Security Analysis in Smart Agriculture: Insights from a Cyber-Physical System Application

Smart agriculture modifies traditional farming practices,and offers innovative approaches to boost production and sustainability by leveraging contemporary technologies.In today’s world where technology is everything,these technologies are utilized to streamline regular tasks and procedures in agriculture,one of the largest and most significant industries in every nation.This research paper stands out from existing literature on smart agriculture security by providing a comprehensive analysis and examination of security issues within smart agriculture systems.Divided into three main sections-security analysis,system architecture and design and risk assessment of Cyber-Physical Systems(CPS)applications-the study delves into various elements crucial for smart farming,such as data sources,infrastructure components,communication protocols,and the roles of different stakeholders such as farmers,agricultural scientists and researchers,technology providers,government agencies,consumers and many others.In contrast to earlier research,this work analyzes the resilience of smart agriculture systems using approaches such as threat modeling,penetration testing,and vulnerability assessments.Important discoveries highlight the concerns connected to unsecured communication protocols,possible threats from malevolent actors,and vulnerabilities in IoT devices.Furthermore,the study suggests enhancements for CPS applications,such as strong access controls,intrusion detection systems,and encryption protocols.In addition,risk assessment techniques are applied to prioritize mitigation tactics and detect potential hazards,addressing issues like data breaches,system outages,and automated farming process sabotage.The research sets itself apart even more by presenting a prototype CPS application that makes use of a digital temperature sensor.This application was first created using a Tinkercad simulator and then using actual hardware with Arduino boards.The CPS application’s defenses against potential threats and vulnerabilities are strengthened by this integrated approach,which distinguishes this research for its depth and usefulness in the field of smart agriculture security.

Hybrid Algorithm-Driven Smart Logistics Optimization in IoT-Based Cyber-Physical Systems

Effectively managing complex logistics data is essential for development sustainability and growth,especially in optimizing distribution routes.This article addresses the limitations of current logistics path optimization methods,such as inefficiencies and high operational costs.To overcome these drawbacks,we introduce the Hybrid Firefly-Spotted Hyena Optimization(HFSHO)algorithm,a novel approach that combines the rapid exploration and global search abilities of the Firefly Algorithm(FO)with the localized search and region-exploitation skills of the Spotted Hyena Optimization Algorithm(SHO).HFSHO aims to improve logistics path optimization and reduce operational costs.The algorithm’s effectiveness is systematically assessed through rigorous comparative analyses with established algorithms like the Ant Colony Algorithm(ACO),Cuckoo Search Algorithm(CSA)and Jaya Algo-rithm(JA).The evaluation also employs benchmarking methodologies using standardized function sets covering diverse objective functions,including Schwefel’s,Rastrigin,Ackley,Sphere and the ZDT and DTLZ Function suite.HFSHO outperforms these algorithms,achieving a minimum path distance of 546 units,highlighting its prowess in logistics path optimization.This comprehensive evaluation authenticates HFSHO’s exceptional performance across various logistic optimization scenarios.These findings emphasize the critical significance of selecting an appropriate algorithm for logistics path navigation,with HFSHO emerging as an efficient choice.Through the synergistic use of FO and SHO,HFSHO achieves a 15%improvement in convergence,heightened operational efficiency and substantial cost reductions in logistics operations.It presents a promising solution for optimizing logistics paths,offering logistics planners and decision-makers valuable insights and contributing substantively to sustainable sectoral growth.

颈椎前路Hybrid手术和颈椎后路单开门椎管扩大成形术治疗多节段脊髓型颈椎病临床疗效分析

目的:分析颈椎前路Hybrid手术和颈椎后路单开门椎管扩大成形术(EODL)治疗多节段脊髓型颈椎病的疗效,探讨多节段脊髓型颈椎病患者手术方式的选择。方法:对2017年7月—2020年7月在首都医科大学附属北京中医医院手术治疗的70例多节段脊髓型颈椎病患者进行回顾性分析,根据手术方式不同,分为前路组35例和后路组35例,前路组患者行Hybrid手术[颈椎前路椎间盘切除融合术(ACDF)联合人工颈椎间盘置换术(ACDR)],后路组患者行EODL。记录2组患者住院时间、手术时间、术中出血量和术后引流量,通过日本骨科协会(JOA)评分、JOA改善率、颈椎残障功能指数(NDI)、疼痛视觉模拟评分(VAS)和术后满意度评分进行疗效评价,统计2组患者术后并发症发生情况。结果:与后路组比较,前路组患者术中出血量、术后引流量、住院时间和手术时间均明显减少(P<0.01),术前各项评分差异无统计学意义(P>0.05)。末次随访时,与后路组比较,前路组患者JOA评分和JOA改善率明显升高(P<0.01),NDI评分和VAS评分明显降低(P<0.01)。与术前比较,末次随访时2组患者JOA评分明显升高(P<0.01),NDI和VAS评分均明显降低(P<0.01)。按术后满意度评分评价,2组患者术后满意度均较高。2组患者术后并发症发生率比较差异无统计学意义(P>0.05)。结论:颈椎前路Hybrid手术和EODL在治疗多节段脊髓型颈椎病方面均取得了较为满意的疗效。Hybrid手术具有出血量少和手术时间短等优点,临床上应根据患者实际情况选择最适宜的术式。

