Variation In Traits Research Articles

Differences in visitation of honeybees and bumblebees to ornamental plant varieties can be explained by floral traits

Global bee populations are rapidly declining. One way of supporting bee populations is by enhancing urban green spaces with plants attractive to bees. Plant breeding has introduced a high degree of variability in floral traits, which can affect the attractiveness and usefulness of ornamental plants to bees. In this study, we investigated how variations in floral traits, including nectar sugar content, corolla tube depth, flower colour, UV-presence and the number of flowers, affected the attractiveness of 119 cultivars from eight ornamental plant genera (Salvia nemorosa, Gaillardia aristata, Delosperma cooperi, Lavandula angustifolia, Lavandula stoechas, Sedum telephium, Perovskia atriplicifolia and Agastache hybrida) to honeybees and bumblebees. Our results show that differences in bee visitation rate among cultivars were directly related to variation in floral traits. For most plant genera, cultivars of the same species varied significantly in attractiveness. Honeybees and bumblebees generally did not find the same cultivars and plant genera attractive. Nectar sugar content and flower colour were important for cultivar attractiveness to both honeybees and bumblebees, with corolla tube depth also being an important factor for honeybees. We found that flower colour was often related to the favourability of other floral traits that promote more rewarding or easily accessible flowers. However, most cultivars were considered unattractive and only a small number of cultivars were highly attractive to honeybees (6%) and bumblebees (10%). Overall, our study gives valuable insights for plant breeders, emphasising how different floral traits affect the attractiveness of ornamental plants which helps to select for floral traits that result in more attractive ornamental plants for bees.

Genome-wide association studies dissect the genetic architecture of seed and yield component traits in cowpea (Vigna unguiculata L. Walp).

The identification of loci related to seed and yield component traits in cowpea constitutes a key step for improvement through marker-assisted selection (MAS). Furthermore, seed morphology has an impact on industrial processing and influences consumer and farmer preferences. In this study, we performed genome-wide association studies (GWAS) on a mini-core collection of cowpea to dissect the genetic architecture and detect genomic regions associated with seed morphological traits and yield components. Phenotypic data were measured both manually and by high-throughput image-based approaches to test associations with 41,533 single-nucleotide polymorphism markers using the FarmCPU model. From genome-associated regions, we also investigated putative candidate genes involved in the variation of the phenotypic traits. We detected 42 marker-trait associations for pod length and hundred seed weight, length, width, perimeter, and area of the seed. Candidate genes encoding leucine-rich repeat-containing (LRR) and F-box proteins, known to be associated with seed size, were identified; in addition, we identified candidate genes encoding PPR (Pentatricopeptide repeat) proteins, recognized to have an important role in seed development in several crops. Our findings provide insights into natural variation in cowpea for yield-related traits and valuable information for MAS breeding strategies in this and other closely related crops.

Digest: Clinal variation in plant traits is shaped by plastic and evolutionary responses to water regimes and herbivory.

How do water regime and herbivory shape phenotypic variation in plants along environmental gradients? Using a multifactorial field common garden approach, Jameel et al. (2024) showed that water availability and herbivore abundance influence the expression of foliar and reproductive traits in the perennial forb Boechera stricta. The concordance between phenotypic plasticity, phenotypic clines, and the direction of selection demonstrates the adaptive nature of plasticity in ecologically relevant traits. Furthermore, the experimental manipulations highlight which agents of selection drive the evolution of these traits.

Development of a set of monosomic alien addition lines from Gossypium raimondii in Gossypium hirsutum toward breeding applications in cotton

