New Phytologist

Arbuscular mycorrhizal (AM) symbiosis is conserved across land plants and is the default nutrient uptake strategy in nature. Within roots, AM colonisation is tightly patterned and dynamically tuned by nutritional cues. Multiple genetic modules contribute to this regulation, including the phosphate starvation response, DWARF14-LIKE (D14L) karrikin signalling, and the common symbiosis signalling pathway (CSSP). Transcriptional overlap among these has led to the hypothesis that phosphate starvation and D14L signalling act upstream of the CSSP. Here, we examined the epistatic relationship between D14L and CSSP in rice. Overexpression of an autoactive gain-of-function CCaMK (gofCCaMKox) restored AM colonisation and symbiosis marker gene expression in d14l mutants to wild-type levels or above, whereas overexpression of wild-type CCaMK did not, confirming that CSSP operates downstream of D14L signalling. However, gofCCaMKox did not rescue the d14l mesocotyl elongation phenotype, supporting a bifurcation of D14L into developmental and symbiotic outputs. Unexpectedly, gofCCaMKox also expanded fungal access to normally restrictive tissue domains (the meristematic zone and endodermis) assigning a role for CCaMK activation in defining root zone and cell-type competence for AM colonisation. Despite restored colonisation, introduction of gofCCaMKox into d14l produced arbuscules, which however were less developed and had increased hyphal septation, revealing a CCaMK-independent role for D14L in intraradical colonisation and arbuscule development. Transcriptome profiling resolved AM-relevant genes into modules controlled by CCaMK activation alone, in combination with D14L, or requiring additional colonisation-associated cues, and further suggested CCaMK primarily acts through AP2 transcription factors. Together, these findings reinforce CCaMK as a master regulator of AM symbiosis at the genetic, transcriptomic and anatomical levels while uncovering CCaMK-independent functions of D14L in arbuscule development.

Plant ecological and evolutionary strategies are shaped by interactions between phylogenetic history and environmental constraints, resulting in leaf and stomatal traits. However, traditional trait-based and phylogenetic approaches often fail to fully explain biochemical mechanisms underlying ecological strategies, particularly for leaf and stomatal traits. Plant metabolomes integrate genetic, physiological, and environmental information and therefore represent a promising intermediate phenotype for investigating links between biochemical diversity, functional traits, and evolutionary patterns. We analysed metabolomic profiles from 74 plant species with various growth forms and ecological types. Using machine learning approaches, we explored whether metabolic variation could predict plant functional divisions, growth forms and phenological types, but also physiological traits related to drought resistance. Metabolomic data contained structured information associated with variation in plant functional traits, ecological strategies, and phylogenetic relationships. Machine learning models identified with high accuracy distinct metabolic signatures linked to differences among plant functional divisions, growth forms, phenology, and trait values. Our study demonstrates that predictive metabolomics provides a powerful and integrative framework to investigate plant ecological and evolutionary strategies. By linking biochemical diversity with plant phylogeny, and ecophysiological traits across multiple species, this approach offers new opportunities to explore the mechanistic basis of plant evolution.

O_LIBackground and Aims: Chemodiversity is a fitness-relevant trait shaped by genetics, environment, and their interaction. Populus trichocarpa naturally inhabits broad climatic gradients and shows extensive variation in specialised metabolism. We investigated whether provenance and climate of origin imprint leaf chemodiversity and class-level relationships under common-garden conditions, and how these patterns relate to gene expression. C_LIO_LIMethods: Leaves from 87 P. trichocarpa genotypes representing 22 provenances from the west coast of North America growing in a common garden were profiled by untargeted FT-ICR-MS (1030 features) and targeted LC-MS/MS. A subset of 41 genotypes was subject to RNA-seq analyses. We tested whether provenance influenced multivariate patterns and whether metabolomic differences were related to geographic and climatic distance, where chemodiversity was quantified as Functional Hill Diversity. C_LIO_LIKey Results: P. trichocarpa metabolomes differed among origins despite shared growth conditions and showed distance-decay with both geography and climate. North-south extremes were well separated, and within-drainage samples shared high similarity. Flavonoid and isoprenoid pools strongly co-varied across individuals, whereas isoprene synthase activity did not predict total isoprenoids. Transcriptomes showed within-pathway coherence but limited overall provenance separation. C_LIO_LIConclusions: Leaf chemistry in P. trichocarpa retains signatures of geographic origin even under common-garden conditions. Coordinated investment in flavonoids and isoprenoids, together with among-origin differences in functional chemodiversity, reveals provenance-linked chemical fingerprints that complement genomic and metabolic trait data for climate-informed deployment. C_LI

