Patterns and effects of gene flow on adaptation across spatial scales: implications for management

Gene flow can have rapid effects on adaptation and is an important evolutionary tool available when undertaking biological conservation and restoration. This tool is underused partly because of the perceived risk of outbreeding  depression and loss of mean fitness when different populations are crossed. In this article, we briefly review some theory and empirical findings on how genetic variation is distributed across species ranges, describe known patterns of gene flow in nature with respect to environmental gradients, and highlight the effects of gene flow on adaptation in small or stressed populations in challenging environments (e.g., at species range limits). We then present a case study involving crosses at varying spatial scales among mountain populations of a trigger plant (Stylidium armeria: Stylidiaceae) in the Australian Alps to highlight how some issues around gene flow effects can be evaluated. We found evidence of outbreeding depression in seed production at greater geographic distances. Nevertheless, we found no evidence of maladaptive gene flow effects in likelihood of germination, plant performance (size), and performance variance, suggesting that gene flow at all spatial scales produces offspring with high adaptive potential. This case study demonstrates a path to evaluating how increasing sources of gene flow in managed wild and restored populations could identify some offspring with high fitness that could bolster the ability of populations to adapt to future environmental changes. We suggest further ways in which managers and researchers can act to understand and consider adaptive gene flow in natural and conservation contexts under rapidly changing conditions.

Predicting species invasiveness with genomic data: Is genomic offset related to establishment probability?

Predicting the risk of establishment and spread of populations outside their native range represents a major challenge in evolutionary biology. Various methods have recently been developed to estimate population (mal)adaptation to a new environment with genomic data via so-called Genomic Offset (GO) statistics. These approaches are particularly promising for studying invasive species but have still rarely been used in this context. Here, we evaluated the relationship between GO and the establishment probability of a population in a new environment using both in silico and empirical data. First, we designed invasion simulations to evaluate the ability to predict establishment probability of two GO computation methods (Geometric GO and Gradient Forest) under several conditions. Additionally, we aimed to evaluate the interpretability of absolute Geometric GO values, which theoretically represent the adaptive genetic distance between populations from distinct environments. Second, utilizing public empirical data from the crop pest species Bactrocera tryoni, a fruit fly native from Northern Australia, we computed GO between "source" populations and a diverse range of locations within invaded areas. This practical application of GO within the context of a biological invasion underscores its potential in providing insights and guiding recommendations for future invasion risk assessment. Overall, our results suggest that GO statistics represent good predictors of the establishment probability and may thus inform invasion risk, although the influence of several factors on prediction performance (e.g., propagule pressure or admixture) will need further investigation.

Local adaptation of Pinus leiophylla under climate and land use change models in the Avocado Belt of Michoacán

Climate change and land use change are two main drivers of global biodiversity decline, decreasing the genetic diversity that populations harbour and altering patterns of local adaptation. Landscape genomics allows measuring the effect of these anthropogenic disturbances on the adaptation of populations. However, both factors have rarely been considered simultaneously. Based on a set of 3660 SNPs from which 130 were identified as outliers by a genome-environment association analysis (LFMM), we modelled the spatial turnover of allele frequencies in 19 localities of Pinus leiophylla across the Avocado Belt in Michoacán state, Mexico. Then, we evaluated the effect of climate change and land use change scenarios, in addition to evaluating assisted gene flow strategies and connectivity metrics across the landscape to identify priority conservation areas for the species. We found that localities in the centre-east of the Avocado Belt would be more vulnerable to climate change, while localities in the western area are more threatened by land conversion to avocado orchards. Assisted gene flow actions could aid in mitigating both threats. Connectivity patterns among forest patches will also be modified by future habitat loss, with central and eastern parts of the Avocado Belt maintaining the highest connectivity. These results suggest that areas with the highest priority for conservation are in the eastern part of the Avocado Belt, including the Monarch Butterfly Biosphere Reserve. This work is useful as a framework that incorporates distinct layers of information to provide a more robust representation of the response of tree populations to anthropogenic disturbances.

Estimates of heterozygosity from single nucleotide polymorphism markers are context-dependent and often wrong

Genetic diversity is frequently described using heterozygosity, particularly in a conservation context. Often, it is estimated using single nucleotide polymorphisms (SNPs); however, it has been shown that heterozygosity values calculated from SNPs can be biased by both study design and filtering parameters. Though solutions have been proposed to address these issues, our own work has found them to be inadequate in some circumstances. Here, we aimed to improve the reliability and comparability of heterozygosity estimates, specifically by investigating how sample size and missing data thresholds influenced the calculation of autosomal heterozygosity (heterozygosity calculated from across the genome, i.e. fixed and variable sites). We also explored how the standard practice of tri- and tetra-allelic site exclusion could bias heterozygosity estimates and influence eventual conclusions relating to genetic diversity. Across three distinct taxa (a frog, Litoria rubella; a tree, Eucalyptus microcarpa; and a grasshopper, Keyacris scurra), we found heterozygosity estimates to be meaningfully affected by sample size and missing data thresholds, partly due to the exclusion of tri- and tetra-allelic sites. These biases were inconsistent both between species and populations, with more diverse populations tending to have their estimates more severely affected, thus having potential to dramatically alter interpretations of genetic diversity. We propose a modified framework for calculating heterozygosity that reduces bias and improves the utility of heterozygosity as a measure of genetic diversity, whilst also highlighting the need for existing population genetic pipelines to be adjusted such that tri- and tetra-allelic sites be included in calculations.

Incorporating adaptive genomic variation into predictive models for invasion risk assessment

Global climate change is expected to accelerate biological invasions, necessitating accurate risk forecasting and management strategies. However, current invasion risk assessments often overlook adaptive genomic variation, which plays a significant role in the persistence and expansion of invasive populations. Here we used Molgula manhattensis, a highly invasive ascidian, as a model to assess its invasion risks along Chinese coasts under climate change. Through population genomics analyses, we identified two genetic clusters, the north and south clusters, based on geographic distributions. To predict invasion risks, we employed the gradient forest and species distribution models to calculate genomic offset and species habitat suitability, respectively. These approaches yielded distinct predictions: the gradient forest model suggested a greater genomic offset to future climatic conditions for the north cluster (i.e., lower invasion risks), while the species distribution model indicated higher future habitat suitability for the same cluster (i.e, higher invasion risks). By integrating these models, we found that the south cluster exhibited minor genome-niche disruptions in the future, indicating higher invasion risks. Our study highlights the complementary roles of genomic offset and habitat suitability in assessing invasion risks under climate change. Moreover, incorporating adaptive genomic variation into predictive models can significantly enhance future invasion risk predictions and enable effective management strategies for biological invasions in the future.

