Search Results (13,313)

Global high-mountain ecosystems are increasingly subjected to intensified anthropogenic disturbances, which facilitate the spread of invasive alien plants and threaten agricultural sustainability and ecological security. Using Laojun Mountain in Yunnan as the study area, this research investigates the relationship between the distribution patterns of invasive plants and land-use changes, based on data from 38 transect surveys conducted in 2023 and 30-m-resolution land-use data spanning 2003–2023. The analysis incorporates a random forest model and a land-use transition matrix. The key findings are as follows: (1) Variable importance analysis revealed elevation as the most critical factor influencing invasion occurrence (mean decrease in Gini index: 8.0), followed by slope, aspect, and land-use type. (2) Cultivated land exhibited the highest probability of invasion, with high-risk areas (>0.8) concentrated in agricultural zones in the central-southern and northeastern regions. (3) From 2003 to 2023, cultivated land increased by a net area of 20.85 km², primarily due to conversion from forests (19.57 km²) and grasslands, while grassland area decreased by 24.70 km². This study concludes that agricultural expansion has intensified habitat fragmentation and anthropogenic disturbances, creating favorable conditions for invasive plant establishment. It is recommended that invasive species monitoring and ecological restoration efforts be strengthened in agroforestry transition zones to enhance landscape resilience against biological invasions. Full article

Mountain ecosystems are susceptible to climate change, and alpine treeline ecotones (ATEs) represent one of the significant responsive indicators of climate-driven environmental change. This study examines long-term spatiotemporal dynamics of the ATE across the Eastern Slopes of the Canadian Rocky Mountains (ESCR) from 1984 to 2023, with the objective of assessing whether regional climate warming has influenced ATE extent and elevation across different aspects and watersheds. Multi-decadal Landsat imagery, ERA5-Land temperature data, and topographic variables were integrated within a Google Earth Engine (GEE) framework to map ATEs using the Alpine Treeline Ecotone Index (ATEI), a probabilistic approach designed to capture transitional vegetation zones. Temporal trends were evaluated using non-parametric statistics, correlation analyses, and watershed- and aspect-based comparisons. Results indicate that the total alpine treeline ecotone (ATE) area in the ESCR was approximately 13.3% larger in 2023 than in 1984. However, the temporal evolution of ATE extent and elevation was non-monotonic, and linear trend analyses did not detect statistically significant increasing or decreasing trends over the full study period. ATE elevation and expansion exhibited pronounced spatial heterogeneity, with greater changes occurring on north- and northwest-facing slopes and within selected watersheds. In contrast, summer (July–September) temperatures increased significantly (+2.84 °C), exceeding global land-only warming rates, and vegetation greenness (NDVI) showed a strong, statistically significant positive relationship with temperature. These findings show that while climate warming has clearly increased vegetation productivity, elevational ATE dynamics remain spatially heterogeneous and temporally non-synchronous with summer temperature trends. Full article

Tea buds are the key raw material for high-quality tea production, and their accurate perception is essential for intelligent harvesting and quality-oriented management. However, tea bud detection in mountainous large-leaf tea plantations remains challenging because small, densely distributed targets are embedded in complex field environments, significantly limiting the stability and accuracy of existing detection methods. To address these challenges, this study proposes an improved tea bud detection model, termed YOLO-LAR, for mountainous large-leaf tea plantations in Yunnan Province, China, which is developed as an enhanced framework based on the YOLOv11 baseline. YOLO-LAR improves feature representation through multi-scale feature fusion, enabling more effective detection of densely distributed small tea buds. In addition, an optimized downsampling strategy is employed to preserve critical spatial information, and a context-enhanced feature aggregation mechanism is introduced to strengthen robustness under complex backgrounds and illumination variations. The results demonstrate that YOLO-LAR achieves precision, recall, mAP@0.50, and mAP@0.50:0.95 of 0.959, 0.908, 0.961, and 0.814, respectively, outperforming mainstream YOLO-based models, including YOLOv11n, YOLOv10n, and YOLOv8n. These results indicate that YOLO-LAR provides an effective and practical solution for accurate tea bud detection, offering strong technical support for intelligent harvesting and precision management in mountainous tea plantation environments. Full article