Distributed dynamic event-based finite-time dissipative synchronization control for semi-Markov switched fuzzy cyber-physical systems against random packet losses

This paper is concerned with the finite-time dissipative synchronization control problem of semi-Markov switched cyber-physical systems in the presence of packet losses, which is constructed by the Takagi–Sugeno fuzzy model. To save the network communication burden, a distributed dynamic event-triggered mechanism is developed to restrain the information update. Besides, random packet dropouts following the Bernoulli distribution are assumed to occur in sensor to controller channels, where the triggered control input is analyzed via an equivalent method containing a new stochastic variable. By establishing the mode-dependent Lyapunov–Krasovskii functional with augmented terms, the finite-time boundness of the error system limited to strict dissipativity is studied. As a result of the help of an extended reciprocally convex matrix inequality technique, less conservative criteria in terms of linear matrix inequalities are deduced to calculate the desired control gains. Finally, two examples in regard to practical systems are provided to display the effectiveness of the proposed theory.

Stochastic Models to Mitigate Sparse Sensor Attacks in Continuous-Time Non-Linear Cyber-Physical Systems

Cyber-Physical Systems are very vulnerable to sparse sensor attacks.But current protection mechanisms employ linear and deterministic models which cannot detect attacks precisely.Therefore,in this paper,we propose a new non-linear generalized model to describe Cyber-Physical Systems.This model includes unknown multivariable discrete and continuous-time functions and different multiplicative noises to represent the evolution of physical processes and randomeffects in the physical and computationalworlds.Besides,the digitalization stage in hardware devices is represented too.Attackers and most critical sparse sensor attacks are described through a stochastic process.The reconstruction and protectionmechanisms are based on aweighted stochasticmodel.Error probability in data samples is estimated through different indicators commonly employed in non-linear dynamics(such as the Fourier transform,first-return maps,or the probability density function).A decision algorithm calculates the final reconstructed value considering the previous error probability.An experimental validation based on simulation tools and real deployments is also carried out.Both,the new technology performance and scalability are studied.Results prove that the proposed solution protects Cyber-Physical Systems against up to 92%of attacks and perturbations,with a computational delay below 2.5 s.The proposed model shows a linear complexity,as recursive or iterative structures are not employed,just algebraic and probabilistic functions.In conclusion,the new model and reconstructionmechanism can protect successfully Cyber-Physical Systems against sparse sensor attacks,even in dense or pervasive deployments and scenarios.

Activation Redistribution Based Hybrid Asymmetric Quantization Method of Neural Networks

The demand for adopting neural networks in resource-constrained embedded devices is continuously increasing.Quantization is one of the most promising solutions to reduce computational cost and memory storage on embedded devices.In order to reduce the complexity and overhead of deploying neural networks on Integeronly hardware,most current quantization methods use a symmetric quantization mapping strategy to quantize a floating-point neural network into an integer network.However,although symmetric quantization has the advantage of easier implementation,it is sub-optimal for cases where the range could be skewed and not symmetric.This often comes at the cost of lower accuracy.This paper proposed an activation redistribution-based hybrid asymmetric quantizationmethod for neural networks.The proposedmethod takes data distribution into consideration and can resolve the contradiction between the quantization accuracy and the ease of implementation,balance the trade-off between clipping range and quantization resolution,and thus improve the accuracy of the quantized neural network.The experimental results indicate that the accuracy of the proposed method is 2.02%and 5.52%higher than the traditional symmetric quantization method for classification and detection tasks,respectively.The proposed method paves the way for computationally intensive neural network models to be deployed on devices with limited computing resources.Codes will be available on https://github.com/ycjcy/Hybrid-Asymmetric-Quantization.

