We used unmanned aerial vehicles (UAVs) to carry out a relatively complete population census of large wild herbivores in Maduo County on the Tibetan Plateau in the spring of 2017. The effective area covered by aerial surveys was 326.6 km², and 23,784 images were acquired. Interpretation tag libraries for UAV images were created for wild animals, including Kiang (Equus kiang), Tibetan gazelle (Procapra picticaudata), and blue sheep (Pseudois nayaur), as well as livestock, including yaks and Tibetan sheep. Large wild herbivores in the survey transect were identified through manual imagery interpretation. Densities ranged from 1.15/km² for Kiang, 0.61/km² for Tibetan gazelle, 0.62/km² for blue sheep, 4.12/km² for domestic yak, and 7.34/km² for domestic sheep. A method based on meadows in the cold and warm seasons was used for estimating the densities and numbers of large wild herbivores and livestock, and was verified against records of livestock numbers. Population estimates for Kiang, Tibetan gazelle, blue sheep, domestic yak, and domestic sheep were 17,109, 15,961, 9324, 70,846, and 102,194, respectively. Based on published consumption estimates, the results suggest that domestic stock consume 4.5 times the amount of vegetation of large wild herbivores. Compared with traditional ground survey methods, performance of UAV remote sensing surveys of large wild herbivore populations was fast, economical and reliable, providing an effective future method for surveying wild animals.

Livestock grazing is an important determinant of species diversity and plant growth. Overgrazing is identified as one of the most important disturbances resulting in grassland degradation. Although many restoration practices have been implemented, grazing exclusion is one of the most effective methods to restore degraded grasslands. We explored the impact of five years of grazing exclusion on plant growth and species diversity in four types of grasslands: temperate steppe (TS), swamp meadow (SM), alpine steppe (AS), and alpine meadow (AM). Our results showed that grazing exclusion increased plant height, coverage, biomass, and species diversity in all four grasslands. The aboveground biomass in AM (180.8%), TS (117.3%), and SW (105.9%) increased significantly more than AS (10.1%). Grazing exclusion in AM had the greatest effect on proportion of palatable species, and the increase in palatable species in AM was higher than that of the other grassland types significantly. Species diversity increased significantly within the enclosure in SM (23.9%) and AM (20.8%). Our results indicate that grazing exclusion is an effective management strategy to restore degraded grasslands and it works best in alpine meadow. This study contributes to the growing theoretical basis for grassland management strategies and has a significant effect on sustainable development for grassland resources and pastoral areas.

Models constitute the primary approaches for predicting terrestrial ecosystem gross primary production (GPP) at regional and global scales. Many satellite-based GPP models have been developed due to the simple algorithms and the low requirements of model inputs. The performances of these models are well documented at the biome level. However, their performances among vegetation subtypes limited by different environmental stresses within a biome remains largely unexplored. Taking grasslands in northern China as an example, we compared the performance of eight satellite-based GPP models, including three light-use efficiency (LUE) models (vegetation photosynthesis model (VPM), modified VPM (MVPM), and moderate resolution imaging spectroradiometer GPP algorithm (MODIS-GPP)) and five statistical models (temperature and greenness model (TG), greenness and radiation model (GR), vegetation index model (VI), alpine vegetation model (AVM), and photosynthetic capacity model (PCM)), between the water-limited temperate steppe and the temperature-limited alpine meadow based on 16 site-year GPP estimates at four eddy covariance (EC) flux towers. The results showed that all the GPP models performed better in the alpine meadow, particularly in the alpine shrub meadow (R² ≥ 0.84), than in the temperate steppe (R² ≤ 0.68). The performance varied greatly among the models in the temperate steppe, while slight intermodel differences existed in the alpine meadow. Overall, MVPM (of the LUE models) and VI (of the statistical models) were the two best-performing models in the temperate steppe due to their better representation of the effect of water stress on vegetation productivity. Additionally, we found that the relatively worse model performances in the temperate steppe were seriously exaggerated by drought events, which may occur more frequently in the future. This study highlights the varying performances of satellite-based GPP models among vegetation subtypes of a biome in different precipitation years and suggests priorities for improving the water stress variables of these models in future efforts.

