Pearl millet genomic vulnerability to climate change in West Africa highlights the need for regional collaboration

Local Selection Shaped the Diversity of European Maize Landraces

The introduction of populations to novel environments can lead to a loss of genetic diversity and the accumulation of deleterious mutations due to selection and demographic changes. We investigate how the recent introduction of maize to Europe shaped the genetic diversity and differentiation of European traditional maize populations and quantify the impact of its recent range expansion and consecutive breeding on the accumulation of genetic load. We use genome-wide genetic markers of almost 2000 individuals from 38 landraces, 155 elite breeding lines, and a large set of doubled haploid lines derived from two landraces to find extensive population structure within European maize, with landraces being highly differentiated even over short geographic distances. Yet, diversity change does not follow the continuous pattern of range expansions. Landraces maintain high genetic diversity that is distinct between populations and does not decrease along the possible expansion routes. Signals of positive selection in European landraces that overlap with selection in Asian maize suggest convergent selection during maize introductions. At the same time, environmental factors partially explain genetic differences across Europe. Consistent with the maintenance of high diversity, we find no evidence of genetic load accumulating along the maize introduction route in European maize. However, modern breeding likely purged highly deleterious alleles but accumulated genetic load in elite germplasm. Our results reconstruct the history of maize in Europe and show that landraces have maintained high genetic diversity that could reduce genetic load in the European maize breeding pools.

Historic rewiring of grass flowering time pathways and implications for crop improvement under climate change

Grasses are fundamental to human survival, providing a large percentage of our calories, fuel, and fodder for livestock, and an enormous global carbon sink. A particularly important part of the grass plant is the grain-producing inflorescence that develops in response to both internal and external signals that converge at the shoot tip to influence meristem behavior. Abiotic signals that trigger reproductive development vary across the grass family, mostly due to the unique ecological and phylogenetic histories of each clade. The time it takes a grass to flower has implications for its ability to escape harsh environments, while also indirectly affecting abiotic stress tolerance, inflorescence architecture, and grain yield. Here, we synthesize recent insights into the evolution of grass flowering time in response to past climate change, particularly focusing on genetic convergence in underlying traits. We then discuss how and why the rewiring of a shared ancestral flowering pathway affects grass yields, and outline ways in which researchers are using this and other information to breed higher yielding, climate-proof cereal crops.

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.

Museum genomics reveals temporal genetic stasis and global genetic diversity in Arabidopsis thaliana

Global patterns of population genetic variation through time offer a window into evolutionary processes that maintain diversity. Over time, lineages may expand or contract their distribution, causing turnover in population genetic composition. At individual loci, migration, drift, environmental change (among other processes) may affect allele frequencies. Museum specimens of widely distributed species offer a unique window into the genetics of understudied populations and changes over time. Here, we sequenced genomes of 130 herbarium specimens and 91 new field collections of Arabidopsis thaliana and combined these with published genomes. We sought a broader view of genomic diversity across the species, and to test if population genomic composition is changing through time. We documented extensive and previously uncharacterized diversity in a range of populations in Africa, populations that are under threat from anthropogenic climate change. Through time, we did not find dramatic changes in genomic composition of populations. Instead, we found a pattern of genetic change every 100 years of the same magnitude seen when comparing Eurasian populations that are 185 km apart, potentially due to a combination of drift and changing selection. We found only mixed signals of polygenic adaptation at phenology and physiology QTL. We did find that genes conserved across eudicots show altered levels of directional allele frequency change, potentially due to variable purifying and background selection. Our study highlights how museum specimens can reveal new dimensions of population diversity and show how wild populations are evolving in recent history.

Effect of vapor pressure deficit on growth and yield of pearl millet germplasm originating from semi-arid, semi-humid and humid regions

Introduction

The increase in vapor pressure deficit (VPD) is among the expected change in futur climate, and understanding its effect on crop growth is of much significance for breeeding programs. Three groups (G1,G2 and G3) of pearl millet germplasm, originating from regions with different rainfall intensities, were grown in the field during period of high and low VPDs. The groups G1,G2 and G3 were respectively from Guinean (rainfall above 1000 mm), Soudanian (rainfall between 600 mm and 900 mm), and Sahelian zones (rainfall between 600 and 300 mm) of Africa. The objective was to assess if there was any growth response difference among the germplasm groups.

