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The rise of the Tubo Kingdom is considered as the key period for the formation of modern groups on the Tibetan Plateau. The ethnic origin of the residents of the Tubo Kingdom is quite complex, and their genetic structure remains unclear. The tombs of the Tubo Kingdom period in Dulan County, Qinghai Province, dating back to the seventh century, are considered to be the remains left by Tubo conquerors or the Tuyuhun people dominated by the Tubo Kingdom. The human remains of these tombs are ideal materials for studying the population dynamics in the Tubo Kingdom. In this paper, we analyzed the genome-wide data of eight remains from these tombs by shotgun sequencing and multiplex PCR panels and compared the results with data of available ancient and modern populations across East Asia. Genetic continuity between ancient Dulan people with ancient Xianbei tribes in Northeast Asia, ancient settlers on the Tibetan Plateau, and modern Tibeto-Burman populations was found. Surprisingly, one out of eight individuals showed typical genetic features of populations from Central Asia. In summary, the genetic diversity of ancient Dulan people and their affiliations with other populations provide an example of the complex origin of the residents in the Tubo Kingdom and their long-distance connection with populations in a vast geographic region across ancient Asia. [ABSTRACT FROM AUTHOR]

Oxygen (O2) is the most abundant molecule in the atmosphere after nitrogen. Previous studies have documented that oxygen concentration remains nearly constant (20.946%) at all altitudes. Here we show for the first time that oxygen concentration varies significantly from earlier consensus and shows strong spatial and seasonal differences. Field observations on the Qinghai-Tibetan Plateau (QTP) indicate oxygen concentration of 19.94–20.66% (2018, n = 80), 19.98–20.78% (2019, n = 166) and 19.97–20.73% (2020, n = 176), all statistically different from earlier reports (p < 0.001) and are lower than the nearly constant. The mean oxygen concentration in summer (20.47%) is 0.31% higher than that of winter (20.16%) (n = 53) at identical locations in 2019, sampled in the Qilian Mountains, northwest QTP. We used LMG (The Lindeman, Merenda and Gold) method to estimate the relative contributions of altitude, air temperature and vegetation index (Fractional Vegetation Cover, FVC and Leaf Area Index, LAI) to oxygen concentration, which are 47%, 32% and 3% (FVC, R2 = 82%); 45%, 30% and 7% (LAI, R2 = 82%), respectively. These findings provide a new perspective for in-depth understanding on population risk in high altitude regions in the context of global climate change, to ensure the health and safety of residents and tourists in high altitude regions and promoting the stability, prosperity and sustainable development of high-altitude regions worldwide. [ABSTRACT FROM AUTHOR]

Livestock snow disaster occurs widely in Central-to-Eastern Asian temperate and alpine grasslands. The effects of snow disaster on livestock involve a complex interaction between precipitation, vegetation, livestock, and herder communities. Quantifying the relationship among livestock mortality, snow hazard intensity, and seasonal environmental stressors is of great importance for snow disaster early warning, risk assessments, and adaptation strategies. Using a wide-spatial extent, long-time series, and event-based livestock snow disaster dataset, this study quantified those relationships and established a quantitative model of livestock mortality for prediction purpose for the Qinghai-Tibet Plateau region. Estimations using generalized additive models (GAMs) were shown to accurately predict livestock mortality and mortality rate due to snow disaster, with adjusted-R 2 up to 0.794 and 0.666, respectively. These results showed that a longer snow disaster duration, lower temperatures during the disaster, and a drier summer with less vegetation all contribute significantly and non-linearly to higher mortality (rate), after controlling for elevation and socioeconomic conditions. These results can be readily applied to risk assessment and risk-based adaptation actions. [ABSTRACT FROM AUTHOR]

• Population of the Tibetan Plateau in 2020 was mapped at a fine grid resolution of 100 m. • Multi-source data and ensemble model enhanced population mapping in complex terrain. • Ensemble models blend strengths and weaknesses of each machine learning method. • Tencent LBS data directly boosted accuracy and simplifies models. • Ensemble models had higher accuracy than other models and data. The Tibetan Plateau, known for its high elevation and sparse population distribution, heavily depends on gridded population data to enhance disaster prevention and management strategies. This study utilizes multi-source physical geographic and socio-economic factors to delineate the population distribution across the plateau. Using data from the seventh National Census in 2020, we apply three individual machine learning methods (Random Forest, GBDT, and XGBoost) and two multi-model ensemble methods (weighted average ensemble and stacking ensemble) to spatialize the population data into a 100-meter grid. The results reveal that the spatialization accuracy of all models exceeds that of the WorldPop dataset. Specifically, the Random Forest model (RMSE = 4061.09, nRMSE = 44.71 %) and the stacking ensemble model (RMSE = 4094.47, nRMSE = 44.26 %) demonstrate the highest accuracy among the individual and ensemble models, respectively. Emphasizing the importance of integrating multi-source big data, Tencent location-based services data emerges as a crucial variable across all models. This study highlights the effectiveness of ensemble models and multi-source big data in improving population mapping accuracy, especially in regions with complex terrains. [ABSTRACT FROM AUTHOR]