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.

How Can Genomics Help or Hinder Wildlife Conservation?

Genomic data are becoming increasingly affordable and easy to collect, and new tools for their analysis are appearing rapidly. Conservation biologists are interested in using this information to assist in management and planning but are typically limited financially and by the lack of genomic resources available for non-model taxa. It is therefore important to be aware of the pitfalls as well as the benefits of applying genomic approaches. Here, we highlight recent methods aimed at standardizing population assessments of genetic variation, inbreeding, and forms of genetic load and methods that help identify past and ongoing patterns of genetic interchange between populations, including those subjected to recent disturbance. We emphasize challenges in applying some of these methods and the need for adequate bioinformatic support. We also consider the promises and challenges of applying genomic approaches to understand adaptive changes in natural populations to predict their future adaptive capacity.

How Fish Population Genomics Can Promote Sustainable Fisheries: A Road Map

Maintenance of genetic diversity in marine fishes targeted by commercial fishing is a grand challenge for the future. Most of these species are abundant and therefore important for marine ecosystems and food security. Here, we present a road map of how population genomics can promote sustainable fisheries. In these species, the development of reference genomes and whole genome sequencing is key, because genetic differentiation at neutral loci is usually low due to large population sizes and gene flow. First, baseline allele frequencies representing genetically differentiated populations within species must be established. These can then be used to accurately determine the composition of mixed samples, forming the basis for population demographic analysis to inform sustainably set fish quotas. SNP-chip analysis is a cost-effective method for determining baseline allele frequencies and for population identification in mixed samples. Finally, we describe how genetic marker analysis can transform stock identification and management.

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.

Assessing genome-wide adaptations associated with range expansion in the pink rice borer, Sesamia inferens

Understanding the genetic basis of adaptive evolution following habitat expansion can have important implications for pest management. The pink rice borer (PRB), Sesamia inferens (Walker), is a destructive pest of rice that was historically restricted to regions south of 34° N latitude in China. However, with changes in global climate and farming practices, the distribution of this moth has progressively expanded, encompassing most regions in North China. Here, 3 highly differentiated subpopulations were discovered using high-quality single-nucleotide polymorphism and structural variant datasets across China, corresponding to northern, southern China regions, and the Yunnan-Guizhou Plateau, with significant patterns of isolation by geographic and environmental distances. Our estimates of evolutionary history indicate asymmetric migration with varying population sizes across the 3 subpopulations. Selective sweep analyses estimated strong selection at insect cuticle glycine-rich cuticular protein genes which are associated with enhanced desiccation adaptability in the northern group, and at the histone-lysine-N-methyltransferase gene associated with range expansion and local adaptation in the Shandong population. Our findings have significant implications for the development of effective strategies to control this pest.

Populus cathayana genome and population resequencing provide insights into its evolution and adaptation

Populus cathayana Rehder, an indigenous poplar species of ecological and economic importance, is widely distributed in a high-elevation range from southwest to northeast China. Further development of this species as a sustainable poplar resource has been hindered by a lack of genome information the at the population level. Here, we produced a chromosome-level genome assembly of P. cathayana, covering 406.55 Mb (scaffold N50 = 20.86 Mb) and consisting of 19 chromosomes, with 35 977 protein-coding genes. Subsequently, we made a genomic variation atlas of 438 wild individuals covering 36 representative geographic areas of P. cathayana, which were divided into four geographic groups. It was inferred that the Northwest China regions served as the genetic diversity centers and a population bottleneck happened during the history of P. cathayana. By genotype-environment association analysis, 947 environment-association loci were significantly associated with temperature, solar radiation, precipitation, and altitude variables. We identified local adaptation genes involved in DNA repair and UV radiation response, among which UVR8, HY5, and CUL4 had key roles in high-altitude adaptation of P. cathayana. Predictions of adaptive potential under future climate conditions showed that P. cathayana populations in areas with drastic climate change were anticipated to have greater maladaptation risk. These results provide comprehensive insights for understanding wild poplar evolution and optimizing adaptive potential in molecular breeding.

Projection of current and future distribution of adaptive genetic units in an alpine ungulate

Climate projections predict major changes in alpine environments by the end of the 21st century. To avoid climate-induced maladaptation and extinction, many animal populations will either need to move to more suitable habitats or adapt in situ to novel conditions. Since populations of a species exhibit genetic variation related to local adaptation, it is important to incorporate this variation into predictive models to help assess the ability of the species to survive climate change. Here, we evaluate how the adaptive genetic variation of a mountain ungulate-the Northern chamois (Rupicapra rupicapra)-could be impacted by future global warming. Based on genotype-environment association analyses of 429 chamois using a ddRAD sequencing approach, we identified genetic variation associated with climatic gradients across the European Alps. We then delineated adaptive genetic units and projected the optimal distribution of these adaptive groups in the future. Our results suggest the presence of local adaptation to climate in Northern chamois with similar genetic adaptive responses in geographically distant but climatically similar populations. Furthermore, our results predict that future climatic changes will modify the Northern chamois adaptive landscape considerably, with various degrees of maladaptation risk.

Space-for-time substitutions in climate change ecology and evolution

In an epoch of rapid environmental change, understanding and predicting how biodiversity will respond to a changing climate is an urgent challenge. Since we seldom have sufficient long-term biological data to use the past to anticipate the future, spatial climate-biotic relationships are often used as a proxy for predicting biotic responses to climate change over time. These 'space-for-time substitutions' (SFTS) have become near ubiquitous in global change biology, but with different subfields largely developing methods in isolation. We review how climate-focussed SFTS are used in four subfields of ecology and evolution, each focussed on a different type of biotic variable - population phenotypes, population genotypes, species' distributions, and ecological communities. We then examine the similarities and differences between subfields in terms of methods, limitations and opportunities. While SFTS are used for a wide range of applications, two main approaches are applied across the four subfields: spatial in situ gradient methods and transplant experiments. We find that SFTS methods share common limitations relating to (i) the causality of identified spatial climate-biotic relationships and (ii) the transferability of these relationships, i.e. whether climate-biotic relationships observed over space are equivalent to those occurring over time. Moreover, despite widespread application of SFTS in climate change research, key assumptions remain largely untested. We highlight opportunities to enhance the robustness of SFTS by addressing key assumptions and limitations, with a particular emphasis on where approaches could be shared between the four subfields.