Quantifying root traits such as root length (RL) and root surface area (RSA) from minirhizotron imagery is a valuable approach for overcoming the phenotyping bottleneck that limits understanding and improvement of crop productivity, resource use efficiency and resilience in field experiments. However, current approaches remain labor-intensive, and deep learning (DL) methods suffer from limited generalization ability. We present RootQuant, an end-to-end DL model that simultaneously predicts RL and RSA directly from minirhizotron images using only whole-image trait values as supervision, thereby eliminating the need for pixel-level annotations. The models generalization ability was evaluated across species and fine-tuning configurations. The practical applicability of the model was further assessed under field conditions by converting image-derived RL estimates into volumetric root length density (vRLD). Using 118,191 maize and soybean images collected between 2009 and 2020, RootQuant trained on both species achieved an R2 of 0.90 and an RMSE of 2.9 mm for RL, and an R2 of 0.88 and an RMSE of 4.2 mm2 for RSA. The same mixed-species model generalized strongly across species, yielding an 8% relative improvement in R2 and a 30% lower RMSE on maize compared with the same architecture trained on a single species and applied zero-shot. Image-derived RL predictions converted to vRLD showed the expected depth-dependent decline in vRLD, as was also found by coincident destructive quantification of roots washed out of soil cores. By providing a generalist backbone model trained on a large dataset from two major crop species, RootQuant enables high-throughput simultaneous estimation of two relevant root traits directly from raw imagery without task-specific fine-tuning, thereby accelerating in situ root system analysis and phenotyping applications.

Adaptation of plant photosynthesis to dynamic light conditions experienced in natural environments is achieved through specific protective mechanisms. Energy-dependent non-photochemical quenching (qE), regulated by Photosystem II Subunit S (PsbS), is a key process facilitating acclimation to fluctuating light in C3 plants, which operate conventional photosynthesis. C4 plants, which include some of the world's most productive and agriculturally important crops, have evolved a distinct high-efficiency photosynthetic pathway. Little is known about the role of specific processes, like qE, in acclimation of C4 plants to dynamic light environments. We generated gene-edited lines of the model C4 grass Setaria viridis lacking PsbS, which were found to be deficient in qE. This deficiency resulted in a modest increase in PSII photoinhibition and a CO2 assimilation penalty under light stress in short-term experiments, but photosynthesis and growth under fluctuating light were unaffected. Instead, keeping Photosystem I oxidised through photosynthetic control, negative feedback regulation of the Cytochrome b6f complex, was critical. Therefore, unlike in C3 plants, qE does not provide a significant adaptive advantage to C4 plants under dynamic light conditions. These findings provide important insights into the biology of C4 plants and help prioritise future strategies for improving the productivity and resilience of C4 crops.

Belowground plant trait research has predominantly focused on trade-offs in fine root traits via the root economics space. Yet, this fine root framework captures only a fraction of the functional strategies plants employ beneath the soil surface. Here, we broaden the perspective on belowground plant functioning by integrating traits related to root system extent, clonality and bud banks, using data from the new UNDERPLOT database. This integration links measurable traits to key belowground functions: resource acquisition, spatial exploration, and persistence. Our analysis shows that the fine root economics space explains less than 5% of the variation in traits related to root system extent, clonality, and bud banks. Instead, an expanded trait analysis reveals three significant dimensions, explaining 62% of total trait variation. The third dimension, represents an independent, persistence-related gradient, not captured by existing root economics frameworks. We propose that understanding belowground plant strategies requires embracing additional functional gradients. The strategy of persistence, in particular, varies significantly across growth forms and is a critical dimension of plant response to resource limitation and stress, becoming increasingly important as global change shifts disturbance regimes.

