Search Results (1,383)

Tourism safety in high altitude destinations is strongly affected by the combined effects of environmental constraints, tourism exposure, and safety support capacity. The Qinghai–Tibet Plateau (QTP), characterized by high altitude, complex terrain, sparse settlements, and limited emergency accessibility in remote areas, provides a representative case for tourism risk assessment in extreme plateau environments. To predict and interpret the spatial pattern of tourism risk on the QTP, this study constructs an assessment framework based on “Hazard–formative factors + Risk exposure + Safety security” and integrates XGBoost with SHAP interpretable machine learning. Eleven indicators representing environmental conditions, tourism exposure, and safety support capacity were used to model tourism risk at a 1 km × 1 km spatial resolution. The optimized XGBoost model achieved an AUC of 0.877, indicating good predictive performance. The results show that tourism risk on the QTP presents a spatial pattern of “high in the northwest and low in the southeast”. High risk and relatively high risk areas account for approximately 74.98% of the study area and are mainly distributed in remote hinterlands and northwestern plateau regions, whereas low risk areas are concentrated around southeastern river valleys, towns, mature scenic areas, and major transport corridors. SHAP analysis indicates that Distance to towns is the most important factor influencing predicted tourism risk, followed by Reception facility kernel density, Relief degree of land surface, and Scenic spot kernel density. Nonlinear and interaction analyses further suggest that remoteness, tourism facilities, terrain relief, and scenic area concentration jointly shape the predicted risk pattern. The findings provide spatial evidence for differentiated tourism risk management, including regular tourism development in relatively safe urban and scenic nodes, controlled management of medium risk tourism corridors, and stricter access management in remote high risk areas. Full article

The invasive plant Datura stramonium L. possesses strong reproductive capacity and ecological adaptability, showing a tendency to spread rapidly, especially in highly human-disturbed habitats. To explore its resource utilization pathway—turning waste into wealth—and to address toxic metal pollution in strongly human-disturbed areas (such as mining regions), this study evaluates its phytoremediation potential in contaminated soils on the Qinghai–Tibet Plateau. We established a non-planted control and three planting density treatments to compare the removal rates of Pb, Cd, Cr, and As. To our knowledge, this is the first study to assess how planting density influences the multi-metal phytoremediation performance of this invasive species in a high-altitude plateau environment. The results showed that planting significantly increased toxic metal removal rates, with overall efficiency generally improving at higher densities, particularly for Cr. Analysis of bioconcentration and translocation factors revealed distinct element-specific accumulation patterns. Pb and As were primarily enriched and retained in the roots. Interestingly, while Cd exhibited a strong localized tendency to accumulate in the leaves, its overall root-to-shoot translocation remained relatively restricted at the whole-plant level, similar to Cr. Overall, D. stramonium functions primarily through root stabilization for Pb, As, and Cr, alongside partial aboveground accumulation for Cd. However, given its toxic and invasive nature, any practical phytoremediation application requires strict post-harvest biomass management and ecological monitoring to prevent secondary spread. Full article

The reliable identification of productive and nutritionally valuable Bromus inermis Leyss. germplasm requires multi–year evaluation because forage performance is strongly influenced by genotype, stand age, and annual environmental variation. We evaluated four experimental genotypes and the cultivar WUSU as a control over three production years at a fixed alpine site on the Qinghai–Tibet Plateau. Agronomic traits, forage yield, dry matter accumulation, and nutritional quality were measured annually. A multi–criteria TOPSIS model was used to integrate yield and quality traits for genotype ranking, while random forest analysis and piecewise structural equation modeling were applied to identify key traits and potential pathways influencing forage performance. Genotype, year, and their interaction significantly affected most agronomic, yield, and nutritional traits. Most traits reached their highest values in the third production year, indicating that this stage was critical for evaluating full productive potential. Among the tested materials, genotype 4–4 showed consistently high biomass production and favorable nutritional performance, whereas WUSU and genotype 1–10 generally ranked lower. Plant height and grass height were positively associated with fresh and hay yield, while fresh forage yield, crude protein content, and stem diameter contributed strongly to model prediction. The SEM results suggested that genotype–year interaction influenced hay yield mainly through changes in stem diameter and acid detergent fiber content. These findings indicate that combining multi–year field evaluation with multi–criteria ranking and pathway analysis can improve the identification of promising B. inermis germplasm. Genotype 4–4 represents a useful candidate for further multi–site validation and breeding for high–yield, high–quality forage production in alpine regions. These findings provide a theoretical basis and candidate germplasm for the genetic improvement of Bromus inermis Leyss. adapted to the Qinghai–Tibet Plateau. Full article