Genomic analyses indicate resilience of a commercially and culturally important marine gastropod snail to climate change

Genomic vulnerability analyses are being increasingly used to assess the adaptability of species to climate change and provide an opportunity for proactive management of harvested marine species in changing oceans. Southeastern Australia is a climate change hotspot where many marine species are shifting poleward. The turban snail, Turbo militaris is a commercially and culturally harvested marine gastropod snail from eastern Australia. The species has exhibited a climate-driven poleward range shift over the last two decades presenting an ongoing challenge for sustainable fisheries management. We investigate the impact of future climate change on T. militaris using genotype-by-sequencing to project patterns of gene flow and local adaptation across its range under climate change scenarios. A single admixed, and potentially panmictic, demographic unit was revealed with no evidence of genetic subdivision across the species range. Significant genotype associations with heterogeneous habitat features were observed, including associations with sea surface temperature, ocean currents, and nutrients, indicating possible adaptive genetic differentiation. These findings suggest that standing genetic variation may be available for selection to counter future environmental change, assisted by widespread gene flow, high fecundity and short generation time in this species. We discuss the findings of this study in the content of future fisheries management and conservation.

Comparative Population Transcriptomics Provide New Insight into the Evolutionary History and Adaptive Potential of World Ocean Krill

Genetic variation is instrumental for adaptation to changing environments but it is unclear how it is structured and contributes to adaptation in pelagic species lacking clear barriers to gene flow. Here, we applied comparative genomics to extensive transcriptome datasets from 20 krill species collected across the Atlantic, Indian, Pacific, and Southern Oceans. We compared genetic variation both within and between species to elucidate their evolutionary history and genomic bases of adaptation. We resolved phylogenetic interrelationships and uncovered genomic evidence to elevate the cryptic Euphausia similis var. armata into species. Levels of genetic variation and rates of adaptive protein evolution vary widely. Species endemic to the cold Southern Ocean, such as the Antarctic krill Euphausia superba, showed less genetic variation and lower evolutionary rates than other species. This could suggest a low adaptive potential to rapid climate change. We uncovered hundreds of candidate genes with signatures of adaptive evolution among Antarctic Euphausia but did not observe strong evidence of adaptive convergence with the predominantly Arctic Thysanoessa. We instead identified candidates for cold-adaptation that have also been detected in Antarctic fish, including genes that govern thermal reception such as TrpA1. Our results suggest parallel genetic responses to similar selection pressures across Antarctic taxa and provide new insights into the adaptive potential of important zooplankton already affected by climate change.

Genomic signals of local adaptation in Picea crassifolia

BACKGROUND

Global climate change poses a grave threat to biodiversity and underscores the importance of identifying the genes and corresponding environmental factors involved in the adaptation of tree species for the purposes of conservation and forestry. This holds particularly true for spruce species, given their pivotal role as key constituents of the montane, boreal, and sub-alpine forests in the Northern Hemisphere.

RESULTS

Here, we used transcriptomes, species occurrence records, and environmental data to investigate the spatial genetic distribution of and the climate-associated genetic variation in Picea crassifolia. Our comprehensive analysis employing ADMIXTURE, principal component analysis (PCA) and phylogenetic methodologies showed that the species has a complex population structure with obvious differentiation among populations in different regions. Concurrently, our investigations into isolation by distance (IBD), isolation by environment (IBE), and niche differentiation among populations collectively suggests that local adaptations are driven by environmental heterogeneity. By integrating population genomics and environmental data using redundancy analysis (RDA), we identified a set of climate-associated single-nucleotide polymorphisms (SNPs) and showed that environmental isolation had a more significant impact than geographic isolation in promoting genetic differentiation. We also found that the candidate genes associated with altitude, temperature seasonality (Bio4) and precipitation in the wettest month (Bio13) may be useful for forest tree breeding.

CONCLUSIONS

Our findings deepen our understanding of how species respond to climate change and highlight the importance of integrating genomic and environmental data in untangling local adaptations.

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.

Adaptive divergence and genetic vulnerability of relict species under climate change: a case study of Pterocarya macroptera

BACKGROUND AND AIMS

Understanding adaptive genetic variation and whether it can keep pace with predicted future climate change is critical in assessing the genetic vulnerability of species and developing conservation management strategies. The lack of information on adaptive genetic variation in relict species carrying abundant genetic resources hinders the assessment of genetic vulnerability. Using a landscape genomics approach, this study aimed to determine how adaptive genetic variation shapes population divergence and to predict the adaptive potential of Pterocarya macroptera (a vulnerable relict species in China) under future climate scenarios.

METHODS

We applied restriction site-associated DNA sequencing (RAD-seq) to obtain 8244 single-nucleotide polymorphisms (SNPs) from 160 individuals across 28 populations. We examined the pattern of genetic diversity and divergence, and then identified outliers by genetic differentiation (FST) and genotype-environment association (GEA) methods. We further dissected the effect of geographical/environmental gradients on genetic variation. Finally, we predicted genetic vulnerability and adaptive risk under future climate scenarios.

KEY RESULTS

We identified three genetic lineages within P. macroptera: the Qinling-Daba-Tianmu Mountains (QDT), Western Sichuan (WS) and Northwest Yunnan (NWY) lineages, which showed significant signals of isolation by distance (IBD) and isolation by environment (IBE). IBD and IBE explained 3.7-5.7 and 8.6-12.8 % of the genetic structure, respectively. The identified GEA SNP-related genes were involved in chemical defence and gene regulation and may exhibit higher genetic variation to adapt to the environment. Gradient forest analysis revealed that the genetic variation was mainly shaped by temperature-related variables, indicating its adaptation to local thermal environments. A limited adaptive potential was suggested by the high levels of genetic vulnerability in marginal populations.

CONCLUSIONS

Environmental gradient mainly shaped the population differentiation of P. macroptera. Marginal populations may be at high risk of extinction, and thus proactive management measures, such as assisted gene flow, are required to ensure the survival of these populations.

Identifying genetic variation associated with environmental gradients and drought-tolerance phenotypes in ponderosa pine

As climate changes, understanding the genetic basis of local adaptation in plants becomes an ever more pressing issue. Combining genotype-environment association (GEA) with genotype-phenotype association (GPA) analysis has an exciting potential to uncover the genetic basis of environmental responses. We use these approaches to identify genetic variants linked to local adaptation to drought in Pinus ponderosa. Over 4 million Single Nucleotide Polymorphisms (SNPs) were identified using 223 individuals from across the Sierra Nevada of California. 927,740 (22.3%) SNPs were retained after filtering for proximity to genes and used in our association analyses. We found 1374 associated with five major climate variables, with the largest number (1151) associated with April 1st snowpack. We also conducted a greenhouse study with various drought-tolerance traits measured in first-year seedlings of a subset of the genotyped trees grown in the greenhouse. 796 SNPs were associated with control-condition trait values, while 1149 were associated with responsiveness of these traits to drought. While no individual SNPs were associated with both the environmental variables and the measured traits, several annotated genes were associated with both, particularly those involved in cell wall formation, biotic and abiotic stress responses, and ubiquitination. However, the functions of many of the associated genes have not yet been determined due to the lack of gene annotation information for conifers. Future studies are needed to assess the developmental roles and ecological significance of these unknown genes.