Aquila Optimization with Machine Learning-Based Anomaly Detection Technique in Cyber-Physical Systems

Cyber-physical system(CPS)is a concept that integrates every computer-driven system interacting closely with its physical environment.Internet-of-things(IoT)is a union of devices and technologies that provide universal interconnection mechanisms between the physical and digital worlds.Since the complexity level of the CPS increases,an adversary attack becomes possible in several ways.Assuring security is a vital aspect of the CPS environment.Due to the massive surge in the data size,the design of anomaly detection techniques becomes a challenging issue,and domain-specific knowledge can be applied to resolve it.This article develops an Aquila Optimizer with Parameter Tuned Machine Learning Based Anomaly Detection(AOPTML-AD)technique in the CPS environment.The presented AOPTML-AD model intends to recognize and detect abnormal behaviour in the CPS environment.The presented AOPTML-AD framework initially pre-processes the network data by converting them into a compatible format.Besides,the improved Aquila optimization algorithm-based feature selection(IAOA-FS)algorithm is designed to choose an optimal feature subset.Along with that,the chimp optimization algorithm(ChOA)with an adaptive neuro-fuzzy inference system(ANFIS)model can be employed to recognise anomalies in the CPS environment.The ChOA is applied for optimal adjusting of the membership function(MF)indulged in the ANFIS method.The performance validation of the AOPTML-AD algorithm is carried out using the benchmark dataset.The extensive comparative study reported the better performance of the AOPTML-AD technique compared to recent models,with an accuracy of 99.37%.

A Deep CNN-LSTM-Based Feature Extraction for Cyber-Physical System Monitoring

A potential concept that could be effective for multiple applications is a“cyber-physical system”(CPS).The Internet of Things(IoT)has evolved as a research area,presenting new challenges in obtaining valuable data through environmental monitoring.The existing work solely focuses on classifying the audio system of CPS without utilizing feature extraction.This study employs a deep learning method,CNN-LSTM,and two-way feature extraction to classify audio systems within CPS.The primary objective of this system,which is built upon a convolutional neural network(CNN)with Long Short Term Memory(LSTM),is to analyze the vocalization patterns of two different species of anurans.It has been demonstrated that CNNs,when combined with mel-spectrograms for sound analysis,are suitable for classifying ambient noises.Initially,the data is augmented and preprocessed.Next,the mel spectrogram features are extracted through two-way feature extraction.First,Principal Component Analysis(PCA)is utilized for dimensionality reduction,followed by Transfer learning for audio feature extraction.Finally,the classification is performed using the CNN-LSTM process.This methodology can potentially be employed for categorizing various biological acoustic objects and analyzing biodiversity indexes in natural environments,resulting in high classification accuracy.The study highlights that this CNNLSTM approach enables cost-effective and resource-efficient monitoring of large natural regions.The dissemination of updated CNN-LSTM models across distant IoT nodes is facilitated flexibly and dynamically through the utilization of CPS.

复杂建设环境下基于Hybrid A^(*)算法的铁路平面线形绿色优化设计

随着“双碳经济下绿色铁路”理念的兴起,将“绿色生态”融入到铁路平面线路优化已成为近年来的研究热点。本文以铁路建设成本与生态破坏成本的协同优化为目标,引入并改进了一种自动驾驶导航算法(Hybrid A^(*)算法),以适应复杂的铁路设计问题,同时考虑最小曲线半径、最大曲线半径、最短曲线长度、最短夹直线长度、缓和曲线长度等铁路线形约束。研究结果表明:(1)改进后算法以离散网格方式整合外部环境因素,实现渐进式全局探索,获取接近全局最优的铁路线路设计结果;(2)该方法在复杂外部环境约束下,无需预设水平交点位置和数量,可自动生成符合线路-环境耦合约束的优化平面线路方案。

Production traits and fertility of reciprocal hybrids between Argopecten irradians irradians and A.i.concentricus