Method

Four trials were conducted, two in the dry season of 2019 (Ds19) and 2020 (Ds20) with avarage VPDs of 3.62 kPa and 2.92 kPa, respectively, and two in the rainy season of 2019 (Rs19) and 2020 (Rs20) with avaerage VPDs of 1.14 kPa and 0.61 kPa, respectively.

Results

In order to avoid possible confounding effects of radiation on millet growth and yield, data were normalized by the quantity of light received during each season. After this normalization, leaf area and grain yield decreased in the highest-VPD seasons whereas tiller number decreased only in Ds19 (one high VPD season). The comparison of the three germplasm groups indicates that G3 the germplasm group from Sahelian regions showed greater tolerance to high VPD than G1 and G2.

Discussion

Germplasm from the G3 group could be a good material for developing tolerant germplasm to future climate that is bound to have high VPD.

Underutilized crops for diverse, resilient and healthy agri-food systems: a systematic review of sub-Saharan Africa

Sub-Saharan Africa (SSA) faces increasing water scarcity, food and nutrition insecurity, poverty and inequality under climate change. Under these circumstances, promoting locally adapted and nutrient-dense crops is touted as a plausible climate adaptation strategy. We reviewed the utility of neglected and underutilized crop species (NUS) as a climate change adaptation strategy to diversify local food systems and diets and improve nutritional health and environmental outcomes in SSA. We conducted a systematic literature review using Web of Science and Scopus research databases. Of the 1,545 studies retrieved, 75 were included following a multi-level screening process on Covidence guided by the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. The review consolidates fragmented knowledge on the application of NUS in different contexts. Despite growing interest, NUS remain gendered and stigmatized crops, marginalized and fragmented in research, development, and marketing efforts and lack explicit support from policy and decision-makers. Despite rhetoric purporting to support them, there is a worrying rise in policies and regulations that inadvertently hinder the development of these crops and reinforce dependence on a narrow basket of crops for food and nutrition security, undermining food sovereignty. Some NUS have received increasing recognition for their potential in the past decade. However, this is neither universal nor systematic, which makes scaling up necessary but challenging. Consequently, progress in mainstreaming NUS in local food systems continues to lag. Despite these challenges, NUS remain sub-Saharan Africa's better-bet option for diversifying food systems and transitioning them to be equitable, inclusive, resilient and healthy; hence, NUS provide positive outcomes for people and the planet under climate change.

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.

Milletomics: a metabolomics centered integrated omics approach toward genetic progression

Producing alternative staple foods like millet will be essential to feeding ten billion people by 2050. The increased demand for millet is driving researchers to improve its genetic variation. Millets include protein, dietary fiber, phenolic substances, and flavonoid components. Its climate resilience makes millet an appealing crop for agronomic sustainability. Integrative omics technologies could potentially identify and develop millets with desirable phenotypes that may have high agronomic value. Millets' salinity and drought tolerance have been enhanced using transcriptomics. In foxtail, finger, and pearl millet, proteomics has discovered salt-tolerant protein, phytohormone-focused protein, and drought tolerance. Metabolomics studies have revealed that certain metabolic pathways including those involving lignin, flavonoids, phenylpropanoid, and lysophospholipids are critical for many processes, including seed germination, photosynthesis, energy metabolism, and the synthesis of bioactive chemicals necessary for drought tolerance. Metabolomics integration with other omics revealed metabolome engineering and trait-specific metabolite creation. Integrated metabolomics and ionomics are still in the development stage, but they could potentially assist in comprehending the pathway of ionomers to control nutrient levels and biofortify millet. Epigenomic analysis has shown alterations in DNA methylation patterns and chromatin structure in foxtail and pearl millets in response to abiotic stress. Whole-genome sequencing utilizing next-generation sequencing is the most proficient method for finding stress-induced phytoconstituent genes. New genome sequencing enables novel biotechnological interventions including genome-wide association, mutation-based research, and other omics approaches. Millets can breed more effectively by employing next-generation sequencing and genotyping by sequencing, which may mitigate climate change. Millet marker-assisted breeding has advanced with high-throughput markers and combined genotyping technologies.

