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PubMed2026

Climate-Driven Niche Tracking and Genomic Resilience Shape Future Distribution of a Widespread Agricultural Weed.

Understanding how agriculturally important species respond to environmental change is critical for maintaining productivity, mitigating agroecosystem threats and sustaining resilience. While crops have traditionally been the focus in agroecosystems, agricultural weeds are integral components that often face even stronger selective pressures, making them powerful models for investigating ecological and evolutionary responses to climatic and human-mediated challenges. Insights from how weeds adapt rapidly under these pressures can inform strategies to improve agricultural outcomes, since both pests and crops evolve under the same multivariate selective pressures. Here, we integrate two centuries of distribution records with whole-genome sequencing from natural populations of the most damaging weed in Europe-Alopecurus myosuroides (blackgrass) - to examine its ecological and evolutionary responses in agroecosystems. Blackgrass largely maintained its historical climatic niche, expanding its range primarily by tracking environments analogous to those it historically occupied. Genome-wide analyses revealed a polygenic basis of environmental responses, with most loci linked to single environmental variables and a subset showing limited environmental pleiotropy, indicating modular adaptation to the complex selective pressures of managed agricultural landscapes. Coupling these genomic-environment relationships with projected climate change and genomic offset analyses indicated that most blackgrass populations will remain well aligned with future conditions. Our findings show that ecological niche tracking and polygenic adaptation allow agricultural weeds like blackgrass to persist under rapid environmental change, offering insights relevant not only for weed management but also for designing resilient cropping systems under future climates.

Plant Weeds↗

PubMed2026

Multimodal Deep Learning and Foundation Models for Early Detection and Forecasting of Plant Diseases.

Plant diseases destroy 20-40% of global food production annually, posing a critical threat to food security for a projected population of 9.7 billion by 2050. Conventional diagnostic approaches relying on expert visual assessment are slow, costly, and unsuitable for modern agricultural scales. While deep convolutional neural networks demonstrated early promise, single-modality, image-centric systems consistently fail under real-world field conditions characterized by variable lighting, co-occurring infections, and cultivar diversity. This review synthesizes a decade of progress across four interconnected frontiers: the evolution of deep learning architectures for plant disease detection; the adaptation of foundation models including CLIP, SAM, and DINOv2 to agricultural contexts; the development of multimodal fusion frameworks integrating imagery, environmental, genomic, and hyperspectral data; and the transition from static disease diagnosis to descriptive comparison of reported metrics, which suggested that multimodal approaches frequently reported improved diagnostic performance relative to corresponding single-modality baselines, although direct cross-study comparison was limited by methodological heterogeneity. A systematic review following PRISMA guidelines identifies eligible comparative studies. Descriptive comparison of reported performance metrics across these studies indicated that multimodal approaches generally achieved higher accuracy and sensitivity than single-modality models, particularly for pre-symptomatic disease detection. Eight critical research gaps are identified, including the absence of a unified agricultural foundation model and limited climate-aware forecasting under non-stationary climate projections. A structured research agenda is proposed to accelerate translation from laboratory performance to globally equitable, field-deployable crop protection systems.

convolutional neural networks↗

PubMed2026

Haplotype Blocks Are Associated With Rapid Local Adaptation to Environmental Shifts in Wild Barley.

Genomic mechanisms of local adaptation must be highly responsive in geographic regions where climate is changing rapidly. The Levant region is a critical biodiversity hotspot and the distribution edge for many species, including the wild ancestor of domesticated barley. This region is under an accelerated desertification process, thus enforcing a rapid genomic response to the projected environmental changes. To elucidate the genomic basis of rapid local adaptation, we studied wild barley populations using an ecological-genetic sampling design that decouples environmental variation from demographic background. We collected and sequenced 300 wild barley individuals and evaluated the phenotypes of 3600 progeny plants over 3 years. Our genomic analyses revealed that local adaptation is associated with clusters of candidate genes forming haplotype blocks. These clusters are enriched with environment and stress responsive genes, including flowering time regulators, drought and heat responsive genes. We identified six candidate adaptive haplotype blocks which span 1-8 Mbp and are distributed across chromosomes 1H, 2H, 4H and 5H, each segregating as two major haplotypes. Additionally, we integrated over 2600 occurrence records into ecological and evolutionary modelling to assess the genomic vulnerability of populations to projected future climates. Our study identifies candidate genomic regions and environmental drivers of local adaptation in wild barley and highlights the advantage of haplotype blocks architecture in orchestrating an efficient response to rapid environmental change. We highlight the ecological factors most strongly associated with the observed evolutionary responses and provide insights and guidelines for biodiversity conservation and implementation of crop wild relatives in breeding.

