Search Results (115)

Fractional Vegetation Cover (FVC) dynamics on the Qinghai–Tibetan Plateau (QTP) are critical indicators for assessing ecosystem condition. However, uncertainties persist in the accuracy of existing FVC products over the QTP due to retrieval differences, scale effects, and limited validation data. This study utilized the Google Earth Engine platform to integrate unmanned aerial vehicle (UAV) observations, Sentinel-2, MODIS, climate, and topography datasets, and proposed a comprehensive framework incorporating dual-index screening, machine learning optimization, and multidimensional validation to systematically assess the accuracy of GEOV3, GLASS, and MuSyQ FVC products in the alpine grasslands. The dual-index screening reduced validation uncertainty by improving the spatial representativeness of ground data. To build a high-precision evaluation dataset with limited inter-class coverage, recursive feature elimination and grid search were applied to optimize five ML models, and CatBoost achieved the superior performance (R² = 0.880, RMSE = 0.122), followed by XGBoost, GBM, LightGBM, and RF models. Four validation scenarios were implemented, including direct validation using 250 m UAV plot FVC and multi-scale validation using a 10 m FVC reference aggregated to product grids. Results show that GEOV3 (R² = 0.909–0.925, RMSE = 0.082–0.103) outperformed GLASS (R² = 0.742–0.771, RMSE = 0.138–0.175) and MuSyQ (R² = 0.739–0.746, RMSE = 0.138–0.181), both of which exhibited systematic underestimation. This framework significantly enhances FVC product validation reliability, providing a robust solution for remote sensing product validation in alpine grassland ecosystems. Full article

Species interactions are crucial for understanding how species will respond to future climate change. Incorporating interspecific relationships into mammalian distribution prediction models will significantly impact model outcomes, especially those for animals on the Qinghai–Tibet Plateau (QTP). Thus, we incorporated interspecific relationships into species distribution models to assess and predict the future distributions of five ungulates, including the Red deer (Cervus elaphus), the Kiang (Equus kiang), the Tibetan gazelle (Procapra picticaudata), the Tibetan antelope (Pantholops hodgsonii), and the Bharal (Pseudois nayaur). We found that (1) the suitable habitats of these five ungulates were all predicted to increase between the present and 2050; (2) the suitable distribution areas of four of these ungulates were predicted to be smaller when interspecific relationships were incorporated into the models, with the exception of the Red deer, whose suitable habitat was estimated to be larger; and (3) the centroids of suitable habitat for the five ungulates were predicted to shift to the southern part of the QTP by 2050. Our results demonstrated that interspecific relationships could influence predictions of species distributions, and thus incorporating interspecific relationships will facilitate better assessments and predictions of the future distributions of species. Full article

The complex terrain of the Qinghai–Tibetan Plateau (QTP) makes low-level wind shear (LLWS) detection challenging. Using May–September 2023 high-resolution Doppler Wind Lidar (DWL) observations, this study analyzed the spatiotemporal characteristics of LLWS and proposed an optimized detection algorithm. A key novelty of this work lies in the development of a hybrid physical–statistical detection scheme that combines horizontal divergence with logistic regression to dynamically modulate the shear field. This approach effectively reduces noise-induced false alarms in complex plateau terrain. The results show that LLWS occurred mainly near the surface at night in June, while in September it appeared more frequently during daytime throughout the boundary layer. Horizontally, the dominant directions of LLWS shifted seasonally from northwest and west in June to south and east in September. The proposed optimization method effectively suppressed false alarms, reducing moderate and strong LLWS frequencies by 30–40%. In June, optimization significantly reduced spurious detections of LLWS in the northeast and southwest. The frequency of LLWS in the northeast direction was reduced by up to 0.03. In September, scattered shear was removed and strong shear became more organized in the southeast, while southwest shear frequency decreased by up to 0.04, confirming LLWS patterns and method effectiveness. Full article