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.

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.

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.

The chromosome-level genome assembly of an endangered herb Bergenia scopulosa provides insights into local adaptation and genomic vulnerability under climate change

BACKGROUND

Global climate change poses severe threats to biodiversity and ecosystem stability. Rapid climate oscillations potentially lead to species geographic range shifts, population declines, and even extinctions. The rare and endangered species, being critical components of regional biodiversity, hold the key to understanding local adaptation and evolutionary processes shaping species distributions. Therefore, assessing the evolutionary mechanisms of local adaptation and population vulnerability under climate change is crucial for developing conservation strategies of endangered species.

RESULTS

In this study, we assembled a high-quality, chromosome-level genome of the rare and endangered herb Bergenia scopulosa in the Qinling Mountains in East Asia and resequenced 37 individual genomes spanning its entire geographic distributional ranges. By integrating population genetics, landscape genomics, and climate datasets, a substantial number of adaptive single-nucleotide polymorphism loci associated with climate variables were identified. The genotype-environment association analysis showed that some cold-tolerant genes have played pivotal roles in cold environmental adaptation of B. scopulosa. These findings are further corroborated through evolutionary analysis of gene family and quantitative PCR validation. Population genomic analysis revealed 2 distinct genetic lineages in B. scopulosa. The western lineage showed higher genomic vulnerability and more rare cold-tolerance alleles, suggesting its heightened sensitivity to impending climate shifts, and should be given priority conservation in the management practices.

CONCLUSIONS

These findings provide novel insights into local adaptation and genomic vulnerability of B. scopulosa under climate change in the Qinling Mountains in East Asia. Additionally, the study also offers valuable guidance for formulating conservation strategies for the rare and endangered plants.

Co-conserving Indigenous and local knowledge systems with seeds

Indigenous and local knowledge (ILK) holders have deep ecological, horticultural, and practical knowledge of plants, but this knowledge is not routinely considered and supported along with seed collections conserved ex situ. In this opinion, conceived collaboratively by a team of botanists, ecologists, ethnobiologists, and practitioners in biodiversity and ILK systems conservation, we propose seven actions towards the co-conservation of seeds and associated knowledge to overcome obstacles and encourage ex situ conservation institutions to support knowledge holders in multiple ways. Success depends on simultaneous changes in conservation practices, new collaborative relationships, and shifts in policy to share and conserve biocultural diversity. Failure to act will witness the continued erosion of ILK and depreciation of ex situ plant collections.

Landscape genomics reveals adaptive genetic differentiation driven by multiple environmental variables in naked barley on the Qinghai-Tibetan Plateau

Understanding the local adaptation of crops has long been a concern of evolutionary biologists and molecular ecologists. Identifying the adaptive genetic variability in the genome is crucial not only to provide insights into the genetic mechanism of local adaptation but also to explore the adaptation potential of crops. This study aimed to identify the climatic drivers of naked barley landraces and putative adaptive loci driving local adaptation on the Qinghai-Tibetan Plateau (QTP). To this end, a total of 157 diverse naked barley accessions were genotyped using the genotyping-by-sequencing approach, which yielded 3123 high-quality SNPs for population structure analysis and partial redundancy analysis, and 37,636 SNPs for outlier analysis. The population structure analysis indicated that naked barley landraces could be divided into four groups. We found that the genomic diversity of naked barley landraces could be partly traced back to the geographical and environmental diversity of the landscape. In total, 136 signatures associated with temperature, precipitation, and ultraviolet radiation were identified, of which 13 had pleiotropic effects. We mapped 447 genes, including a known gene HvSs1. Some genes involved in cold stress and regulation of flowering time were detected near eight signatures. Taken together, these results highlight the existence of putative adaptive loci in naked barley on QTP and thus improve our current understanding of the genetic basis of local adaptation.

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.