Unmanned aerial vehicle surveys were conducted in the summer season of 2016 and the winter season of 2017 to investigate the large wild herbivore population, including kiangs, Tibetan gazelles and bharals, in Madoi County; the source region of the Yellow River. The study generated forage grass production data in 30 m spatial resolution in Madoi County in 2016 using a downscaling algorithm; estimated a forage-livestock balance including wild animals and domestic animals; and analyzed the effect of the large wild herbivore population on the balance between forage grass and herbivory in Madoi County. The large wild herbivore population was estimated based on the density of the animals in the survey sample strip and compared and verified with available statistical data and the two survey results from the summer season of 2016 and winter season of 2017. The results showed that: (1) in the winter season of 2017, the populations of kiang, Tibetan gazelle and bharal were 17,100, 16,000 and 9300, respectively, while the populations of domestic yak, Tibetan sheep and horse were 70,800, 102,200 and 1200, respectively. The total population of large wild herbivores and domestic animals was 475,000 (sheep units). The ratio (in sheep units) between large wild herbivores and domestic animals was 1:4.5; (2) When only considering domestic animals, the grazing pressure index was 1.13, indicating slight overloading of the grassland. When considering domestic animals and large wild herbivores (kiang, Tibetan gazelle and bharal), the grazing pressure index was 1.38, suggesting moderate overloading of the grassland; (3) If large wild herbivores are not taken into consideration when the forage-livestock balance is calculated, the grazing pressure will be under-estimated by 22%. Overgrazing is the major cause of grassland degradation in Madoi County. An additional 79,000 tons of hay or a 30% reduction in domestic animals is required to maintain a forage-livestock balance in Madoi County.

The spatial pattern of soil bulk density in the grasslands of northern China largely remains undefined, which raised uncertainty in understanding and modeling various soil processes in large spatial scale. Based on the measured data of soil bulk density available from soil survey reports from the grasslands of northern China, we constructed a soil Stratified Pedotransfer function (SPTF) from the surface soil bulk density. Accordingly, the stratified bulk density data of soil vertical profile was reconstructed, and the estimation of soil bulk density data in horizontal space was performed. The results demonstrated that the soil bulk density of the grasslands of northern China was typically high in the central and northwestern regions and low in the eastern and mountainous regions. Mean soil bulk density of the grasslands was 1.52 g·cm⁻³. According to geographical divisions, the highest soil bulk density was observed in the Tarim basin, with mean soil bulk density of 1.91 g·cm⁻³. Conversely, the lowest soil bulk density was observed in the Tianshan Mountain area, with mean soil bulk density of 1.01 g·cm⁻³. Based on data obtained on various types of grasslands, the soil bulk density of alpine meadow was the lowest, with a mean soil bulk density of 0.75 g·cm⁻³, whereas that of temperate desert was the highest, with mean soil bulk density of 1.80 g·cm⁻³. Mean prediction error, root mean square deviation, relative error, and multiple correlation coefficient of soil bulk density data pertaining to surface layer (0–10 cm) in the grasslands of northern China were 0.018, 0.223, 16.2%, and 0.5386, respectively. The approach of employing multiple data sources via soil transfer function improved the estimation accuracy of soil bulk density from stratified soils data at the large scale. Our study would promote the accurate assessment of grassland carbon storage and fine land characteristics mapping.

Emerging infectious diseases (EIDs), whether newly identified or re-emerging in human and animal populations, pose significant threats to global public health. China has experienced multiple EIDs outbreaks in recent years, underscoring the need for robust surveillance and early warning systems. Although China has established surveillance systems for events affecting climate, wildlife, livestock and poultry, and humans, the current systems remain inadequate for the early detection, monitoring, and prevention of zoonotic spillover events. The “One Health” approach, which integrates human, animal, and environmental health, offers a comprehensive strategy for mitigating EIDs risks. This study reviews China's national-level surveillance and early warning systems from a “One Health” perspective, highlighting key limitations and proposing future directions to enhance preparedness and response capabilities. The findings are intended to inform policy improvements and strengthen interdisciplinary collaboration for effective EIDs management.