In this study, we identified 87 members of the SAUR gene family within the Populus simonii genome, a notably extensive family with a central role in auxin responses. Phylogenetic analysis classified these genes into five distinct subfamilies. Chromosomal mapping revealed an uneven distribution of PsSAUR genes across 19 chromosomes, with notable clustering on chromosomes 4 and 9. Structural analysis indicated that members of the same subfamily share highly conserved protein domain architectures and motif organizations. Promoter analysis identified various cis-acting elements associated with light, phytohormones, abiotic stress, and general growth and development. Transcriptome profiling revealed tissue-specific and stress-responsive expression patterns, underscoring the transcriptional plasticity of PsSAUR genes in developmental regulation and environmental adaptation. These findings were further validated through RT-qPCR analysis, confirming the differential expression of selected PsSAURs. Notably, PsSAUR66 was identified as a key gene in the interaction network. Functional validation using transient ProPsSAUR66:GUS assays in tobacco demonstrated that its promoter activity is strongly induced by heat, cold, and salt stresses. These findings provide novel insights into the regulatory roles of PsSAUR genes and lay the groundwork for further functional characterization in woody plants.

Class III peroxidases are plant-specific enzymes that play indispensable roles in catalyzing oxidative–reductive reactions, which are integral to numerous biochemical processes in plants. In this study, we identified 69 members of the class III peroxidase (POD) gene family in the Populus simonii genome and classified them into four subfamilies based on phylogenetic analysis. Chromosomal localization revealed that these PsPOD genes are unevenly distributed across 19 chromosomes, with chromosomes 3 and 7 harboring the highest densities. Conserved domain and motif analyses demonstrated that all PsPOD proteins contain the characteristic peroxidase domain and share highly conserved motif structures. Cis-acting element analysis of promoter regions revealed the presence of numerous regulatory elements associated with light responsiveness, phytohormone signaling, stress responses, and plant growth and development. Transcriptome data showed that the expression of PsPOD genes varies significantly across different tissues and organs and under various stress conditions, suggesting their involvement in both developmental processes and abiotic stress responses. These findings were further validated by qRT-PCR analysis of selected PsPOD genes. Notably, PsPOD45, PsPOD69, PsPOD33, and PsPOD64 were identified as central hub genes in the protein–protein interaction network, making them promising candidates for further functional characterization. Overall, this study provides a comprehensive overview of the PsPOD gene family in P. simonii, laying a solid foundation for future functional studies and offering valuable insights for comparative research in other plant species.

With the advancement of industrial aquaculture, intelligent fish feeding has become pivotal in reducing feed and labor costs while enhancing fish welfare. Computer vision, as a non-invasive and efficient approach, has made significant strides in this domain. However, current research still faces three major issues: qualitative labels lead to models that produce only qualitative outputs; redundant information in images causes interference; and the high complexity of models hinders real-time application. To address these challenges, this study innovatively proposes the quantification of fish feeding behaviors through satiety experiments, enabling the generation of quantitative data labels. A two-stage recognition network is then designed to eliminate redundant information and enhance model performance. This network utilizes pose detection to extract key features, while a graph convolutional network (GCN) effectively models the topological relationships between fish posture and distribution, achieving a satiety classification accuracy of 98.1%. Furthermore, to reduce model complexity, lightweight RepSELAN and SPPSF modules were developed, resulting in a 31.4% reduction in parameters and a 26.2% decrease in computational load, with only a 0.11% decrease in mAP(B) and a 0.95% increase in mAP(P). Compared with existing methods, this approach outperforms conventional models in both accuracy and efficiency, providing a novel and efficient model foundation for developing intelligent feeding strategies.

Unmanned aerial vehicles (UAV) aerial imagery presents significant challenges for object detection due to small, densely packed targets and complex backgrounds. This study introduces an enhanced model, MGT-YOLOv8n(Freeze), extending the YOLOv8n architecture to address these challenges. We propose a novel training paradigm with a classification-focused pre-training phase and a detection phase that leverages inherited classification weights to enhance feature extraction capabilities. The pre-training phase involves cropping instances from the detection dataset for category-specific classification, with weights frozen and inherited during detection.We enhance the YOLOv8n architecture through modifications: integration of the VSS module in the backbone network for improved detail feature capture, employing the GDFPN structure in the neck for optimized multi-scale feature fusion and lightweight dynamic up-sampling, and incorporating the TADH module in the head for improved task alignment. Additionally, we introduce the WSIoU loss function to reduce low-quality frame impacts. Experimental results demonstrate the superiority of our training paradigm, On the HIT-UAV dataset, MGT-YOLOv8n(Freeze) achieves substantial gains of 8.7% and 9.1% in mAP@0.5 and mAP@0.95, respectively, while improving mAP@0.5 and mAP@0.95 on VisDrone2019 by 2.7% and 2.2%, significantly outperforming traditional direct training approaches. These comprehensive results validate the effectiveness of our improved model for small target detection in UAV aerial imagery and the viability of the proposed training paradigm.