The potentialities of omics resources for millet improvement

Millets are nutrient-rich (nutri-rich) cereals with climate resilience attributes. However, its full productive potential is not realized due to the lack of a focused yield improvement approach, as evidenced by the available literature. Also, the lack of well-characterized genomic resources significantly limits millet improvement. But the recent availability of genomic data and advancement in omics tools has shown its enormous potential to enhance the efficiency and precision faced by conventional breeding in millet improvement. The development of high throughput genotyping platforms based on next-generation sequencing (NGS) has provided a low-cost method for genomic information, specifically for neglected nutri-rich cereals with the availability of a limited number of reference genome sequences. NGS has created new avenues for millet biotechnological interventions such as mutation-based study, GWAS, GS, and other omics technologies. The simultaneous discovery of high-throughput markers and multiplexed genotyping platform has aggressively aided marker-assisted breeding for millet improvement. Therefore, omics technology offers excellent opportunities to explore and combine useful variations for targeted traits that could impart high nutritional value to high-yielding cultivars under changing climatic conditions. In millet improvement, an in-depth account of NGS, integrating genomics data with different biotechnology tools, is reviewed in this context.

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.

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.

Niche divergence at the intraspecific level in an endemic rare peony (Paeonia rockii): A phylogenetic, climatic and environmental survey

Ecological factors have received increasing attention as drivers of speciation but also in the maintenance of postspeciation divergence. However, the relative significance of the responses of species to climate oscillations for driving niche divergence or conservatism in the evolution of many species that pass through diverse environments and limited geographical boundaries remains poorly understood. Paeonia rockii (one of the ancient species of Paeonia) comprising two subspecies called Paeonia rockii subsp. rockii and Paeonia rockii subsp. taibaishanica is an endemic, rare, and endangered medicinal plant in China. In this study, we integrated whole chloroplast genomes, and ecological factors to obtain insights into ecological speciation and species divergence in this endemic rare peony. RAxML analysis indicated that the topological trees recovered from three different data sets were identical, where P. rockii subsp. rockii and P. rockii subsp. taibaishanica clustered together, and molecular dating analyses suggested that the two subspecies diverged 0.83 million years ago. In addition, ecological niche modeling showed that the predicted suitable distribution areas for P. rockii subsp. rockii and P. rockii subsp. taibaishanica differed considerably, although the predicted core distribution areas were similar, where the population contracted in the last interglacial and expanded in the last glacial maximum. Under the emissions scenarios for the 2050s and 2070s, the suitable distribution areas were predicted to contract significantly, where the migration routes of the two subspecies tended to migrate toward high latitudes and elevations, thereby suggesting strong responses of the distributions of the two subspecies to climate change. These findings combined with the phylogeographic relationships provide comprehensive insights into niche variation and differentiation in this endemic rare peony, and they highlight the importance of geological and climatic changes for species divergence and changes in the population geographic patterns of rare and endangered medicinal plants in East Asia.

Genomic footprints of selection in early-and late-flowering pearl millet landraces

Pearl millet is among the top three-cereal production in one of the most climate vulnerable regions, sub-Saharan Africa. Its Sahelian origin makes it adapted to grow in poor sandy soils under low soil water regimes. Pearl millet is thus considered today as one of the most interesting crops to face the global warming. Flowering time, a trait highly correlated with latitude, is one of the key traits that could be modulated to face future global changes. West African pearl millet landraces, can be grouped into early- (EF) and late-flowering (LF) varieties, each flowering group playing a specific role in the functioning and resilience of Sahelian smallholders. The aim of this study was thus to detect genes linked to flowering but also linked to relevant traits within each flowering group. We thus investigated genomic and phenotypic diversity in 109 pearl millet landrace accessions, i.e., 66 early-flowering and 43 late-flowering, grown in the groundnut basin, the first area of rainfed agriculture in Senegal dominated by dry cereals (millet, maize, and sorghum) and legumes (groundnuts, cowpeas). We were able to confirm the role of PhyC gene in pearl millet flowering and identify several other genes that appear to be as much as important, such as FSR12 and HAC1. HAC1 and two other genes appear to be part of QTLs previously identified and deserve further investigation. At the same time, we were able to highlight a several genes and variants that could contribute to the improvement of pearl millet yield, especially since their impact was demonstrated across flowering cycles.