Phenotypic plasticity can buffer the potential fitness consequences of environmental change, yet limited understanding of its genetic basis constrains its application to predicting population response to future climates. Hybrid zones provide powerful systems to study the genetic basis of plasticity because admixture can create novel allele combinations that generate new reaction norms for selection to act upon. Here, we combine two clonally replicated common gardens of Populus trichocarpa x P. balsamifera genotypes with whole-genome resequencing to identify genetic variation underlying plasticity for physiological traits. Admixed genotypes exhibited broader, and in some cases novel, reaction norms relative to parental genotypes, particularly for key stomatal traits. Admixture mapping of genotype-specific reaction norms identified ten candidate genes on chromosome 15, including TWIST, associated with plasticity in adaxial stomatal occurrence and density. Using random forest models, we projected allele-specific responses to climate warming within the hybrid zone to link genetic variation in plasticity with predicted warming. Random forest models forecast that future climates would favor P. trichocarpa alleles at TWIST, while P. balsamifera alleles would be maintained in heterozygous genotypes. These results suggest that hybridization can expand reaction norms and maintain genetic variation that may facilitate rapid phenotypic response needed to adapt to climate change.

Ensuring global food security under rapid climate change demands accelerated genetic gain and breeding strategies that address complex Genotype-by-Environment (GxE) interactions. Traditional genomic selection models often fail to account for novel or extreme climates.Furthermore, integrating mechanistic crop growth models (CGMs) using traditional Bayesian frameworks to solve this issue presents severe computational bottlenecks. Here, we introduce DeepBioGS, a novel hybrid framework that integrates genomic selection with biophysical growth modelling via a fully differentiable deep learning architecture. DeepBioGS utilises a parameter-prediction multi-layer perceptron to map high-dimensional genomic markers to latent, highly heritable physiological traits (Genotype-Specific Parameters; GSP). These parameters mechanistically predict crop phenology across diverse environments. Using two multi-environment wheat datasets comprising over 6,000 genotypes, DeepBioGS extracted latent traits with near-perfect SNP-based heritability values (0.95-1.00). Crucially, the framework demonstrated superior or comparable predictive accuracy (up to r2 = 0.77) against standard genomic best linear unbiased prediction (GBLUP) and traditional Bayesian CGM-WGP models. Its architecture drastically improved computational scalability by enabling standard backpropagation, effectively bypassing the stochastic sampling limitations of approximate Bayesian methods. Most importantly for climate adaptation, DeepBioGS allowed accurate forecasting of genotype performance in entirely unobserved environmental conditions. By merging the representational power of deep learning with the structural constraints of biophysics, DeepBioGS provides a highly scalable, interpretable tool to navigate GxE interactions, enabling the assessment of cultivars under future climate scenarios, thus optimising crop breeding for a changing global environment.

Rafflesiaceae, known for producing the largest flowers in the world, are obligate parasites that exclusively infect Tetrastigma sp. (Vitaceae). Despite their unique biology, the interactions between parasitic tissues and host roots remain poorly understood, particularly during the flower morphogenesis phase, where parasitic tissue erupts through the host root. Here, we performed dual transcriptome analyses of two Rafflesiaceae species and their respective Tetrastigma hosts: Sapria himalayana with T. cauliflorum and Rafflesia speciosa with T. magnum. Our findings reveal species-specific transcriptional responses in Tetrastigma, suggesting divergent parasitism strategies between Rafflesia and Sapria. Moreover, we identify molecular signatures of parasitism that parallel plant gall formation, particularly in genes governing cell wall modification and host tissue reorganization. Unlike bacterial or insect-induced galls, these mechanisms may involve fungal symbionts, highlighting the unique nature of these interactions. Together, our results demonstrate that Rafflesiaceae parasitism represents a complex tripartite relationship among host, holoparasite, and associated microbes, offering new insights into the hidden biology of these remarkable parasitic plants.

Phenotyping stomatal traits and their developmental plasticity is time-consuming but holds potential to improve water use efficiency and photosynthesis for designing stress-tolerant crops under climate change. Here, we develop a robust, high-throughput pipeline for phenotyping 14 stomatal traits in winter wheat related to size, variation, maximum conductance, and spatial patterning. We (1) analyze over 25,000 images from 60 wheat cultivars grown in growth chamber, greenhouse, and field conditions; (2) investigate the impact of light, temperature, and reduced water and nitrogen supply on stomatal traits and their developmental plasticity across adaxial and abaxial surfaces; and (3) evaluate genetic diversity and breeding progress of stomatal traits. Stomatal traits were highly broad-sense heritable, were largely plastic in response to environmental conditions, and showed genotype-specific responses. Stomatal traits of third leaves under controlled environments with stable light and temperature conditions reliably captured the genetic variance of flag leaves under field conditions. Our data suggests that the upper leaf surface contributed more to transpiration and cooling through consistently higher stomatal density, area, and maximum conductance, while the lower surface facilitated CO2 diffusion via systematic proper patterning and spacing. Breeding maintains the genetic diversity of stomatal traits, and our pipeline facilitates breeders to target them to enhance water use efficiency in high-yielding modern cultivars.

