Hydrologic Setting Dictates the Sensitivity of Ecosystem Metabolism to Climate Variability in Lakes

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.

National-scale remotely sensed lake trophic state from 1984 through 2020

Lake trophic state is a key ecosystem property that integrates a lake's physical, chemical, and biological processes. Despite the importance of trophic state as a gauge of lake water quality, standardized and machine-readable observations are uncommon. Remote sensing presents an opportunity to detect and analyze lake trophic state with reproducible, robust methods across time and space. We used Landsat surface reflectance data to create the first compendium of annual lake trophic state for 55,662 lakes of at least 10 ha in area throughout the contiguous United States from 1984 through 2020. The dataset was constructed with FAIR data principles (Findable, Accessible, Interoperable, and Reproducible) in mind, where data are publicly available, relational keys from parent datasets are retained, and all data wrangling and modeling routines are scripted for future reuse. Together, this resource offers critical data to address basic and applied research questions about lake water quality at a suite of spatial and temporal scales.