Chronosequence of invasion reveals minimal losses of population genomic diversity, niche expansion, and trait divergence in the polyploid, leafy spurge

Rapid evolution may play an important role in the range expansion of invasive species and modify forecasts of invasion, which are the backbone of land management strategies. However, losses of genetic variation associated with colonization bottlenecks may constrain trait and niche divergence at leading range edges, thereby impacting management decisions that anticipate future range expansion. The spatial and temporal scales over which adaptation contributes to invasion dynamics remain unresolved. We leveraged detailed records of the ~130-year invasion history of the invasive polyploid plant, leafy spurge (Euphorbia virgata), across ~500 km in Minnesota, U.S.A. We examined the consequences of range expansion for population genomic diversity, niche breadth, and the evolution of germination behavior. Using genotyping-by-sequencing, we found some population structure in the range core, where introduction occurred, but panmixia among all other populations. Range expansion was accompanied by only modest losses in sequence diversity, with small, isolated populations at the leading edge harboring similar levels of diversity to those in the range core. The climatic niche expanded during most of the range expansion, and the niche of the range core was largely non-overlapping with the invasion front. Ecological niche models indicated that mean temperature of the warmest quarter was the strongest determinant of habitat suitability and that populations at the leading edge had the lowest habitat suitability. Guided by these findings, we tested for rapid evolution in germination behavior over the time course of range expansion using a common garden experiment and temperature manipulations. Germination behavior diverged from the early to late phases of the invasion, with populations from later phases having higher dormancy at lower temperatures. Our results suggest that trait evolution may have contributed to niche expansion during invasion and that distribution models, which inform future management planning, may underestimate invasion potential without accounting for evolution.

The pursuit of genetic gain in agricultural crops through the application of machine-learning to genomic prediction

Current practice in agriculture applies genomic prediction to assist crop breeding in the analysis of genetic marker data. Genomic selection methods typically use linear mixed models, but using machine-learning may provide further potential for improved selection accuracy, or may provide additional information. Here we describe SelectML, an automated pipeline for testing and comparing the performance of a range of linear mixed model and machine-learning-based genomic selection methods. We demonstrate the use of SelectML on an in silico-generated marker dataset which simulated a randomly-sampled (mixed) and an unevenly-sampled (unbalanced) population, comparing the relative performance of various methods included in SelectML on the two datasets. Although machine-learning based methods performed similarly overall to linear mixed models, they performed worse on the mixed dataset and marginally better on the unbalanced dataset, being more affected than linear mixed models by the imposed sampling bias. SelectML can assist in the training, comparison, and selection of genomic selection models, and is available from https://github.com/darcyabjones/selectml.

Evolutionary potential mitigates extinction risk under climate change in the endangered southwestern willow flycatcher

The complexity of global anthropogenic change makes forecasting species responses and planning effective conservation actions challenging. Additionally, important components of a species' adaptive capacity, such as evolutionary potential, are often not included in quantitative risk assessments due to lack of data. While genomic proxies for evolutionary potential in at-risk species are increasingly available, they have not yet been included in extinction risk assessments at a species-wide scale. In this study, we used an individual-based, spatially explicit, dynamic eco-evolutionary simulation model to evaluate the extinction risk of an endangered desert songbird, the southwestern willow flycatcher (Empidonax traillii extimus), in response to climate change. Using data from long-term demographic and habitat studies in conjunction with genome-wide ecological genomics research, we parameterized simulations that include 418 sites across the breeding range, genomic data from 225 individuals, and climate change forecasts spanning 3 generalized circulation models and 3 emissions scenarios. We evaluated how evolutionary potential, and the lack of it, impacted population trajectories in response to climate change. We then investigated the compounding impact of drought and warming temperatures on extinction risk through the mechanism of increased nest failure. Finally, we evaluated how rapid action to reverse greenhouse gas emissions would influence population responses and species extinction risk. Our results illustrate the value of incorporating evolutionary, demographic, and dispersal processes in a spatially explicit framework to more comprehensively evaluate the extinction risk of threatened and endangered species and conservation actions to promote their recovery.

Where to draw the line? Expanding the delineation of conservation units to highly mobile taxa

Conservation units (CUs) are an essential tool for maximizing evolutionary potential and prioritizing areas across a species' range for protection when implementing conservation and management measures. However, current workflows for identifying CUs on the basis of neutral and adaptive genomic variation largely ignore information contained in patterns of isolation by distance (IBD), frequently the primary signal of population structure in highly mobile taxa, such as birds, bats, and marine organisms with pelagic larval stages. While individuals located on either end of a species' distribution may exhibit clear genetic, phenotypic, and ecological differences, IBD produces subtle changes in allele frequencies across space, making it difficult to draw clear boundaries for conservation purposes in the absence of discrete population structure. Here, we highlight potential pitfalls that arise when applying common methods for delineating CUs to continuously distributed organisms and review existing methods for detecting subtle breakpoints in patterns of IBD that can indicate barriers to gene flow in highly mobile taxa. In addition, we propose a new framework for identifying CUs in all organisms, including those characterized by continuous genomic differentiation, and suggest several possible ways to harness the information contained in patterns of IBD to guide conservation and management decisions.

The genomic and epigenetic footprint of local adaptation to variable climates in kiwifruit

A full understanding of adaptive genetic variation at the genomic level will help address questions of how organisms adapt to diverse climates. Actinidia eriantha is a shade-tolerant species, widely distributed in the southern tropical region of China, occurring in spatially heterogeneous environments. In the present study we combined population genomic, epigenomic, and environmental association analyses to infer population genetic structure and positive selection across a climatic gradient, and to assess genomic offset to climatic change for A. eriantha. The population structure is strongly shaped by geography and influenced by restricted gene flow resulting from isolation by distance due to habitat fragmentation. In total, we identified 102 outlier loci and annotated 455 candidate genes associated with the genomic basis of climate adaptation, which were enriched in functional categories related to development processes and stress response; both temperature and precipitation are important factors driving adaptive variation. In addition to single-nucleotide polymorphisms (SNPs), a total of 27 single-methylation variants (SMVs) had significant correlation with at least one of four climatic variables and 16 SMVs were located in or adjacent to genes, several of which were predicted to be involved in plant response to abiotic or biotic stress. Gradient forest analysis indicated that the central/east populations were predicted to be at higher risk of future population maladaptation under climate change. Our results demonstrate that local climate factors impose strong selection pressures and lead to local adaptation. Such information adds to our understanding of adaptive mechanisms to variable climates revealed by both population genome and epigenome analysis.

