Understanding the extent and directionality of the impact of human activities on ecosystems is directly related to their management and protection. However, the lack of historical data limits our understanding of ecosystem changes with long-term exposure to human activities. Recently, lake sedimentary DNA (sedDNA) has become a powerful tool for revealing changes in ecosystems at the century and millennium scales. Here, we used sedDNA to reveal the dynamic of the microbial community (including bacteria and micro-eukaryotes) in Lake Chao over the past 150 years, and further explored the effects of long-term nutrient and heavy metal loads on these communities. Our data show that nutrient and heavy metal loads in Lake Chao have increased by ca. 2 to 4-fold since the 1960s. In response, the community structure, diversity, and ecological network of bacteria and micro-eukaryotes changed significantly during the 1960s, the 1980s and the 2010s. Importantly, community structure was more sensitive to human activities than diversity. We also found that the relative abundance of some taxa associated with nitrification and algal blooms (e.g., taxa in Nitrospira sp., Peridinales) has increased ca. 100-fold since the 1960s. Nutrient could better explain the variation in the bacterial community (ca. twice as much as heavy metal), while heavy metal explained micro-eukaryotes better (ca. 3 or 5-fold as much as nutrient). In particular, based on parsimonious models from distance-based linear model (distLM), we further identified that Pb is the key factor affecting the bacterial and micro-eukaryotes community in Lake Chao in addition to nutrient. Our study reveals the impacts of long-term human activities on lake ecosystems from multiple perspectives of nutrient and heavy metal loads, community structure, diversity and ecological network, these findings will contribute to the management and conservation of lakes in the future.

Rivers are among the most threatened freshwater ecosystems, and anthropogenic activities are affecting both river structures and water quality. While assessing the organisms can provide a comprehensive measure of a river's ecological status, it is limited by the traditional morphotaxonomy-based biomonitoring. Recent advances in environmental DNA (eDNA) metabarcoding allow to identify prokaryotes and eukaryotes in one sequencing run, and could thus allow unprecedented resolution. Whether such eDNA-based data can be used directly to predict the pollution status of rivers as a complementation of environmental data remains unknown. Here we used eDNA metabarcoding to explore the main stressors of rivers along which community structure changes, and to identify the method's potential for predicting pollution status based on eDNA data. We showed that a broad range of taxa in bacterial, protistan, and metazoan communities could be profiled with eDNA. Nutrients were the main driving stressor affecting communities' structure, alpha diversity, and the ecological network. We specifically observed that the relative abundance of indicative OTUs was significantly correlated with nutrient levels. These OTUs data could be used to predict the nutrient status up to 79% accuracy on testing data sets. Thus, our study gives a novel approach to predicting the pollution status of rivers by eDNA data.