Data-driven, participatory characterization of farmer varieties discloses teff breeding potential under current and future climates

In smallholder farming systems, traditional farmer varieties of neglected and underutilized species (NUS) support the livelihoods of millions of growers and consumers. NUS combine cultural and agronomic value with local adaptation, and transdisciplinary methods are needed to fully evaluate their breeding potential. Here, we assembled and characterized the genetic diversity of a representative collection of 366 Ethiopian teff (Eragrostis tef) farmer varieties and breeding materials, describing their phylogenetic relations and local adaptation on the Ethiopian landscape. We phenotyped the collection for its agronomic performance, involving local teff farmers in a participatory variety evaluation. Our analyses revealed environmental patterns of teff genetic diversity and allowed us to identify 10 genetic clusters associated with climate variation and with uneven spatial distribution. A genome-wide association study was used to identify loci and candidate genes related to phenology, yield, local adaptation, and farmers’ appreciation. The estimated teff genomic offset under climate change scenarios highlighted an area around lake Tana where teff cropping may be most vulnerable to climate change. Our results show that transdisciplinary approaches may efficiently propel untapped NUS farmer varieties into modern breeding to foster more resilient and sustainable cropping systems.

Small farms support the livelihoods of about two billion people worldwide. Smallholder farmers often rely on local varieties of crops and use less irrigation and fertilizer than large producers. But smallholdings can be vulnerable to weather events and climate change. Data-driven research approaches may help to identify the needs of farmers, taking into account traditional knowledge and cultural practices to enhance the sustainability of certain crops.

Teff is a cereal crop that plays a critical role in the culture and diets of Ethiopian communities. It is also a super food appreciated on international markets for its nutritional value. Rural smallholder farmers in Ethiopia rely on the crop for subsistence and income and make up the bulk of the country’s agricultural system. Many grow local varieties with tremendous genetic diversity. Scientists, in collaboration with farmers, could tap that diversity to produce more productive or climate-resilient types of teff, both for national and international markets.

Woldeyohannes, Iohannes et al. produced the first large-scale genetic, agronomic and climatic study of traditional teff varieties. In the experiments, Woldeyohannes and Iohannes et al. sequenced the genomes of 366 Ethiopian teff varieties and evaluated their agronomic value in common gardens. The team collaborated with 35 local farmers to understand their preference of varieties and traits. They then conducted a genome-wide association study to assess the crops’ productivity and their adaptations to local growing conditions and farmer preferences. Genetic changes that speed up teff maturation and flowering time could meet small farmers’ needs to secure teff harvest. Woldeyohannes, Iohannes et al. also identified a region in Ethiopia, where local teff varieties may struggle to adapt to climate change. Genetic modifications may help the crop to adapt to frequent droughts that may be a common characteristic of future climates.

The experiments reveal the importance of incorporating traditional knowledge from smallholder farmers into data-driven crop improvement efforts considering genetics and climate science. This multidisciplinary approach may help to improve food security and protect local genetic diversity on small farms. It may also help to ensure that agricultural advances fairly and equitably benefit small farmers.

The combination of genomic offset and niche modelling provides insights into climate change-driven vulnerability

Global warming is increasingly exacerbating biodiversity loss. Populations locally adapted to spatially heterogeneous environments may respond differentially to climate change, but this intraspecific variation has only recently been considered when modelling vulnerability under climate change. Here, we incorporate intraspecific variation in genomic offset and ecological niche modelling to estimate climate change-driven vulnerability in two bird species in the Sino-Himalayan Mountains. We found that the cold-tolerant populations show higher genomic offset but risk less challenge for niche suitability decline under future climate than the warm-tolerant populations. Based on a genome-niche index estimated by combining genomic offset and niche suitability change, we identified the populations with the least genome-niche interruption as potential donors for evolutionary rescue, i.e., the populations tolerant to climate change. We evaluated potential rescue routes via a landscape genetic analysis. Overall, we demonstrate that the integration of genomic offset, niche suitability modelling, and landscape connectivity can improve climate change-driven vulnerability assessments and facilitate effective conservation management.