Applying molecular and genetic methods to trees and their fungal communities

Forests provide invaluable economic, ecological, and social services. At the same time, they are exposed to several threats, such as fragmentation, changing climatic conditions, or increasingly destructive pests and pathogens. Trees, the inherent species of forests, cannot be viewed as isolated organisms. Manifold (micro)organisms are associated with trees playing a pivotal role in forest ecosystems. Of these organisms, fungi may have the greatest impact on the life of trees. A multitude of molecular and genetic methods are now available to investigate tree species and their associated organisms. Due to their smaller genome sizes compared to tree species, whole genomes of different fungi are routinely compared. Such studies have only recently started in forest tree species. Here, we summarize the application of molecular and genetic methods in forest conservation genetics, tree breeding, and association genetics as well as for the investigation of fungal communities and their interrelated ecological functions. These techniques provide valuable insights into the molecular basis of adaptive traits, the impacts of forest management, and changing environmental conditions on tree species and fungal communities and can enhance tree-breeding cycles due to reduced time for field testing. It becomes clear that there are multifaceted interactions among microbial species as well as between these organisms and trees. We demonstrate the versatility of the different approaches based on case studies on trees and fungi. KEY POINTS: • Current knowledge of genetic methods applied to forest trees and associated fungi. • Genomic methods are essential in conservation, breeding, management, and research. • Important role of phytobiomes for trees and their ecosystems.

Biological mechanisms matter in contemporary wildlife conservation

Given limited resources for wildlife conservation paired with an urgency to halt declines and rebuild populations, it is imperative that management actions are tactical and effective. Mechanisms are about how a system works and can inform threat identification and mitigation such that conservation actions that work can be identified. Here, we call for a more mechanistic approach to wildlife conservation and management where behavioral and physiological tools and knowledge are used to characterize drivers of decline, identify environmental thresholds, reveal strategies that would restore populations, and prioritize conservation actions. With a growing toolbox for doing mechanistic conservation research as well as a suite of decision-support tools (e.g., mechanistic models), the time is now to fully embrace the concept that mechanisms matter in conservation ensuring that management actions are tactical and focus on actions that have the potential to directly benefit and restore wildlife populations.

Re-thinking the environment in landscape genomics

Detecting the extrinsic selective pressures shaping genomic variation is critical for a better understanding of adaptation and for forecasting evolutionary responses of natural populations to changing environmental conditions. With increasing availability of geo-referenced environmental data, landscape genomics provides unprecedented insights into how genomic variation and underlying gene functions affect traits potentially under selection. Yet, the robustness of genotype-environment associations used in landscape genomics remains tempered due to various limitations, including the characteristics of environmental data used, sampling designs employed, and statistical frameworks applied. Here, we argue that using complementary or new environmental data sources and well-informed sampling designs may help improve the detection of selective pressures underlying patterns of local adaptation in various organisms and environments.

Prepping for climate change by introgressive hybridization

Species and populations may adapt to climate change by microevolutionary processes. However, standing genetic variation can be insufficient for this to occur. An interesting new study of a system of rainbowfish species shows that intraspecific hybridization enriches gene pools with adaptive variation that may allow persistence in a changing climate.

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.

Estimating resistance surfaces using gradient forest and allelic frequencies

Understanding landscape connectivity has become a global priority for mitigating the impact of landscape fragmentation on biodiversity. Connectivity methods that use link-based methods traditionally rely on relating pairwise genetic distance between individuals or demes to their landscape distance (e.g., geographic distance, cost distance). In this study, we present an alternative to conventional statistical approaches to refine cost surfaces by adapting the gradient forest approach to produce a resistance surface. Used in community ecology, gradient forest is an extension of random forest, and has been implemented in genomic studies to model species genetic offset under future climatic scenarios. By design, this adapted method, resGF, has the ability to handle multiple environmental predicators and is not subjected to traditional assumptions of linear models such as independence, normality and linearity. Using genetic simulations, resistance Gradient Forest (resGF) performance was compared to other published methods (maximum likelihood population effects model, random forest-based least-cost transect analysis and species distribution model). In univariate scenarios, resGF was able to distinguish the true surface contributing to genetic diversity among competing surfaces better than the compared methods. In multivariate scenarios, the gradient forest approach performed similarly to the other random forest-based approach using least-cost transect analysis but outperformed MLPE-based methods. Additionally, two worked examples are provided using two previously published data sets. This machine learning algorithm has the potential to improve our understanding of landscape connectivity and inform long-term biodiversity conservation strategies.

Climate-induced range shifts drive adaptive response via spatio-temporal sieving of alleles

Quaternary climate fluctuations drove many species to shift their geographic ranges, in turn shaping their genetic structures. Recently, it has been argued that adaptation may have accompanied species range shifts via the "sieving" of genotypes during colonisation and establishment. However, this has not been directly demonstrated, and knowledge remains limited on how different evolutionary forces, which are typically investigated separately, interacted to jointly mediate species responses to past climatic change. Here, through whole-genome re-sequencing of over 1200 individuals of the carnation Dianthus sylvestris coupled with integrated population genomic and gene-environment models, we reconstruct the past neutral and adaptive landscape of this species as it was shaped by the Quaternary glacial cycles. We show that adaptive responses emerged concomitantly with the post-glacial range shifts and expansions of this species in the last 20 thousand years. This was due to the heterogenous sieving of adaptive alleles across space and time, as populations expanded out of restrictive glacial refugia into the broader and more heterogeneous range of habitats available in the present-day inter-glacial. Our findings reveal a tightly-linked interplay of migration and adaptation under past climate-induced range shifts, which we show is key to understanding the spatial patterns of adaptive variation we see in species today.

Neutral and adaptive genetic diversity in plants: An overview

Genetic diversity is a prerequisite for evolutionary change in all kinds of organisms. It is generally acknowledged that populations lacking genetic variation are unable to evolve in response to new environmental conditions (e.g., climate change) and thus may face an increased risk of extinction. Although the importance of incorporating genetic diversity into the design of conservation measures is now well understood, less attention has been paid to the distinction between neutral (NGV) and adaptive (AGV) genetic variation. In this review, we first focus on the utility of NGV by examining the ways to quantify it, reviewing applications of NGV to infer ecological and evolutionary processes, and by exploring its utility in designing conservation measures for plant populations and species. Against this background, we then summarize the ways to identify and estimate AGV and discuss its potential use in plant conservation. After comparing NGV and AGV and considering their pros and cons in a conservation context, we conclude that there is an urgent need for a better understanding of AGV and its role in climate change adaptation. To date, however, there are only a few AGV studies on non-model plant species aimed at deciphering the genetic and genomic basis of complex trait variation. Therefore, conservation researchers and practitioners should keep utilizing NGV to develop relevant strategies for rare and endangered plant species until more estimates of AGV are available.