BackgroundGossypium raimondii Ulbr is a diploid wild cotton (2n = 26, D5D5) that originated in west-central Peru of South America and possesses desirable characteristics that are absent in the Upland cotton G. hirsutum. Many beneficial genes were lost from G. hirsutum in the process of domestication, leading to a narrowed genetic base and greater vulnerability to biotic and abiotic stresses. This genetic base can be expanded through distant hybridization using the superior genes of G. raimondii.ResultsIn this study, putative hexaploid F1 plants of G. hirsutum - G. raimondii were generated by interspecific hybridization. Analysis of its mitotic metaphase plates revealed the presence of 78 chromosomes, with each of the six chromosome-specific fluorescence in situ hybridization (FISH) probes (3D5, 5D5, 6D5, 7D5, 9D5, and 10D5) of G. raimondii exhibiting bright and distinct signals on its respective pair of chromosomes. Then, the fertile hexaploid F1 plants were continuously backcrossed with G. hirsutum and a set of G. hirsutum - G. raimondii monosomic alien addition lines (MAALs) were developed using SSR markers in successive backcrosses and self-crossing from BC2F1 to BC4F2. These MAALs were confirmed by chromosome-specific anchored SSRs and FISH. This set of MAALs exhibited abundant variation in morphological traits, agronomic characteristics, yield, and fiber quality traits, as well as in drought and salt resistance at seedling stage. Notably, MAAL_9D5 and MAAL_10D5 demonstrated excellent fiber length (FL), fiber uniformity (FU), fiber strength (FS), micronaire value, and fiber elongation (FE); At seeding stage, MAAL_8D5, MAAL9D5, MAAL_10D5, MAAL_12D5, and MAAL_13D5 showed salt resistance potential; while MAAL_1D5, MAAL_3D5, MAAL_4D5, MAAL_7D5, MAAL_8D5, MAAL_12D5, and MAAL_13D5 exhibited drought resistance potential. These MAALs will provide important genetic bridge materials for gene transfer from G. raimondii as well as for the study of Gossypium species genomes and their evolution.ConclusionsA set of Gossypium hirsutum - Gossypium raimondii MAALs were developed and they showed abundant variation in morphological, agronomic, yield, and fiber quality traits, as well as in drought and salt resistance at seedling stage.

Root and Leaf Traits of Alfalfa Exhibit Distinct Responses to Soil Microbial Communities and Environmental Stresses

ABSTRACTOngoing climate change is negatively impacting crop productivity globally. Past research has highlighted that a diverse soil microbial community and variation in plant traits for resource acquisition can mitigate the negative impacts of climate change factors on crop productivity. This study investigates the effects of two major environmental stressors—drought and salinity stress, on plant productivity, biomass allocation, and root and leaf trait responses under distinct soil microbial diversities. Our results showed that salinity stress had stronger negative impacts on plant productivity than drought stress. Shoot biomass decreased by 30% and 32.5% under drought and salinity stress, respectively, whereas the root biomass decreased by 32% only under salinity stress. Soil microbial diversity did not affect plant productivity. Next, root traits were mainly impacted by drought and salinity stress, whereas leaf traits were impacted by both environmental stresses and soil microbial diversity. Specific root length and specific root area decreased under drought, and root tissue density was minimal under salinity stress. Root traits were not affected by soil microbial communities. In contrast, the leaf nitrogen content increased, whereas pheophytin content (a breakdown product of chlorophyll) decreased when plants were grown in diverse microbial communities under environmental stresses, especially drought. These results highlight the importance of soil microbial diversity in impacting plant traits in response to environmental stresses. We showed that the soil microbial diversity influences both aboveground and belowground plant traits, indicating the need for better management practices to conserve and promote soil microbial diversity.

Characterization of microbiota signatures in Iberian pig strains using machine learning algorithms

BackgroundThere is a growing interest in uncovering the factors that shape microbiome composition due to its association with complex phenotypic traits in livestock. Host genetic variation is increasingly recognized as a major factor influencing the microbiome. The Iberian pig breed, known for its high-quality meat products, includes various strains with recognized genetic and phenotypic variability. However, despite the microbiome’s known impact on pigs’ productive phenotypes such as meat quality traits, comparative analyses of gut microbial composition across Iberian pig strains are lacking. This study aims to explore the gut microbiota of two Iberian pig strains, Entrepelado (n = 74) and Retinto (n = 63), and their reciprocal crosses (n = 100), using machine learning (ML) models to identify key microbial taxa relevant for distinguishing their genetic backgrounds, which holds potential application in the pig industry. Nine ML algorithms, including tree-based, kernel-based, probabilistic, and linear algorithms, were used.ResultsBeta diversity analysis on 16 S rRNA microbiome data revealed compositional divergence among genetic, age and batch groups. ML models exploring maternal, paternal and heterosis effects showed varying levels of classification performance, with the paternal effect scenario being the best, achieving a mean Area Under the ROC curve (AUROC) of 0.74 using the Catboost (CB) algorithm. However, the most genetically distant animals, the purebreds, were more easily discriminated using the ML models. The classification of the two Iberian strains reached the highest mean AUROC of 0.83 using Support Vector Machine (SVM) model. The most relevant genera in this classification performance were Acetitomaculum, Butyricicoccus and Limosilactobacillus. All of which exhibited a relevant differential abundance between purebred animals using a Bayesian linear model.ConclusionsThe study confirms variations in gut microbiota among Iberian pig strains and their crosses, influenced by genetic and non-genetic factors. ML models, particularly CB and RF, as well as SVM in certain scenarios, combined with a feature selection process, effectively classified genetic groups based on microbiome data and identified key microbial taxa. These taxa were linked to short-chain fatty acids production and lipid metabolism, suggesting microbial composition differences may contribute to variations in fat-related traits among Iberian genetic groups.