Eclogites exhumed from subduction channels are pivotal for deciphering the thermal structure of continental subduction zones. However, heterogeneities in bulk-rock composition and evolutionary history within the subduction channel can lead to variations in petrographic textures and elemental characteristics among eclogites. Therefore, investigating the pressure–temperature (P-T) evolution of eclogites from different outcrops is crucial for refining dynamic models of convergent plate boundaries. The Western Dabie Mountain represents an ideal locality for studying the petro-thermodynamics of continental subduction channels. This study focuses on samples collected from the Dongyuemiao area, situated at the boundary between the high-pressure and ultrahigh-pressure metamorphic belts in the Western Dabie. We integrate petrographic observations, mineral chemistry, phase equilibrium modeling, Zr-in-rutile thermometry and hornblende-plagioclase thermobarometry to constrain the P-T evolution of the eclogite. The samples exhibit a consistent mineral assemblage: garnet + omphacite + amphibole + quartz + phengite, with accessory minerals including rutile and titanite. Garnet grains display characteristic “cloudy-core” and “atoll” textures. Major and trace element analyses of large garnet porphyroblasts reveal pronounced growth zoning in divalent cations, with cores showing enrichment in light rare earth elements (LREEs). Based on phase equilibrium modeling and calculated isopleths for garnet (Ca, Mg) and phengite (Si content), we interpret that the garnet core mineral assemblage (glaucophane + rutile + sphene) records a blueschist-facies metamorphic stage, situated near the rutile-titanite transition. A prograde P-T path is reconstructed, comprising an initial stage of isobaric heating (from ~480 °C at 20 kbar to ~550 °C at 21 kbar), followed by an isothermal compression to the Pmax stage (from ~550 °C at 21 kbar to ~575 °C at 26 kbar). Subsequent retrograde evolution is characterized by decompression and cooling, with symplectite formation recording conditions of ~570 °C and 13 kbar. This study demonstrates that the reconstructed P-T path for the Dongyuemiao eclogites shows stepped geothermal gradient for the prograde stage, and that fluid activity during exhumation resulted from a combination of internal and external factors. Full article

Soil texture exerts fundamental control over nutrient retention in arid ecosystems; however, its mechanistic coupling with nutrient stoichiometry in wind-eroded desert steppes remains poorly resolved. We investigated soil particle-size distribution and nutrient characteristics across contrasting vegetation types in a desert steppe on the northern slope of the Yinshan Mountains. The interactions between soil texture and nutrient distribution were quantified through field sampling and laboratory analyses. The Caragana grassland was dominated by fine-textured soils, with a silt-to-sand ratio of 21.58% and a fractal dimension ranging from 2.1 to 3.95, indicating a complex soil structure with strong nutrient-retention capacity. In contrast, the Leymus grassland and desert sites were characterized by higher sand content, with a median particle size of 1.67 mm and sorting coefficients ranging from 0.06 to 4.2, reflecting a simpler structure and comparatively lower nutrient levels. Overall, soils in the region were nutrient-deficient, with widespread phosphorus and potassium limitations, whereas nitrogen was relatively more abundant. Total nitrogen (<0.75 mg kg⁻¹), total phosphorus (0.2–0.4 mg kg⁻¹), total potassium and available nutrients were predominantly classified as ‘deficient’ to ‘extremely deficient’, exhibiting a clear surface accumulation pattern. The Poaceae meadow surface layer showed the highest total nitrogen and phosphorus contents. The sorting coefficient and fractal dimension were identified as key particle-size parameters regulating soil nutrient stoichiometric ratios. The silt-to-sand ratio exerted negative path effects (−0.11 to −0.18) on SOC/TN and AK/AN, whereas fractal dimension showed positive path effects (0.17–0.23) on AK/AN. These findings provide a scientific basis for ecological restoration and soil management in the region. Full article