How genomics can help biodiversity conservation

The availability of public genomic resources can greatly assist biodiversity assessment, conservation, and restoration efforts by providing evidence for scientifically informed management decisions. Here we survey the main approaches and applications in biodiversity and conservation genomics, considering practical factors, such as cost, time, prerequisite skills, and current shortcomings of applications. Most approaches perform best in combination with reference genomes from the target species or closely related species. We review case studies to illustrate how reference genomes can facilitate biodiversity research and conservation across the tree of life. We conclude that the time is ripe to view reference genomes as fundamental resources and to integrate their use as a best practice in conservation genomics.

Population Genomics for Insect Conservation

Insects constitute vital components of ecosystems. There is alarming evidence for global declines in insect species diversity, abundance, and biomass caused by anthropogenic drivers such as habitat degradation or loss, agricultural practices, climate change, and environmental pollution. This raises important concerns about human food security and ecosystem functionality and calls for more research to assess insect population trends and identify threatened species and the causes of declines to inform conservation strategies. Analysis of genetic diversity is a powerful tool to address these goals, but so far animal conservation genetics research has focused strongly on endangered vertebrates, devoting less attention to invertebrates, such as insects, that constitute most biodiversity. Insects' shorter generation times and larger population sizes likely necessitate different analytical methods and management strategies. The availability of high-quality reference genome assemblies enables population genomics to address several key issues. These include precise inference of past demographic fluctuations and recent declines, measurement of genetic load levels, delineation of evolutionarily significant units and cryptic species, and analysis of genetic adaptation to stressors. This enables identification of populations that are particularly vulnerable to future threats, considering their potential to adapt and evolve. We review the application of population genomics to insect conservation and the outlook for averting insect declines.

Molecular ecology meets systematic conservation planning

Integrative and proactive conservation approaches are critical to the long-term persistence of biodiversity. Molecular data can provide important information on evolutionary processes necessary for conserving multiple levels of biodiversity (genes, populations, species, and ecosystems). However, molecular data are rarely used to guide spatial conservation decision-making. Here, we bridge the fields of molecular ecology (ME) and systematic conservation planning (SCP) (the 'why') to build a foundation for the inclusion of molecular data into spatial conservation planning tools (the 'how'), and provide a practical guide for implementing this integrative approach for both conservation planners and molecular ecologists. The proposed framework enhances interdisciplinary capacity, which is crucial to achieving the ambitious global conservation goals envisioned for the next decade.

Genetic approaches to exploit landraces for improvement of Triticum turgidum ssp. durum in the age of climate change

Addressing the challenges of climate change and durum wheat production is becoming an important driver for food and nutrition security in the Mediterranean area, where are located the major producing countries (Italy, Spain, France, Greece, Morocco, Algeria, Tunisia, Turkey, and Syria). One of the emergent strategies, to cope with durum wheat adaptation, is the exploration and exploitation of the existing genetic variability in landrace populations. In this context, this review aims to highlight the important role of durum wheat landraces as a useful genetic resource to improve the sustainability of Mediterranean agroecosystems, with a focus on adaptation to environmental stresses. We described the most recent molecular techniques and statistical approaches suitable for the identification of beneficial genes/alleles related to the most important traits in landraces and the development of molecular markers for marker-assisted selection. Finally, we outline the state of the art about landraces genetic diversity and signature of selection, already identified from these accessions, for adaptability to the environment.

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

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

A novel synthesis of two decades of microsatellite studies on European beech reveals decreasing genetic diversity from glacial refugia

Genetic diversity influences the evolutionary potential of forest trees under changing environmental conditions, thus indirectly the ecosystem services that forests provide. European beech (Fagus sylvatica L.) is a dominant European forest tree species that increasingly suffers from climate change-related die-back. Here, we conducted a systematic literature review of neutral genetic diversity in European beech and created a meta-data set of expected heterozygosity (He) from all past studies providing nuclear microsatellite data. We propose a novel approach, based on population genetic theory and a min-max scaling to make past studies comparable. Using a new microsatellite data set with unprecedented geographic coverage and various re-sampling schemes to mimic common sampling biases, we show the potential and limitations of the scaling approach. The scaled meta-dataset reveals the expected trend of decreasing genetic diversity from glacial refugia across the species range and also supports the hypothesis that different lineages met and admixed north of the European mountain ranges. As a result, we present a map of genetic diversity across the range of European beech which could help to identify seed source populations harboring greater diversity and guide sampling strategies for future genome-wide and functional investigations of genetic variation. Our approach illustrates how to combine information from several nuclear microsatellite data sets to describe patterns of genetic diversity extending beyond the geographic scale or mean number of loci used in each individual study, and thus is a proof-of-concept for synthesizing knowledge from existing studies also in other species.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11295-022-01577-4.

Understanding Local Adaptation to Prepare Populations for Climate Change

Adaptation within species to local environments is widespread in nature. Better understanding this local adaptation is critical to conserving biodiversity. However, conservation practices can rely on species’ trait averages or can broadly assume homogeneity across the range to inform management. Recent methodological advances for studying local adaptation provide the opportunity to fine-tune efforts for managing and conserving species. The implementation of these advances will allow us to better identify populations at greatest risk of decline because of climate change, as well as highlighting possible strategies for improving the likelihood of population persistence amid climate change. In the present article, we review recent advances in the study of local adaptation and highlight ways these tools can be applied in conservation efforts. Cutting-edge tools are available to help better identify and characterize local adaptation. Indeed, increased incorporation of local adaptation in management decisions may help meet the imminent demands of managing species amid a rapidly changing world.

Genomic insights into local adaptation and future climate-induced vulnerability of a keystone forest tree in East Asia

Rapid global climate change is posing a substantial threat to biodiversity. The assessment of population vulnerability and adaptive capacity under climate change is crucial for informing conservation and mitigation strategies. Here we generate a chromosome-scale genome assembly and re-sequence genomes of 230 individuals collected from 24 populations for Populus koreana, a pioneer and keystone tree species in temperate forests of East Asia. We integrate population genomics and environmental variables to reveal a set of climate-associated single-nucleotide polymorphisms, insertion/deletions and structural variations, especially numerous adaptive non-coding variants distributed across the genome. We incorporate these variants into an environmental modeling scheme to predict a highly spatiotemporal shift of this species in response to future climate change. We further identify the most vulnerable populations that need conservation priority and many candidate genes and variants that may be useful for forest tree breeding with special aims. Our findings highlight the importance of integrating genomic and environmental data to predict adaptive capacity of a key forest to rapid climate change in the future.

