Search Results (327)

Monacha cartusiana (O. F. Müller, 1774), native to the Mediterranean region and Europe, is a terrestrial gastropod recognized as a highly destructive agricultural pest that causes significant damage to crop plants, fruit trees, vegetables, ornamentals, and natural ecosystems. Despite its broad geographic distribution, the evolutionary history and phylogeographic relationships of M. cartusiana populations remain globally unexplored. This study reports the first molecularly confirmed record of M. cartusiana in Pakistan and investigates its genetic diversity and phylogeographic structure within a global context using mitochondrial markers. After morphological identification, genomic DNA was extracted from collected specimens using the CTAB method, followed by amplification and sequencing of the mitochondrial COI and 16S rRNA genes. The resulting sequences were subsequently analyzed using DnaSP and PopART software to estimate genetic diversity, perform neutrality tests, and construct haplotype networks. Published sequences of M. cartusiana retrieved from GenBank were incorporated to provide a global comparative framework. The COI dataset (555 bp) revealed 52 haplotypes, whereas the 16S rRNA dataset (269 bp) identified 14 haplotypes across global populations. High haplotype diversity (Hd = 0.946 for COI; Hd = 0.831 for 16S rRNA) and moderate nucleotide diversity (π = 0.010 for COI; π = 0.01253 for 16S rRNA) indicated substantial genetic variability within the species. Neutrality tests produced negative and insignificant values for Tajima’s D for COI and significant values for 16S rRNA (−1.428 for COI; −0.20586 for 16S rRNA) and Fu’s Fs (−29.776 for COI; −1.263 for 16S rRNA), suggesting historical population expansion. Phylogenetic reconstruction and haplotype network analyses identified two major clades (Clade A and Clade B), reflecting genetic relationships among populations from different geographic regions. AMOVA based on COI and 16S rRNA sequences revealed significant population structuring, with 29.98–51.30% of the total genetic variation occurring among populations and high fixation indices (FST = 0.299–0.51398, p = 0.001), indicating pronounced genetic differentiation and restricted gene flow. Pairwise FST analyses indicated that the Pakistani population is most closely related to populations from Italy and Central Europe, suggesting a closer genetic affinity with Southern or Central European populations. However, FST alone does not allow definitive inference of introduction directionality, and additional analyses would be required to robustly identify the source population. Overall, this study provides the first comprehensive molecular and phylogeographic assessment of the M. cartusiana species from Pakistan within a global context. These findings contribute important baseline data for understanding the evolutionary dynamics, dispersal history, and population connectivity of this economically important pest species. The pronounced genetic differentiation among populations and the suggested genetic affinity of the Pakistani population with European lineages have direct implications for biosecurity monitoring, invasion pathway tracing, and targeted pest management strategies. Future research integrating nuclear markers with the mitochondrial data presented here will be essential for a more complete understanding of gene flow and local adaptation in this species. Full article

Genetic Programming (GP) for evolving Behavior Trees (BTs) in autonomous robots often suffer from premature convergence, even when adaptive mutation mechanisms are employed. This paper proposes a novel hybrid framework that integrates Large Language Model (LLM) supervision into GP, in which the LLM performs holistic population analysis, adaptively regulates mutation rates, and generates targeted BTs to proactively address behavioral gaps in the evolving population. Unlike conventional evolutionary operators, the LLM introduces high-level semantic guidance by seeding underrepresented behavioral archetypes, thereby complementing stochastic genetic variation with structured exploration. The proposed method is evaluated in a Unity-based multi-task robotic simulation environment. Experimental results show that the hybrid approach significantly outperforms baseline GP with standard adaptive mutation, achieving a 71.7% faster emergence of Complete Robots, a 65.2% faster emergence of Excellent Robots, and a 28% increase in behavioral diversity. Notably, the two systems exhibit opposite mutation dynamics: the LLM-guided system progressively reduces mutation rates to promote exploitation, whereas the baseline maintains a high mutation rate. In addition, the LLM generates approximately 40 targeted BTs per run, proactively seeding the population with underrepresented behavioral archetypes. These performance gains are obtained with only a 13% computational overhead. Full article

