Search Results (92)

Symplocos is an ecologically important genus that plays vital roles in subtropical evergreen broad-leaved mountain forests, including contributing to nutrient cycling, providing shelter and habitats for various organisms, and supporting overall plant diversity across East and Southeast Asia. Many species exhibit high levels of endemism and sensitivity to environmental change. China, with its wide range of ecosystems and climatic zones, is home to 18 endemic Symplocos species. Studies revealed that global warming is driving shifts in species diversity, particularly in mountains. Our study explores the current and projected richness patterns of endemic Symplocos species in China under climate change scenarios, emphasizing the implications for conservation planning. We applied stacked species distribution models (SSDMs), using key bioclimatic and environmental variables to predict current and future habitat suitability for endemic Symplocos species, evaluated model performance through multiple accuracy metrics, and generated ensemble projections to assess richness patterns under climate change scenarios. To assess the spatial configuration and fragmentation patterns of the endemic species richness under current and future climate scenarios, landscape metrics were calculated based on classified richness maps. The produced models demonstrated high accuracy with AUC > 0.9 and TSS > 0.75, highlighting the critical role of bioclimatic variables, particularly precipitation and temperature, in shaping endemic Symplocos distribution. Our analysis identifies the current hotspots of Symplocos endemism along southeastern China, particularly in Zhejiang, Fujian, Jiangxi, Hunan, southern Anhui, and northern Guangdong and Guangxi. These areas are at high risk, with up to 35% of endemic Symplocos species richness predicted to be lost over the next 60 years due to climate change. The study predicts a high decrease in endemic Symplocos species richness, especially in South China (e.g., Fujian, Guangdong, Guizhou, Yunnan, southern Shaanxi), and mid-level decreases in East China (e.g., Heilongjiang, Jilin, eastern Inner Mongolia, Liaoning). Conversely, potential increases in endemic Symplocos species richness are projected in northern and western Xinjiang, western Tibet, and parts of eastern Sichuan, Guangxi, Hunan, Hebei, and Anhui, suggesting these regions may serve as future refugia for endemic Symplocos species. The analysis of the landscape structure and configuration revealed relatively minor but notable variations in the spatial structure of endemic Symplocos richness patterns under current and future climate scenarios. However, under the SSP585 scenario by 2080, the medium richness class showed a more pronounced decrease in aggregation index and increase in number of patches relative to other richness classes, suggesting that higher emissions may drive fragmentation of moderately rich areas, potentially isolating populations of Symplocos. These structural changes suggest a potential reduction in habitat quality and connectivity, posing significant risks to the persistence of endemic Symplocos populations, which underscores the urgent need for targeted smart-climate conservation strategies that prioritize both current hotspots and potential future refugia to enhance the resilience of endemic Symplocos forests and their ecosystems in the face of climate change. Full article

The Qinghai–Tibet Plateau is a crucial ecological security barrier in China and Asia. Its grassland ecosystem has high ecological service value. Scientific assessments and classifications of grasslands are crucial for determining the value of grassland resources and implementing refined management. Traditional grassland classification methods have used expert knowledge and linear models, which are subjective and cannot describe complex nonlinear relationships. We conducted a case study in Hongyuan County, Sichuan Province, in the water conservation area of the Qinghai–Tibet Plateau, using multi-source data including Landsat 8 (15 m/30 m), MOD15A2 (500 m), ALOS imagery (12.5 m), and 435 field survey samples, combined with machine learning models such as convolutional neural network (CNN), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), histogram gradient boosting (HistGradientBoosting), and random forest (RF). The objective was to develop a novel grassland classification method that integrates multi-source remote sensing data with machine learning algorithms. Based on the evaluation metrics of SHAP values, mean annual precipitation (MAP, 0.675), >0 °C Accumulated Temperature (AT, 0.591), and aspect (ASPECT, 0.548) were the most critical factors influencing alpine grasslands, revealing a driving mechanism characterized by climate dominance, topographic regulation, soil support, and vegetation response. The XGBoost model demonstrated the best performance (with an accuracy of 0.829, Precision of 0.818, Recall of 0.829, weighted F1-score of 0.820, and an AUC value of 0.870). The pixel-by-pixel absolute difference calculation between the model-predicted and the actual classification results showed that regions with no discrepancy (absolute value = 0) accounted for 75.82%, those with a minor discrepancy (absolute value = 1) accounted for 23.63%, and regions with a major discrepancy (absolute value = 2) accounted for only 0.54%. This study has established a replicable paradigm for the precise management and conservation of alpine grassland resources. Through the synergistic application of deep learning and machine learning, it generated superior baseline data, quantitatively uncovered a grassland differentiation mechanism dominated by hydrothermal factors and fine-tuned by topography in the complex Qinghai–Tibet Plateau, and delivered high-precision spatial distribution maps of grassland classes. Full article