BACKGROUND:GRAS proteins constitute a plant-specific family of transcription factors involved in growth, development, and stress responses. Although the GRAS gene family has been extensively studied in various plant species, the comprehensive examination of the GRAS gene family in Populus simonii remains poorly characterized. In particular, its classification, evolutionary relationships, and potential functions in Populus simonii remain largely unexplored. RESULTS:In this study, a total of 89 GRAS gene family members were identified in the Populus simonii genome. These genes were found to be unevenly distributed across 19 chromosomes, with chromosomes 1 and 7 harboring the highest number of GRAS genes. Based on the classification framework established for Arabidopsis thaliana, the Populus simonii GRAS genes were categorized into ten distinct subgroups. Sequence conservation analysis revealed that all PSGRAS proteins possess the conserved GRAS domain, and share several highly conserved motifs. Analysis of cis-acting elements in the promoter regions indicated the presence of multiple regulatory elements associated with light responsiveness, phytohormone signaling, stress tolerance, and developmental regulation. Expression profiling showed that PSGRAS genes exhibit tissue-specific and stress-responsive expression patterns, suggesting their functional diversification in growth, development, and abiotic stress adaptation in Populus simonii. Notably, PSGRAS20 was identified as a central hub in the predicted protein-protein interaction network, implying a potential regulatory role in coordinating the expression of other GRAS family members. CONCLUSIONS:This study systematically identified and characterized 89 GRAS genes in Populus simonii, revealing their uneven chromosomal distribution, conserved structural features, and diverse expression patterns across tissues and stress conditions. The presence of cis-acting elements related to hormone signaling, stress response, and development suggests their broad regulatory roles. Notably, PSGRAS20 was identified as a potential central regulator within the gene interaction network. These findings enhance our understanding of the GRAS gene family's biological functions in Populus simonii and provide a foundation for future functional genomics and breeding applications.

Pruning is a common forest-tending method; its purpose is to promote growth and improve the overall stand quality. Poplar is a fast-growing, broad-leaved tree species with high ecological and economic value. It is a common management method to promote its growth by pruning and adjusting the spatial structure of the stand, but its potential regulatory mechanism remains unclear. In this study, transcriptome and metabolome data of different parts at all pruning intensities were determined and analyzed. The results showed that 7316 differentially expressed genes were identified in this study. In the plant hormone signal transduction pathway, candidate genes were found in eight kinds of plant hormones, among which the main expression was gibberellin, auxin, and brassinosteroid. Some candidate gene structures (beta-glucosidase, endoglucanase, hexokinase, glucan endo-1, 3-beta-D-glucosidase, beta-fructofuranosidase, fructokinase, maltase-glucoamylase, phosphoglucomutase, and sucrose) were specifically associated with starch and sucrose biosynthesis. In the starch and sucrose biosynthesis pathway, D-fructose 6-phosphate, D-glucose 1,6-bisphosphate, and glucose-1-phosphate were the highest in stems and higher in the first round of pruning than in no pruning. The bHLH plays a key role in the starch and sucrose synthetic pathway, and AP2/ERF-ERF is important in the plant hormone signal transduction pathway. These results laid a foundation for understanding the molecular mechanism of starch and sucrose biosynthesis and provided a theoretical basis for promoting tree growth through pruning.