How do aboveground and belowground functional traits correlate with the demography of tree seedlings regenerated after landslide disturbances?

ABSTRACT Trait-based plant ecology is commonly used to study plant demography in forests, especially after disturbances. However, how functional traits affect the demography of regenerating seedlings after landslides remains largely unknown. This study conducted a 2-year monitoring of Abies sachalinensis (evergreen species) and Acer mono (deciduous species) seedlings dominantly regenerating in artificial landslide areas in northern Japan. We investigated the correlations between intraspecific variations in functional traits and two demographic properties (growth and mortality). We found that growth of evergreen tree seedlings was correlated with functional traits related to leaf and root construction costs and light capture capacity. However, there were no significant correlations between mortality and the functional traits for either species in the post-landslide forests. Our results emphasize the importance of both aboveground and belowground functional traits in understanding vegetation recovery after landslides. This work suggests that the intraspecific trait-demography relationships can vary depending on the demographic properties in post-landslide forests.

Wood Anatomical and Hydraulic Traits of Tamarix Species Across a Large Eurasian Gradient Show a Stronger Climatic Than Phylogenetic Signal

ABSTRACTAimTamarisk (Tamarix) is the predominant but taxonomically complex genus of the Tamaricaceae. The morphologically similar shrub or tree species grow in arid, salt‐influenced habitats of Eurasia and Africa. We sampled woody shoots of seven species at the eastern margin (NW China; T. ramosissima) and the centre (Uzbekistan; T. hispida) of the Indo‐Turanian region as well as at the eastern (Israel; T. aphylla, T. negevensis, T. nilotica) and western margin (southern Spain; T. boveana, T. gallica) of the Mediterranean region, the two diversity centres of the genus. To explore the drivers of intrageneric trait variability, we investigated whether differences in the anatomical‐hydraulic wood traits among the species are related to climate or phylogeny.LocationEurasia.TaxonTamarix L. (Tamaricaceae).MethodsWe determined features of wood anatomy and calculated the hydraulic conductivity (kt) and the water potential at 50% loss of hydraulic conductance (P50). We related these traits to climate variables and to the phylogenetic distances among the species.ResultsTamarix nilotica, T. negevensis and T. ramosissima exhibited large conduit areas, large hydraulic diameters and high kt, whereas the Spanish species T. gallica and T. boveana displayed high wood densities and a small fraction of large conduits. The phylogenetically distant species T. aphylla and T. hispida took intermediate positions. Tamarix ramosissima, which grows in regions with cold winters and hot‐dry summers, exhibited the most negative P50 values, indicative of a low susceptibility to a failure of the water‐conducting system. Trait differences among the species were unrelated to the species' phylogenetic relatedness but correlated with climate variables.Main ConclusionsDespite strong similarity in morphology and habitat preferences, Tamarix species displayed significant differences in their anatomical‐hydraulic traits. These differences were related to climate conditions rather than phylogeny and are indicative of specific local adaptations to environmental conditions.

Genome Scan Analysis for Advancing Knowledge and Conservation Strategies of Primitivo Clones (Vitis vinifera L.)