Changing hydrothermal regime leads to pronounced changes in growth and ranges of Siberian tree species that are mostly negative at the southern part of the trees’ habitat. Here we analyzed the response of Larix sibirica and Populus laurifolia to moisture changes in unique refugia that border the Mongolian desert in Southern Siberia. The great age of old-growth larch trees (>500 years) suggests that the refugia have existed throughout the Holocene. We aimed to (1) analyze larch and poplar growth and range response to the changing temperature and moisture regime, (2) explore the potential migration of trees into the desert, and (3) analyze Gross Primary Productivity (GPP) dynamics within the refugia and adjacent desert. We used on-ground surveys, remote sensing data, and dendroecological analysis. We found that since the warming onset (c. 1980), larch and poplar trees have increased their growth and population within and beyond the refugia (+300% for poplar and +45% for larch). Both species’ growth has been controlled by atmospheric and soil droughts (measured by the Standardized Precipitation Evapotranspiration Index (SPEI) and Self-Calibrating Palmer Drought Severity Index (scPDSI)) and by microtopography-dependent moistening. Summer winds impair trees’ growth via increased evapotranspiration. Both species were migrating to the southern sandy dunes. Although poplar is less drought-resistant than larch, it was shifting ahead of larch (5.6 m/year vs. 0.8 m/year). The mean and maximum treeline shifts were 260 and 450 m for poplar and 35 m and 70 m for larch. P. laurifolia occupied new climate-caused niches ahead of drought-resistant L. sibirica due to its higher prolificacy. We found a “desert greening” phenomenon, i.e., a significantly increasing GPP trend (R² = 0.31) in both refugia and sandy dunes. The GPP increase correlated with tree growth increase (r² = 0.36–0.39). The larch and poplar migration to the desert contradicts the predicted shrinkage of the tree ranges within their southern boundary. However, the projected increase in the moisture deficit by 2080–2100 may impair this phenomenon. Nevertheless, current changes in the hydrology regime are favorable for larch and poplar growth and expansion into the adjacent Mongolian desert. Full article

Background/Objectives: The STAT (Signal Transducer and Activator of Transcription) family of seven transcription factors mediates cytokine and growth-factor signaling, regulating proliferation, differentiation, and immunity. While STAT3/STAT5 are established oncogenes and STAT1/STAT2 are classically viewed as tumor suppressors, emerging evidence indicates context-dependent roles in tumorigenesis. This study aimed to integrate evolutionary analysis with bulk transcriptomic, regulon, single-cell, and exploratory chromatin-binding analyses of the STAT family in human solid tumors. Methods: Orthologs and paralogs of human STAT genes (81 sequences total) were retrieved across vertebrates and invertebrates; a phylogenetic tree was constructed using MUSCLE alignment and Neighbor-Joining in MEGA12. Differential expression was assessed in TCGA solid tumors versus GTEx normal tissues. Master-regulator activity was inferred using the corto algorithm. Single-cell RNA-seq datasets were used to compare malignant and non-malignant cell populations. STAT1 chromatin binding was examined via ChIP-seq in interferon-stimulated HeLa and K562 cells. Results: Phylogeny resolved seven conserved vertebrate clades, with endocrine-responsive STAT3/STAT5 showing higher conservation and immune-associated STAT1/STAT2/STAT4/STAT6 exhibiting faster divergence. The majority of STAT genes were frequently upregulated across multiple solid tumors, with activated regulons confirming functional transcriptional engagement. Single-cell analysis demonstrated tumor-cell-autonomous upregulation of STAT1 and STAT2 in the HNSCC dataset. STAT1 ChIP-seq revealed asymmetric forward/reverse-strand read density around peak summits, supporting non-canonical DNA recognition. Conclusions: The STAT family operates as an evolutionarily conserved, broadly activated transcriptional module in human solid cancers, combining quantitative upregulation with qualitative shifts in DNA-binding dynamics. These findings refine our understanding of JAK/STAT signaling in oncology and highlight opportunities for network-targeted therapies. Full article