Kobresia humilis, an alpine meadow-constructive species, has significant ecological and economic importance on the Qinghai–Tibetan Plateau (QTP). Understanding the diversity and structure of the rhizosphere microbiota associated with K. humilis is essential for advancing microbiome engineering aimed at promoting sustainable ecosystem functioning in alpine meadows. However, little is known about the composition of bacterial community associated with K. humilis and the environmental drivers affecting microbiota assembly on a larger scale. This study revealed that bacterial communities inhabiting the rhizosphere exhibited greater diversity and higher compositional dissimilarity than those within the root compartment (ANOSIM, R = 0.86, p = 0.001). The bacterial genus Sphingomonas was identified as the predominant taxon in both microbial niches. A total of 196 and 176 core genera were detected in the roots and rhizosphere, respectively, with chemoheterotrophy and aerobic chemoheterotrophy representing the dominant functional groups. Co-occurrence network analysis identified hub genera, including Sphingomonas, Rhodomicrobium, Rhizobacter, and Phyllobacterium within root, and Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium, Acidibacter, RB41, and Sphingomonas in the rhizosphere. Among the sampling sites, Haiyan (HY) emerged as the central hub (EIC_HY = 1), followed by Tianjun (EIC_root = 0.98; EIC_soil = 0.99) and Xinghai (EIC_root = 0.97; EIC_soil = 0.95). Redundancy analysis indicated that bacterial abundance in roots was significantly influenced by geographic variables, temperature, and edaphic factors, whereas bacterial communities in the rhizosphere were primarily affected by latitude, altitude, pH, and climatic conditions (p < 0.05). Furthermore, the core bacterial genera exhibited stronger correlations with geographic and edaphic parameters than with climatic factors (p < 0.05). Collectively, these results enhance the current understanding of K. humilis–microbe–environment interactions within the alpine meadow ecosystems of the QTP. Full article

The outburst of Zonag Lake in the permafrost region of the Qinghai-Tibet Plateau (QTP) has significantly altered the local environment, particularly affecting surface conditions and permafrost dynamics. By employing remote sensing and GIS tools, this study analyzed the spatial and temporal variations in surface environmental changes (surface temperature, vegetation, and dryness) within the Zonag–Salt Lake basin. The results indicate that the outburst caused higher surface temperatures and reduced vegetation cover around Zonag Lake. Analysis using the Temperature–Vegetation Dryness Index (TVDI) reveals higher dryness levels in downstream areas, especially from Kusai Lake to Salt Lake, compared to the upstream Zonag Lake. Temporal trends from 2000 to 2023 show a decrease in average Land Surface Temperature (LST) and an increase in the Normalized Difference Vegetation Index (NDVI). Geographical centroid shifts in environmental indices demonstrate migration patterns influenced by seasonal climate changes and the outburst event. Desertification around Zonag Lake accelerates permafrost development, while the wetting environment around Salt Lake promotes permafrost degradation. The Zonag Lake region is also an ecologically significant area, serving as a key calving ground for the Tibetan antelope (Pantholops hodgsonii), a nationally protected species. Thus, the environmental changes revealed in this study carry important implications for biodiversity conservation on the Tibetan Plateau. These findings highlight the profound impact of the Zonag Lake outburst on the surface environment and permafrost dynamics in the region, providing critical insights for understanding environmental responses to lake outbursts in high-altitude regions. Full article