The success of the Primitivo variety underscores the critical need for the managing of clone genetic conservation, utilization, and improvement. By combining genomic and environmental data, breeders can better predict the performance of varieties, thereby improving breeding efficiency and enabling more targeted development of high-quality grapevine cultivars. In this study, 35 Primitivo clones were analysed, including selected and certified clones that have been propagated over several years in Apulia. Genetic variability among the Primitivo clones was assessed through genotyping by sequencing. Using 38,387 filtered SNPs, pairwise identity-by-state (IBS) analysis demonstrated the uniqueness of the 35 clones (IBS < 0.75), indicating a high degree of variability among the samples. Genetic diversity analysis revealed three primary groups, which were differentiated based on geographic origin. The clones from Gioia del Colle were grouped into two distinct clusters, which aligns with the observed variation in grape-related traits. The fixation index (FST > 0.50) identified numerous loci putatively associated with stress responses and developmental traits, including genes involved in key plant biological processes, stress response regulation, and adaptation to environmental conditions such as glutamate receptors, auxin, and ethylene signalling.

Harnessing Genetic Diversity And Vigor Of Onion From Multi-generatio Study

This study investigates the genetic variation, vigor, and performance of onion generations resulting from crosses. The experiment was conducted at the Spices Research Centre, Shibganj, Bogura, during 2021-22, following diallel mating design with three parental varieties, BARI piaz1, BARI piaz4, and BARI piaz6. The research focuses on evaluating yield and yield-contributing traits, including bulb dimensions, bulbing index, and individual bulb weight. The results demonstrate significant genetic diversity among onion generations, offering opportunities for the breeders to select and develop superior lines. Hybrid vigor and heterosis, particularly evident in F1 generations, suggest that combining genetically diverse parental lines can lead to progeny with superior performance compared to their parents. However, the choice of parental lines significantly influences the stability and performance of hybrid progeny, with certain varieties demonstrating consistent performance across generations (Parent, F1, F2). Trait segregation and variability within F2 hybrid populations underscore the importance of evaluating large populations to identify individuals with desired trait combinations. Overall, the findings highlight the importance of leveraging genetic diversity and hybrid vigor in onion breeding programs to develop cultivars. By employing breeding strategies the high-performing onion varieties capable of thriving in diverse agro-climatic conditions in onion farming. Bangladesh J. Agri. 2024, 49(2): 31-44

Plant trait networks reveal the ecological strategies of Arabidopsis thaliana along ontogeny

AbstractPlant trait networks (PTNs) quantitatively describe the trait correlation patterns, which constrain the dynamic responses and individual fitness of plants under climate change. Despite renewed attention directed toward intraspecific trait correlation within trait‐based ecology in the last decade, thorough descriptions of how trait correlation patterns within species respond to changing environments ontogenetically and influence ecological strategies are still lacking. In this study, a controlled experiment was conducted on Arabidopsis thaliana wild‐type (Columbia), with three treatment groups (control/drought/heat) over three stages of the life cycle (vegetative/inflorescence/reproductive stage). Thirty traits (aboveground biomass and seed biomass were not used in trait correlation analyses) were obtained to investigate how trait correlation patterns in particular life stages (three T‐PTNs constructed based on trait variation across treatments within life stages) and during whole‐life cycles (three L‐PTNs constructed based on trait variation across life stages in particular treatments) respond to stress. The results showed that traits were correlated along an axis within life stages, reflecting an economic trade‐off between acquisitive strategies (in control plants) and conservative strategies (in drought/heat plants). With ontogeny, the higher sensitivity of central traits (highly connected with other plant traits in a network) in T‐PTNs to stress resulted in more significant separation among treatment groups along the axis. Second, based on life cycles, stress decoupled trait correlations in L‐PTNs and decreased the value of central traits (related to carbon assimilation/accumulation) compared with the ambient control, resulting in lower fitness. These results suggested that central traits in PTNs drove the shifts in ecological strategies across changing environments, and the network topology (the connection frequency or clustering degree among traits in a network, describing the correlation patterns) constrained the resource utilization efficiency in plants; both affect plant fitness collectively. These insights will facilitate more accurate and broader applications (e.g., the assessment of plant fitness or sensitivity to stress) of PTNs in trait‐based ecology.

Systematic shifts in the variation among host individuals must be considered in climate-disease theory.