Biological invasions are transforming ecosystems worldwide, yet the genomic bases enabling certain species to dominate new environments remain poorly understood. Phragmites australis, a widespread wetland grass with invasive and native subspecies co-occurring in North America, provides a powerful system to investigate genomic mechanisms of invasiveness. We generated independent chromosome-scale genome assemblies for invasive P. australis ssp. australis and co-occurring native ssp. americanus and used comparative genomic and transcriptomic analyses to identify lineage-specific innovations associated with invasive success. The invasive subspecies exhibits genomic novelties through functionally-biased single-copy orthologs, intronless genes, and subgenome expression asymmetry, along with a stress-ready basal transcriptome relative to the native subspecies. Following the removal of aboveground shoots ("cutback"), which measures the ability to recover from damage, the invasive subspecies undergoes stronger transcriptional reprogramming, increased shoot production, and higher biomass accumulation compared to the native. It also displays expansion of gene families and coordinately expressed gene modules that support resource mobilization, growth responses to light, and stress tolerance. Beyond Phragmites, comparative analyses across multiple grass genomes, including eight invasive species with related non-invasive species, revealed repeated expansion of gene families associated with abiotic stress tolerance and developmental regulation, suggesting convergent adaptive strategies in the grass family for invasive success. Together, these results demonstrate genomic architecture linked to invasion success and highlight potential targets for managing invasive grasses.

Root carbon (C)-nutrient functional balance underpins root economic strategies, yet previous models neglected the root-length dimension. Here, we develop a three-dimensional C-nutrient functional balance model for first-order roots that explicitly incorporates root length, and validates it using a global root trait dataset. For subtropical woody species, root cortex volume x root length scaled isometrically with the fourth power of stele diameter (slope{approx}1.0), supporting the model. Herbaceous species from the Qinghai-Tibetan plateau showed a significantly lower slope (0.61), likely due to extreme environmental impacts on transport or metabolism. Woody species preferentially invest in cortical area (thicker roots), supporting mycorrhizal symbiosis, whereas herbaceous species favor root length extension for autonomous soil exploration. By integrating root length, this model provides a novel mechanistic explanation for the formation of both the collaboration and conservation axes within the root economics space, advancing the theoretical framework of root functional strategies.

Accurate segmentation of plant disease symptoms is essential for crop monitoring and phenotyping, yet it typically requires costly pixel-level annotations. Weakly supervised semantic segmentation (WSSS) alleviates this burden using image-level labels, but its performance depends on the quality of spatial priors such as class activation maps (CAMs). We investigate whether text-guided segmentation with the Segment Anything Model 3 (SAM3) can serve as an alternative weak supervision signal. Three pseudo-mask generation strategies are compared: (i) CAMs refined with SAM or SAM3, (ii) zero-shot text-guided SAM3, and (iii) a hybrid approach combining weak spatial cues with text prompts. The resulting pseudo-masks are used to train a DeepLabV3 model. Text guidance alone matches or outperforms conventional WSSS, achieving up to 0.46 IoU without spatial supervision and 0.61 IoU on a public dataset, although performance is sensitive to text prompt formulation. The hybrid strategy improves robustness, reaching 0.50 IoU on the primary dataset and 0.58 IoU on the additional dataset while reducing prompt sensitivity. Overall, text guidance is a promising alternative to conventional weak supervision, while hybrid approaches provide a more robust solution for plant disease segmentation.