Challenges in quantifying genome erosion for conservation

Massive defaunation and high extinction rates have become characteristic of the Anthropocene. Genetic effects of population decline can lead populations into an extinction vortex, where declining populations show lower genetic fitness, in turn leading to lower populations still. The lower genetic fitness in a declining population due to a shrinking gene pool is known as genetic erosion. Three different types of genetic erosion are highlighted in this review: overall homozygosity, genetic load and runs of homozygosity (ROH), which are indicative of inbreeding. The ability to quantify genetic erosion could be a very helpful tool for conservationists, as it can provide them with an objective, quantifiable measure to use in the assessment of species at risk of extinction. The link between conservation status and genetic erosion should become more apparent. Currently, no clear correlation can be observed between the current conservation status and genetic erosion. However, the high quantities of genetic erosion in wild populations, especially in those species dealing with habitat fragmentation and habitat decline, may be early signs of deteriorating populations. Whole genome sequencing data is the way forward to quantify genetic erosion. Extra screening steps for genetic load and hybridization can be included, since they could potentially have great impact on population fitness. This way, the information yielded from genetic sequence data can provide conservationists with an objective genetic method in the assessment of species at risk of extinction. However, the great complexity of genome erosion quantification asks for consensus and bridging science and its applications, which remains challenging.

Aggregate population-level models informed by genetics predict more suitable habitat than traditional species-level model across the range of a widespread riparian tree

Identifying and predicting how species ranges will shift in response to climate change is paramount for conservation and restoration. Ecological niche models are the most common method used to estimate potential distributions of species; however, they traditionally omit knowledge of intraspecific variation that can allow populations to respond uniquely to change. Here, we aim to test how population X environment relationships influence predicted suitable geographic distributions by comparing aggregated population-level models with species-level model predictions of suitable habitat within population ranges and across the species’ range. We also test the effect of two variable selection methods on these predictions–both addressing the possibility of local adaptation: Models were built with (a) a common set, and number, of predictors and, (b) a unique combination and number of predictors specific to each group’s training extent. Our study addresses the overarching hypothesis that populations have unique environmental niches, and specifically that (1) species-level models predict more suitable habitat within the ranges of genetic populations than individual models built from those groups, particularly when compared models are built with the same set of environmental predictors; and (2) aggregated genetic population models predict more suitable habitat across the species’ range than the species-level model, an = d this difference will increase when models are trained with individualized predictors. We found the species models predicted more habitat within population ranges for two of three genetic groups regardless of variable selection, and that aggregated population models predicted more habitat than species’ models, but that individualized predictors increased this difference. Our study emphasizes the extent to which changes to model predictions depend on the inclusion of genetic information and on the type and selection of predictors. Results from these modeling decisions can have broad implications for predicting population-level ecological and evolutionary responses to climate change.

Genic distribution modelling predicts adaptation of the bank vole to climate change

The most likely pathway for many species to survive future climate change is by pre-existing trait variation providing a fitness advantage under the new climate. Here we evaluate the potential role of haemoglobin (Hb) variation in bank voles under future climate change. We model gene-climate relationships for two functionally distinct Hb types, HbS and HbF, which have a north-south distribution in Britain presenting an unusually tractable system linking genetic variation in physiology to geographical and temporal variation in climate. Projections to future climatic conditions suggest a change in relative climatic suitability that would result in HbS being displaced by HbF in northern Britain. This would facilitate local adaptation to future climate—without Hb displacement, populations in northern Britain would likely be suboptimally adapted because their Hb would not match local climatic conditions. Our study shows how pre-existing physiological differences can influence the adaptive capacity of species to climate change.

Haemoglobin variation in British bank voles combined with climate models predict future regional allelic replacement reflecting capacity for adaptation to climate change.

Repeated genetic adaptation to altitude in two tropical butterflies

Repeated evolution can provide insight into the mechanisms that facilitate adaptation to novel or changing environments. Here we study adaptation to altitude in two tropical butterflies, Heliconius erato and H. melpomene, which have repeatedly and independently adapted to montane habitats on either side of the Andes. We sequenced 518 whole genomes from altitudinal transects and found many regions differentiated between highland (~ 1200 m) and lowland (~ 200 m) populations. We show repeated genetic differentiation across replicate populations within species, including allopatric comparisons. In contrast, there is little molecular parallelism between the two species. By sampling five close relatives, we find that a large proportion of divergent regions identified within species have arisen from standing variation and putative adaptive introgression from high-altitude specialist species. Taken together our study supports a role for both standing genetic variation and gene flow from independently adapted species in promoting parallel local adaptation to the environment.

Here, the authors study adaptation to altitude in 518 whole genomes from two species of tropical butterflies. They find repeated genetic differentiation within species, little molecular parallelism between these species, and introgression from closely related species, concluding that standing genetic variation promotes parallel local adaptation.

Genome–Environment Associations, an Innovative Tool for Studying Heritable Evolutionary Adaptation in Orphan Crops and Wild Relatives

Leveraging innovative tools to speed up prebreeding and discovery of genotypic sources of adaptation from landraces, crop wild relatives, and orphan crops is a key prerequisite to accelerate genetic gain of abiotic stress tolerance in annual crops such as legumes and cereals, many of which are still orphan species despite advances in major row crops. Here, we review a novel, interdisciplinary approach to combine ecological climate data with evolutionary genomics under the paradigm of a new field of study: genome–environment associations (GEAs). We first exemplify how GEA utilizes in situ georeferencing from genotypically characterized, gene bank accessions to pinpoint genomic signatures of natural selection. We later discuss the necessity to update the current GEA models to predict both regional- and local- or micro-habitat–based adaptation with mechanistic ecophysiological climate indices and cutting-edge GWAS-type genetic association models. Furthermore, to account for polygenic evolutionary adaptation, we encourage the community to start gathering genomic estimated adaptive values (GEAVs) for genomic prediction (GP) and multi-dimensional machine learning (ML) models. The latter two should ideally be weighted by de novo GWAS-based GEA estimates and optimized for a scalable marker subset. We end the review by envisioning avenues to make adaptation inferences more robust through the merging of high-resolution data sources, such as environmental remote sensing and summary statistics of the genomic site frequency spectrum, with the epigenetic molecular functionality responsible for plastic inheritance in the wild. Ultimately, we believe that coupling evolutionary adaptive predictions with innovations in ecological genomics such as GEA will help capture hidden genetic adaptations to abiotic stresses based on crop germplasm resources to assist responses to climate change.