As an efficient and environment-friendly method, electrostatic separation has gradually replaced flotation methods in the separation of magnesite in recent years. In the process of triboelectrostatic separation, the mineral particles are tribocharged driven by the air flow, then the trajectory is shifted under the action of the electric field, so as to realize the separation. The useful mineral in magnesite is MgCO3, but the theoretical research related to the charge characteristics of MgCO3 is not sufficient. Particle image velocimetry (PIV), as an indirect measurement technique, is able to obtain the velocity field of the fluids from images. However, the particles moving in the air have the issues such as excessive speed and small particle size, which make the traditional PIV has low accuracy in estimating the motion of particles. In this paper, a high-speed camera is used to capture the motion trajectory of tribocharged MgCO3 particles in a parallel electric field. A new optical flow method LFN-en-A network based on LiteFlowNet-en network is proposed to compute the particle motion trajectory by combining the deep learning method with the traditional PIV, which realizes the displacement estimation of particles moving in the air. It ultimately realizes the calculation of the charge-to-mass ratio on single particles. Analyzing the accuracy of the LFN-en-A network's estimation in the experiments, the estimation of LiteFlowNet-en was compared. Changing the shooting frame rate analyzes the optimal one required by the LFN-en-A network. Combining the estimation results of LFN-en-A to calculate the particle charge-to-mass ratio (Q/m), the Q/m of MgCO3 particle was analyzed by changing the experimental conditions in the process of particles' tribocharging, which provided a new method for particle-to-charge ratio measurement.

Accurate estimation of fish length is crucial aquaculture processes. Precise and real-time estimation of fish length aids fish farmers in formulating effective farming strategies. Existing methods for estimating the length of underwater fish have made significant advancements in overcoming the interferences present in complex underwater environments. Nevertheless, the measurement results of some methods continue to be limited by variations in the refractive index of multiple media and deviations in imaging angles, resulting in measurement errors and diminished practicality. Consequently, we propose a measurement method that aims to address the aforementioned challenges in fisheries aquaculture by providing more accurate and cost-effective solutions. This method initially introduces the concept of regressing multiple media into a single medium to correct underwater depth estimation and enhance underwater imaging quality. Subsequently, an accurate plane fitting algorithm is applied to precisely fit the fish body plane and extract its corresponding normal vector information. Finally, taking into account the light propagation path and accounting for the limitations of precise values, we utilize the geometric relationship between the length of the bounding box and the actual length of the fish to calculate a more accurate estimation of fish body length. Experimental validation showcases the applicability of our measurement method to diverse fish species and substantiates its exceptional performance in both experimental and real-world scenarios. The maximum average relative errors for each group are within 8%, and the average relative error is 3.28% under the exact value constraints presented in the experiments. This method makes a significant contribution to fish length estimation and offers valuable insights for applications in fisheries and aquaculture.

Melatonin is widely recognized as involved in a wide range of plant growth and development functions. Research has primarily focused on herbaceous plants, with few findings on the function of melatonin in supporting tree growth. In this study, it was demonstrated that melatonin, as an endogenous hormone, considerably contributed towards the increased tolerance of poplar to salt-alkali stress. Transcriptome and metabolome analysis revealed that melatonin may enhance the cellular protection and antioxidant mechanisms of poplar and improve salt-alkali tolerance by regulating genes and metabolites involved in photosynthesis, antioxidant enzyme systems, plant hormone signal transduction pathways, and in the flavonoid as well as the melatonin biosynthesis pathway. The results of this study not only aid our increased understanding of the potential roles of melatonin in salt-alkali stress but also shed light on the potential mechanisms of the economically important woody model plant, poplar, involved in resisting abiotic stress.

This paper presents a novel path planning algorithm for a picking robotic arm in a multi-obstacle environment, based on deep reinforcement learning. The proposed method introduces a new state representation technique that accurately captures the real-time state information of the robotic arm and multiple obstacles using a finite-dimensional representation. This state is represented by the direction vector of the nearest distance to the nearest obstacle around each axis of the robot arm, the real-time angle of the robotic arm, the three-dimensional coordinates of the picking target, and the three-dimensional coordinates of the end effector. The effectiveness of this method is validated through tests in a simulation environment.

BackgroundPopulus simonii, a notable native tree species in northern China, demonstrates impressive resistance to stress, broad adaptability, and exceptional hybridization potential. DOF family is a class of specific transcription factors that only exist in plants, widely participating in plant growth and development, and also playing an important role in abiotic stress response. To date, there have been no reported studies on the DOF gene family in P. simonii, and the expression levels of this gene family in different tissues of poplar, as well as its expression patterns under cold, heat, and other stress conditions, remain unclear.MethodsIn this study, DOF gene family were identified from the P. simonii genome, and various bioinformatics data on the DOF gene family, gene structure, gene distribution, promoters and regulatory networks were analyzed. Quantitative real time PCR technology was used to verify the expression patterns of the DOF gene family in different poplar tissues.ResultsThis research initially pinpointed 41 PSDOF genes in P. simonii genome. The chromosomal localization results revealed that the PSDOF genes is unevenly distributed among 19 chromosomes, with the highest number of genes located on chromosomes 4, 5, and 11. A phylogenetic tree was constructed based on the homology between Arabidopsis thaliana and P. simonii, dividing the 41 PSDOF genes into seven subgroups. The expression patterns of PSDOF genes indicated that specific genes are consistently upregulated in various tissues and under different stress conditions, suggesting their pivotal involvement in both plant development and response to stress. Notably, PSDOF35 and PSDOF28 serve as pivotal hubs in the interaction network, playing a unique role in coordinating with other genes within the family.ConclusionThe analysis enhances our comprehension of the functions of the DOF gene family in tissue development and stress responses within P. simonii. These findings provide a foundation for future exploration into the biological roles of DOF gene family.