Understanding how medicinal plant distributions shift in response to climate change is essential for developing forward-looking conservation strategies. Cibotium barometz (L.) J. Sm., a tree fern from the family Dicksoniaceae, is not only ecologically significant but also holds considerable medicinal value. Despite its importance, wild populations of this species have been steadily declining due to ongoing habitat loss and unsustainable harvesting. To address this concern, we constructed a multi-model ensemble framework that integrated nine different algorithms, including Generalized Linear Models, various machine learning approaches, and a MaxEnt model optimized through ENMeval using a regularization multiplier of 2 and a feature class of LQH. Using this modeling framework, we simulated the habitat suitability dynamics of C. barometz under current climate conditions (1970–2000) and two future periods (2050s and 2090s) across four Shared Socioeconomic Pathways (SSP126, SSP245, SSP370, and SSP585). Our analysis identified water availability and low temperature stress as the primary factors limiting the species’ distribution. The suitable range for precipitation during the driest quarter extends from 3.25 to 640.20 mm, with optimal conditions occurring when precipitation reaches at least 96.84 mm. Annual precipitation suitable for the species lies between 74.58 and 4209.60 mm, and the most favorable range falls between 3834.10 and 4209.60 mm. While the minimum temperature of the coldest month can vary from −35.41 to 22.35 °C, optimal survival requires temperatures of 8.79 °C or higher. In addition, the species grows best within an annual temperature range of 16.25 to 27.92 °C, with an optimum around 20.47 °C. Projections based on the multi model ensemble suggest that future climate warming may lead to a southwestward shift in the centroid of suitable habitat for this species. By the 2090s, under the SSP245, SSP370, and SSP585 scenarios, the centroid shifts southwestward by 331.3 km, 335.1 km, and 180.2 km, respectively. Meanwhile, areas with high habitat suitability are expected to retreat toward mid-to-high elevation zones, especially in southeastern Yunnan, southern Guizhou, and western Guangxi. The effects of different emission pathways vary considerably; under the high-emission SSP585 scenario, the reduction in total suitable area is projected to be more severe and habitat fragmentation more extensive compared to the low-emission SSP126 pathway. In contrast, implementing ambitious emissions reduction measures could play a key role in supporting the long-term stability of C. barometz populations. This study clarifies how this species responds to climate change and the spatial strategies it may adopt, providing a scientific basis and spatial references for conserving its germplasm resources, restoring its habitats, and advancing its sustainable use. Full article

Multi-UAV path planning in dynamic and complex environments is a challenging constrained optimization problem because it must simultaneously consider path efficiency, obstacle avoidance, altitude feasibility, flight smoothness, and inter-UAV path diversity. Existing methods often struggle to maintain search diversity, balance exploration and exploitation, and avoid premature convergence in high-dimensional search spaces. To address this issue, this paper proposes a Q-learning-guided Harris Hawk Optimization-Genetic Algorithm (QHHO_GA), which integrates Genetic Algorithm (GA), Harris Hawk Optimization (HHO), Q-learning, prioritized experience replay, entropy-based state partitioning, and a Rapidly exploring Random Tree (RRT)-based stagnation adjustment mechanism. In the proposed framework, GA enhances population quality and diversity, HHO performs the core search, Q-learning adaptively guides HHO behaviors, and stagnation monitoring with RRT-based stagnation adjustment improves the ability to escape locally trapped regions. Experimental results on the CEC2017 benchmark suite and a multi-UAV path planning task demonstrate the effectiveness of the proposed method. On the CEC2017 benchmark, QHHO_GA ranks among the top two on 18 out of 30 test functions and achieves the best overall ranking among the compared algorithms. In the UAV path planning experiments, it achieves an average ranking of 3.44 and also achieves the best overall rank among all compared methods. These results indicate that QHHO_GA is a robust and competitive method for high-dimensional constrained optimization, and is particularly effective for complex multi-UAV path planning tasks. Full article

Amidst global urbanization and rising psychological stress, urban green spaces are increasingly recognized as critical infrastructure for sustainable urban development and public health. However, the mechanisms by which summer vegetation structure mediates both physiological and psychological restoration, and the interplay between these two dimensions, remain poorly understood. Understanding these mechanisms is essential for designing sustainable, health-promoting urban environments that can support growing urban populations in a warming climate. This study employed a controlled field experiment in Xi’an during summer to examine the effects of five vegetation structure types (Single-Layer Grassland, single-layer woodland, tree–shrub–grass composite woodland, tree–grass composite woodland, and a non-vegetated square) on university students’ physiological (heart rate variability) and psychological (perceived restorativeness and affective states) restoration. Following stress induction, 300 participants engaged with the green spaces through both quiet sitting and walking. The results revealed three key findings: (1) the tree–shrub–grass composite woodland consistently showed the most favorable trends other vegetation types across all psychological restoration dimensions, while also showing favorable trends in physiological recovery, underscoring the importance of structural complexity for restorative quality; (2) walking significantly enhanced physiological recovery compared to seated observation across all settings, confirming the role of physical activity as a critical activator of green space benefits; (3) correlation analysis identified a specific cross-system association: the R-R interval recovery value showed a weak but significant correlation with positive affect (PA) scores, suggesting that physiological calmness and positive emotional experience are linked, yet their weak coupling under short-term exposure indicates they may operate as parallel processes with distinct temporal dynamics. These findings indicate that the restorative potential of summer green spaces emerges from an integrated framework combining vegetation complexity and activity support. We propose that future sustainable landscape design should prioritize multi-layered vegetation structures as nature-based solutions that simultaneously enhance human well-being and urban resilience. These findings provide empirical evidence for integrating health-promoting green infrastructure into sustainable urban planning frameworks, supporting multiple Sustainable Development Goals (SDGs), including SDG 3 (Good Health and Well-being), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action). Full article