Tibetan sheep are an important livestock breed adapted to the extreme environment of the Qinghai–Tibet Plateau (QTP). Their energy metabolism and environmental adaptability are highly dependent on the rumen microbiome. However, systematic comparisons of the rumen microbiome, its functions, and the resistome between plateau-adapted breeds and lowland breeds remain insufficient. In this study, 6 Tibetan sheep (TS) and 6 Hu sheep (HS) were selected. All the selected sheep had a body weight of 34 kg (±0.5 kg) and an age of 1 year (±1 month) and were all managed under local traditional natural grazing (without supplementary feeding). Using metagenomics and metabolomics techniques, systematic comparative analysis was conducted on the differences in rumen microbial community structure, functions, resistome, and metabolites between the two breeds. Metagenomic analysis showed that at the phylum level, the abundance of Bacteroidetes in the rumen of TS was significantly higher than that in HS (p < 0.05); at the genus level, the abundance of Bacteroides in TS was also significantly higher (p < 0.05). Carbohydrate-active enzymes (CAZy) analysis indicated that the abundance of Glycosyltransferases (GTs) and Carbohydrate-Binding Modules (CBMs) in the rumen of TS were significantly upregulated (p < 0.05), while HS was rich in various Glycoside Hydrolases (GHs). Comprehensive Antibiotic Resistance Database (CARD) analysis revealed that more than 60% of the Antibiotic Resistance Genes (ARGs) in the rumen of HS were present at higher levels than those in TS. Metabolomics identified a large number of differential metabolites, among which metabolites such as 2E,6Z,8Z,12E-hexadecatetraenoic acid, Leukotriene F4, and Tenurin were significantly upregulated in the rumen of TS. Correlation analysis showed that rumen microbial flora and their metabolites jointly act to regulate rumen ARGs. Specifically, microorganisms including Firmicutes and Succiniclasticum had a significantly positive correlation with ARGs such as rpoB2 (p < 0.05), while differential metabolites like endomorphin-1 and Purothionin AII exhibited a significantly negative correlation with ARGs such as rpoB2 (p < 0.05). Therefore, compared with HS, the synergistic effect of the rumen microbial flora, their metabolites, and the resistome in TS is an important characteristic and strategy for their adaptation to the hypoxic environment of the QTP, and also contributes to the formation of their unique rumen resistome. Despite being reared in the same plateau environment, the rumen microbiome of HS still retains low-altitude characteristics, which are manifested as high GHs activity and high ARGs abundance. Full article

The Complementary Relationship (CR) principle of evapotranspiration provides an efficient approach for estimating actual evapotranspiration (ET_a), owing to its simplified computation and effectiveness in utilizing meteorological factors. Accurate estimation of actual evapotranspiration (ET_a) is crucial for understanding surface energy and water cycles, especially in permafrost regions. This study aims to evaluate the applicability of two Complementary Relationship (CR)-based methods—Bouchet’s in 1963 and Brutsaert’s in 2015—for estimating ETa on the Qinghai–Tibetan Plateau (QTP), using observations from Eddy Covariance (EC) systems. The potential evapotranspiration (ET_p) was calculated using the Penman equation with two wind functions: the Rome wind function and the Monin–Obukhov Similarity Theory (MOST). The comparison revealed that Bouchet’s method underestimated ET_a during frozen soil periods and overestimated it during thawed periods. In contrast, Brutsaert’s method combined with the MOST yielded the lowest RMSE values (0.67–0.70 mm/day) and the highest correlation coefficients (r > 0.85), indicating superior performance. Sensitivity analysis showed that net radiation (Rn) had the strongest influence on ET_a, with a daily sensitivity coefficient of up to 1.35. This study highlights the improved accuracy and reliability of Brutsaert’s CR method in cold alpine environments, underscoring the importance of considering freeze–thaw dynamics in ET modeling. Future research should incorporate seasonal calibration of key parameters (e.g., ε) to further reduce uncertainty. Full article