Transcriptomic analysis of methyl jasmonate treatment reveals gene networks involved in drought tolerance in pearl millet

Water deficit stress at the early stage of development is one of the main factors limiting pearl millet production. One practice to counteract this limitation would be to resort to the application of hormones to stimulate plant growth and development at critical stages. Exogenous methyl jasmonate (MeJA) can improve drought tolerance by modulating signaling, metabolism, and photosynthesis pathways, therefore, we assumed that can occur in pearl millet during the early stage of development. To decipher the molecular mechanisms controlling these pathways, RNAseq was conducted in two pearl millet genotypes, drought-sensitive SosatC88 and drought-tolerant Souna3, in response to 200 μM of MeJA. Pairwise comparison between the MeJA-treated and non-treated plants revealed 3270 differentially expressed genes (DEGs) among 20,783 transcripts in SosatC88 and 127 DEGs out of 20,496 transcripts in Souna3. Gene ontology (GO) classification assigned most regulated DEGs in SosatC88 to heme binding, oxidation-reduction process, response to oxidative stress and membrane, and in Souna3 to terpene synthase activity, lyase activity, magnesium ion binding, and thylakoid. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis reveals that DEGs in SosatC88 are related to the oxidation-reduction process, the biosynthesis of other secondary metabolites, the signal transduction, and the metabolism of terpenoids, while in Souna3, DEGs are related to the metabolism of terpenoids and the energy metabolism. Two genes encoding a diterpenoid biosynthesis-related (Pgl_GLEAN_10009413) and a Glutathione S transferase T3 (Pgl_GLEAN_10034098) were contra-regulated between SosatC88 and Souna3. Additionally, five random genes differentially expressed by RNAseq were validated using qPCR, therefore, they are potential targets for the development of novel strategies breeding schemes for plant growth under water deficit stress. These insights into the molecular mechanisms of pearl millet genotype tolerance at the early stage of development contribute to the understanding of the role of hormones in adaptation to drought-prone environments.

Evolutionary and Ecological Drivers of Local Adaptation and Speciation in a North American Avian Species Complex

Throughout the speciation process, genomic divergence can be differentially impacted by selective pressures, as well as gene flow and genetic drift. Disentangling the effects of these evolutionary mechanisms remains challenging, especially for non-model organisms. Accounting for complex evolutionary histories and contemporary population structure often requires sufficient sample sizes, for which the expense of full genomes remains prohibitive. Here, we demonstrate the utility of partial-genome sequence data for range-wide samples to shed light into the divergence process of two closely related ducks, the Mexican duck (Anas diazi) and mallard (A. platyrhynchos). We determine the role of selective and neutral processes during speciation of Mexican ducks by integrating evolutionary and demographic modelling with genotype-environment and genotype-phenotype association testing. First, evolutionary models and demographic analyses support the hypothesis that Mexican ducks originally diverged ~300,000 years ago in a climate refugia arising during a glacial period in in a southwestern North America, and that subsequent environmental selective pressures played a key role in divergence. Mexican ducks then showed cyclical demographic patterns that likely reflected repeated range expansions and contractions, along with bouts of gene flow with mallards during glacial cycles. Finally, we provide evidence that sexual selection acted on several phenotypic traits as a co-evolutionary process, facilitating the development of reproductive barriers that initially arose due to strong ecological selection. More broadly, this work reveals that the genomic and phenotypic patterns observed across species complexes are the result of myriad factors that contribute in dynamic ways to the evolutionary trajectories of a lineage.

Entailing the Next-Generation Sequencing and Metabolome for Sustainable Agriculture by Improving Plant Tolerance