In the domain of aquaculture, the act of feeding fish is a pivotal factor influencing both the growth of the fish and the associated cultivation costs. The implementation of intelligent feeding strategies is a crucial prerequisite for maintaining fish health and minimizing costs, with the accurate discernment of fish feeding behavior serving as the fundamental basis for the realization of such strategies. Addressing the issues of high data redundancy and substantial noise content inherent in the datasets utilized by existing identification models, as well as the intricate design and suboptimal execution efficiency of the model structures, this study introduces a two-stage framework for discriminating fish feeding behavior. In the initial stage, a re-parameterizable multi-scale object detection model is established, facilitating the acquisition of the spatial distribution of fish schools. This process effectively eliminates redundant data and noise, decouples the training and inference processes of the model, equivalently transforms the complex network training weights, and simplifies the inference process of the model. An analysis of the dataset characteristics is conducted, leading to the optimization of the model detector’s design and a reduction in both the model parameters and computational requirements. In the subsequent stage, responding to the key data characteristics of the spatial distribution of fish schools, a lightweight behavior recognition model is designed. This model enables the rapid and accurate identification of fish feeding behavior. A plethora of experimental results demonstrate that the proposed method can achieve a high recognition accuracy (Acc 83.33%) while operating under the constraints of minimal model parameters and computations (6.45M Params, 8.135G FLOPs). This provides a robust model foundation for the industrial application of the algorithm, underscoring the significant potential of the proposed method in advancing intelligent feeding strategies in aquaculture.

Feed costs constitute a significant part of the expenses in the aquaculture industry. However, feeding practices in fish farming often rely on the breeder’s experience, leading to feed wastage and environmental pollution. To achieve precision in feeding, it is crucial to adjust the feed according to the fish’s feeding state. Existing computer vision-based methods for assessing feeding intensity are limited by their dependence on a single spatial feature and manual threshold setting for determining feeding status constraints. These models lack practicality due to their specificity to certain scenarios and objectives. To address these limitations, we propose an integrated approach that combines computer vision technology with a Convolutional Neural Net-work (CNN) to assess the feeding intensity of farmed fish. Our method incorporates temporal, spatial, and data statistical features to provide a comprehensive evaluation of feeding intensity. Using computer vision techniques, we preprocessed feeding images of pearl gentian grouper, extracting temporal features through optical flow, spatial features via binarization, and statistical features using the gray-level co-occurrence matrix. These features are input into their respective specific feature discrimination networks, and the classification results of the three networks are fused to construct a three-stream network for feeding intensity discrimination. The results of our proposed three-stream network achieved an impressive accuracy of 99.3% in distinguishing feeding intensity. The model accurately categorizes feeding states into none, weak, and strong, providing a scientific basis for intelligent fish feeding in aquaculture. This advancement holds promise for promoting sustainable industry development by minimizing feed wastage and optimizing environmental impact.

The cone is a crucial component of the whole life cycle of gymnosperm and an organ for sexual reproduction of gymnosperms. In Pinus koraiensis, the quantity and development process of male and female cones directly influence seed production, which in turn influences the tree’s economic value. There are, however, due to the lack of genetic information and genomic data, the morphological development and molecular mechanism of female and male cones of P. koraiensis have not been analyzed. Long-term phenological observations were used in this study to document the main process of the growth of both male and female cones. Transcriptome sequencing and endogenous hormone levels at three critical developmental stages were then analyzed to identify the regulatory networks that control these stages of cones development. The most significant plant hormones influencing male and female cones growth were discovered to be gibberellin and brassinosteroids, according to measurements of endogenous hormone content. Additionally, transcriptome sequencing allowed the identification of 71,097 and 31,195 DEGs in male and female cones. The synthesis and control of plant hormones during cones growth were discovered via enrichment analysis of key enrichment pathways. FT and other flowering-related genes were discovered in the coexpression network of flower growth development, which contributed to the growth development of male and female cones of P. koraiensis. The findings of this work offer a cutting-edge foundation for understanding reproductive biology and the molecular mechanisms that control the growth development of male and female cones in P. koraiensis.

