. Therefore, scientists from around the world are directing their efforts to breed crops for future environments to sustainably produce more nutritious food. Herein, we propose scientific avenues to be reinforced in selecting varieties, including crop wild relatives, either for monoculture or mixed cropping systems, taking advantage of plant-microbial interactions, while considering the diversity of organisms associated with crops and unlocking combinatorial nutritional stresses.

Genomics for monitoring and understanding species responses to global climate change

All life forms across the globe are experiencing drastic changes in environmental conditions as a result of global climate change. These environmental changes are happening rapidly, incur substantial socioeconomic costs, pose threats to biodiversity and diminish a species' potential to adapt to future environments. Understanding and monitoring how organisms respond to human-driven climate change is therefore a major priority for the conservation of biodiversity in a rapidly changing environment. Recent developments in genomic, transcriptomic and epigenomic technologies are enabling unprecedented insights into the evolutionary processes and molecular bases of adaptation. This Review summarizes methods that apply and integrate omics tools to experimentally investigate, monitor and predict how species and communities in the wild cope with global climate change, which is by genetically adapting to new environmental conditions, through range shifts or through phenotypic plasticity. We identify advantages and limitations of each method and discuss future research avenues that would improve our understanding of species' evolutionary responses to global climate change, highlighting the need for holistic, multi-omics approaches to ecosystem monitoring during global climate change.

Crop adaptation to climate change: An evolutionary perspective

The disciplines of evolutionary biology and plant and animal breeding have been intertwined throughout their development, with responses to artificial selection yielding insights into the action of natural selection and evolutionary biology providing statistical and conceptual guidance for modern breeding. Here we offer an evolutionary perspective on a grand challenge of the 21st century: feeding humanity in the face of climate change. We first highlight promising strategies currently under way to adapt crops to current and future climate change. These include methods to match crop varieties with current and predicted environments and to optimize breeding goals, management practices, and crop microbiomes to enhance yield and sustainable production. We also describe the promise of crop wild relatives and recent technological innovations such as speed breeding, genomic selection, and genome editing for improving environmental resilience of existing crop varieties or for developing new crops. Next, we discuss how methods and theory from evolutionary biology can enhance these existing strategies and suggest novel approaches. We focus initially on methods for reconstructing the evolutionary history of crops and their pests and symbionts, because such historical information provides an overall framework for crop-improvement efforts. We then describe how evolutionary approaches can be used to detect and mitigate the accumulation of deleterious mutations in crop genomes, identify alleles and mutations that underlie adaptation (and maladaptation) to agricultural environments, mitigate evolutionary trade-offs, and improve critical proteins. Continuing feedback between the evolution and crop biology communities will ensure optimal design of strategies for adapting crops to climate change.

Climate and Soil Microsite Conditions Determine Local Adaptation in Declining Silver Fir Forests

Ongoing climatic change is threatening the survival of drought-sensitive tree species, such as silver fir (Abies alba). Drought-induced dieback had been previously explored in this conifer, although the role played by tree-level genetic diversity and its relationship with growth patterns and soil microsite conditions remained elusive. We used double digest restriction-site-associated DNA sequencing (ddRADseq) to describe different genetic characteristics of five silver fir forests in the Spanish Pyrenees, including declining and non-declining trees. Single nucleotide polymorphisms (SNPs) were used to investigate the relationships between genetics, dieback, intraspecific trait variation (functional dendrophenotypic traits and leaf traits), local bioclimatic conditions, and rhizosphere soil properties. While there were no noticeable genetic differences between declining and non-declining trees, genome-environment associations with selection signatures were abundant, suggesting a strong influence of climate, soil physicochemical properties, and soil microbial diversity on local adaptation. These results provide novel insights into how genetics and diverse environmental factors are interrelated and highlight the need to incorporate genetic data into silver fir forest dieback studies to gain a better understanding of local adaptation.