The Qinghai–Tibetan Plateau (QTP), often referred to as the “Third Pole” of the world, harbors alpine grassland ecosystems that play an essential role as global carbon sinks, helping to mitigate the pace of climate change. Nonetheless, alterations in natural environmental conditions coupled with escalating human activities have disrupted the seasonal growth cycles of grasslands, thereby intensifying degradation processes. To date, the key drivers and lifecycle dynamics of Grassland Depletion across the QTP remain contentious, limiting our comprehension of its ecological repercussions and regulatory mechanisms. This study comprehensively investigates grassland degradation on the Qinghai–Tibetan Plateau, analyzing its drivers and changes in ecological suitability during the growing season. By integrating natural factors (e.g., precipitation and temperature) and anthropogenic influences (e.g., population density and grazing intensity), it examines observational data from over 160 monitoring stations collected between the 1980s and 2020. The findings reveal three distinct phases of grassland degradation: an acute degradation phase in 1990 (GDI, Grassland Degradation Index = 2.53), a partial recovery phase from 1996 to 2005 (GDI < 2.0) during which the proportion of degraded grassland decreased from 71.85% in 1990 to 51.22% in 2005, and a renewed intensification of degradation after 2006 (GDI > 2.0), with degraded grassland areas reaching 56.39% by 2020. Among the influencing variables, precipitation emerged as the most significant driver, interacting closely with anthropogenic factors such as grazing practices and population distribution. Specifically, the combined impacts of precipitation with population density, grazing pressure, and elevation were particularly notable, yielding interaction q-values of 0.796, 0.767, and 0.752, respectively. Our findings reveal that while grasslands exhibit superior carbon sink potential relative to forests, their productivity and ecological functionality are undergoing considerable declines due to the compounded effects of multiple interacting factors. Consequently, the spatial distribution of ecologically suitable zones has contracted significantly, with the remaining high-suitability regions concentrating in the “twin-star” zones of Baingoin and Zanda grasslands, areas recognized as focal points for future ecosystem preservation. Furthermore, the effects of climate change and intensifying anthropogenic activity have driven the reduction in highly suitable grassland areas, shrinking from 41,232 km² in 1990 to 24,485 km² by 2020, with projections indicating a further decrease to only 2844 km² by 2060. This study sheds light on the intricate mechanisms behind Grassland Depletion, providing essential guidance for conservation efforts and ecological restoration on the QTP. Moreover, it offers theoretical underpinnings to support China’s carbon neutrality and peak carbon emission goals. Full article

Complete chloroplast genome sequences are widely used in the analyses of phylogenetic relationships among angiosperms. As a species-rich genus, species diversity centers of Saxifraga L. include mountainous regions of Eurasia, such as the Alps and the Qinghai–Tibetan Plateau (QTP) sensu lato. However, to date, datasets of chloroplast genomes of Saxifraga have been concentrated on the QTP species; those from European Alps are largely unavailable, which hinders comprehensively comparative and evolutionary analyses of chloroplast genomes in this genus. Here, complete chloroplast genomes of 19 Saxifraga species were de novo sequenced, assembled and annotated, and of these 15 species from Alps were reported for the first time. Subsequent comparative analysis and phylogenetic reconstruction were also conducted. Chloroplast genome length of the 19 Saxifraga species range from 149,217 bp to 152,282 bp with a typical quadripartite structure. All individual chloroplast genome included in this study contains 113 unique genes, including 79 protein-coding genes, four rRNAs and 30 tRNAs. The IR boundaries keep relatively conserved with minor expansion in S. consanguinea. mVISTA analysis and identification of polymorphic loci for molecular markers shows that six intergenic regions (ndhC-trnV, psbE-petL, rpl32-trnL, rps16-trnQ, trnF-ndhJ, trnS-trnG) can be selected as the potential DNA barcodes. A total of 1204 SSRs, 433 tandem repeats and 534 Large sequence repeats were identified in the 19 Saxifraga chloroplast genomes. The codon usage analysis revealed that Saxifraga chloroplast genome codon prefers to end in A/T. Phylogenetic reconstruction of 33 species (31 Saxifraga species included) based on 75 common protein coding genes received high bootstrap support values for nearly all identified nodes, and revealed a tree topology similar to previous studies. Full article

Noxious weeds (NWs) are increasingly recognized as a significant threat to the native alpine grassland ecosystems of the Qinghai–Tibetan Plateau (QTP). However, large-scale quantification of their continuous fractional cover remains challenging. This study proposes a pixel-level estimation framework utilizing time-series Sentinel-2 imagery. A Dynamic Mask Non-Stationary Transformer (DMNST) model was developed and trained using multi-temporal multispectral data to map the spatial distribution of NWs in the Three-River Headwaters Region. The model was calibrated and validated using field data collected from 170 plots (1530 quadrats). The results demonstrated that both the dynamic masking module and the non-stationary normalization significantly enhanced the prediction accuracy and robustness, particularly when applied jointly. The model performance varied across different combinations of spectral bands and temporal inputs, with the optimal configurations achieving a test R² of 0.770, MSE of 0.009, and RMSE of 0.096. These findings underscore the critical role of the input configuration and architectural enhancements in accurately modeling the fractional cover of NWs. This study confirms the applicability of Sentinel-2 time-series imagery for modeling the continuous fractional cover of NWs and provides a scalable tool for invasive species monitoring and ecological risk assessment in alpine ecosystems. Full article