Conventional axial sprayers are poorly suited to orchards located in sensitive contexts (near water bodies, frequented by bystanders) or to large-volume trees. The result is often poor distribution within the canopy leading to more or less effective disease or insect control, off-target drift leading to environmental pollution and economic inefficiency. Trunk microinjection of plant protection products (PPPs) as a target-precise delivery system could greatly reduce the drift and improve the PPPs application. This study investigated the efficacy of five PPPs (flonicamid, spirotetramat, azadirachtin, lambda-cyhalothrin and deltamethrin) microinjected into the trunk of apple trees in 2022, 2023 and 2024 for managing Apple Rosy Aphid (Dysaphis plantaginea). Observations focused on aphid colonization and residue dynamics in buds, leaves and fruits. Under the conditions of the experiments, azadirachtin microinjection significantly reduced autumn infestation from 87 to 100% and spring infestation from 88 to 97%. The results obtained with flonicamid showed greater variability: from 50 to 80% fall infestation reduction and from 26 to 89% spring infestation reduction depending on the strategy and year. Spirotetramat and the two pyrethroids tested did not provide satisfactory control of populations. Residue levels varied by injected compound and the analyzed tissues. This study demonstrated that trunk microinjection could be an effective delivery method for existing PPPs, depending on the active ingredient, for controlling rosy apple aphids in orchards. Full article

In recent years, the Turkish real estate market has emerged as a key driver of economic growth while simultaneously shaping the dynamics of social life. This study employs multivariate analysis methods to classify metropolitan cities and potential metropolitan candidate provinces that exhibit similarities in terms of housing market characteristics, based on 22 socio-economic and sectoral variables influencing the real estate sector. Additionally, the study identifies the metropolitan clusters to which the 10 candidate provinces structurally correspond within this classification framework. To achieve this, conventional classification techniques such as Decision Trees and K-Nearest Neighbors (k-NN) were integrated with artificial intelligence-based methods, including Random Forest (RF) and Support Vector Machines (SVM). The analysis resulted in the categorization of 40 metropolitan and candidate provinces into five distinct groups. Findings indicate that multivariate indicators capturing demographic, economic, and structural differences across metropolitan areas play a critical role in shaping the housing market and guiding strategic urban development decisions. Furthermore, the results highlight that determining metropolitan status solely based on population figures is insufficient and that a more scientific and comprehensive approach—grounded in a broader set of socio-economic and structural indicators yields more meaningful classifications. Full article

Avocado (Persea americana Mill.) is one of the most widely consumed fruit tree crops worldwide, with cultivation expanding rapidly beyond its Mesoamerican and northwest South America center of origin. In emerging secondary diversity centers such as East Africa, farmers have long propagated seedling naturalized populations that may hold valuable reservoirs of genetic diversity, yet these resources remain underexplored. To help fill this gap, this study developed the first genomic resources for avocados in Tanzania, where avocado has a long history of introduction and diversification dating to the first Arab incursions and Catholic missionary missions. Low-coverage whole-genome resequencing (lcWGS) data were obtained from 95 trees sampled in Tanzania across the low- to mid-altitude Morogoro region (n = 25) and the Southern Highlands—i.e., the Iringa (n = 20), Mbeya (n = 30) and Ruvuma (n = 20) regions. In order to guide racial assignation, sequences were merged with NCBI-available lcWGS data from 205 avocado trees, including 42 commercial varieties, with reported ancestry. Population stratification as inferred via maximum likelihood phylogenetic inference, genetic principal component analysis, and ADMIXTURE unsupervised clustering suggested that the sampled Tanzanian avocado trees were genetically closer to the West Indian race and more distant from the northwest South American Caribbean and Andean groups. Additionally, while the trees from the low- to mid-altitude region of Morogoro were almost exclusively West Indian type, some trees from the Southern Highlands aligned more closely with West Indian × Guatemalan and West Indian × Mexican hybrids. These trends were equally supported by a subset of 10,460 high-coverage (10×) SNP markers. Together these findings clarify the dynamics of avocado diversification in a secondary center in East Africa, spanning recent introductions from a single Mesoamerican race, adaptation to a wide range of locally geographic conditions, and farmer-driven selection matching local tribal preferences. Characterizing these locally adapted resources is key for identifying underrepresented yet promising provenances, developing resilient and sustainable horticultural production systems, and safeguarding the species’ global genetic heritage. Full article