The Sichuan–Yunnan region, a primary seismic-prone zone on the Qinghai–Tibet Plateau, has experienced heightened seismic exposure due to rapid urbanisation. In order to address the issue of disaster risks and to promote sustainable urban development, this study establishes an integrated urban seismic resilience evaluation framework based on the DPSIR (Driving–Pressure–State–Impact–Response) model. The CRITIC–AHP combined weighting method was utilised to determine indicator weights, and data from 37 prefecture-level cities (2010, 2015, 2020) were analysed to reveal spatial–temporal evolution patterns and correlations. The results demonstrate a consistent improvement in regional seismic resilience, with the overall index increasing from 0.501 in 2010 to 0.526 in 2020. Sichuan exhibited a “decline-then-rise” trend (0.570 to 0.566 to 0.585), while Yunnan demonstrated continuous growth (0.517 to 0.557). The spatial pattern underwent an evolution from “west–low, central–eastern–high” to “south–high, north–low”, with over half of the cities attaining relatively high resilience by 2020. Chengdu and Kunming have been identified as dual high-resilience cores, diffusing resilience outward to neighbouring regions. In contrast, mountainous areas such as Garze and Aba have been found to exhibit low resilience levels, primarily due to high seismic stress and limited socioeconomic capacity. Subsystem analysis has revealed divergent resilience pathways across provinces, while spatial autocorrelation has demonstrated fluctuating global Moran’s I values and temporary local clustering. This research provides a scientific foundation for seismic disaster mitigation and offers a transferable analytical framework for enhancing urban resilience in earthquake-prone regions globally. Full article

Human settlements’ suitability in ecologically fragile regions is critical for sustainable development and ecological security. However, comprehensive assessments that integrate multiple natural environmental factors are insufficient. Here, we establish a human settlements suitability index (HSI) to assess human settlements’ suitability on the Qinghai–Tibet Plateau, including Relief Degree of Land Surface (RDLS), Temperature–Humidity Index (THI), Land Surface Water Abundance Index (LSWAI), and Land Cover Index (LCI). The results show that: (1) The RDLS of the Qinghai–Tibet Plateau was generally high, reflecting elevated terrain and steep topography, with strong regional variation. THI increases from the northwest arid region to the southeast, while high LSWAI and LCI were concentrated and show a zonal distribution. (2) The HSI ranged from 0.07 to 1, with seven suitability types. Low-suitability was distributed in the Kunlun, Gangdise, Himalayas, and the northern and southern parts of the Tibetan valleys. Mid-suitability appeared in the Sichuan–Tibet Alpine Canyon, while high-suitability was concentrated in the northeast (Qaidam Basin, Qilian, Hengduan Mountains), the west (Menyu), and the Qaidam Basin. (3) Low-suitability covered over 70% of the Qinghai–Tibet Plateau but hosts only 20% of the population. Mid-suitability occupied about 20% of the land, yet contained nearly 70% of the population. High-suitability (HSI > 0.7) areas were limited but concentrated populations in the Qaidam Basin, southern Tibetan valleys, and eastern Sichuan–Tibet Alpine Valleys. Future development should target these high-suitability regions to support sustainable population growth and reduce land pressure. These findings provide a scientific basis for regional planning, population distribution, and ecological security on the Qinghai–Tibet Plateau. Full article