This study uses Gonjo County, Chamdo City, Tibet, as the study area and addresses the challenges of lithological complexity and low efficiency of conventional geological surveys in the Qinghai–Tibet Plateau. This study applies the first systematic application of Chinese GF-5 AHSI data to conduct detailed lithological classification in a plateau environment. Three types of datasets were constructed, including the full-band (FB) dataset, shortwave infrared diagnostic bands (SWIR), and feature-selected bands (FS). Four classification models—Support Vector Machine (SVM), Long Short-Term Memory network (LSTM), Multi-Scale Convolutional Neural Network (MSCNN), and Spectral-Spatial Unified Network (SSUN)—were comparatively evaluated to systematically assess the performance of spectral feature selection and deep learning methods for hyperspectral lithological classification. The experimental results explicitly demonstrate the superiority of spectral-spatial feature extraction. Specifically, compared to the baseline Support Vector Machine (SVM) model, which achieved an overall accuracy of 74.67% and a kappa coefficient of 0.6952, the proposed SSUN model demonstrated an advantage, reaching an overall accuracy of 90.94% and a kappa coefficient of 0.8917. By jointly extracting spectral sequence features and spatial contextual information, SSUN effectively suppresses noise and enhances the spatial continuity of lithological boundaries. The results demonstrate the high practical applicability and spectral fidelity of GF-5 AHSI data for lithological identification in plateau stratigraphic environments. The shortwave infrared region is confirmed to be a critical spectral domain for lithological discrimination, and spectral-spatial deep learning models can maintain high classification accuracy after feature dimensionality reduction, achieving a balance between classification efficiency and accuracy. This study provides reliable methodological support for remote sensing lithological mapping and mineral resource exploration in complex plateau geological environments. Full article

Low-temperature stress significantly limits plant growth, development, and productivity, posing a major environmental constraint. The potato (Solanum tuberosum L.) is particularly vulnerable to low temperatures, underscoring the crucial need to enhance cold tolerance in potato breeding efforts for sustainable production. Basic leucine zipper (bZIP) transcription factors serve as central regulators of plant developmental processes and stress responses; however, their functional role in cold tolerance in tetraploid potato remains poorly understood. Here, we report a systematic characterization of the bZIP gene family in tetraploid potato and provide preliminary evidence that StbZIP104 enhances plant cold tolerance. A total of 191 StbZIP genes were identified and classified into 11 subfamilies, exhibiting uneven chromosomal distribution and expansion primarily driven by whole-genome and segmental duplication. Promoter cis-element analysis, together with GO and KEGG enrichment analyses, indicated that StbZIP genes are broadly associated with hormone signaling, stress responses, signal transduction, and environmental adaptation. Expression profiling under low-temperature treatment revealed eight cold-inducible StbZIP genes (log₂FC ≥ 1 and FDR < 0.05), among which StbZIP104 was strongly induced (log₂FC ≥ 2) and showed 5.36-fold higher expression in highly cold-resistant cultivars than in cold-sensitive cultivars. Subcellular localization confirmed that StbZIP104 is a nuclear-localized protein. Functional validation confirmed that overexpressing StbZIP104 notably improved cold tolerance in transgenic Samsun NN tobacco (Nicotiana tabacum cv. Samsun NN). This was supported by heightened superoxide dismutase and peroxidase activities, increased levels of soluble protein and soluble sugars, and decreased malondialdehyde content compared to the wild type under cold stress. This study establishes a basis for the functional characterization of the bZIP gene family in tetraploid potato and serves as a theoretical reference for understanding the mechanisms that govern cold tolerance in this species. Full article