O_LIBetacyanins are red pigments characteristic of Caryophyllales and show considerable structural diversity, yet the enzymatic basis underlying 6-O-glucosylated betacyanins such as gomphrenin I has remained unclear. In particular, how alternative glucosylation patterns contribute to betacyanin diversification is poorly understood. C_LIO_LIHere, we identified cyclo-DOPA glucosyltransferases from Basella alba and Gomphrena globosa and examined their roles in gomphrenin I biosynthesis using transient expression assays and tobacco BY-2 cell systems. Phylogenetic analyses, structural modelling and site-directed mutagenesis were employed to investigate their functional and structural characteristics. C_LIO_LIBacDOPA5/6GTs catalysed both 5-O- and 6-O-glucosylation of cyclo-DOPA, leading to the production of betanin and gomphrenin I, whereas GgcDOPA6GT specifically mediated gomphrenin I formation. These enzymes belong to distinct subclades within the cDOPA-GT family, and mutational analyses demonstrated essential roles for conserved histidine residues and an -helical region adjacent to the catalytic site. C_LIO_LIThermal stability analyses further showed that gomphrenin I is more thermally stable than betanin, likely due to the formation of an intramolecular hydrogen bond. Together, these results reveal an additional cDOPA6GT-mediated route for gomphrenin I biosynthesis and provide insight into the diversification and functional specialization of betacyanins, linking the position of glucosylation to pigment stability and biochemical properties. C_LI

While whole genome sequencing captures millions of single nucleotide polymorphisms (SNPs) and hyperspectral imaging (HSI) enables non destructive plant phenotyping, integrating these modalities to link genotype to phenotype remains challenging due to their high dimensionality and non linearity. This study presents DeepPheno a deep learning framework that predicts SNP genotypes from HSI data, using model predictability as a proxy for genotype phenotype association. HSI data were acquired from 194 lettuce genotypes under field conditions. HSI data patches (20 x 20 pixels x 224 spectral bands) were used to train a hybrid CNN to predict the variant of a specific SNP. The framework was validated on SNPs with known phenotypic effects (anthocyanin, leaf serration, pale pigmentation), achieving high predictive performance (AUC ranging from 0.806 to 0.935), whereas models trained on randomly shuffled labels performed at chance (mean AUC {approx} 0.51). Extending the workflow to 50 randomly selected putatively neutral SNPs, most yielded low predictability, but two showed high performance (AUC > 0.76), suggesting uncharacterized genotype phenotype links. Explainable AI, including SHAP and Grad CAM, identified relevant spectral and spatial features driving these predictions, particularly the green and red edge wavelengths associated with pigment dynamics and leaf structure. These results establish a framework for understanding complex genotype phenotype interactions in plants and extracting these links from HSI data without predefining the exact trait values. It provides an avenue for high throughput trait discovery and description and extends the integration of image based phenomics with plant genetics.

O_LIThere is growing interest in developing perennial cereal crops for sustainable production, yet key differences in trait syndromes between annual and perennial grasses and their influence on environmental adaptation remain poorly understood. C_LIO_LIWe measured 25 traits in 16 annual and perennial Hordeum species (45 accessions), including barley, grown over three seasons in a common garden. Using a phylogenetic framework and repeated transitions between annual and perennial forms, we (i) identified traits distinguishing these life strategies and (ii) tested how they relate to climate at the accessions origins. C_LIO_LIWild and cultivated barley are distinguished within the Hordeum clade by high growth rates and large organs, which may have predisposed wild barley to domestication. Annual and perennial accessions differed in resource allocation: annuals had higher harvest index and leaf and grain nitrogen, while perennials produced carbon-rich tissues and sustained vegetative growth. Seasonal temperature variation shaped trait syndromes: annual traits aligned with temperature in the driest quarter, reflecting selection under terminal stress, while perennial traits correlated with temperature in the wettest quarter, the main growth phase shaping long-term performance and survival. C_LIO_LIWe provide important information on traits and climate adaptations underlying perennial persistence and annual resource strategies, relevant for developing perennial cereal crops. C_LI

Shifts in flowering phenology are one of the most well studied plant responses to global climate change. Many studies have documented these shifts and their drivers, including some that describe altered patterns of co-flowering among taxonomically broad species within communities. In comparison, few analyses have examined systematic changes in co-flowering between closely related, interfertile species, where co-flowering can have unique evolutionary consequences. To address such shifts in co-flowering among close relatives, we investigate phenological responses to climate change and its effect on patterns of co-flowering over the past 124 years in 52 species of North America violets (Viola). This genus has many co-occurring species that reproductively interact via shared pollinators and hybridization. We use ~14,000 herbarium records along with environmental and species trait data to model the magnitude of recent flowering phenology shifts, environmental variables and/or species traits associated with these shifts, and resulting changes in co-flowering among species. While both the magnitude and direction of phenological shifts varied among Viola species, nearly half (25/52) show significant changes in flowering day. Regardless of whether flowering was advanced or delayed, flowering date was most consistently associated with local mean temperature. Of six species-level traits, geographical region also significantly predicted flowering shifts, consistent with environment and geography together explaining broad phenological responses across this group. These shifts have produced significant changes to pairwise patterns of co-flowering among species -- ranging from a 59 day increase in co-flowering to complete loss of co-flowering overlap. Sympatric pairs specifically have experienced both increases and decreases in co-flowering, with a geographic pattern of increased co-flowering occurring mainly in eastern US and decreased co-flowering common in western US. Because Viola species are generalist pollinated and already known to hybridize, these new co-flowering patterns could further undermine reproductive barriers among species in this genus.