Hordeum↗

PubMed2026

Modelling peak microbial pollution events caused by combined sewer overflows in a source-to-sea system.

Predicting peak microbial pollution events in downstream coastal bathing waters caused by combined sewer overflows (CSOs) is essential for protecting public health. In urban areas, wastewater effluents, CSOs, and surface runoff can contribute to elevated microorganism loads to downstream waters. These pressures are likely to be intensified by growing population density and more frequent heavy rainfalls due to climate change. This study developed a process-based model to simulate Escherichia coli (E. coli) emissions, transport, and fate from the initial sources to coastal beaches. A three-year retrospective simulation (2017-2019) shows that E. coli concentrations in CSO discharges varied widely across the catchment (4.6 - 7.3 (log10 CFU 100 ml-1)). 99th percentile E. coli concentrations (4.0 (log10 CFU 100 ml-1)) at the inland water outlet were dominated by local CSO emissions, whereas 90th percentile E. coli concentrations (3.6 (log10 CFU 100 ml-1)) reflected cumulative upstream contributions from both CSO and effluent emissions. With the simulation accuracy of 89%, the model reliably reproduced the E. coli dynamics on the downstream beach and showed strong performance in representing peak concentrations based on Complementary Cumulative Distribution Function (CCDF) analysis. The process-based model enables quantitative tracking of source contributions and identification of pollution hotspots, providing support for mitigation measures. The study lays down a source-to-sea modelling framework for representing pollution transport across the aquatic continuum and provides a transferable tool for microbial pollution forecasting and climate adaptation planning.

Climate projection↗

PubMed2025

Comparative genomic analysis of Acer tsinglingense and A. davidii provides insights into nervonic acid biosynthesis, population evolution and genome vulnerability of endangered A. tsinglingense.

Global biodiversity is facing threats from climate change, habitat fragmentation, and anthropogenic activities-pressures that particularly endanger endemic and narrowly distributed species. In this study, the high-quality chromosome-level genomes of two ecologically divergent maples were assembled: the endangered and range-restricted Acer tsinglingense (791.40 Mb) and its widespread congener Acer davidii (1291.99 Mb). Phylogenomic analysis indicates that the two species diverged ~16.3 million years ago, with A. tsinglingense showing notable gene family expansions in secondary metabolite pathways. Notably, the 3-ketoacyl-CoA synthase gene family, which is involved in nervonic acid biosynthesis, underwent significant expansion and tandem duplication in A. tsinglingense, exhibiting high expression in buds. Population genomic analysis revealed that, compared with the widely distributed A. davidii, A. tsinglingense possesses lower genetic diversity, higher harmful mutation load, and signatures of a severe population bottleneck during the Late Pleistocene. Genome-environment association analysis further identified climate-adaptive genomic variations linked to five key environmental factors and projected potential genomic offsets under future climate scenarios. The southern lineage of A. tsinglingense exhibited greater climate sensitivity and genomic vulnerability under strong selective pressures, underscoring its importance as a conservation priority. Our research reveals that metabolic specializations in A. tsinglingense (such as the synthesis of nervonic acid) may confer competitive advantages in specific habitats. However, factors including its restricted distribution, historical population bottlenecks, and accumulated genetic load severely constrain its evolutionary potential to cope with rapid climate change. These findings emphasize the importance of elucidating the genomic basis and mechanisms of endangerment in metabolically specialized and threatened plant species to inform effective conservation strategies.

Genome, Plant↗

PubMed2026

Impacts of climate-driven yield changes on the affordability of healthy diets: a modelling study.