Soil total nitrogen (TN) and soil total phosphorus (TP) are key soil quality indicators and provide critical ecological functions in the grasslands. This study analyzed the driving factors of TN/TP in the grasslands of Qinghai Province based on Shapley additive interpretation (SHAP) analysis. Four machine learning methods, namely random forest (RF), XGBoost 3.2.0, support vector machine, and Cubist, were used to establish spatial prediction models for TN/TP. Vegetation factors (Net Primary Production and Normalized Difference Vegetation Index) and precipitation-related factors (Aridity Index and Mean Annual Precipitation) were the most important variables for TN, indicating plant productivity and precipitation are strongly associated with TN accumulation. Elevation and temperature-related factors (Mean Annual Temperature and evapotranspiration) were the most important variables for TP, demonstrating that elevation-mediated temperature was the major factor affecting the TP accumulation. XGBoost and RF were the optimal models for TN and TP, respectively. TN exhibited a decreasing spatial trend from east to west, while the northwestern and southwestern areas showed relatively higher and lower TP, respectively. Total TN and TP stocks were estimated to be 3.57 × 10⁸ t and 0.88 × 10⁸ t, respectively. This study provides data support and suggestions for sustainable soil nutrient management in the grasslands on the Qinghai-Tibet Plateau. Full article

The Lhalu Wetland National Nature Reserve, the largest natural urban wetland on the Qinghai–Tibet Plateau, plays a critical role in maintaining regional ecological balance and biodiversity. However, the baseline biodiversity of this reserve remains unclear because of the extensive temporal span of historical records, shifts in taxonomic systems, and inconsistent survey methodologies, which impedes a robust scientific understanding of its ecological dynamics. This study systematically compiled and taxonomically verified species records from over 50 sources spanning the 1950s to the present. The records cover plants, fish, birds, and amphibians/reptiles, thereby resolving issues of synonyms, homonyms, and misidentifications. Each species record is annotated with its original survey time, allowing users to distinguish historically reported occurrences from those recorded in recent surveys. Species accumulation curves were constructed for major taxa and compared with 45-year climatic trends (1979–2023) and socioeconomic indicators for Lhasa City. A total of 438 vascular plant species (82 families, 251 genera) and 311 animal species (39 orders, 98 families), including 30 fishes, 174 birds, and 11 amphibians/reptiles, were documented. Invasive species comprised 55 alien plants and 13 alien fishes, while 4 plant and 46 animal species are under national protection. Temporal synchrony between increases in alien taxa and anthropogenic pressures (gross domestic product (GDP) and population growth, infrastructure development) suggests that human activities may be a potential driver of biodiversity change, but formal causal inference is precluded by heterogeneity in survey methods and sampling effort. This work provides a structured dataset of the biodiversity baseline of the Lhalu Wetland and offers a descriptive assessment of its temporal patterns in relation to climate and human disturbance, while explicitly acknowledging data limitations. It provides essential data and theoretical support for the scientific management and targeted conservation of plateau urban wetlands. Full article