The paper presents the evaluation and research undertaken to propose an optimal solution for the Capra–Bâlea road tunnel, within the framework of rehabilitating and modernizing the entire road section, with the objective of ensuring uninterrupted vehicular traffic during the winter season. The Capra–Bâlea road tunnel is the longest operational and under exploitation tunnel in Romania, measuring 887 m, and the highest-altitude road tunnel structure in the country, at 2042 m above sea level. It serves as a connection between the historic regions of Tara Romaneasca and Transylvania via the DN7C national road, commonly referred to as the Transfagarasan, which is among Romania’s most significant tourist routes, and contains five of the ten existing road tunnels in the country. The tunnel passes through crystalline metamorphic rocks typical of the Fagaras mountains. The construction method was typical of the 1970s, combining drill-and-blast in the central section with cut-and-cover execution at the two ends. The technical condition of the tunnel, evaluated through a detailed technical inspection, is presented, highlighting defects and proposing rehabilitation or restoration solutions. The existing cross sections are described and comparatively analyzed against the currently recommended cross-sections in accordance with present standards and gauge requirements. A three-dimensional simulation of both the current and original cross-sections was performed to investigate the behavior of this type of structure, and solutions for tunnel rehabilitation and modernization are recommended. Finally, the advantages of the proposed solution are discussed. Full article

The main objective of this study is to automatically detect real-time snow-related road surface conditions using imagery captured from existing roadside webcams along interstate freeways. Blowing snow is considered one of the most hazardous roadway weather phenomena because it significantly reduces driver visibility and adversely affects vehicle operation. A comprehensive image preprocessing and reduction process was conducted to construct two reference datasets. The first dataset consisted of two categories (blowing snow and no blowing snow), while the second dataset included five surface condition categories: blowing snow, dry, slushy, snow covered, and snow patched. Eight pre-trained convolutional neural networks (CNNs), including AlexNet, SqueezeNet, ShuffleNet, ResNet18, GoogleNet, ResNet50, MobileNet-V3, and EfficientNet-B0, were evaluated for roadway surface condition classification. For Dataset 1, ResNet50 achieved the highest detection accuracy of 97.88%, while AlexNet demonstrated competitive performance with 97.56% accuracy and significantly shorter training time. Among the lightweight architectures, MobileNet-V3 achieved 95.56% accuracy, demonstrating strong computational efficiency. EfficientNet-B0 achieved 93.56% accuracy while maintaining reduced model complexity. For Dataset 2, ResNet18 achieved the highest multi-class detection accuracy of 96.10%, while AlexNet required the shortest training time among the evaluated models. A comparative analysis between deep CNN models and traditional machine learning approaches showed that deep CNNs significantly outperform feature-based methods in detecting blowing snow conditions. The proposed framework provides an automated, accurate, and scalable solution for roadway surface condition monitoring and supports real-time applications in intelligent transportation systems. Full article

In recent years, global climate change, combined with increased human activities, has led to habitat degradation and range shifts in rare medicinal plants, potentially affecting the quality of medicinal herbs. In this study, we assessed how key environmental variables shape the potential distribution of Apocynum venetum L. based on 281 wild occurrence records and nine environmental variables using the MaxEnt model. The results revealed that the mean temperature of the coldest quarter, solar radiation in June, and elevation are the most significant factors affecting the distribution of A. venetum, with optimal values ranging from −10 to 5 °C, 21,000 to 23,000 kJ m⁻² day⁻¹, and 200 to 1500 m, respectively. Ecological niche modeling indicated that highly suitable habitats are primarily located in Xinjiang, Gansu, Shaanxi, Shanxi, Henan, Hebei, Jiangsu, Shandong, and Inner Mongolia. However, future projections under climate change suggest an expansion of these suitable areas, shifting towards higher latitudes in the northwestern regions and high-altitude mountains. These findings provide a scientific basis for guiding the production and sustainable utilization of A. venetum resources. Full article