Scots pine (Pinus sylvestris L.) spans most of Eurasia, yet southern and mountainous populations may retain distinctive genetic components shaped by long-term isolation and complex postglacial dynamics. We genotyped 186 trees from four Scots pine stands in Armenia (AM1-AM4) and reference stands from Germany, Russia and Montenegro with the PiSy50k SNP array and integrated these data with published European array datasets from Finland, Poland and the Baltic region. After quality checks and conservative SNP filtering, 627 individuals from 47 populations and 3659 SNP loci were retained. Within-population diversity was generally high; Armenian stands AM2–AM4 were among the most diverse, whereas AM1 showed reduced diversity and the highest differentiation relative to the remainder of the dataset (FST vs. rest = 0.0047). Direct pairwise F_ST and hierarchical AMOVA confirmed pronounced heterogeneity among Armenian stands, with AM1 the most differentiated stand, AM2 and AM4 closest to the broader Eurasian background, and AM3 intermediate. Principal component analysis (PC1 = 1.42%, PC2 = 0.76%) again separated AM1 strongly from all non-Armenian samples, while AM2 overlapped with the central/eastern European cluster and AM3 and AM4 combined continental-like and AM1-like individuals. Structure-like inference with LEA/sNMF showed a broad cross-entropy plateau from approximately K = 4 to K = 6; we therefore use K = 5 as a practical summary, which highlighted a dominant AM1-associated ancestry component and variable continental admixture in AM2–AM4. KING kinship estimates provided little evidence for within-stand family clustering in Armenian stands; no second-degree-or-closer pairs were observed in AM1–AM4. Together, the results reveal pronounced heterogeneity among Armenian Scots pine stands and identify AM1 as a highly differentiated but unresolved genomic component, providing a genomic baseline to support conservation planning, provenance evaluation and the management of forest reproductive material in the Lesser Caucasus. Full article

Passiflora species are recognized for their intense evolutionary dynamics within the chloroplast genome (cp), serving as a primary model for studying structural variations in natural populations. This study investigates the phylogenetic relationships of 47 Passiflora accessions from Boyacá, Colombia, utilizing a multilocus approach with chloroplast genes (trnL), non-coding regions (psbA-trnH), and nuclear markers (ITS). Multiple sequence alignments identified fragment sizes of ~264 bp (trnL), ~333 bp (psbA-trnH), and ~706 bp (ITS), each displaying distinct nucleotide compositions. Evolutionary patterns and substitution rates were evaluated using the Maximum Likelihood approach in IQ-TREE, with best-fit models selected via the Bayesian Information Criterion (BIC). The analysis revealed that transitional substitution rates consistently exceeded transversions across all markers, with the trnL region exhibiting a notably high G-to-A substitution rate of 22.15. These genomic data resolved robust evolutionary proximity between P. edulis f. edulis and P. edulis f. flavicarpa, while highlighting significant genetic divergence from P. ligularis and P. maliformis. The results demonstrate that while these specific markers effectively clarify lineage relationships, an integrated multi-marker system is essential to provide a precise understanding of the complex evolutionary divergence patterns in Andean Passiflora, offering a foundational genetic background for regional biodiversity conservation. Full article

The spongy moth is a significant Lepidopteran species across Europe, where it occurs in oak stands. Tree species composition has a crucial effect on larval development, population density, and outbreaks. Host switching is more likely to occur in a mixed forest than in a monospecific forest. We aimed to better understand the effect of host alternation on the development of the spongy moth. In a laboratory, we reared spongy moth larvae on either (a) Turkey oak (Quercus cerris L.) or (b) European hornbeam (Carpinus betulus L.) only or on host plants that were changed from Turkey oak to European hornbeam (c) in the early (L₃) or (d) late (L₅) larval instar. Both Q. cerris and C. betulus proved suitable hosts for the spongy moth larvae. However, the larvae fed exclusively on Turkey oak leaves had better developmental indicators than the others. The groups that switched hosts had weaker developmental indicators than the larvae fed only on Turkey oak but showed better development than the group reared only on Hornbeam leaves. The results of our laboratory research on host switching may offer valuable insights into the developmental dynamics of spongy moths in monospecific forests versus those with higher biodiversity. Full article