Background: Morus alba L. (family Moraceae) is widely cultivated across the world and is well-known for its medicinal and nutritional value, especially its leaves. This study investigates the regional variation in mulberry leaf metabolites, focusing on alkaloids and flavonoids, and explores the influence of climatic and environmental factors on their biosynthesis using an integrated metabolomic and environmental analysis. Mulberry leaves, known for their medicinal and nutritional value, were collected from six regions across China, including Sichuan, Xinjiang, and Tibet. Methods: Untargeted metabolomics via UHPLC-MS was conducted. Differential metabolites were identified through multivariate analysis and annotated using the KEGG database. Redundancy analysis was used to link metabolite profiles with climatic data. Results: Mulberry leaves from six Chinese regions showed significant variation in total flavonoid content (TFC), total polyphenol content (TPC), and 1-Deoxynojirmycin (DNJ), with Tibet having the highest TFC and TPC, and Panzhihua the highest DNJ. Metabolomic analysis identified 3794 metabolites, revealing distinct regional clustering. A total of 79 differential metabolites were identified, which are enriched in pathways such as galactose metabolism and phenylalanine biosynthesis. Environmental factors, especially bio3, bio10, bio2, bio5, and bio20, strongly influenced metabolite profiles. Conclusions: The biosynthesis and accumulation of secondary metabolites in mulberry leaves are significantly influenced by region-specific environmental factors, particularly temperature, precipitation, and light. The identified differential metabolites are mainly enriched in galactose metabolism, arginine, and proline metabolism, and phenylalanine, tyrosine, and tryptophan biosynthesis. These pathways are closely associated with plant stress responses and the synthesis of secondary metabolites. The pronounced regional differences in metabolite profiles underscore the critical role of environmental factors in determining the chemical composition of mulberry leaves. This research provides valuable insights into the influence of climatic factors affecting the chemical composition of plants. It lays a theoretical foundation for the quality assessment and grading of mulberry leaves, providing scientific guidance for their targeted cultivation and utilization. Full article

In global cold regions and seasonal frozen soil areas, frost heave failure of rock slopes severely endangers infrastructure safety, particularly along China’s Sichuan–Tibet and Qinghai–Tibet Railways. To address this, a meshless numerical model based on the smoothed particle hydrodynamics (SPH) method was developed to simulate progressive frost heave and fracture of water-saturated fissured rock masses—its novelty lies in avoiding grid distortion and artificial crack path assumptions of FEM as well as complex parameter calibration of DEM by integrating the maximum tensile stress criterion (with a binary fracture marker for particle failure), thermodynamic phase change theory (classifying fissure water into water, ice-water mixed, and ice particles), and the equivalent thermal expansion coefficient method to quantify frost heave force. Systematic simulations of fissure parameters (inclination angle, length, number, and row number) revealed that these factors significantly shape failure modes: longer fissures and more rows shift failure from strip-like to tree-like/network-like, more fissures accelerate crack coalescence, and larger inclination angles converge stress to fissure tips. This study clarifies key mechanisms and provides a theoretical/numerical reference for cold region rock slope stability control. Full article

Methane (CH₄), the second-largest global greenhouse gas and a key driver of tropospheric ozone formation, critically influences climate change and air quality. As the world’s largest CH₄ emitter, China must develop targeted mitigation strategies to support its carbon peak and neutrality goals while reducing ozone pollution. Here, we analyzed the spatiotemporal evolution of provincial CH₄ emissions in China from 2000 to 2023 using spatial autocorrelation, hotspot detection, trend analysis, and K-means clustering. Our results revealed a triphasic emission trajectory—rapid growth followed by stabilization and a recent resurgence—with all provinces except Tibet showing increasing trends. The energy sector emerged as the primary contributor, particularly in Inner Mongolia, Shanxi, and Shaanxi, whereas agricultural emissions dominated in pastoral regions, such as Inner Mongolia and Sichuan, and rice-growing areas, such as Hunan and Hubei. Coastal provinces, including Shandong, Jiangsu, and Guangdong, exhibited waste disposal as their predominant CH₄ source. Based on these patterns, we classified the emission zones into four distinct typologies: coal-dominant, waste-dominant, oil-agriculture composite, and multifactorial systems, proposing tailored mitigation frameworks that integrate CH₄ and ozone co-reduction. This study provides a spatially resolved foundation for synergistic climate and air quality governance in China. Full article