In aquaculture, the accurate recognition of fish underwater has outstanding academic value and economic benefits for scientifically guiding aquaculture production, which assists in the analysis of aquaculture programs and studies of fish behavior. However, the underwater environment is complex and affected by lighting, water quality, and the mutual obscuration of fish bodies. Therefore, underwater fish images are not very clear, which restricts the recognition accuracy of underwater targets. This paper proposes an improved YOLO-V7 model for the identification of Takifugu rubripes. Its specific implementation methods are as follows: (1) The feature extraction capability of the original network is improved by adding a sizeable convolutional kernel model into the backbone network. (2) Through ameliorating the original detection head, the information flow forms a cascade effect to effectively solve the multi-scale problems and inadequate information extraction of small targets. (3) Finally, this paper appropriately prunes the network to reduce the total computation of the model; meanwhile, it ensures the precision of the detection. The experimental results show that the detection accuracy of the improved YOLO-V7 model is better than that of the original. The average precision improved from 87.79% to 92.86% (when the intersection over union was 0.5), with an increase of 5.07%. Additionally, the amount of computation was reduced by approximately 35%. This shows that the detection precision of the proposed network model was higher than that for the original model, which can provide a reference for the intelligent aquaculture of fishes.

The auxin/indole-3-acetic acid (Aux/IAA) and auxin response factor (ARF) genes are two crucial gene families in the plant auxin signaling pathway. Nonetheless, there is limited knowledge regarding the Aux/IAA and ARF gene families in Populus simonii. In this study, we first identified 33 putative PsIAAs and 35 PsARFs in the Populus simonii genome. Analysis of chromosomal location showed that the PsIAAs and PsARFs were distributed unevenly across 17 chromosomes, with the greatest abundance observed on chromosomes 2. Furthermore, based on the homology of PsIAAs and PsARFs, two phylogenetic trees were constructed, classifying 33 PsIAAs and 35 PsARFs into three subgroups each. Five pairs of PsIAA genes were identified as the outcome of tandem duplication, but no tandem repeat gene pairs were found in the PsARF family. The expression profiling of PsIAAs and PsARFs revealed that several genes exhibited upregulation in different tissues and under various stress conditions, indicating their potential key roles in plant development and stress responses. The variance in expression patterns of specific PsIAAs and PsARFs was corroborated through RT-qPCR analysis. Most importantly, we instituted that the PsIAA7 gene, functioning as a central hub, exhibits interactions with numerous Aux/IAA and ARF proteins. Furthermore, subcellular localization findings indicate that PsIAA7 functions as a protein localized within the nucleus. To conclude, the in-depth analysis provided in this study will contribute significantly to advancing our knowledge of the roles played by PsIAA and PsARF families in both the development of P. simonii tissue and its responses to stress. The insights gained will serve as a valuable asset for further inquiries into the biological functions of these gene families.

The C-Repeat Binding Factor (CBF) gene family has been identified and characterized in multiple plant species, and it plays a crucial role in responding to low temperatures. Presently, only a few studies on tree species demonstrate the mechanisms and potential functions of CBFs associated with cold resistance, while our study is a novel report on the multi-aspect differences of CBFs among three tree species, compared to previous studies. In this study, genome-wide identification and analysis of the CBF gene family in Acer truncatum, Acer pseudosieboldianum, and Acer yangbiense were performed. The results revealed that 16 CBF genes (five ApseCBFs, four AcyanCBFs, and seven AtruCBFs) were unevenly distributed across the chromosomes, and most CBF genes were mapped on chromosome 2 (Chr2) and chromosome 11 (Chr11). The analysis of phylogenetic relationships, gene structure, and conserved motif showed that 16 CBF genes could be clustered into three subgroups; they all contained Motif 1 and Motif 5, and most of them only spanned one exon. The cis-acting elements analysis showed that some CBF genes might be involved in hormone and abiotic stress responsiveness. In addition, CBF genes exhibited tissue expression specificity. High expressions of ApseCBF1, ApseCBF3, AtruCBF1, AtruCBF4, AtruCBF6, AtruCBF7, and ApseCBF3, ApseCBF4, ApseCBF5 were detected on exposure to low temperature for 3 h and 24 h. Low expressions of AtruCBF2, AtruCBF6, AtruCBF7 were detected under cold stress for 24 h, and AtruCBF3 and AtruCBF5 were always down-regulated under cold conditions. Taken together, comprehensive analysis will enhance our understanding of the potential functions of the CBF genes on cold resistance, thereby providing a reference for the introduction of Acer species in our country.