The two bay scallop subspecies,Argopecten irradians irradians(NN)and A.i.concentricus(SS),are fast growing and major cultured bivalves in China.However,their relatively small sizes and decreasing production traits caused by long-term inbreeding have been major concerns to the industry in the last two decades.Hybridization between the two bay scallop subspecies may provide a new approach to breed a new variety with superior production traits for the industry.For this end,in this study,we hybridized the two bay scallop subspecies in order to obtain a new strain that incorporates the genes of both subspecies.No significant difference was found in fertilization rate,hatching rate and metamorphosis rate between the purebred and crossbred cohorts(NN♀×SS♂,denoted as NS;SS♀×NN♂,denoted as SN).Both mating strategy(intra-vs.inter-population crosses)and egg origin had significant effects on growth and survival at the larval stage.Heterosis was observed in the crossbred and was more pronounced in older stages.Genetic diversity of the reciprocal hybrids,especially that of SN,was increased compared with the purebred cohorts.Almost all hybrids were completely fertile and able to reproduce by selffertilization or by backcrossing with either parent.Apparently,male sterile individuals whose gonads were fully occupied by the ovary part at mature stage were found in the hybrids for the first time.The hybrids,especially SN,may provide precious germplasm resources for the production of ternary hybrids with the Peruvian scallop,A.purpuratus.

Total Fat, Fatty Acid Composition, Tocopherol and Tocotrienol Profiles in Fermented Beans of Ten Controlled Pollinated Cocoa (Theobroma cacao L.) Hybrids from Cameroon

This study aimed to discriminate ten Cameroonian cocoa hybrids according to their total fat, fatty acid composition, tocopherol and tocotrienol profiles. Six cocoa clones from the gene banks of the Cameroon Cocoa Development Corporation were used to create hybrids. The determination of fatty acid composition was carried out by using a gas chromatography (GC) apparatus coupled by a flame ion detector (FID). Tocopherol and tocotrienol analysis was performed by upper high-performance liquid chromatography (UHPLC). Information on the impact of the genotype on the cocoa fat composition was provided. The major fatty acids (FA) in fermented samples are stearic (34.57%), palmitic (26.13%), oleic (34.13%) and linoleic (3.16%) acids. (35.05% to 35.6%). SCA12 × ICS40, SCA12 × SNK13, SNK13 × T79/501 have the least hard cocoa butters. Tocopherols analysis showed a predominance of γ-tocopherols (94.64 ± 1.51 to 292.16 ± 3.17 µg∙g−1), whereas only a small amount of β and δ-tocopherol (from 0.46 to 2.78 µg∙g−1 and 0.12 to 5.82 respectively) was observed. No γ-tocotrienol was found in fermented samples. A differentiation in terms of total fat and tocopherol content was observed amongst hybrids with the same mother-clone, suggesting an impact of pollen on these compounds.

Recent progress of hybrid cathode interface layer for organic solar cells

Organic solar cells(OSCs)have gained conspicuous progress during the past few decades due to the development of materials and upgrading of the device structure.The power conversion efficiency(PCE)of the single-junction device had surpassed 19%.The cathode interface layer(CIL),by optimizing the connection between the active layer and the cathode electrode,has become a momentous part to strengthen the performances of the OSCs.Simultaneously,CIL is also indispensable to illustrating the working mechanism of OSCs and enhancing the stability of the OSCs.In this essay,hybrid CILs in OSCs have been summarized.Firstly,the advancement and operating mechanism of OSCs,and the effects and relevant design rules of CIL are briefly concluded;secondly,the significant influence of CIL on enhancing the stability and PCE of OSCs is presented;thirdly,the characteristics of organic hybrid CIL and organic-inorganic hybrid CIL are introduced.Finally,the conclusion and outlook of CIL are summarized.

Hybrid Skin-Topological Effect Induced by Eight-Site Cells and Arbitrary Adjustment of the Localization of Topological Edge States

Hybrid skin-topological effect(HSTE)in non-Hermitian systems exhibits both the skin effect and topological protection,offering a novel mechanism for localization of topological edge states(TESs)in electrons,circuits,and photons.However,it remains unclear whether the HSTE can be realized in quasicrystals.

MXene-based hybrid materials for electrochemical and photoelectrochemical H_(2) generation

The conversion of solar energy to produce clean hydrogen fuel through water splitting is an emerging strategy for efficiently storing solar energy in the form of solar fuel.This aligns with the increasing global demand for the development of an ideal energy alternative to fossil fuels that does not emit greenhouse gases.Electrochemical(EC) and photoelectrochemical(PEC) water splitting technologies have garnered significant attention worldwide for advanced hydrogen solar fuel production in recent decades.To achieve sustainable green H_(2) production,it is essential to create efficient catalyst materials that are low-cost and can replace expensive noble metal-based catalysts.These characteristics make them an ideal catalyst material for the process.Two-dimensional MXenes with M_(n+1)X_(n) structure have been identified as a promising option for EC and PEC water splitting due to their superior hydrophilicity,metal-like conductivity,large surface area,and adjustable surface chemistry.Here,we present a summary of recent advancements in the synthesis and performance enhancement methods for MXene hybrid materials in hydrogen production through EC and PEC water splitting.Furthermore,we examine the challenges and insights associated with the rational design of MXene-based hybrid materials to facilitate efficient water splitting for sustainable solar fuel production.