Landslides are major geological hazards that pose serious threats to life and property, particularly in the high-mountain canyon regions of Sichuan, Yunnan, and southeastern Tibet. Displacement prediction plays a critical role in disaster prevention and mitigation. In recent years, machine learning methods based on InSAR data have achieved significant breakthroughs in landslide forecasting. However, models relying solely on a single data-driven approach may fail to fully capture the complex physical mechanisms of landslides, affecting both the reliability and interpretability of predictions. Therefore, developing effective landslide displacement prediction models is essential. The paper introduces a model designed to forecast the landslide displacement using Variational Mode Decomposition (VMD), Bayesian Optimization (BO), and Gated Recurrent Units (GRU). First, wavelet analysis is employed to identify the trend component in the landslide displacement data. Then, the total displacement is separated into its trend and periodic components through the application of the Variational Mode Decomposition (VMD) technique. A wide range of influencing factors is introduced, and Utilizing Grey Relational Analysis, we evaluate the interplay between contributing factors and all components of landslide displacement, both trend and periodic. Prediction models incorporate the trend and periodic terms, alongside the contributing factors, as input variables. The overall displacement is computed by summing the trend and periodic terms series using the Mianshawan landslide as a case study, experimental studies were conducted with landslide data from January 2019 to December 2022 with a Root Mean Squared Error (RMSE) of 0.402, Mean Absolute Error (MAE) of 0.187, Mean Absolute Percentage Error (MAPE) of 2.05%, and a coefficient of determination (R²) of 0.998. These findings indicate that, compared to traditional methods, our model delivers remarkable improvements in performance, offering higher prediction accuracy and greater reliability in the landslide forecasting task for the Mianshawan area. Full article

The Southwest Alpine Canyon Area (SACA) is a typical ecologically sensitive location in China; therefore, constructing and optimizing an ecological network for this area is essential to ensure the regional ecological security of its fragile ecosystems. This study employed the InVEST model to quantitatively assess the habitat quality of the SACA for the years 2000, 2010, and 2020. The ecological sources were determined based on the results of a habitat quality assessment and a Morphological Spatial Pattern Analysis (MSPA). Finally, ecological corridors, ecological pinch points, and ecological barrier points were identified using circuit theory. The results indicated that the SACA’s habitat quality was relatively good, but experienced slight degradation from 0.87 in 2000 to 0.84 in 2020. Anthropogenic activities have been identified as the primary contributor to habitat quality decline in the region. Geographically, the habitat quality is significantly poorer in the southeast and northwest of the SACA. A total of 319 ecological sources were identified, predominantly located in the southwest and northeast of the SACA, comprising 43.27% of the total area. Furthermore, 94 ecological corridors were delineated, covering an area of 74,015.61 km² and extending over 182.80 km in length in total. A total of 38 ecological pinch points and 39 ecological barrier points were distinguished, with a noticeable concentration in regions undergoing ecological degradation. Overall, while the ecological network structure in the SACA is complex and highly interconnected, it faces challenges relating to material cycling and ecological network circulation. Future ecological restoration and protection efforts should focus on areas along the border between the ecological maintenance area in southeastern Tibet (Region I) and the water conservation area in eastern Tibet–western Sichuan (Region II). Additionally, the establishment of ecological protection belts around potential ecological corridors is proposed to enhance ecosystem connectivity. These findings could provide a robust scientific foundation for territorial spatial planning, ecological preservation, and restoration in the SACA. Full article