Hulless barley (Hordeum vulgare L. var. nudum) on the Qinghai–Tibet Plateau is consistently exposed to intense solar irradiance, yet whether and how reproductive spikes and flag leaves partition photoprotection remains unclear. Here, we compared a pigmented black landrace (Cai Peng Zi, CPZ) with a white cultivar (Zang Qing 3000, ZQ3000) across early, middle, and late spike coloration stages under field conditions. By integrating measurements of anthocyanin and chlorophyll contents, chlorophyll fluorescence parameters, and rapid light-response curves, we dissected organ-specific strategies in photochemistry and energy dissipation in spikes and flag leaves. The results showed that anthocyanin accumulation in CPZ spikes increased significantly during spike coloration, while chlorophyll a and the chlorophyll a/b ratio declined, indicating a shift from light harvesting to photoprotection in reproductive tissues. This pigment transition coincided with reduced PSII performance (declines in QY_max, qP, and qL) but stable non-photochemical quenching (NPQ and qN), pointing to reduced photochemical capacity with relatively stable energy dissipation in the spike. In contrast, CPZ leaves maintained higher QY_max than ZQ3000 but exhibited a pronounced decline in NPQ and qN at late stages, reflecting CPZ’s attenuated regulated energy dissipation capacity. Rapid light-response analysis further supported differences between organs and cultivars. Under high PAR, ZQ3000 spikes exhibited steeper declines in Y(II) and stronger downregulation of ETR(II), whereas CPZ spikes showed more moderate decreases; in leaves, ZQ3000 maintained consistently lower Y(NO) and higher Y(NPQ), indicating greater reliance on regulated energy dissipation. Collectively, our results reveal how pigment-mediated screening in reproductive structures and dynamic regulation of energy dissipation in leaves are coordinated to optimize light-use efficiency in high-altitude environments, providing physiological insights for breeding resilient hulless barley varieties. Full article

Yak (Bos grunniens) is a vital livestock resource on the Qinghai–Tibet Plateau, and its body measurement parameters play a crucial role in growth and development assessment, health monitoring, and breeding improvement. To overcome the limitations of traditional manual measurements—such as low efficiency, unstable accuracy, and the tendency to induce animal stress—this study proposes an intelligent yak body measurement prediction method that integrates keypoint detection with regression modeling, termed UST-YOLO11Pose-TRM. Within the YOLO11-Pose framework, three attention mechanisms—UIB, SENetV2, and TripleAttention—are incorporated to construct a lightweight yet high-precision keypoint detection model, UST-YOLO11Pose, thereby enhancing channel feature representation, global contextual modeling, and spatial dependency perception. Meanwhile, a Transformer-based regression model is designed, leveraging multi-head self-attention to characterize global geometric relationships among keypoints and to achieve accurate prediction of key body measurement parameters, including body length, body height, oblique body length, chest girth, and cannon circumference. Experimental results demonstrate that UST-YOLO11Pose achieves an mAP of 0.958, a Precision of 0.967, and a Recall of 0.955 in keypoint detection tasks, significantly outperforming both same-series and cross-series comparative models with a parameter size of only 10.06 MB. In the body measurement regression task, the Transformer-based regression model attains an RMSE of 0.185, an MAE of 0.122, an MAPE of 2.3%, and a coefficient of determination (R²) of 0.962 on the test set, indicating excellent predictive accuracy and robust fitting stability. In summary, UST-YOLO11Pose-TRM enables accurate, efficient, non-contact yak body measurement, showing strong potential for smart pasture development and precision livestock management. Full article

The Sanjiangyuan Region is among China’s most critical ecological function zones and serves as an important habitat for rare wildlife species such as brown bears and snow leopards. Driven by factors including climate change and intensified human activities, human–wildlife conflicts have become increasingly frequent on the Qinghai–Tibet Plateau, threatening the living space of both herders and wildlife. This study centers on the Yushu Tibetan Autonomous Prefecture in Qinghai Province, integrating multi-source remote sensing data with field survey data, and employs the Maximum Entropy Model (MaxEnt) MaxEnt model and the BIOMOD2 framework to simulate high-risk areas for brown bear incidents. Results indicate that the BIOMOD2 ensemble model (EMca) achieved the highest predictive accuracy, with the Random Forest (RF) model demonstrating strong robustness among individual models. Digital Elevation Model (DEM), Soil Surface Moisture (SSM), Fractional Vegetation Cover (FVC), and Human Footprint (HFP) were identified as the primary factors influencing the spatial distribution of brown bear incidents. High-risk areas exhibited significant clustering, mainly concentrated in the southern and southeastern regions of Qumalai, Nangchen, and Chindu; the eastern part of Zadoi County; and the central and southern parts of Yushu City, particularly within the elevation range of 4304–4544 m, where human activity intensity is relatively low. The core high-risk zone is located along the Tongtian River in southern Qumalai County, demonstrating strong spatial connectivity. By investigating the spatial distribution patterns and driving mechanisms of brown bear incidents in Yushu Prefecture, this study offers some references for government agencies to formulate strategies that promote harmonious coexistence between humans and nature. Full article