To make more informed predictions of host-pathogen interactions under climate change, studies have incorporated the thermal performance of host, vector and pathogen traits into disease models to quantify effects on average transmission rates. However, this body of work has omitted the fact that variation in susceptibility among individual hosts affects disease spread and long-term patterns of host population dynamics. Furthermore, and especially for ectothermic host species, variation in susceptibility is likely to be plastic, influenced by variables such as environmental temperature. For example, as host individuals respond idiosyncratically to temperature, this could affect the population-level variation in susceptibility, such that there may be predictable functional relationships between variation in susceptibility and temperature. Quantifying the relationship between temperature and among-host trait variation will therefore be critical for predicting how climate change and disease will interact to influence host-pathogen population dynamics. Here, we use a model to demonstrate how short-term effects of temperature on the distribution of host susceptibility can drive epidemic characteristics, fluctuations in host population sizes and probabilities of host extinction. Our results emphasize that more research is needed in disease ecology and climate biology to understand the mechanisms that shape individual trait variation, not just trait averages.

A Genome-Wide Association Study and Rare Variant Analysis for Dupuytren Disease in a North American Population

Although European genome-wide association studies (GWAS) have aided in defining genetic associations in Dupuytren disease (DD), North American populations have been infrequently analyzed. Additionally, there are a paucity of rare variant analyses (RVA) for DD, which can help define both trait variability and risk for low-frequency variants. Our purpose was to perform a GWAS and RVA for DD using a North American database. The study cohort (cases and controls) consisted of patients from our institutional MyCode Community Health Initiative, an unselected clinical cohort. A GWAS was performed controlling for age, sex and body mass index. For the RVA, sequence kernel association test analysis was performed on the most significant genes from the GWAS. Sequence kernel association test is a regression method to test associations between common and rare genetic variants in a defined region and a specific trait while adjusting for covariates. A total of 1,123 DD cases and 130,822 controls were included. DD cases were significantly older, more likely to be male, and had higher body mass indices. The GWAS yielded variants in two genes with a statistically significant difference between cases and controls: WNT7B and EPDR1. WNT7B variants rs9330811 (odds ratio, 1.96; 95% confidence interval, 1.73-2.23) and rs10448585 (odds ratio, 1.68; 95% confidence interval, 1.44-1.96) were the top hits. Variant rs2122625 in EPDR1 also reached genome-wide significance. The RVA indicated that WNT7B, DUXA, LOXL1, CSMD2, and TACC2 were significantly associated with a diagnosis of DD. In our North American population, GWAS yielded variants in two genes that were significantly associated with DD (WNT7B and EPDR), which likely contribute to abnormal proliferation of fibroblasts. Five rare variants (WNT7B, DUXA, LOXL1, CSMD2, and TACC2) were also significantly associated with DD. As disease-modifying treatments are explored, these data add to a growing body of literature defining genetic variants in DD.

Understanding molecular mechanisms of vertebral number of variations on Mongolian sheep using candidate genes analysis.

This study aimed to investigate the genetic link between variations in vertebral number and meat production traits, such as body weight and body measurements (body length, body height, heart girth, and shin width) in Mongolian (Bayantsagaan) sheep. Additionally, we examined the association of single-nucleotide polymorphisms (SNPs) in candidate genes, particularly vertnin (VRTN), nuclear receptor subfamily 6, group A, member 1 (NR6A1), and synapse differentiation-inducing 1-like (SYNDIG1L), with vertebral number variations and their potential impact on meat production traits. The study involved 220 Bayantsagaan sheep from Bayantsagaan soum, Tov province, Mongolia, including 104 sheep with extra vertebrae group and 116 individuals with typical vertebral number as the control group. Morphological data, including body weight and body measurements, were collected, and genetic samples were obtained. The impact of vertebral number on morphological traits was estimated using a general linear model. The SNPs in the VRTN, NR6A1, and SYNDIG1L genes were sequenced, and their association with vertebral number was analyzed using one-way analysis of variance. Bayantsagaan sheep with extra vertebrae were, on average, 4.45 kg heavier and exhibited higher variability in body size traits compared to the control group. Four polymorphic sites were identified at the VRTN gene, with one polymorphic locus (VRTN1716) showing a significant association with vertebrae number and body size. Sheep with C/C genotype at VRTN1716 locus, had more vertebrae and larger body size compared to other genotypes. The findings suggest that variations in vertebral number and VRTN gene polymorphisms are linked to favorable meat production traits in Bayantsagaan sheep. The identified SNP (VRTN1716) associated with vertebral number and body size offers the potential for marker-assisted selection in breeding programs. These results provide valuable insights into the genetic basis of meat production traits in Bayantsagaan sheep and may contribute to the development of more efficient breeding strategies.