BACKGROUND: Food security is central to global nutrition improvement and public health goals, and healthy diets represent a higher-level aspiration beyond merely avoiding hunger. Climate change poses an increasing threat to food systems by affecting crop yields and food prices. Although climate change-driven risks to hunger have been widely studied, the extent to which climate change undermines the affordability of healthy diets while accounting for socioeconomic responses and regional inequalities remains insufficiently understood. This study aimed to quantify the effects of climate change on the future affordability of healthy diets under alternative socioeconomic and climate scenarios. METHODS: We developed an integrated modelling framework that explicitly couples multimodel crop-yield projections with an integrated assessment model (Global Change Analysis Model [GCAM]). Yield responses from six global gridded crop models driven by four climate models were integrated into GCAM, allowing endogenous socioeconomic adjustments such as land-use shifts, production reallocation, and price responses to emerge under shared socioeconomic pathways (SSPs). Diet affordability was then assessed using the Food and Agriculture Organization of the UN's Cost and Affordability of a Healthy Diet framework across three socioeconomic-climate scenarios (SSP1-2.6, SSP2-4.5, and SSP3-6.0). FINDINGS: Under a high-emissions pathway (ie, SSP3-6.0), climate change was projected to render healthy diets unaffordable for a model-mean of 119 million people globally by 2100, even when CO2 fertilisation effects are included, with the upper end of the model ensemble reaching about 1·6 billion people. In contrast, climate-induced affordability losses were found to be negligible under both a low-emissions pathway (ie, SSP1-2.6; -0·3 million) and a medium-emission pathway (SSP2-4.5; +0·2 million). Under a high-emission pathway, model-mean projections indicated that diet costs could increase by up to 12% in the most affected regions by the end of the century. Under medium emissions, cost increases were projected to remain below 4%, whereas under low emissions, affordability changes were projected to be minimum across regions (within approximately 0·5%). Substantial regional disparities emerged, with the largest and most consistent affordability losses concentrated in low-income regions that contributed least to historical greenhouse gas emissions. Under SSP3-6.0, these disparities persisted particularly in regions of Africa and Asia despite projected three-to-five-fold increases in income over the century, with climate-induced disruptions to food systems increasing the number of people unable to afford a healthy diet through mid-century. INTERPRETATION: Climate change is likely to exacerbate global nutritional inequalities by disproportionately increasing the affordability risks of healthy diets in regions that have contributed least to historical greenhouse gas emissions. Under high-warming scenarios, socioeconomic development alone is insufficient to fully offset these risks, highlighting the structural vulnerability of low-income food systems to climate-driven price shocks. These findings suggest that in the absence of targeted interventions, climate change could continue to undermine progress towards equitable and health-oriented nutrition outcomes. FUNDING: Ministry of Science and Technology of the People's Republic of China; National Natural Science Foundation of China; National Aeronautics and Space Administration Goddard Institute for Space Studies Climate Impacts Group; Future of Life Institute; and Global Alliance for Improved Nutrition.

Journal Article↗

PubMed2025

Genomic Insights Into Local Adaptation Across Heterogeneous Understory Habitats and Climate Change Vulnerability.

Understanding adaptive evolution and survival risks in understory herbs is crucial for the effective conservation of biodiversity. How environmental gradients shape species local adaptation patterns is not well understood, nor is how populations of understory herbs respond to a changing climate. In this study, we conducted population genomic analyses of Adenocaulon himalaicum (Asteraceae) with a pan-East Asian distribution, representing a good model for dominant understory herbs to elucidate adaptation mechanisms in heterogeneous forest ecosystems. Based on 34,398 putatively neutral single nucleotide polymorphisms (SNPs) across 27 populations, we identified three genetic lineages accompanied by high levels of genetic differentiation between populations. Our isolation by environment results (IBE) indicated a significant effect of environmental gradients on genomic variation of A. himalaicum (r = 0.18, p = 0.03). To decompose the relative contributions of climate, geography and population structure in explaining genetic variance, our partial RDA found that the prominent contribution of environmental effects (climatic and soil variables) explained 29% and 36% of the neutral and adaptive genetic variation, respectively. Using two genotype-environment association (GEA) methods, we identified 13 SNPs as candidates for core climate-related adaptation loci, with two of these loci further validated by qRT-PCR experiments. Projections of spatiotemporal genomic vulnerability under different future climate scenarios revealed that populations in the southeastern edge of the Himalayas, near the Sichuan Basin, the southernmost region of Northeast China and the northern Korean Peninsula, as well as northern Japan, were identified as the most vulnerable and should be prioritised for conservation. Therefore, our current study provides the genomic foundations for conservation and management strategies to elucidate how these understory herbs cope with future climate changes.

Climate Change↗

PubMed2026

Artificial intelligence-driven advancements in agricultural biotechnology.