Deep genome skimming reveals the hybrid origin of Pseudosasa gracilis (Poaceae: Bambusoideae)

Pseudosasa gracilis(Poaceae:Bambusoideae)is a temperate woody bamboo species endemic to Southcentral China with a narrow distribution.Previous phylogenetic studies revealed an unexpected,isolated phylogenetic position of Ps.gracilis.Here we conducted phylogenomic analysis by sampling populations of Ps.gracilis and its sympatric species Ps.nanunica and Sinosasa polytricha reflecting different genomic signals,by deep genome skimming.Integrating molecular evidence from chloroplast genes and genome-wide SNPs,we deciphered the phylogenetic relationships of Ps.gracilis.Both plastid and nuclear data indicate that Ps.gracilis is more closely related to Sinosasa,which is discordant with the taxonomic treatment.To further explore this molecular-morphological conflict,we screened 411“perfect-copy”syntenic genes to reconstruct phylogenies using both the concatenation and coalescent methods.We observed extensive discordance between gene trees and the putative species tree.A significant hybridization event was detected based on 411 genes from the D subgenome,showing Ps.gracilis was a hybrid descendant between Sinosasa longiligulata and Ps.nanunica,with 63.56%and 36.44%inheritance probabilities of each parent.Moreover,introgression events were detected in the C subgenome between Ps.gracilis and S.polytricha in the same distribution region.Our findings suggest that sympatric hybridization and introgression play a crucial role in the origin of Ps.gracilis.By providing an empirical example of bamboo of hybrid origin using comprehensive analyses based on genomic data from different inheritance systems and morphological characters,our study represents a step forward in understanding of reticulate evolution of bamboos.

Transcriptomic and metabolomic analysis provides insights into lignin biosynthesis and accumulation and differences in lodging resistance in hybrid wheat

The use of hybrid wheat is one way to improve the yield in the future.However,greater plant heights increase lodging risk to some extent.In this study,two hybrid combinations with differences in lodging resistance were used to analyze the stem-related traits during the filling stage,and to investigate the mechanism of the difference in lodging resistance by analyzing lignin synthesis of the basal second internode(BSI).The stem-related traits such as the breaking strength,stem pole substantial degree(SPSD),and rind penetration strength(RPS),as well as the lignin content of the lodging-resistant combination(LRC),were significantly higher than those of the lodgingsensitive combination(LSC).The phenylpropanoid biosynthesis pathway was significantly and simultaneously enriched according to the transcriptomics and metabolomics analysis at the later filling stage.A total of 35 critical regulatory genes involved in the phenylpropanoid pathway were identified.Moreover,42%of the identified genes were significantly and differentially expressed at the later grain-filling stage between the two combinations,among which more than 80%were strongly up-regulated at that stage in the LRC compared with LSC.On the contrary,the LRC displayed lower contents of lignin intermediate metabolites than the LSC.These results suggested that the key to the lodging resistance formation of LRC is largely the higher lignin synthesis at the later grain-filling stage.Finally,breeding strategies for synergistically improving plant height and lodging resistance of hybrid wheat were put forward by comparing the LRC with the conventional wheat applied in large areas.

Degradation and biocompatibility of one-step electrodeposited magnesium thioctic acid/magnesium hydroxide hybrid coatings on ZE21B alloys for cardiovascular stents

Constructing a functional hybrid coating appears to be a promising strategy for addressing the poor corrosion resistance and insufficient endothelialization of Mg-based stents.Nevertheless,the steps for preparing composite coatings are usually complicated and time-consuming.Herein,a novel composite coating,composed of bioactive magnesium thioctic acid(MTA)layer formed by deposition and corrosion-resistant magnesium hydroxide(Mg(OH)_(2))layer grown in situ,is simply fabricated on ZE21B alloys via one-step electrodeposition.Scanning electron microscopy(SEM)shows that the electrodeposited coating has a compact and uniform structure.And the high adhesion of the MTA/Mg(OH)_(2)hybrid coating is also confirmed by the micro-scratch test.Electrochemical test,scanning kelvin probe(SKP),and hydrogen evolution measurement indicate that the hybrid coating effectively reduces the degradation rate of Mg substrates.Haemocompatibility experiment and cell culture trial detect that the composite coating is of fine biocompatibility.Finally,the preparation mechanism of MTA/Mg(OH)_(2)hybrid coatings is discussed and proposed.This coating shows a great potential application for cardiovascular stents.