Although bud banks are key components of vegetation regeneration in degraded alpine meadows, their relationships with soil conditions on the Qinghai–Tibet Plateau remain insufficiently understood. In this study, we investigated bud bank composition and density, plant functional group biomass, soil physicochemical properties, and soil microbial biomass across five degradation stages of alpine meadows in a long-term controlled grazing experiment. Field sampling was conducted in mid-August 2021, and the relationships between bud bank densities, plant biomass, and soil variables were evaluated using comparative statistical analyses, redundancy analysis, and structural equation modeling. Bud bank density increased from non-degraded to moderately degraded meadows, reaching 3075 buds m⁻², but declined sharply in severely degraded meadows to 183 buds m⁻². Regarding distinct bud types, rhizome and tiller bud densities peaked in moderately degraded alpine meadows (1217 and 1750 buds m⁻², respectively), whereas dicot bud density peaked in lightly degraded meadows. Bud bank density was positively associated with higher soil moisture content and negatively associated with increased soil bulk density. Moreover, bud bank density was positively correlated with soil organic carbon, total phosphorus, ammonium nitrogen, and soil microbial biomass carbon, nitrogen, and phosphorus. Our findings indicate that soil conditions may favor the maintenance of high bud bank density in moderately degraded meadows with high soil moisture, low bulk density, and more nutrient-rich soil conditions in moderately degraded meadows. Overall, our results indicate that alpine meadow degradation influences belowground regenerative capacity through changes in soil conditions and associated shifts in bud bank dynamics. Therefore, assessments and restoration of degraded alpine meadows should consider bud bank persistence in addition to aboveground vegetation characteristics. Full article

This study aimed to investigate differences and patterns in fungal communities within the root-zone soil of Lamiophlomis rotata across varying altitudes. Specifically, it analyzed the characteristics of soil fungal communities at altitudes of 3600, 3800, 4000, and 4200 m and examined their relationships with key bioactive medicinal constituents and soil nutrients. The results indicated that Ascomycota, Mortierellomycota, and Basidiomycota were the dominant fungal phyla in the L. rotata root-zone soil, with Pseudosperma and Clavaria as the predominant genera. The Shannon and Chao1 diversity indices of soil fungi initially decreased and subsequently increased with increasing altitude. At the same altitude, these indices were higher in the root-zone soil than in the non-root-zone soil. Redundancy analysis revealed that available phosphorus was the primary factor influencing fungal communities in the non-root-zone soil. In conclusion, altitude significantly affected the characteristics of fungal communities in root-zone soil, which differed significantly from those in the non-root-zone soils. These findings provide valuable data to support the conservation of L. rotata resources on the Qinghai–Tibet Plateau. Full article

Accurate prediction and assessment of carbon storage are crucial in the context of global climate change. However, existing research has largely focused on large-scale regions, while studies on small-scale ecologically fragile alpine regions remain insufficient. This study focuses on Zoige County, integrating the PLUS model, InVEST model, and Random Forest model to form a composite analysis workflow. Through this workflow, we simulated the distribution of land use types in 2030 and quantified carbon storage from 1990 to 2030, subsequently analyzing their spatial distribution and driving factors. The key findings include: (1) Under the natural development scenario (NDS) and the ecological protection scenario (EPS) for 2030, the primary land use transition involved the conversion of grassland to forest and wetland. Conversely, wetland was converted into cropland under the economic development scenario (EDS). (2) Under the NDS, EDS, and EPS, carbon storage would be 8.396 × 10⁷ t, 8.252 × 10⁷ t, and 8.432 × 10⁷ t, respectively. The EPS yielded the largest increase in carbon storage. (3) In all three scenarios, carbon storage showed a clustered distribution. Compared with 2020, the carbon storage hot spot areas under both NDS and EPS showed an expansion trend, whereas the cold spot areas also expanded in three scenarios. (4) The key drivers of carbon storage include slope, elevation, soil type, and mean annual temperature. This study concludes that the EPS represents the most favorable development pathway for carbon storage accumulation. This finding can provide a basis for future carbon storage dynamics and land use planning for Zoige County. Full article

