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• Structural stable state triggers a change in functional stable state. • Long-term ungrazed steppe had an abrupt transition in plant functional type and followed a shift in ecosystem function. • Moderate grazed activity no abrupt transitions for stable states. Theoretical models suggest that ecosystems can be found in one of several possible alternative stable states, and a shift in structural stable state (SSS) can trigger a change in functional stable state (FSS). But we still lack the empirical evidence to confirm these states and transitions, and to inform the rates of change. Here, a 30-yr dataset from long-term ungrazed and grazed temperate grasslands was analyzed to determine whether abrupt transitions of SSS and FSS can occur. We found that the long-term ungrazed grassland experienced abrupt transitions in the dominant plant functional type (shift in SSS) that was followed by a transition between carbon sink and source 1–2 year later (shift in FSS). A directional shift in precipitation and temperature accounted for 40% of the variation in the SSS transition, while the SSS transition explained 20% of the variation in the FSS transition. In contrast, no abrupt transitions for SSS and FSS were observed in the long-term moderately grazed grassland. These findings provide important insight into the interacting effects of climate change and livestock grazing on ecosystem transitions in temperate grasslands. Moderate utilization of production in ecosystems that have co-evolved with herbivores can offset structural and functional transitions induced by climate change. [ABSTRACT FROM AUTHOR]

• Causality models were applied to discover ecosystem causal networks. • Growing season temperature dominated changes in ecosystem structure and function. • Only weak causal links existed between ecosystem structure and function. • Relative causality-driven intensities were identified. The understanding of the interplay between the environment, ecosystem structure, and function is crucial for effectively managing ecosystems in the face of climate change and environmental disturbances. Numerous observations and simulation experiments relying on correlation analysis have yielded the conclusion that environmental factors can directly or indirectly affect ecosystem community structure and function. However, the limitations of traditional ecological approaches lie in their inadequate control over confounding factors, thus hindering our comprehensive understanding of the true causal relationships. Our study aims to address these limitations and obtain a more accurate picture of causality about environment, ecosystem structure and function within a long-term time series data of an ecosystem. Here, utilizing a 31-year dataset of typical temperate grassland ecosystems, we developed an advanced causal discovery model and further enhanced it to effectively identify the causal network within the ecosystem, while also quantifying the intensity of causal pathways connecting the environment, ecosystem structure, and function. We found that the primary driver for changes in ecosystem structure and function was the temperature during the growing season, with soil water content being the secondary factor. However, there was only a weak causal relationship between ecosystem structure and gross primary productivity (GPP), which was mediated by eurytopic xerophyte plants. These findings challenge the conventional notion that precipitation is a key driver of changes in ecosystem carbon fluxes, highlighting the importance of plant-available water in regulating ecosystem structure and function. Our results suggest that the effects of global warming and severe droughts on plant growth and carbon sequestration capacity necessitate significant attention and further mitigation measures in arid and semi-arid regions. [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]

• A sustainable livelihoods indicator across a geographical gradient of aridity was developed. • Sustainable livelihoods vary greatly across dryland zones. • Semi-arid zones have the most vulnerable livelihood rather than the arid zones. • The lack of physical capital and social capital is an important obstacle to sustainable livelihoods development in drylands. • Call for integrated land management and planning approach in drylands. Drylands cover about 41% of the Earth's land surface and are inhabited by more than two billion people, who rely on the diversified ecosystem services provided by drylands for their livelihoods. Achieving sustainable livelihoods (SLs) is a key component of achieving the sustainable development goals set by the agenda in 2030. Although it has aroused extensive interest, research on SLs in drylands at a regional scale is still limited. This paper aims to address this research gap by evaluating SLs through a geographic gradient of aridity in Inner Mongolia. A sustainable livelihood index (SLI) was developed using a wide range of indicators in a sustainable livelihood framework (SLF). The weight of the indicators was determined by the entropy weight method, and the characteristics of the spatial distribution of the SLI were analyzed. The results showed that the SLI varies greatly across aridity zones. In terms of livelihood assets, the SLI in the dry sub-humid zone was 15% higher than in the arid zones, while, surprisingly, semi-arid zones were found to have the most vulnerable livelihoods (rather than the arid zones). The reason for this is that land management and planning approaches are necessary in drylands. In further detail, Moran's I index illustrated that the overall performance of the SLI of each league or city has a positive spatial correlation, while through local spatial correlation it was found that Hinggan and Chifeng are hot spot areas and Hohhot is a cold spot area. The lack of physical and social capital is an important obstacle for SLs. Based on the analysis of SLs in Inner Mongolia, the characteristics of the sustainable development of local residents were revealed. In this paper, we call for an integrated (i.e., focusing on natural and human capital) land management and planning approach for drylands to reflect the nature of the tightly coupled socio-ecological systems. [ABSTRACT FROM AUTHOR]