“I shall endeavor to find out how nature’s forces act upon one another, and in what manner the geographic environment exerts its influence on animals and plants. In short, I must find out about the harmony in nature” Alexander von Humboldt—Letter to Karl Freiesleben, June 1799.

Linking ecological niche models and common garden experiments to predict phenotypic differentiation in stressful environments: Assessing the adaptive value of marginal populations in an alpine plant

Environmental variation within a species' range can create contrasting selective pressures, leading to divergent selection and novel adaptations. The conservation value of populations inhabiting environmentally marginal areas remains in debate and is closely related to the adaptive potential in changing environments. Strong selection caused by stressful conditions may generate novel adaptations, conferring these populations distinct evolutionary potential and high conservation value under climate change. On the other hand, environmentally marginal populations may be genetically depauperate, with little potential for new adaptations to emerge. Here, we explored the use of ecological niche models (ENMs) linked with common garden experiments to predict and test for genetically determined phenotypic differentiation related to contrasting environmental conditions. To do so, we built an ENM for the alpine plant Silene ciliata in central Spain and conducted common garden experiments, assessing flowering phenology changes and differences in leaf cell resistance to extreme temperatures. The suitability patterns and response curves of the ENM led to the predictions that: (1) the environmentally marginal populations experiencing less snowpack and higher minimum temperatures would have delayed flowering to avoid risks of late-spring frosts and (2) those with higher minimum temperatures and greater potential evapotranspiration would show enhanced cell resistance to high temperatures to deal with physiological stress related to desiccation and heat. The common garden experiments revealed the expected genetically based phenotypic differentiation in flowering phenology. In contrast, they did not show the expected differentiation for cell resistance, but these latter experiments had high variance and hence lower statistical power. The results highlight ENMs as useful tools to identify contrasting putative selective pressures across species ranges. Linking ENMs with common garden experiments provides a theoretically justified and practical way to study adaptive processes, including insights regarding the conservation value of populations inhabiting environmentally marginal areas under ongoing climate change.

Evolution in response to climate in the native and introduced ranges of a globally distributed plant

The extent to which species can adapt to spatiotemporal climatic variation in their native and introduced ranges remains unresolved. To address this, we examined how clines in cyanogenesis (hydrogen cyanide [HCN] production-an antiherbivore defense associated with decreased tolerance to freezing) have shifted in response to climatic variation in space and time over a 60-year period in both the native and introduced ranges of Trifolium repens. HCN production is a polymorphic trait controlled by variation at two Mendelian loci (Ac and Li). Using phenotypic assays, we estimated within-population frequencies of HCN production and dominant alleles at both loci (i.e., Ac and Li) from 10,575 plants sampled from 131 populations on five continents, and then compared these frequencies to those from historical data collected in the 1950s. There were no clear relationships between changes in the frequency of HCN production, Ac, or Li and changes in temperature between contemporary and historical samples. We did detect evidence of continued evolution to temperature gradients in the introduced range, whereby the slope of contemporary clines for HCN and Ac in relation to winter temperature became steeper than historical clines and more similar to native clines. These results suggest that cyanogenesis clines show no clear changes through time in response to global warming, but introduced populations continue to adapt to their contemporary environments.

Climate Warming Since the Holocene Accelerates West-East Communication for the Eurasian Temperate Water Strider Species Aquarius paludum

Holocene climate warming has dramatically altered biological diversity and distributions. Recent human-induced emissions of greenhouse gases will exacerbate global warming and thus induce threats to cold-adapted taxa. However, the impacts of this major climate change on transcontinental temperate species are still poorly understood. Here, we generated extensive genomic datasets for a water strider, Aquarius paludum, which was sampled across its entire distribution in Eurasia and used these datasets in combination with ecological niche modeling (ENM) to elucidate the influence of the Holocene and future climate warming on its population structure and demographic history. We found that A. paludum consisted of two phylogeographic lineages that diverged in the middle Pleistocene, which resulted in a “west–east component” genetic pattern that was probably triggered by Central Asia-Mongoxin aridification and Pleistocene glaciations. The diverged western and eastern lineages had a second contact in the Holocene, which shaped a temporary hybrid zone located at the boundary of the arid–semiarid regions of China. Future predictions detected a potentially novel northern corridor to connect the western and eastern populations, indicating west–east gene flow would possibly continue to intensify under future warming climate conditions. Further integrating phylogeographic and ENM analyses of multiple Eurasian temperate taxa based on published studies reinforced our findings on the “west–east component” genetic pattern and the predicted future northern corridor for A. paludum. Our study provided a detailed paradigm from a phylogeographic perspective of how transcontinental temperate species differ from cold-adapted taxa in their response to climate warming.

Genomic reaction norms inform predictions of plastic and adaptive responses to climate change

Genomic reaction norms represent the range of gene expression phenotypes (usually mRNA transcript levels) expressed by a genotype along an environmental gradient. Reaction norms derived from common‐garden experiments are powerful approaches for disentangling plastic and adaptive responses to environmental change in natural populations. By treating gene expression as a phenotype in itself, genomic reaction norms represent invaluable tools for exploring causal mechanisms underlying organismal responses to climate change across multiple levels of biodiversity.

Our goal is to provide the context, framework and motivation for applying genomic reaction norms to study the responses of natural populations to climate change.

Here, we describe the utility of integrating genomics with common‐garden‐gradient experiments under a reaction norm analytical framework to answer fundamental questions about phenotypic plasticity, local adaptation, their interaction (i.e. genetic variation in plasticity) and future adaptive potential.

An experimental and analytical framework for constructing and analysing genomic reaction norms is presented within the context of polygenic climate change responses of structured populations with gene flow. Intended for a broad eco‐evo readership, we first briefly review adaptation with gene flow and the importance of understanding the genomic basis and spatial scale of adaptation for conservation and management of structured populations under anthropogenic change. Then, within a high‐dimensional reaction norm framework, we illustrate how to distinguish plastic, differentially expressed (difference in reaction norm intercepts) and differentially plastic (difference in reaction norm slopes) genes, highlighting the areas of opportunity for applying these concepts.

We conclude by discussing how genomic reaction norms can be incorporated into a holistic framework to understand the eco‐evolutionary dynamics of climate change responses from molecules to ecosystems. We aim to inspire researchers to integrate gene expression measurements into common‐garden experimental designs to investigate the genomics of climate change responses as sequencing costs become increasingly accessible.