Pollen development and fertilization are considered the most heat-sensitive stages of plant reproduction. While heat stress severely impairs pollen germination and tube growth, the physiological diversity within a single flowers pollen load suggests that subpopulations may exhibit differential climate resilience. In this study, we tested the hypothesis that this heterogeneity reflects a dormancy-based reserve mechanism that preserves fertilization under heat stress. Using flow cytometry and fluorescence-activated cell sorting in Arabidopsis thaliana and Solanum lycopersicum (MicroTom), we resolved pollen subpopulations by reactive oxygen species (ROS) status and examined their behavior under increasing heat stress. In both species, ROS-defined metabolic state was tightly associated with pollen size: high-ROS pollen was larger and readily germination-competent, whereas low-ROS pollen was smaller and showed low basal germination, consistent with dormancy. Heat stress preferentially depleted the high-ROS fraction, whereas the low-ROS fraction persisted and, under heat stress, increased metabolic activity and size. By isolating low-ROS and high-ROS pollen, we further show that a brief heat treatment suppresses germination of active high-ROS pollen but promotes germination of dormant low-ROS pollen. These findings provide direct evidence that heat can release dormancy in low-ROS pollen and support a conserved model in which dormant pollen serves as a heat-resilient reproductive reserve.

Plant architecture and developmental timing are influenced by light availability, especially in high-density cropping systems, where canopy shading modifies both light intensity and spectral quality. Despite their ecological and agronomical importance, the genetic basis of these responses in wheat remains poorly understood. Here, using a Recombinant Inbred Lines (RILs) population, we investigated phenological and morphological traits under sunlight and simulated canopy shade. We identify a major QTL on chromosome 5A with light-dependent allelic effects, indicating genotype-by-environment variation in developmental responses. This QTL corresponds to a structural rearrangement, consistent with an inversion in the wild emmer reference genome encompassing PHYTOCHROME C (PHYC-A) and VERNALIZATION-1 (VRN-A1), as well as coding polymorphism in PHYC-A. Analysis of a tetraploid wheat diversity panel further showed that natural variation at PHYC-A and an early stop codon in the BB genome copy of PHYTOCHROME A (PHYA-B) on chromosome 4B are associated with differences in heading time. Functional analysis using TILLING-derived phytochrome mutants confirms distinct and complementary roles for PHYA and PHYC in regulating flowering time, plant height, and leaf elongation under simulated canopy shade. These findings highlight the contribution of phytochrome variation to developmental plasticity under canopy-like light environment, thereby extending model insights to agronomically relevant conditions.

Waterlogging is a major constraint on barley productivity, yet its dynamic, multi-phase nature makes it challenging to dissect using traditional phenotyping approaches. High-throughput phenotyping (HTP) platforms address this by enabling temporal, multi-sensor imaging of large populations, but generate complex datasets that demand new analytical frameworks. Here, we imaged 230 barley accessions over 14 days of waterlogging stress and seven days of recovery using visible, chlorophyll fluorescence, and hyperspectral sensors. Explainable AI was applied to classify stress responses into early stress, late stress, and recovery phases, achieving 86% classification accuracy, and to identify the hyperspectral indices most informative for each phase. Water index (WATER1) and structure insensitive pigment index (SIPI) emerged as primary predictors of stress response. Longitudinal genome-wide association studies (GWAS), using a treatment-by-marker interaction model, identified 236 significant loci across 12 linkage disequilibrium blocks, implicating candidate genes involved in oxidative stress regulation, transcriptional control, and auxin transport. MYB transcription factors were consistently identified across all stress phases, underscoring their central role in waterlogging adaptation. To support interpretation of longitudinal GWAS results, we developed 3D-QTLVis, an interactive visualisation tool that extends Manhattan plots across time, enabling clearer identification of dynamic genomic regions underlying stress tolerance.