The need for faster and more informative data processing for better decision-making is driving the adoption of artificial intelligence (AI) in the agricultural sector. Thanks to recent advancements in computer science and the increase in computational powers of modern computers, AI is not only augmenting traditional solutions, but also helping in developing novel solutions to existing challenging matters. AI-driven models have an exceptional ability to identify patterns and combine a diverse collection of data together and make inference. The increasing pressure on farmlands posed by the growing global population and climate change is lessening growth, yield, and productivity ultimately posing risk to food security worldwide. Incorporation of AI in agriculture has the potential to drive farming efficiency to new heights. This comprehensive review critically evaluates the evolution of AI in agricultural biotechnology from a theoretical concept to a global phenomenon. A comprehensive literature search was performed using major scientific databases, including PubMed, Web of Science, Embase, Scopus, Lens and the Cochrane Library. In this review, we empirically demonstrate the fields advancement toward more capable AI systems and discuss the current applications of AI across crop improvement and precision agriculture such as crop improvement and genetic engineering, genomic selection and plant breeding, pest and disease detection, precision agriculture and smart farming, soil health and nutrient management, climate resilient crop development, livestock biotechnology, challenges and ethical considerations in AI based agricultural biotechnology. Furthermore, this review addresses the exponential growth of commercial intellectual property in the field and contrast it with academic publication outputs. Finally, we critically assess the ethical challenges impeding equitable adoption of AI including data sovereignty and digital divide, while projecting future frontiers involving quantum computing. This review will help build sustainable agricultural systems capable of adapting to climate change, contribute to the development of climate-resilient and high-yielding crops, and address global food security challenges.

Agriculture↗

PubMed2026

Landscape Genomics Reveals Divergent Adaptation Modes and Predicts Climate Vulnerability in Xinjiang Indigenous Sheep.

Climate change increasingly endangers precious indigenous sheep germplasm resources distributed across diverse Chinese landscapes, and systematically decoding their polygenic climate-adaptive genetic mechanisms is essential for targeted breed conservation and long-term sustainable pastoral production. Whole-genome resequencing data from 93 individuals covering six representative local sheep breeds were analyzed in this work. After filtering highly collinear climate variables, three mature landscape genomic approaches were jointly applied to identify environment-linked gene variants, while two predictive metrics across ten CMIP6 future climate scenarios quantified each breed's long-term adaptive risks. Six temperature- and water-related environmental factors jointly drove sheep population genetic differentiation, with temperature fluctuation indices showing markedly stronger explanatory power. Detected adaptive genes were significantly enriched in ion transport, energy metabolism and cellular stress response pathways. Future projections indicated western breeds (Bayinbuluke, Cele Black, Xiahe) face severe maladaptation risks under high-emission SSP370 scenarios by 2100, whereas central and eastern breeds possess much broader climate tolerance. This study systematically reveals the core genomic basis of ovine climate adaptation and quantifies distinct breed-specific climate vulnerability, providing solid reliable theoretical support for precision germplasm conservation and selective breeding of climate-resilient sheep varieties.

adaptive loci↗

PubMed2026

Population and landscape genomics provide insights into the adaptive genetic variation and future climate-induced vulnerability of the endangered tree species Phoebe bournei.

Elucidating the genomic underpinnings of adaptive variation is highly important for the conservation, landscape application, and management of ornamental trees against the backdrop of global climate change. However, research on the genetic mechanisms underlying climate adaptation in Phoebe bournei-a near-threatened subtropical tree species endemic to China, which is endowed with exceptionally high ornamental and ecological value-remains scarce. Whole-genome resequencing was conducted on 362 individuals from 27 natural populations across the geographical range of the species. Genome-environment association analyses were employed to identify 1556 climate-associated variants and 167 candidate genes associated with temperature and precipitation variables. Through functional annotation and expression profiling, pivotal genes, including TRX-M4 and FBD1, were identified as integral to drought and heat stress responses, with adaptive alleles displaying distinct geographic frequency distributions and significant phenotypic differentiation. Divergent evolutionary trajectories were deduced among populations, with southeastern populations distinguished by elevated genetic diversity and strong signatures of local adaptation. Nevertheless, projections derived from the Risk of Non-Adaptedness and gradient forest models suggest that these southeastern populations will face substantial genomic offset under future climate scenarios, signaling heightened vulnerability and the need for prioritized conservation and management. This study provides the first genome-wide perspective into the adaptive evolution of P. bournei and offers a robust foundation for its conservation and climate-resilient management.

Journal Article↗

PubMed2025

Patterns of Genetic Diversity Within Three California Quail Species Are Best Explained by Climate and Landscape Changes.

