Optimizing land use systems of an agricultural watershed in China to meet ecological and economic requirements for future sustainability

Seasonal dynamics of environmental heterogeneity augment microbial interactions by regulating community structure in different trophic lakes

Understanding how environmental heterogeneity drives microbial communities in lakes is essential for developing effective strategies to manage and restore aquatic ecosystems. However, the mechanisms by which environmental heterogeneity influences microbial community structure, network patterns, and interactions remain largely unexplored. To bridge this gap, we collected 84 water samples from four typical lakes in China (Fuxian, Tianmu, Taihu, and Xingyun) representing a range of trophic levels, across wet and dry seasons. We assessed environmental heterogeneity using 14 water quality parameters, analyzed community structure with Jaccard and Bray-Curtis dissimilarity indices, and developed a comprehensive index to elucidate microbial network complexity. Our study reveals three key findings: (1) Environmental heterogeneity was significantly greater in dry season compared to wet season across all lakes (P < 0.05). (2) Increased environmental heterogeneity led to higher bacterioplankton community dissimilarity, with greater β-diversity observed in dry season (P < 0.05). (3) Shifts in community structure due to increased environmental heterogeneity further enhanced microbial interactions, as evidenced by more complex and interconnected co-occurrence networks in the dry season. In summary, our study demonstrates that environmental heterogeneity significantly impacts bacterioplankton community structure and subsequently enhances microbial interactions. These findings underscore the importance of considering environmental heterogeneity in lake ecosystem management, as it plays a crucial role in regulating microbial community dynamics and interactions.

Spatiotemporal Patterns of Cultivated Land Quality Integrated with Multi-Source Remote Sensing: A Case Study of Guangzhou, China

Scientifically revealing the spatiotemporal patterns of cultivated land quality (CLQ) is crucial for increasing food production and achieving United Nations Sustainable Development Goal (SDG) 2: Zero Hunger. Although studies on the evaluation of CLQ have been conducted, an effective evaluation system that is suitable for the macro-regional scale has not yet been developed. In this study, we first defined the CLQ from four aspects: soil fertility, natural conditions, construction level, and cultivated land productivity. Then, eight indicators were selected by integrating multi-source remote sensing data to create a new CLQ evaluation system. We assessed the spatiotemporal patterns of CLQ in Guangzhou, China, from 2010 to 2018. In addition, we identified the main factors affecting the improvement of CLQ. The results showed that the CLQ continuously improved in Guangzhou from 2010 to 2018. The area of high-quality cultivated land increased by 13.7%, which was mainly distributed in the traditional agricultural areas in the northern and eastern regions of Guangzhou. The areas of medium- and low-quality cultivated land decreased by 8.1% and 5.6%, respectively, which were scattered throughout the whole study area. The soil fertility and high productivity capacity were the main obstacle factors that affected the improvement of CLQ. Simultaneously, the obstacle degree of stable productivity capacity gradually increased during the study period. Therefore, the targeted improvement measures could be put forward by applying biofertilizers, strengthening crop management and constructing well-facilitated farmland. The new CLQ evaluation system we proposed is particularly practical at the macro-regional scale, and the results provided targeted guidance for decision makers to improve CLQ and promote food security.