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[Display omitted] • The influence of meteorological and land surface hydrothermal on LAI in mountainous areas was assessed. • A clear greening trend was observed in Yan Mountains, and the greening trend was the largest in summer. • LAI was negatively correlated with land surface temperature and soil moisture but positively correlated with precipitation and air temperature. • The hysteresis of LAI of different vegetation types to changes in hydrothermal conditions was explored. Changes in hydrothermal conditions have significant effects on vegetation, but there is still a lack of understanding of how vegetation responds to land surface (surface temperature and soil moisture) and meteorological (temperature and precipitation) conditions in mountain regions. This study examined the trends of leaf area index (LAI) in the Yan Mountains over the last four decades using Global Land Surface Satellite (GLASS) data. The results showed a persistent increase of LAI (greening) over 20 % to 80 % of the study area in growing season, spring, summer and autumn. Anthropogenic activities caused the greening trend by crop management before 2000 and afforestation after 2000. The increasing rate of LAI varied with elevation, and the most significant increase occurred in areas between 300 and 900 m, and the lowest increase occurred in areas below 300 m. Moreover, we found that LAI was negatively correlated with land surface temperature and soil moisture, but positively correlated with precipitation and air temperature. The time-lag effect was found between hydrothermal factors and LAI in the past four decades. There was a time lag of 2–3 months between LAI changes and temperature/precipitation during the early and late stages of the growing season, and a time lag of 0–1 month during the middle stage. Specifically, there was no time lag in vegetation response to surface soil moisture, and a time lag of 2–3 months in vegetation response to land surface temperature from July to October. Our findings provide insights into how vegetation adapts to land surface and climatic hydrothermal conditions in mountain regions and can be used by governments to develop policies for ecological protection. [ABSTRACT FROM AUTHOR]

• Grassland degradation is predictable on a large scale by Random Forest. • Specific microbial groups, mainly Solirubrobacter, were screened as credible biomarkers. • Grassland degradation exhibited a longitudinal pattern and was correlated with current Net primary productivity. Extensive grassland degradation under climate change and intensified human activities has threatened ecological security and caused a variety of environmental problems. However, it is still challenging to predict the grassland degradation status on a large scale because it is a multi-factorial phenomenon with complex changes in ecosystem structure and function, which is hard to be fully characterized through mechanism models. The emergence of machine learning algorithms provides a potential to model complex systems and mine information from multi-source data without elucidating underlying mechanisms. Here, we utilized random forest and neural network algorithms to predict the grassland degradation represented by the net primary productivity (NPP) changing rate based on multi-source data including geographic, meteorological, plant traits, land use type and microbial variables in the Chinese Northern grassland. Particularly, the microbial roles in determining the degradation status were concerned. Results show that a high prediction precision was achieved by random forest model, rather than by neural network model, with a mean relative error of 16.9% and a mean square error of 9.273e-05. Besides identified longitude, arid index and current NPP state, specific soil microbial groups, mainly Solirubrobacter, were screened as credible biomarkers. Regarding model fitting, geographic, meteorological and plant variables explained 61.8% of the total variance, which was enhanced up to 72.8% by the rest microbial markers. These findings provide a theoretical basis to establish a pre-warning system for grassland management and policy-making. [ABSTRACT FROM AUTHOR]

[Display omitted] • The relationships between network topological characteristics and the different ESVs were analyzed. • The important sources and corridors are mainly located along the sides of the Wei River and the Fen River. • Higher ESVs related to water resources have better network connectivity. • The "one central source and three forested belts" radially ecological arrangement is planned. There are numerous ecological challenges that are of considerable concern caused by urbanization and natural calamities. The spatial pattern of the landscape is of great significance in maintaining species diversity and regulating material cycles and individual ecophysiological processes as a medium for ecosystem service functions. Resolving the conflicting relationships between people and land necessitates the precise identification of essential ecological sources. Therefore, there is an urgent need to investigate the relationship between ecological sources and ecosystem service values (ESVs) and to optimize the matter and energy.connectivity network. The study optimizes ecological network simulation using a penetration threshold, population density inverse distance formula, and geodetector weight analysis, constructs ecological spatial networks and evaluates their structure using the minimum cumulative resistance (MCR) model and complex network analysis, and discusses the relationship between network topological characteristics (NTCs) and the spatial pattern of the various ESVs. The results show that the major sources and corridors are primarily along the banks of the Wei River and the Fen River. The clustering coefficient has a significant negative correlation with both water supply and water regulation service values. It is found that the sources with higher ecological service values related to water resources have better network connectivity. This result plays a major reference role for the identification of several crucial ecological components, such as sources, nodes and stepping stones, in the middle Yellow River basin (MYRB). Ultimately, we propose strengthening the ecological protection of the eastern part of the Qinling Mountains and increasing the ecological communication capacity of the forestland on both sides of the river valley to create an optimized ecological pattern. [ABSTRACT FROM AUTHOR]