Many North American game animals experienced severe population declines during the 19th century due to market hunting. However, estimates of the timing and magnitude of these declines often rely on anecdotal evidence, which makes it difficult to understand the lasting impacts of hunting pressures versus climate or landscape changes on the genetic diversity of contemporary populations. Historical reports suggest the California quail (Callipepla californica) suffered more significant hunting pressure in the late 19th century relative to either Gambel's (Callipepla gambelii) or mountain quail (Oreortyx pictus). Genomic data can help illuminate the extent to which historical exploitation moulded the genetic health of modern quail populations. We compared whole genome sequences from these three quail species to evaluate whether reported differences in hunting pressure affected contemporary patterns of genetic diversity. Contrary to our expectations, California quail did not exhibit any evidence for population declines until the late 20th century, long after the era of market hunting ended. California quail also exhibited the highest levels of genetic diversity across most analyses with evidence for population expansion over the past 500,000 years. In contrast, the mountain quail exhibited a long-term population decline beginning in the middle of the last ice age 30-40 thousand years ago. The Gambel's quail appears to have suffered a more recent bottleneck in association with a major drought that impacted the desert southwest during the mid-20th century. Gambel's quail also exhibited increased realised genetic load for mild and moderately deleterious genetic variants. Together, our results demonstrate that market hunting had little lasting impact on the genetic diversity of these quail species, whereas landscape and climate changes have led to fluctuations in effective population size (Ne) and the buildup of genetic load.

Animals↗

PubMed2026

Predictive evolutionary genomics: principles, validation, and practice.

Climate change and habitat loss are driving rapid evolutionary responses in populations world-wide, which creates an urgent need for evolutionary forecasting in conservation and agriculture. Such forecasting can be categorized into three time scales: trait-based models that use multivariate quantitative genetic equations to project correlated phenotypic responses up to c. 20 generations, allele-based analyses that model allele frequency dynamics up to 100 generations, and composite adaptation scores that aggregate many small effects to yield predictions across longer horizons. However, these approaches have remained largely disconnected. Here, we present a Bayesian framework that integrates these three complementary approaches for evolutionary prediction. Our framework combines genomic, phenotypic, and environmental data to yield probabilistic predictions with explicit uncertainty. We show how predictive evolutionary forecasts can be validated with experimental evolution, field experimentation, historical specimens, and reciprocal transplants. These validated forecasts can help advance conservation and agricultural programmes by helping predict which populations are at risk of future extinction, optimizing breeding programmes for future climates, and planning ecosystem management under environmental change. By supporting a shift towards more predictive approaches in evolutionary biology, this framework may help improve our ability to manage biodiversity and food security in a changing world.

Genomics↗

PubMed2024

Geospatial Analysis of Multilevel Socioenvironmental Factors Impacting the Campylobacter Burden among Infants in Rural Eastern Ethiopia: A One Health Perspective.

Increasing attention has focused on health outcomes of Campylobacter infections among children younger than 5 years in low-resource settings. Recent evidence suggests that colonization by Campylobacter species contributes to environmental enteric dysfunction, malnutrition, and growth faltering in young children. Campylobacter species are zoonotic, and factors from humans, animals, and the environment are involved in transmission. Few studies have assessed geospatial effects of environmental factors along with human and animal factors on Campylobacter infections. Here, we leveraged Campylobacter Genomics and Environmental Enteric Dysfunction project data to model multiple socioenvironmental factors on Campylobacter burden among infants in eastern Ethiopia. Stool samples from 106 infants were collected monthly from birth through the first year of life (December 2020-June 2022). Genus-specific TaqMan real-time polymerase chain reaction was performed to detect and quantify Campylobacter spp. and calculate cumulative Campylobacter burden for each child as the outcome variable. Thirteen regional environmental covariates describing topography, climate, vegetation, soil, and human population density were combined with household demographics, livelihoods/wealth, livestock ownership, and child-animal interactions as explanatory variables. We dichotomized continuous outcome and explanatory variables and built logistic regression models for the first and second halves of the infant's first year of life. Infants being female, living in households with cattle, reported to have physical contact with animals, or reported to have mouthed soil or animal feces had increased odds of higher cumulative Campylobacter burden. Future interventions should focus on infant-specific transmission pathways and create adequate separation of domestic animals from humans to prevent potential fecal exposures.

Humans↗

PubMed2026

ERGA-BGE reference genome of Eunicella cavolini, an IUCN Near Threatened Gorgonian of the Mediterranean Sea.

The Eunicella cavolini reference genome provides an important resource to study the adaptation of this species to different environments and anthropic pressures. This species is impacted by human activities, including climate change, and this reference genome will be useful to study the genomic evolution of this species. The entirety of the genome sequence was assembled into 17 contiguous chromosomal pseudomolecules. This chromosome-level assembly encompasses 0.49 Gb, composed of 159 contigs and 46 scaffolds, with contig and scaffold N50 values of 7.7 Mb and 51.1 Mb, respectively.

Biodiversity Genomics Europe↗