Variations in Leaf Functional Traits of Pseudotsuga sinensis Across Forests With Varying Levels of Rocky Desertification.

Pseudotsuga sinensis is a distinctive plant species endemic to China, predominantly found in areas affected by varying degrees of rocky desertification. Despite its wide distribution, the physiological mechanisms underlying its adaptation to harsh environments remain unclear. In this study, we investigated 16 leaf traits, including the morphological, anatomical, and chemical characteristics of the leaves of P. sinensis across forests with mild, moderate, severe, and extremely severe rocky desertification to elucidate the adaptive strategies of P. sinensis in response to arid conditions and nutrient-poor soils. Our findings revealed that P. sinensis leaves from forests with mild and moderate rocky desertification exhibited higher specific leaf area (SLA) and magnesium concentrations but lower leaf dry matter content (LDMC), abaxial epidermis thickness, and adaxial epidermis thickness than in those from forests with severe and extremely severe desertification. Principal component analysis indicated that forests with mild to moderate desertification employ resource acquisition strategies characterized by greater SLA and magnesium concentrations than those in forests with severe and extremely severe desertification. In contrast, forests with severe to extremely severe desertification adopted resource-conserving strategies, as evidenced by higher LDMC, epidermal thickness, and calcium concentrations than those in forests with mild to moderate desertification. The N:P ratio of P. sinensis across all desertification levels was consistently below 14, suggesting nitrogen limitation in P. sinensis in regions with rocky desertification. Thus, these results provide valuable reference for guiding vegetation restoration under degraded habitats.

Genetic Diversity Analysis and Comprehensive Evaluation of “M82” in EMS-Mutagenized Tomato

Background: Ethyl methyl sulfonate (EMS) mutagenesis is widely used because of its advantages of inducing point mutations and no need for genetic transformation. To identify germplasm resources of processed tomatoes with superior comprehensive traits suitable for cultivation in Xinjiang. Methods: In this study, tomato seeds were treated with 2% EMS reagent for 12 h, 21 quality traits and 20 quantitative traits of 33 processed tomatoes derived from EMS-mutagenized“M82”were evaluated. Results: The results indicated that for traits such as hypocotyl color, growth habit, plant type, leaf type, and leaf shape, the range of quantitative trait variation was 8.45–37.25%, with a genetic diversity index ranging from 1.25 to 2.07. Conclusions: Cluster analysis of quantitative traits categorized the 33 EMS-mutagenized “M82” processed tomato resources into five groups: Group I contained 22 robust germplasm samples; Group II consisted of a single potential high-quality germplasm; Group III comprised five germplasm with a small and extreme plant type; Group IV included four high-yield germplasm; and Group V represented one moderate, conventional germplasm. Raw data from 15 quantitative traits across the 33 accessions were standardized using the “extreme method” to extract six comprehensive factors. The top 10 germplasm resources based on the comprehensive score were 76, 137, 97, 102, 19, 104, 21, 108, 17, and 147. It provides some theoretical basis for realizing the high-yield and high-quality cultivation and variety breeding of processed tomatoes in Xinjiang.

Mean plant toxicity modulates the effects of plant defense variability.

Plant trait variation is thought to suppress herbivore performance, but experiments typically manipulate only a single mean level of the trait. We manipulated the mean and variation of the concentration of a plant toxin in a model plant-herbivore system across three field and greenhouse experiments. Plants with leaves painted with a higher mean toxin concentration exhibited increased fitness and resistance to herbivores; however, at high mean concentrations, variation reduced the defensive effect, while at lower mean concentrations, variation enhanced it. This reversal aligns with models that include herbivore food selectivity, but our simulations revealed that the benefits of food selectivity for herbivores were minimal. Instead, nonlinear averaging and physiological tracking effects likely drove patterns in plant fitness and resistance to herbivores. We suggest that high defense variation in plants may be a widespread defensive phenotype, but for well-defended plants, variation may inadvertently promote herbivore niche expansion.