The spread of Xylella fastidiosa, a xylem-limited bacterial pathogen, has caused widespread mortality among olive trees in Apulian region, Italy in more than a decade, and represents a significant threat to Mediterranean agroecosystems. To encourage evidence-based containment strategies, we developed a stochastic, spatiotemporal simulation model that represents pathogen transmission at the individual-tree level. This work integrates high-resolution georeferenced olive-tree data and implicitly incorporates vector population dynamics through a tree-specific vulnerability index, which considers local host density and landscape connectivity. Vector dispersal is approximated using a radial transmission kernel, which preserves host–vector spatial interactions while avoiding the explicit modeling of insect trajectories. The system’s spatial structure is additionally formulated as a proximity graph, facilitating network-based analysis of spread pathways. A series of Monte Carlo simulation experiments is employed for calibration against the observed epidemic footprint, while validation utilizes independent infection records and global sensitivity analysis of key parameters. The findings indicate that the model effectively replicates realistic propagation patterns, and its calibrated parameters are consistent with out-of-sample data. This makes it an appropriate exploratory tool for scenario testing, assessing the potential impact of intervention strategies, and offering risk-based decision support for handling Xylella fastidiosa outbreaks. Subsequently, graph centrality metrics are used to identify epidemiologically critical trees that function as transmission bridges, thus representing priority targets for surveillance or removal efforts. Thus, multiple tests have been conducted using betweenness and closeness centrality, while comparing both methods leads to effective node-tree removal decisions. Full article

Ungulate browsing is a major driver of forest regeneration dynamics and habitat structure in managed temperate forests, influencing species composition, regeneration success, and long-term stand development. Traditional assessments of browsing impacts often rely on field-based indicators such as regeneration density or visual cover, but these metrics provide limited insight into three-dimensional habitat structure. Mobile handheld LiDAR offers highly detailed measurements of forest structure, enabling objective and reproducible quantification of structural complexity that complements and extends conventional field-based methods. In this study, we applied handheld LiDAR as an innovative indicator for habitat structure within the ungulate browsing zone (<2 m height) to evaluate structural development across sites differing in management context. Paired fenced and unfenced plots (12 × 12 m) were surveyed within the WiWaldI project framework in 2019 and 2023 and compared across three hunting regimes representing different degrees of ungulate population management. Structural complexity was quantified by deriving box-counting dimensions from LiDAR point clouds, providing a measure of spatial arrangement and density relevant to ungulate–vegetation interactions. To support interpretation and ecological context, we complemented LiDAR indicators with streamlined field assessments. Based on this framework, we assessed whether forest structural complexity and visual cover differ among regions and over time, and whether ungulate browsing induces detectable structural differences between fenced whether structural differences between fenced and unfenced plots are detectable. We further examined the relative importance of tree species composition, plant architecture, and hunting regime as drivers of three-dimensional habitat structure. A simplified octant method characterized the spatial distribution of woody regeneration, while a silhouette-based approach quantified visual cover from the perspective of a standard ungulate profile. These auxiliary measures contextualize visual and spatial aspects of structure that LiDAR metrics capture with minimal observer bias. LiDAR studies have previously demonstrated potential for linking high-resolution structural data to ungulate habitat use, and our approach extends this by focusing on structural complexity as a habitat indicator. Results show a consistent increase in LiDAR-derived structural complexity between 2019 and 2023 across all regions. This increase occurred across management contexts and was not consistently explained by fencing or hunting regime effects, suggesting that site conditions, forest composition, and successional processes were dominant drivers during the observation period. Hunting regime showed no statistically significant and no consistent effect on structural complexity across regions or years. Visual cover metrics varied strongly among regions and species and declined over time. These findings suggest that three-dimensional habitat structure information has the potential to enhance the evaluation of ungulate impacts and may support evidence-based forest and wildlife management, particularly when interpreted in the context of site conditions and successional dynamics. Beyond ungulate impact assessment, the presented handheld LiDAR approach provides a scalable remote sensing framework for precision forestry by capturing three-dimensional structural attributes that are directly linked to forest stability, resilience, growth dynamics, and stand-level species mixing, thereby supporting evidence-based forest management recommendations. Full article