The Western Sichuan Plateau, an ecologically critical transition zone between the Qinghai–Tibet Plateau and the Sichuan Basin, is also a typical fragile and sensitive area in China’s ecological security. This study established a multi-process evaluation model using the Spatial Distance Index Method, integrating cluster analysis, Sen–Mann–Kendall trend detection, and OWA-based scenario simulations to assess composite ecological sensitivity dynamics. The optimal geodetector was further applied to quantitatively determine the driving mechanisms underlying these sensitivity dynamics. The research showed the following findings: (1) From 2000 to 2020, the ecological environment of the Western Sichuan Plateau exhibited a phased pattern characterized by significant improvement, partial rebound, and overall stabilization. The composite ecological sensitivity grading index showed a declining trend, indicating a gradual reduction in ecological vulnerability. The effectiveness of ecological restoration projects became evident after 2010, with the area of medium- to high-sensitivity zones decreasing by 24.29% at the regional scale compared to the 2010 baseline. (2) The spatial pattern exhibited a gradient-decreasing characteristic from west to east. Scenario simulations under varying decision-making behaviors prioritized Jiuzhaigou, Xiaojin, Jinchuan, Danba, and Yajiang counties as ecologically critical. (3) Driving force analysis revealed a marked increase in the explanatory power of freeze-thaw erosion, with its q-value rising from 0.49 to 0.80. Moreover, its synergistic effect with landslide disasters spans 74.19% of county-level units. Dominant drivers ranked: annual temperature range (q = 0.32) > distance to faults (q = 0.17) > slope gradient (q = 0.16), revealing a geomorphic-climatic-tectonic interactive mechanism. This study provided methodological innovations and decision-making support for sustainable environmental development in plateau transitional zones. Full article

In recent decades, honeybee populations have declined, dramatically owing to destructive honey harvesting practices and the loss of foraging grounds and nesting sites. Among them, Apis laboriosa Smith, 1871 (Hymenoptera, Apidae), an important pollinator species found in the Himalayan region, holds significant economic and ecological value. However, conservation efforts and intraspecific taxonomic studies regarding it have been rather limited, and thus its full geographic range remains elusive. This study is the first to research A. laboriosa in Sichuan. Through a systematic study integrating morphological feature analysis and genomic data, the following conclusions are drawn. Whole-genome resequencing data analysis reveals that the Sichuan population forms a new monophyletic group (Bootstraps = 100). In the past ten thousand years, the population sizes of A. laboriosa in four different regions of China have been decreasing rapidly. Measures should be taken to protect them across the entire distribution range, especially the populations in Tibet and Sichuan, due to their relatively large genetic differences and low intra-population genetic diversity. Based on the significant difference analysis, the following four wing vein morphological features with extremely significant differences were identified: the width of the right forewing (FB), the cubital index a/b (Ci), the forewing vein angle (E9), and the forewing vein angle (K19). These findings are expected to offer a valuable reference for future A. laboriosa conservation endeavors, particularly in protecting populations with a high level of genetic differentiation. Full article

Nitrogen oxides (NO_x) are key precursors of tropospheric ozone and particulate matter. The sparse local observations make it challenging to understand NO_x cycling across the Tibetan Plateau (TP), which plays a crucial role in regional and global atmospheric processes. Here, we utilized Geostationary Environment Monitoring Spectrometer (GEMS) data to examine the tropospheric NO₂ vertical column density (Ω_NO2) spatiotemporal variability over TP, a pristine environment marked with natural sources. GEMS observations revealed that the Ω_NO2 over TP is generally low compared with surrounding regions with significant surface emissions, such as India and the Sichuan basin. A spatial decreasing trend of Ω_NO2 is observed from the south and center to the north over Tibet. Unlike the surrounding regions, the TP exhibits opposing seasonal patterns and a negative correlation between the surface NO₂ and Ω_NO2. In the Lhasa and Nam Co areas within Xizang, the highest Ω_NO2 in spring contrasts with the lowest surface concentration. Diurnally, a midday increase in Ω_NO2 in the warm season reflects some external sources affecting the remote area. Trajectory analysis suggests strong convection lifted air mass from India and Southeast Asia into the upper troposphere over the TP. These findings highlight the mixing interplay of nonlocal and local NO_x sources in shaping NO₂ variability in a high-altitude environment. Future research should explore these transport mechanisms and their implications for atmospheric chemistry and climate dynamics over the TP. Full article