Developing pangenomes for large and complex plant genomes and their representation formats.

The development of pangenomes has revolutionized genomic studies by capturing the complete genetic diversity within a species. Pangenome assembly integrates data from multiple individuals to construct a comprehensive genomic landscape, revealing both core and accessory genomic elements. This approach enables the identification of novel genes, structural variations, and gene presence-absence variations, providing insights into species evolution, adaptation, and trait variation. Representing pangenomes requires innovative visualization formats that effectively convey the complex genomic structures and variations. This review delves into contemporary methodologies and recent advancements in constructing pangenomes, particularly in plant genomes. It examines the structure of pangenome representation, including format comparison, conversion, visualization techniques, and their implications for enhancing crop improvement strategies. Earlier comparative studies have illuminated novel gene sequences, copy number variations, and presence-absence variations across diverse crop species. The concept of a pan-genome, which captures multiple genetic variations from a broad spectrum of genotypes, offers a holistic perspective of a species' genetic makeup. However, constructing a pan-genome for plants with larger genomes poses challenges, including managing vast genome sequence data and comprehending the genetic variations within the germplasm. To address these challenges, researchers have explored cost-effective alternatives to encapsulate species diversity in a single assembly known as a pangenome. This involves reducing the volume of genome sequences while focusing on genetic variations. With the growing prominence of the pan-genome concept in plant genomics, several software tools have emerged to facilitate pangenome construction. This review sheds light on developing and utilizing software tools tailored for constructing pan-genomes in plants. It also discusses representation formats suitable for downstream analyses, offering valuable insights into the genetic landscape and evolutionary dynamics of plant species. In summary, this review underscores the significance of pan-genome construction and representation formats in resolving the genetic architecture of plants, particularly those with complex genomes. It provides a comprehensive overview of recent advancements, aiding in exploring and understanding plant genetic diversity.

UAV-Based Multiple Sensors for Enhanced Data Fusion and Nitrogen Monitoring in Winter Wheat Across Growth Seasons

Unmanned aerial vehicles (UAVs) equipped with multi-sensor remote sensing technologies provide an efficient approach for mapping spatial and temporal variations in vegetation traits, enabling advancements in precision monitoring and modeling. This study’s objective was to analyze UAV multiple sensors’ performance in monitoring winter wheat chlorophyll content (SPAD), plant nitrogen accumulation (PNA), and N nutrition index (NNI). A two-year field experiment with five N fertilizer treatments was carried out. The color indices (CIs, from RGB sensors), vegetation indices (VIs, from multispectral sensors), and temperature indices (TIs, from thermal sensors) were derived from the collected images. XGBoost (extreme gradient boosting) was applied to develop the models, using 2021 data for training and 2022 data for testing. The excess green minus excess red index, red green ratio index, and hue (from CIs), and green normalized difference vegetation index, normalized difference red-edge index, and normalized difference vegetation index (from VIs), showed high correlations with three N indicators. At the pre-heading stage, the best performing CIs correlated better than the VIs; this was reversed in the post-heading stage. CIs outperformed VIs in SPAD (CIs: R2(coefficient of determination) = 0.66, VIs: R2 = 0.61), PNA (CIs: R2 = 0.68, VIs: R2 = 0.64), and NNI (CIs: R2 = 0.64, VIs: R2 = 0.60) in the pre-heading stage, whereas VI-based models achieved slightly higher accuracies in post-heading and all stages compared to CIs. Models built with CIs + VIs significantly improved the models’ performance compared to single-sensor models. Adding TIs to CIs and CIs + VIs further improved the models’ performance slightly, especially at the post-heading stage, resulting in the best model performance with three sensors. These findings highlight the effectiveness of UAV systems in estimating wheat N and establish a framework for integrating RGB, multispectral, and thermal sensors to enhance model accuracy in precision vegetation monitoring.

Genetic variability and association of traits in common bean ( Phaseolus vulgaris L.) genotypes in Sekota, North Eastern Ethiopia

Genetic variability and association of traits in common bean ( <i>Phaseolus vulgaris</i> L.) genotypes in Sekota, North Eastern Ethiopia