Crop production is a serious challenge to provide food for the 10 billion individuals forecasted to live across the globe in 2050. The scientists' emphasize establishing an equilibrium among diversity and quality of crops by enhancing yield to fulfill the increasing demand for food supply sustainably. The exploitation of genetic resources using genomics and metabolomics strategies can help generate resilient plants against stressors in the future. The innovation of the next-generation sequencing (NGS) strategies laid the foundation to unveil various plants' genetic potential and help us to understand the domestication process to unmask the genetic potential among wild-type plants to utilize for crop improvement. Nowadays, NGS is generating massive genomic resources using wild-type and domesticated plants grown under normal and harsh environments to explore the stress regulatory factors and determine the key metabolites. Improved food nutritional value is also the key to eradicating malnutrition problems around the globe, which could be attained by employing the knowledge gained through NGS and metabolomics to achieve suitability in crop yield. Advanced technologies can further enhance our understanding in defining the strategy to obtain a specific phenotype of a crop. Integration among bioinformatic tools and molecular techniques, such as marker-assisted, QTLs mapping, creation of reference genome, de novo genome assembly, pan- and/or super-pan-genomes, etc., will boost breeding programs. The current article provides sequential progress in NGS technologies, a broad application of NGS, enhancement of genetic manipulation resources, and understanding the crop response to stress by producing plant metabolites. The NGS and metabolomics utilization in generating stress-tolerant plants/crops without deteriorating a natural ecosystem is considered a sustainable way to improve agriculture production. This highlighted knowledge also provides useful research that explores the suitable resources for agriculture sustainability.

Crop genetic erosion: understanding and responding to loss of crop diversity

Crop diversity underpins the productivity, resilience and adaptive capacity of agriculture. Loss of this diversity, termed crop genetic erosion, is therefore concerning. While alarms regarding evident declines in crop diversity have been raised for over a century, the magnitude, trajectory, drivers and significance of these losses remain insufficiently understood. We outline the various definitions, measurements, scales and sources of information on crop genetic erosion. We then provide a synthesis of evidence regarding changes in the diversity of traditional crop landraces on farms, modern crop cultivars in agriculture, crop wild relatives in their natural habitats and crop genetic resources held in conservation repositories. This evidence indicates that marked losses, but also maintenance and increases in diversity, have occurred in all these contexts, the extent depending on species, taxonomic and geographic scale, and region, as well as analytical approach. We discuss steps needed to further advance knowledge around the agricultural and societal significance, as well as conservation implications, of crop genetic erosion. Finally, we propose actions to mitigate, stem and reverse further losses of crop diversity.

The evolutionary genomics of species' responses to climate change

Climate change is a threat to biodiversity. One way that this threat manifests is through pronounced shifts in the geographical range of species over time. To predict these shifts, researchers have primarily used species distribution models. However, these models are based on assumptions of niche conservatism and do not consider evolutionary processes, potentially limiting their accuracy and value. To incorporate evolution into the prediction of species' responses to climate change, researchers have turned to landscape genomic data and examined information about local genetic adaptation using climate models. Although this is an important advancement, this approach currently does not include other evolutionary processes-such as gene flow, population dispersal and genomic load-that are critical for predicting the fate of species across the landscape. Here, we briefly review the current practices for the use of species distribution models and for incorporating local adaptation. We next discuss the rationale and theory for considering additional processes, reviewing how they can be incorporated into studies of species' responses to climate change. We summarize with a conceptual framework of how manifold layers of information can be combined to predict the potential response of specific populations to climate change. We illustrate all of the topics using an exemplar dataset and provide the source code as potential tutorials. This Perspective is intended to be a step towards a more comprehensive integration of population genomics with climate change science.

Introgression among North American wild grapes (Vitis) fuels biotic and abiotic adaptation

BACKGROUND

Introgressive hybridization can reassort genetic variants into beneficial combinations, permitting adaptation to new ecological niches. To evaluate evolutionary patterns and dynamics that contribute to introgression, we investigate six wild Vitis species that are native to the Southwestern United States and useful for breeding grapevine (V. vinifera) rootstocks.

RESULTS

By creating a reference genome assembly from one wild species, V. arizonica, and by resequencing 130 accessions, we focus on identifying putatively introgressed regions (pIRs) between species. We find six species pairs with signals of introgression between them, comprising up to ~ 8% of the extant genome for some pairs. The pIRs tend to be gene poor, located in regions of high recombination and enriched for genes implicated in disease resistance functions. To assess potential pIR function, we explore SNP associations to bioclimatic variables and to bacterial levels after infection with the causative agent of Pierce's disease (Xylella fastidiosa). pIRs are enriched for SNPs associated with both climate and bacterial levels, suggesting that introgression is driven by adaptation to biotic and abiotic stressors.

CONCLUSIONS

Altogether, this study yields insights into the genomic extent of introgression, potential pressures that shape adaptive introgression, and the evolutionary history of economically important wild relatives of a critical crop.