Moving beyond heritability in the search for coral adaptive potential

Global environmental change is happening at unprecedented rates. Coral reefs are among the ecosystems most threatened by global change. For wild populations to persist, they must adapt. Knowledge shortfalls about corals' complex ecological and evolutionary dynamics, however, stymie predictions about potential adaptation to future conditions. Here, we review adaptation through the lens of quantitative genetics. We argue that coral adaptation studies can benefit greatly from "wild" quantitative genetic methods, where traits are studied in wild populations undergoing natural selection, genomic relationship matrices can replace breeding experiments, and analyses can be extended to examine genetic constraints among traits. In addition, individuals with advantageous genotypes for anticipated future conditions can be identified. Finally, genomic genotyping supports simultaneous consideration of how genetic diversity is arrayed across geographic and environmental distances, providing greater context for predictions of phenotypic evolution at a metapopulation scale.

Understanding local plant extinctions before it is too late: bridging evolutionary genomics with global ecology

Understanding evolutionary genomic and population processes within a species range is key to anticipating the extinction of plant species before it is too late. However, most models of biodiversity risk under global change do not account for the genetic variation and local adaptation of different populations. Population diversity is critical to understanding extinction because different populations may be more or less susceptible to global change and, if lost, would reduce the total diversity within a species. Two new modeling frameworks advance our understanding of extinction from a population and evolutionary angle: Rapid climate change-driven disruptions in population adaptation are predicted from associations between genomes and local climates. Furthermore, losses of population diversity from global land-use transformations are estimated by scaling relationships of species' genomic diversity with habitat area. Overall, these global eco-evolutionary methods advance the predictability - and possibly the preventability - of the ongoing extinction of plant species.

Genetic Adaptation of Siberian Larch (Larix sibirica Ledeb.) to High Altitudes

Forest trees growing in high altitude conditions offer a convenient model for studying adaptation processes. They are subject to a whole range of adverse factors that are likely to cause local adaptation and related genetic changes. Siberian larch (Larix sibirica Ledeb.), whose distribution covers different altitudes, makes it possible to directly compare lowland with highland populations. This paper presents for the first time the results of studying the genetic differentiation of Siberian larch populations, presumably associated with adaptation to the altitudinal gradient of climatic conditions, based on a joint analysis of altitude and six other bioclimatic variables, together with a large number of genetic markers, single nucleotide polymorphisms (SNPs), obtained from double digest restriction-site-associated DNA sequencing (ddRADseq). In total, 25,143 SNPs were genotyped in 231 trees. In addition, a dataset of 761 supposedly selectively neutral SNPs was assembled by selecting SNPs located outside coding regions in the Siberian larch genome and mapped to different contigs. The analysis using four different methods (PCAdapt, LFMM, BayeScEnv and RDA) revealed 550 outlier SNPs, including 207 SNPs whose variation was significantly correlated with the variation of some of environmental factors and presumably associated with local adaptation, including 67 SNPs that correlated with altitude based on either LFMM or BayeScEnv and 23 SNPs based on both of them. Twenty SNPs were found in the coding regions of genes, and 16 of them represented non-synonymous nucleotide substitutions. They are located in genes involved in the processes of macromolecular cell metabolism and organic biosynthesis associated with reproduction and development, as well as organismal response to stress. Among these 20 SNPs, nine were possibly associated with altitude, but only one of them was identified as associated with altitude by all four methods used in the study, a nonsynonymous SNP in scaffold_31130 in position 28092, a gene encoding a cell membrane protein with uncertain function. Among the studied populations, at least two main groups (clusters), the Altai populations and all others, were significantly genetically different according to the admixture analysis based on any of the three SNP datasets as follows: 761 supposedly selectively neutral SNPs, all 25,143 SNPs and 550 adaptive SNPs. In general, according to the AMOVA results, genetic differentiation between transects or regions or between population samples was relatively low, although statistically significant, based on 761 neutral SNPs (F_ST = 0.036) and all 25,143 SNPs (F_ST = 0.017). Meanwhile, the differentiation based on 550 adaptive SNPs was much higher (F_ST = 0.218). The data showed a relatively weak but highly significant linear correlation between genetic and geographic distances (r = 0.206, p = 0.001).