Wetlands are vital ecosystems that regulate water, store carbon and support biodiversity, but they are highly vulnerable to climate variability and human pressures. In semi-arid South Africa, montane wetlands remain understudied despite their ecological and socioeconomic importance. The study analyzed 1996–2023 climate variability and vegetation response across the Waterberg Mountain Complex (WMC) using station temperature/precipitation, Rainfall Anomaly Index (RAI), 6-month wet-season Standardized Precipitation Index (SPI) and site-level Normalized Difference Vegetation Index (NDVI) for 11 wetlands. Maximum temperatures increased at all stations, led by Warmbath (0.009 °C/month). No statistically significant changes in minimum temperature were detected. Precipitation trajectories diverged, Mokopane exhibited a statistically significant wetting trend whereas Lephalale and Marken experienced progressive drying. ENSO-driven droughts (2002/2003, 2015/2016 and 2019/2020) intensified hydroclimatic stress and shortened wetland hydroperiods. NDVI trends revealed strong coupling with rainfall variability, with high-altitude wetlands demonstrating greater resilience, while lowland systems declined in greenness. These findings highlight topography as a determinant of wetland vulnerability, positioning upland wetlands as potential climate refugia. Site-specific adaptation and conservation strategies are essential to safeguard ecosystem services and biodiversity, contributing to global sustainability goals (SDGs 6, 13 and 15). Full article

Water levels in the Great Salt Lake (GSL), UT, USA, have been declining overall since 1989, leading to a 70% decrease in surface area. To understand GSL’s future, we seek to image fresh groundwater input and lithologic variation along the lake’s boundary. Determining the amount of groundwater recharge into GSL is crucial for lake management but currently unknown. During the Fall of 2024 and Spring 2025, we conducted 16 electrical resistivity tomography (ERT) and six transient electromagnetic (TEM) surveys along the southern shore of GSL between Burmester Road (to the West), Saltair, and Lee’s Creek (to the East). These measurements indicate a low-resistivity layer consistent with brine pore-water, with variable thickness ranging from 7.1 ± 0.1 m at Burmester to 9.6 ± 0.2 m at Saltair. The Saltair region shows a high-resistivity layer, consistent with a 4.4 ± 0.05 m thick layer of mirabilite. This layer contains vertical conduits that allow saline pore-water to upwell onto the surface forming evaporite deposits. Near Lee’s Creek, we find evidence of high resistivities consistent with fresher groundwater as shallow as 2.8 ± 0.03 m, where increased permeability along the paleo-Jordan River corridor may provide a path for groundwater recharge from the Wasatch Mountains. Full article

Apples in China are planted mainly in nutrient-poor mountain soil, and a large amount of fertilizer input results in resource waste and a decrease in nutrient utilization efficiency. Controlled-release fertilizer (CRF) has been shown to be environmentally friendly and increase crop yield, but nutrient release cannot be precisely synchronized with apple demand. Here, a suitable secondary fertilization method was established by a two-year apple field experiment with CRF and common compound fertilizer (CF) at various ratios under a 25% reduction in application. The application of CF and CRF changes the temporal and spatial distributions of soil NPK nutrients, decreasing NPK losses and NH₃ emissions. The NH₃ emissions under CF and CRF decreased by 17.98–44.86%, as N loss decreased by 11.59–29.81% and by 4.45–8.19%, with respect to those under CF alone, while the soil pH and electrical conductivity increased by 8.28–17.12% and 10.73–18.29%, compared with those under CF alone. The increase in soil P and K also decreased losses by 8.28–17.12% and 10.73–18.29%. The combined application of CF and CRF can increase soil microbial diversity and functional taxa and nutrient cycling genes, resulting in efficient nutrient transformation and supply for apple trees. The regulation of nutrients and microbes by the secondary application of CF and CRF drives an increase in apple yield of 23.71–54.32%, resulting in high economic benefits. In total, the application ratio of CF and CRF at 3:7 in March and July was an effective way to balance apple productivity and the soil ecological environment, providing a sustainable solution for mountainous orchard ecosystems globally. Full article