In 2024 and 2025, researchers investigated the breeding ecology of the Twite Linaria flavirostris in riparian shrubland habitats at an elevation of 3400 m in the northeastern Qinghai–Tibet Plateau. This species lays eggs from late June to mid-July, capitalizing on the region’s brief warm season. Nests are typically open-cup structures built in Hippophae spp. shrubs. The population predominantly exhibits monogamous mating, with a mean clutch size of 4.7 ± 0.49 (3~5). Incubation is performed solely by the female and lasts 11.52 ± 1.65 days. Both parents provision the nestlings, and the nestling period lasts 12.43 ± 2.39 days. Morphological measurements of nestling body mass and external organs all fit well to the Logistic growth curve equation. By fledging, tarsus length and bill length reach over 90% of adult values, conferring substantial terrestrial mobility. However, flight-related feathers, primaries and rectrices, remain markedly underdeveloped compared to adults, resulting in extremely poor flight capability; further post-fledging development is thus required. Based on reproductive outcomes from this single breeding season, a total of 121 eggs were laid, of which 81 successfully hatched, and ultimately 79 fledglings survived to leave the nest. The overall hatching success was 66.94%, fledging success (among hatchlings) was 97.53%, and overall offspring survival (from eggs to fledglings) was 65.29%. The apparent nesting success rate was 76.0%, based on a total of 50 nests monitored over two years. Daily nest survival rates were estimated using Mayfield’s method and program MARK, resulting in nest success probabilities of 0.587 and 0.219, respectively. Comparing populations across different geographic regions, the results indicate that Twites breeding in environments with higher levels of environmental stress produce smaller clutch sizes and larger eggs, and exhibit a prolonged nestling period. This life-history strategy likely represents an evolutionary adaptation to spatially variable environmental conditions. Full article

To address the bottleneck of poor biological nitrogen removal efficiency caused by the extremely low carbon-to-nitrogen (C/N) ratio of domestic sewage in alpine plateau regions, this study used Hordeum vulgare var. coeleste L., a characteristic crop endemic to the Qinghai–Tibet Plateau, as raw material and adopted pretreated highland barley straw as an external carbon source. Three parallel experiments were carried out using the anaerobic–aerobic–anoxic sequencing batch reactor (AOA-SBR) process to investigate the nitrogen removal performance and functional succession of the microbial community in the AOA-SBR system under three C/N ratio ranges: 5~7, 7~9, and 9~11. The results showed that the addition of an external carbon source significantly improved nitrogen removal efficiency. The optimal C/N ratio range for nitrogen removal in this study was determined to be 7~9. A weakly alkaline environment was conducive to denitrification. The fermentation broth prepared by alkali pretreatment contained a large amount of readily biodegradable organic matter with low toxicity, and achieved excellent nitrogen removal performance, helping to realize cost reduction and efficiency improvement in wastewater treatment. At the optimal C/N ratio of 7~9, the average removal efficiencies of ammonia nitrogen (NH₄⁺-N) and total nitrogen (TN) reached 94.46% and 61.32%, respectively, which were significantly improved compared with the blank control group without external carbon addition. During the experimental period, no obvious changes were observed in microbial abundance at the phylum level, whereas the community structure at the genus level responded significantly to the addition of a straw carbon source. Among them, genera with specific degradation capabilities for straw hydrolysates, such as norank_f__Chitinophagaceae and unclassified_f__Comamonadaceae, were highly sensitive to variations in the C/N ratio. These genera could partially replace the nitrification and denitrification functions of other microorganisms and played a key role in the nitrogen removal process. In contrast, Thauera, a typical conventional heterotrophic denitrifier, showed no significant response to changes in the C/N ratio, indicating that the straw-based external carbon source mainly affected microbial genera with specific hydrolysate-degrading functions. Full article