The Qinghai–Tibetan Plateau (QTP) is under serious desertification stress, which has been receiving increasing attention. Although the restoration of surface vegetation is crucial, the growth of plants is often hindered by unfavorable nutrient-deficient conditions. The plant-associated endophytic microbiome is considered the secondary genome of the host and plays a significant role in host survival under environmental stresses. However, the community compositions and functions of plant-endophytic microorganisms in the QTP desertification environments remain unclear. Therefore, this study investigated the endophytic microbiome of the pioneer plant Gueldenstaedtia verna on the QTP and its contribution to host growth under stressful conditions. The results showed that nutrient-deficient stresses strongly influenced the microbial community structures in the rhizosphere. The impacts of these stresses, however, decreased from the rhizosphere community to the plant endophytes, resulting in consistent plant endophytic microbial communities across different sites. Members of Halomonas were recognized as keystone taxa in the endophytic microbiome of G. verna. Correlation analysis, metagenome-assembled genomes (MAGs), and comparative genome analyses have shown that the keystone taxa of the plant endophytic microbiome may promote plant growth through pathways such as nitrogen fixation, IAA, and antioxidant production, which are important for improving plant nutrient acquisition and tolerance. This finding may provide a crucial theoretical foundation for future phytoremediation efforts in desertification environments on the Qinghai-Tibet Plateau. Full article

Plateau pika (Ochotona curzoniae, hereafter pika) is a key species in the alpine grasslands on the Qinghai-Tibetan Plateau (QTP). They are susceptible to the influence of external disturbance and may present great variation, which is important to evaluate their ecological role in alpine grasslands. However, our knowledge regarding their interannual variation and the influencing mechanism is still limited due to the lack of long-term observation of pika density. This study aimed to investigate the spatiotemporal variations in pika and the associated key influencing factors by aerial photographing at 181 sites in Gannan Tibetan Autonomous Prefecture in 2016, 2019, and 2022. Our findings showed that: (1) pika primarily distributed in the central and northeastern Maqu County and the southwestern part of Luqu County, and their average density was in a range of 9.87 ha⁻¹ to 14.43 ha⁻¹ from 2016 to 2022; (2) high pika density were found in 1.22 to 3.61 °C for annual mean temperature, 12.86 to 15.06 °C for diurnal temperature range, 3400 to 3800 m for DEM and less than 3° for slope; and (3) pika density showed varied response to interannual changes in mean diurnal range, annual precipitation and precipitation of the driest month in different years. Our results concluded that pika density showed significant spatiotemporal variations, and climate and terrain variables dominantly affected pika density. Given the great interannual fluctuation of climate variables and different responses of pika density to these variables, our results suggested that long-term monitoring of pika is crucial to reveal their real distribution, response mechanism to habitat environment, and role in alpine grasslands. Moreover, unmanned aerial vehicles are cost-effective tools for the long-term monitoring of pika. Full article

Satellite and reanalysis-based precipitation products have played a crucial role in addressing the challenges associated with limited ground-based observational data. These products are widely utilized in hydrometeorological research, particularly in data-scarce regions like the Qinghai–Tibetan Plateau (QTP). This study proposed an ensemble streamflow simulation method using remote sensing precipitation data as input. By employing a 1D Convolutional Neural Networks (1D CNN), streamflow simulations from multiple models are integrated and a Shapley Additive exPlanations (SHAP) interpretability analysis was conducted to examine the contributions of individual models on ensemble streamflow simulation. The method is demonstrated using GPM IMERG (Global Precipitation Measurement Integrated Multi-satellite Retrievals) remote sensing precipitation data for streamflow estimation in the upstream region of the Ganzi gauging station in the Yalong River basin of QTP for the period from 2010 to 2019. Streamflow simulations were carried out using models with diverse structures, including the physically based BTOPMC (Block-wise use of TOPMODEL) and two machine learning models, i.e., Random Forest (RF) and Long Short-Term Memory Neural Networks (LSTM). Furthermore, ensemble simulations were compared: the Simple Average Method (SAM), Weighted Average Method (WAM), and the proposed 1D CNN method. The results revealed that, for the hydrological simulation of each individual models, the Kling–Gupta Efficiency (KGE) values during the validation period were 0.66 for BTOPMC, 0.71 for RF, and 0.74 for LSTM. Among the ensemble approaches, the validation period KGE values for SAM, WAM, and the 1D CNN-based nonlinear method were 0.74, 0.73, and 0.82, respectively, indicating that the nonlinear 1D CNN approach achieved the highest accuracy. The SHAP-based interpretability analysis further demonstrated that RF made the most significant contribution to the ensemble simulation, while LSTM contributed the least. These findings highlight that the proposed 1D CNN ensemble simulation framework has great potential to improve streamflow estimations using remote sensing precipitation data as input and may provide new insight into how deep learning methods advance the application of remote sensing in hydrological research. Full article