Cryptic diversity and rampant hybridization in annual gentians on the Qinghai-Tibet Plateau revealed by population genomic analysis

Understanding the evolutionary and ecological processes involved in population differentiation and speciation provides critical insights into biodiversity formation. In this study, we employed 29,865 single nucleotide polymorphisms(SNPs) and complete plastomes to examine genomic divergence and hybridization in Gentiana aristata, which is endemic to the Qinghai-Tibet Plateau(QTP) region. Genetic clustering revealed that G. aristata is characterized by geographic genetic structures with five clusters(West, East, Central, South and North). The West cluster has a specific morphological character(i.e., blue corolla) and higher values of FSTcompared to the remaining clusters, likely the result of the geological barrier formed by the Yangtze River. The West cluster diverged from the other clusters in the Early Pliocene;these remaining clusters diverged from one another in the Early Quaternary. Phylogenetic reconstructions based on SNPs and plastid data revealed substantial cyto-nuclear conflicts. Genetic clustering and D-statistics demonstrated rampant hybridization between the Central and North clusters,along the Bayankala Mountains, which form the geological barrier between the Central and North clusters. Species distribution modeling demonstrated the range of G. aristata expanded since the Last Interglacial period. Our findings provide genetic and morphological evidence of cryptic diversity in G. aristata, and identified rampant hybridization between genetic clusters along a geological barrier.These findings suggest that geological barriers and climatic fluctuations have an important role in triggering diversification as well as hybridization, indicating that cryptic diversity and hybridization are essential factors in biodiversity formation within the QTP region.

Enhanced and asymmetric signatures of hybridization at climatic margins: Evidence from closely related dioecious fig species

Hybridization plays a significant role in biological evolution. However, it is not clear whether ecological contingency differentially influences likelihood of hybridization, particularly at ecological margins where parental species may exhibit reduced fitnesses. Moreover, it is unknown whether future ecosystem change will increase the prevalence of hybridization. Ficus heterostyla and F. squamosa are closely related species co-distributed from southern Thailand to southwest China where hybridization, yielding viable seeds, has been documented. As a robust test of ecological factors driving hybridization, we investigated spatial hybridization signatures based on nuclear microsatellites from extensive population sampling across a widespread contact range. Both species showed high population differentiation and strong patterns of isolation by distance. Admixture estimates exposed asymmetric interspecific gene flow.Signatures of hybridization increase significantly towards higher latitude zones, peaking at the northern climatic margins. Geographic variation in reproductive phenology combined with ecologically challenging marginal habitats may promote this phenomenon. Our work is a first systematic evaluation of such patterns in a comprehensive, latitudinally-based clinal context, and indicates that tendency to hybridize appears strongly influenced by environmental conditions. Moreover, that future climate change scenarios will likely alter and possibly augment cases of hybridization at ecosystem scales.

Cryptic divergences and repeated hybridizations within the endangered “living fossil” dove tree(Davidia involucrata) revealed by whole genome resequencing

The identification and understanding of cryptic intraspecific evolutionary units(lineages) are crucial for planning effective conservation strategies aimed at preserving genetic diversity in endangered species.However, the factors driving the evolution and maintenance of these intraspecific lineages in most endangered species remain poorly understood. In this study, we conducted resequencing of 77 individuals from 22 natural populations of Davidia involucrata, a “living fossil” dove tree endemic to central and southwest China. Our analysis revealed the presence of three distinct local lineages within this endangered species, which emerged approximately 3.09 and 0.32 million years ago. These divergence events align well with the geographic and climatic oscillations that occurred across the distributional range.Additionally, we observed frequent hybridization events between the three lineages, resulting in the formation of hybrid populations in their adjacent as well as disjunct regions. These hybridizations likely arose from climate-driven population expansion and/or long-distance gene flow. Furthermore, we identified numerous environment-correlated gene variants across the total and many other genes that exhibited signals of positive evolution during the maintenance of two major local lineages. Our findings shed light on the highly dynamic evolution underlying the remarkably similar phenotype of this endangered species. Importantly, these results not only provide guidance for the development of conservation plans but also enhance our understanding of evolutionary past for this and other endangered species with similar histories.