As the net gain of carbon by plants after accounting for respiration, vegetation net primary productivity (NPP) plays a central role in the terrestrial carbon cycle. However, a systematic and quantitative analysis of the spatiotemporal evolution and driving mechanisms of vegetation NPP on Hainan Island, a tropical region, is still lacking. Focusing on Hainan Island, this study employs an integrated approach—including the coefficient of variation, Mann–Kendall test, Hurst exponent, geographical detector, and PLS-SEM—to investigate the spatiotemporal dynamics of vegetation NPP and its underlying drivers from 2001 to 2022. The main conclusions as follows: (1) Vegetation NPP on Hainan Island showed a fluctuating upward trend from 2001 to 2022, with a mean annual increase of 3.6 g C·m⁻²·yr⁻¹, and displayed a spatial pattern of decrease from the central-southern mountainous areas toward the coastal regions. (2) NPP changes were generally stable; historically, areas showing an increasing trend exceeded those with a decreasing trend by 30.55%. In the future, the predominant projected trends are “persistent decrease” and “increase to decrease,” which together account for over 80% of the total area. (3) Topography and climate were the dominant drivers of NPP spatial heterogeneity. Elevation had the strongest explanatory power, followed by evapotranspiration and temperature. A significant, nonlinear enhancement effect was observed in the interaction between any two factors. (4) Topographic, climatic, anthropogenic, and vegetation factors all exerted direct positive effects on vegetation NPP. Anthropogenic activities also indirectly promoted NPP by influencing pathways such as vegetation growth. The conclusions of this research provide support for the implementation and evaluation of land-use planning, afforestation projects, and ecological protection and restoration measures on Hainan Island. Full article

This study investigates four rock–soil profiles developed from Proterozoic intermediate–acid rocks in the Maoniushan area of Xichang, Sichuan Province. Through systematic geochemical analysis of major and trace elements and X-ray diffraction analysis of clay minerals, we aim to clarify the dominant controlling factors and environmental response mechanisms of chemical weathering under similar lithological and soil-forming age conditions. The results indicate the following: (1) Major element geochemistry shows that the Chemical Index of Alteration (CIA) of all profiles ranges from 61 to 74, while Na/K ratios and A-CN-K diagrams collectively reveal that the profiles are in a transitional stage from weak weathering (Ca and Na depletion) to moderate weathering (K depletion), with the weathering intensity ranking in the order TP1711 > TP1709 > TP1714 ≈ TP2801. (2) Trace elements exhibit significant differences among profiles: Cu, Zn, and Pb are significantly leached relative to Al₂O₃ in the TP1711 profile, whereas most trace elements are enriched in the TP1714, TP1709, and TP2801 profiles. Variations in ∑LREE/∑HREE ratios further support differences in the weathering stages of the profiles. (3) The clay mineral assemblages are dominated by illite, chlorite, and vermiculite. The TP1714 profile lacks vermiculite and has the highest illite content (54–60%), reflecting a relatively cold and dry local microclimate. In contrast, the other profiles show widespread vermiculite development, accompanied by minor kaolinite, indicating moderate weathering intensity under warm and humid climatic conditions. This study confirms that under similar lithological and soil-forming age conditions, the microclimatic differentiation induced by altitude variation is the key external controlling factor leading to spatial differences in the chemical weathering intensity of granite–soil profiles in the Maoniushan area. Full article

The Indian Himalayan Region is an important ecological location, but it is now suffering from serious air pollution due to activities like vehicular emissions, industrial activities, biomass burning, and regional atmospheric circulation, which have led to increased air pollution and threatened ecosystems, human health, and the climate. This paper employs qualitative document analysis through reviews of the national climate policies, institutional frameworks, state documents, and technology-based solutions. It concludes that despite comprehensive national policies, many gaps exist between the policy design and ground-level implementation. Our findings reveal three critical governance gaps: (i) altitude-specific regulatory failures in vehicular emission standards, (ii) Institutional fragmentation limiting enforcement capacity, particularly for diffuse sources, (iii) economic barriers preventing sustained adoption of clean fuels despite subsidy programs. According to this research, we propose a three-pillar framework integrating (i) investment in sustainable technology and green infrastructure, (ii) strengthening institutions and policies, and (iii) fostering behavioral change and public awareness. The study contributes to the limited literature on region-specific air quality governance and offers a strategic framework to support climate resilience in the Himalayas. Full article