The southeastern margin of the Tibet Plateau represents one of the most seismically active zones in China and serves as a natural laboratory for investigating the uplift dynamics and lateral expansion mechanisms of the plateau. The Litang fault zone (LTFZ) lies within the northwest Sichuan sub-block on the southeastern margin of the Tibet Plateau, running almost parallel to the Xianshuihe fault zone and forming a V-shaped conjugate structure system with the Batang fault zone (BTFZ). The Maoyaba fault (MYBF) is a significant component of the northwestern part of the LTFZ, exhibiting activity in the late Quaternary. It triggered the ancient Luanshibao landslide and caused the Litang earthquake in 1729 AD, demonstrating intense seismic activity. Employing high-resolution remote sensing interpretation, field surveys, UAV photogrammetry, and UAV LiDAR, this study further examines the geometric distribution and kinematic properties of the MYBF, as well as paleoearthquake events recorded by the fault scarps. Combined with the geometric distribution and kinematic properties of the Hagala fault (HGLF) and Zimeihu fault (ZMHF), this study discusses the late Quaternary structural deformation style and seismic potential of the MYBF. The MYBF could produce earthquakes of approximately Mw 6.7 ± 0.3, with an average co-seismic slip of about 0.68 m and an average recurrence interval of strong earthquakes since the late Quaternary ranging from 0.9 to 1.1 ky. The likelihood of surface rupture earthquakes occurring in the near future is low; however, the expansion of the HGLF could induce moderate to strong earthquakes in the MYB area. The variation in the local tectonic stress field, which is influenced by the Litang–Batang V-shaped structure system and lithological differences, results in the formation of an extensional horsetail structure in the northwestern segment of the LTFZ. Both the HGLF and ZMHF remain active faults. Under the influence of nearly north–south tensile stress, these faults and the Litang–Batang V-shaped structure system collectively regulate the movement of regional crustal material. Full article

Hippophae neurocarpa is a relatively new member of the Rhamnus genus that has various potential edible and medicinal values, but still needs to be further developed. To better develop H. neurocarpa, it is crucial to determine its current and future population distribution. This study utilized the “Biomod2” package in R to integrate five individual models and investigate the effects of climate change on the potential distribution of H. neurocarpa, as well as the key climatic factors influencing its distribution. The results indicated that, under the current scenario, the potential distribution of H. neurocarpa is mainly concentrated in the eastern parts of the Loess Plateau and the Qinghai–Tibet Plateau. In the future, its potential suitable habitats will undergo varying degrees of change: the area of medium/low suitability will decrease, while the area of high suitability will shift westward and increase. In the analysis of area changes, it was found that some potential suitable habitats in Sichuan and Shaanxi will directly transition from highly suitable to unsuitable areas. Key environmental variable analysis showed that temperature, particularly low temperature, is a crucial factor affecting the distribution of H. neurocarpa. Additionally, altitude also has a significant impact on its distribution. This study predicted the potential suitable habitats of H. neurocarpa, which will aid in its future development and provide reference for selecting regions suitable for its cultivation. Full article

The coordination between poverty alleviation and ecological protection is both a crucial requirement and a long-standing challenge for sustainable development. China’s implementation of a targeted poverty alleviation strategy has completed the task of eliminating extreme poverty. However, the evaluation of the corresponding ecosystem changes in the entire poverty-alleviated areas is still insufficient. This study investigated the spatiotemporal changes in ecosystem vulnerability across China’s 832 national poverty-stricken counties from 2005 to 2020. A habitat–structure–function framework was applied to develop an evaluation index, along with a factor analysis of environmental and socio-economic indicators conducted through the Geodetector model. Finally, the implications of China’s practices to balance poverty alleviation and ecological protection were explored. The results show that ecosystem vulnerability decreased from 2005 to 2020, with an even greater decrease observed after 2013, which was twice the amount of the decrease seen before 2013. The post-2013 changes were mainly brought about by the enhancement of the ecosystem function in critical zones such as the Qinghai–Tibet Plateau Ecoregion, Yangtze River and Sichuan–Yunnan Key Ecoregion, and Yellow River Key Ecoregion. From 2013 to 2020, the influence of the gross domestic product (GDP) surpassed that of other factors, playing a significant positive role in diminishing ecosystem vulnerability in the three regions mentioned. The results suggest that China’s poverty-alleviated areas have found a “win–win” solution for poverty alleviation and ecological protection, that is, they have built a synergistic mechanism that combines government financial support with strict protection policies (e.g., more ecological compensation, eco-jobs, and ecological public welfare positions for poor areas or the poor). These findings elucidate the mechanisms behind China’s targeted poverty alleviation outcomes and their ecological implications, establishing a practical framework for coordinated development and environmental stewardship in comparable regions. Full article