Biotic mechanisms associated with species diversity are expected to stabilize communities in theoretical and experimental studies but may be difficult to detect in natural communities exposed to large environmental variation. We investigated biotic stability mechanisms in a multi-site study across Inner Mongolian grassland characterized by large spatial variations in species richness and composition and temporal fluctuations in precipitation. We used a new additive-partitioning method to separate species synchrony and population dynamics within communities into different species-abundance groups. Community stability was independent of species richness but was regulated by species synchrony and population dynamics, especially of abundant species. Precipitation fluctuations synchronized population dynamics within communities, reducing their stability. Our results indicate generality of biotic stability mechanisms in natural ecosystems and suggest that for accurate predictions of community stability in changing environments uneven species composition should be considered by partitioning stabilizing mechanisms into different species-abundance groups.

Microbial stoichiometry and its potential driving factors play crucial roles in understanding the balance of chemical elements in ecological interactions and nutrient limitations along the aridity gradient.

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Extending knowledge on ecosystem stability to larger spatial scales is urgently needed because present local-scale studies are generally ineffective in guiding management and conservation decisions of an entire region with diverse plant communities. We investigated stability of plant productivity across spatial scales and hierarchical levels of organization and analyzed impacts of dominant species, species diversity, and climatic factors using a multisite survey of Inner Mongolian grassland. We found that regional stability across distant local communities was related to stability and asynchrony of local communities. Using only dominant instead of all-species dynamics explained regional stability almost equally well. The diversity of all or only dominant species had comparatively weak effects on stability and synchrony, whereas a lower mean and higher variation of precipitation destabilized regional and local communities by reducing population stability and synchronizing species dynamics. We demonstrate that, for semi-arid temperate grassland with highly uneven species abundances, the stability of regional communities is increased by stability and asynchrony of local communities and these are more affected by climate rather than species diversity. Reduced amounts and increased variation of precipitation in the future may compromise the sustainable provision of ecosystem services to human well-being in this region.

As the largest producing country of municipal solid waste (MSW) around the world, China is always challenged by a lower utilization rate of MSW due to a lack of a smart MSW forecasting strategy. This paper mainly aims to construct an effective MSW prediction model to handle this problem by using machine learning techniques. Based on the empirical analysis of provincial panel data from 2008 to 2019 in China, we find that the Deep Neural Network (DNN) model performs best among all machine learning models. Additionally, we introduce the SHapley Additive exPlanation (SHAP) method to unravel the correlation between MSW production and socioeconomic features (e.g., total regional GDP, population density). We also find the increase of urban population and agglomeration of wholesales and retails industries can positively promote the production of MSW in regions of high economic development, and vice versa. These results can be of help in the planning, design, and implementation of solid waste management system in China.