Alpine grassland greening on the Northern Tibetan Plateau driven by climate change and human activities considering extreme temperature and soil moisture.

Alpine grassland is among the world's most vulnerable ecosystems, characterized by a high sensitivity to climate change (CC) and human activities (HA). Quantifying the relative contributions of CC and HA to grassland change plays a crucial role in safeguarding grassland ecological security and devising sustainable grassland management strategies. Although there were adequate studies focusing on the separate impacts of CC and HA on alpine ecosystem, insufficient attention has been given to investigating the effects of extreme temperatures and soil moisture. In this study, the spatiotemporal variations of alpine grassland were analyzed based on MODIS NDVI during the growing season from 2000 to 2020 in Naqu, using partial least squares regression and residual analysis methods to analyze the importance of climate factors and the impacts of CC and HA on grassland change. The results show that the NDVI during the growing season in Naqu exhibited an increasing trend of 0.0046/10a. At the biome scale, the most significant and rapid increase was observed in alpine desert and alpine desert grassland. Extreme temperature and soil moisture (SM) exerted a more significant importance on alpine grassland at whole scale. SM always showed a significant importance at biome and grid scale. The contributions of CC and HA to the change during the growing season were calculated as 0.0032/10a and 0.0015/10a, respectively, accounting for 68.05 % and 31.05 %. CC dominated the increase in NDVI during the growing season; HA contributed positively to NDVI in most areas of Naqu. The results are expected to enhance our understanding of grassland variations under CC and HA and provide a scientific basis for future ecological conservation in alpine regions.

Effects of precipitation and temperature on precipitation use efficiency of alpine grassland in Northern Tibet, China.

Precipitation use efficiency (PUE) is crucial in understanding the coupling between ecosystem carbon and water cycling. In this study, we used a time series (2000-2013) dataset of net primary productivity (NPP) based on the Carnegie-Ames-Stanford Approach (CASA) model together with precipitation to reveal the spatial and temporal patterns of alpine grassland PUE in Northern Tibet. The mean annual PUE values of alpine meadow, alpine meadow steppe, alpine steppe, alpine desert steppe, and alpine desert were 0.48, 0.39, 0.36, 0.29 and 0.23 gc m-2 mm-1, respectively. The spatial patterns of PUE of alpine grassland demonstrated an initial increase in the arid region and a subsequent decrease in the humid region along the precipitation gradient and peaked at approximately 500 mm. To evaluate the temporal patterns, the sensitivity [Formula: see text] and the Pearson correlation coefficient [Formula: see text] between the PUE and climatic factors were calculated. The inter-annual variability of PUE exhibited a significant negative correlation with annual precipitation (P < 0.05), which implies that NPP had a lower sensitivity to precipitation in most regions. The relationship between PUE and the mean annual temperature is different for different regions. Our findings have an important role in understanding the impacts of precipitation availability on climate change and in the scientific management of the alpine grassland ecosystems.

Spatial pattern of land use change and its driving force in Jiangsu Province.

Scientific interpretation of the mechanism of land use change is important for government planning and management activities. This study analyzes the land use change in Jiangsu Province using three land use maps of 2000, 2005 and 2008. The study results show that there was a significant change in land use. The change was mainly characterized by a continuous built-up land expansion primarily at the expense of cropland loss, and the trend became increasingly rapid. There was an obvious regional difference, as most of the cropland loss or built-up land expansion took place in southern Jiangsu, where the rate of built-up land expansion was faster than in central and northern Jiangsu. Meanwhile, the spatial pattern changed remarkably; in general, the number of patches (NumP) showed a declining trend, and the mean patch size (MPS) and patch size standard deviation (PSSD) displayed increase trends. Furthermore, the relative importance of selected driven factors was identified by principal component analysis (PCA) and general linear model (GLM). The results showed that not only the relative importance of a specific driving factor may vary, but the driven factors may as well. The most important driven factor changed from urban population (UP), secondary gross domestic product (SGDP) and gross domestic product (GDP) during 2000-2005 to resident population (RP), population density (POD) and UP during 2005-2008, and the deviance explained (DE) decreased from 91.60% to 81.04%. Policies also had significant impacts on land use change, which can be divided into direct and indirect impacts. Development policies usually had indirect impacts, particularly economic development policies, which promote the economic development to cause land use change, while land management policies had direct impacts. We suggest that the government should think comprehensively and cautiously when proposing a new development strategy or plan.