Alpine grassland degradation in permafrost regions seriously affects the provision of ecosystem services, posing a threat to ecological security. Revegetation is a key strategy for the restoration of alpine grassland ecosystems on the Qinghai–Tibetan Plateau (QTP). However, there is a lack of comprehensive research evaluating ecosystem services after revegetation, especially in permafrost regions. In this study, we assessed the changes in ecosystem services following revegetation in the alpine permafrost regions of the QTP through on-site monitoring and sampling, using extremely degraded alpine grassland as a control. In addition, we analyzed trade-offs among ecosystem services and identified key drivers. Our results indicate that (1) revegetation significantly increased forage supply, carbon storage, and soil retention values (p < 0.05), while water retention and permafrost stability showed no significant changes (p > 0.05); (2) vegetation restoration effectively reduced the trade-offs among ecosystem services; and (3) the main drivers were vegetation coverage, precipitation, belowground biomass, and restoration duration. Overall, this study demonstrates that revegetation improves ecosystem services. The enhancement in these services provides valuable data for future research on ecosystem services in alpine grassland. Full article

Water use efficiency (WUE) reflects the quantitative relationship between vegetation gross primary productivity (GPP) and surface evapotranspiration (ET), serving as a crucial indicator for assessing the coupling of carbon and water cycles in ecosystems. As a sensitive region to climate change, the Qinghai Tibetan Plateau’s WUE dynamics are of significant scientific interest for understanding carbon water interactions and forecasting future climate trends. However, due to the scarcity of observational data and the unique environmental conditions of the plateau, existing studies show substantial errors in GPP simulation accuracy and considerable discrepancies in ET outputs from different models, leading to uncertainties in current WUE estimates. This study addresses these gaps by first employing a machine learning approach (random forest) to integrate observed GPP flux data with multi-source environmental information, developing a predictive model capable of accurately simulating GPP in the Qinghai Tibetan Plateau (QTP). The accuracy of the random forest simulation results, RF_GPP (R² = 0.611, RMSE = 69.162 gC·m⁻²·month⁻¹), is higher than that of the multiple linear regression model, regGPP (R² = 0.429, RMSE = 86.578 gC·m⁻²·month⁻¹), and significantly better than the accuracy of the GLASS product, GLASS_GPP (R² = 0.360, RMSE = 91.764 gC·m⁻²·month⁻¹). Subsequently, based on observed ET flux data, we quantitatively evaluate ET products from various models and construct a multiple regression model that integrates these products. The accuracy of REG_ET, obtained by integrating five ET products using a multiple linear regression model (R² = 0.601, RMSE = 21.04 mm·month⁻¹), is higher than that of the product derived through mean processing, MEAN_ET (R² = 0.591, RMSE = 25.641 mm·month⁻¹). Finally, using the optimized GPP and ET data, we calculate the WUE during the growing season from 1982 to 2018 and analyze its spatiotemporal evolution. In this study, GPP and ET were optimized based on flux observation data, thereby enhancing the estimation accuracy of WUE. On this basis, the interannual variation of WUE was analyzed, providing a data foundation for studying carbon water coupling in QTP ecosystems and supporting the formulation of policies for ecological construction and water resource management in the future. Full article