Advancing crop genomics from lab to field

Crop genomics remains a key element in ensuring scientific progress to secure global food security. It has been two decades since the sequence of the first plant genome, that of Arabidopsis thaliana, was released, and soon after that the draft sequencing of the rice genome was completed. Since then, the genomes of more than 100 crops have been sequenced, plant genome research has expanded across multiple fronts and the next few years promise to bring further advances spurred by the advent of new technologies and approaches. We are likely to see continued innovations in crop genome sequencing, genetic mapping and the acquisition of multiple levels of biological data. There will be exciting opportunities to integrate genome-scale information across multiple scales of biological organization, leading to advances in our mechanistic understanding of crop biological processes, which will, in turn, provide greater impetus for translation of laboratory results to the field.

A high-resolution downscaled CMIP5 projections dataset of essential surface climate variables over the globe coherent with the ERA5 reanalysis for climate change impact assessments

A high-resolution climate projections dataset is obtained by statistically downscaling climate projections from the CMIP5 experiment using the ERA5 reanalysis from the Copernicus Climate Change Service. This global dataset has a spatial resolution of 0.25°x 0.25°, comprises 21 climate models and includes 5 surface daily variables at monthly resolution: air temperature (mean, minimum, and maximum), precipitation, and mean near-surface wind speed. Two greenhouse gas emissions scenarios are available: one with mitigation policy (RCP4.5) and one without mitigation (RCP8.5). The downscaling method is a Quantile Mapping method (QM) called the Cumulative Distribution Function transform (CDF-t) method that was first used for wind values and is now referenced in dozens of peer-reviewed publications. The data processing includes quality control of metadata according to the climate modeling community standards and value checking for outlier detection.

Experimental support for genomic prediction of climate maladaptation using the machine learning approach Gradient Forests

Gradient Forests (GF) is a machine learning algorithm that is gaining in popularity for studying the environmental drivers of genomic variation and for incorporating genomic information into climate change impact assessments. Here we (i) provide the first experimental evaluation of the ability of "genomic offsets" - a metric of climate maladaptation derived from Gradient Forests - to predict organismal responses to environmental change, and (ii) explore the use of GF for identifying candidate SNPs. We used high-throughput sequencing, genome scans, and several methods, including GF, to identify candidate loci associated with climate adaptation in balsam poplar (Populus balsamifera L.). Individuals collected throughout balsam poplar's range also were planted in two common garden experiments. We used GF to relate candidate loci to environmental gradients and predict the expected magnitude of the response (i.e., the genetic offset metric of maladaptation) of populations when transplanted from their "home" environment to the common gardens. We then compared the predicted genetic offsets from different sets of candidate and randomly selected SNPs to measurements of population performance in the common gardens. We found the expected inverse relationship between genetic offset and performance: populations with larger predicted genetic offsets performed worse in the common gardens than populations with smaller offsets. Also, genetic offset better predicted performance than did "naive" climate transfer distances. However, sets of randomly selected SNPs predicted performance slightly better than did candidate SNPs. Our study provides evidence that genetic offsets represent a first order estimate of the degree of expected maladaptation of populations exposed to rapid environmental change and suggests GF may have some promise as a method for identifying candidate SNPs.

What processes must we understand to forecast regional-scale population dynamics?

An urgent challenge facing biologists is predicting the regional-scale population dynamics of species facing environmental change. Biologists suggest that we must move beyond predictions based on phenomenological models and instead base predictions on underlying processes. For example, population biologists, evolutionary biologists, community ecologists and ecophysiologists all argue that the respective processes they study are essential. Must our models include processes from all of these fields? We argue that answering this critical question is ultimately an empirical exercise requiring a substantial amount of data that have not been integrated for any system to date. To motivate and facilitate the necessary data collection and integration, we first review the potential importance of each mechanism for skilful prediction. We then develop a conceptual framework based on reaction norms, and propose a hierarchical Bayesian statistical framework to integrate processes affecting reaction norms at different scales. The ambitious research programme we advocate is rapidly becoming feasible due to novel collaborations, datasets and analytical tools.