为解决红鳍东方鲀养殖密度不均导致图像分割精度低和小目标分割效果差的问题,提出一种改进的轻量版SOLOv2实例分割方法.首先进行可变形卷积(deformable convolutional networks,DCN)网络结构的优化调整,通过在卷积核上增加偏移参数,调整卷积的感受野,使感受野与物体的实际形状更加贴近;再在残差模块最后一层引入无参数注意力机制SimAM,捕捉图像中更多的局部信息,获得不同尺度的目标特征,优化模型对小目标分割的性能.试验结果显示,改进后的轻量版SOLOv2模型较原有模型平均分割精度提高了3.7个百分点,对小目标的分割精度提升了1.4个百分点,同时加入DCN和SimAM注意力模块后,模型的分割精度提高到65.2％.结果表明,改进后的SOLOv2模型可以提高边界处的细节感知能力,强化模型对小目标鱼群特征的提取能力,可用于高密度场景下的精准实例分割,实现红鳍东方鲀鱼群目标精准像素级分割.

Tilia amurensis is a significant ornamental and economically-important tree species, known for its fragrant flowers, which are a source of high-quality honey production. However, the regulatory mechanisms involved in aroma formation during flower development in T. amurensis remains limited. The current study revealed the detection of plant hormones at every assessed stage of flower development. Among them, auxin and brassinosteroid contents significantly increased at stage 3, potentially regulating crucial functions during T. amurensis flower development. Moreover, the study examined the levels and change patterns of secondary metabolites and employed a combination of transcriptomics and metabolomics to comprehensively assess essential structural genes implicated in the biosynthesis pathways of terpenoid and phenylpropanoid. A comprehensive set of 89,526 differentially expressed genes (DEGs) was uncovered, including candidate structural genes ACAT, HDS, TPS, 4CL, CAD, and CCOAMT, which are specifically involved in the biosynthesis of terpenoids and phenylpropanoids. Maslinic acid, 2α,3α-dihydroxyursolic acid, and betulinic acid were accumulated in the terpenoid biosynthesis pathway. In contrast, metabolites with differential accumulation, such as phenylalanine, coniferyl alcohol, and cinnamic acid, were specifically enriched in the phenylpropanoid biosynthesis pathway. The C2H2, MYB, and NAC transcription factor families are crucially associated with the terpenoid and phenylpropanoid biosynthesis pathways. Two transcription factors, C2H2-17 and MYB-24, exhibited strong co-expression with structural genes in two networks, and were identified as central regulatory factors. These findings establish a solid groundwork for elucidating the generation of floral fragrance and provide comprehensive genetic and metabolic information for further studies on T. amurensis.

Pinus koraiensis, Pinus sibirica, and Pinus pumila are the major five-needle pines in northeast China, with substantial economic and ecological values. The phenotypic variation, environmental adaptability and evolutionary relationships of these three five-needle pines remain largely undecided. It is therefore important to study their genetic differentiation and evolutionary history. To obtain more genetic information, the needle transcriptomes of the three five-needle pines were sequenced and assembled. To explore the relationship of sequence information and adaptation to a high mountain environment, data on needle morphological traits [needle length (NL), needle width (NW), needle thickness (NT), and fascicle width (FW)] and 19 climatic variables describing the patterns and intensity of temperature and precipitation at six natural populations were recorded. Geographic coordinates of altitude, latitude, and longitude were also obtained. The needle morphological data was combined with transcriptome information, location, and climate data, for a comparative analysis of the three five-needle pines. We found significant differences for needle traits among the populations of the three five-needle pine species. Transcriptome analysis showed that the phenotypic variation and environmental adaptation of the needles of P. koraiensis, P. sibirica, and P. pumila were related to photosynthesis, respiration, and metabolites. Analysis of orthologs from 11 Pinus species indicated a closer genetic relationship between P. koraiensis and P. sibirica compared to P. pumila. Our study lays a foundation for genetic improvement of these five-needle pines and provides insights into the adaptation and evolution of Pinus species.