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The prevalence and spread of antibiotic resistance genes (ARGs) have been a significant concern for global public health in recent years. Small rural watersheds are the smallest units of factor mobility for agricultural production in China, and their ARG profiles are the best scale of the contamination status, but the mapping and the distribution and diffusion of ARGs in the water and soil of small rural watersheds are inadequate. In this study, based on microbial metagenomics, we invested prevalence maps of 209 ARGs corresponding to typical commonly used antibiotics (including multidrug, aminoglycoside, macrolide-lincosamide-streptogramin B (MLSB), and β-Lactamase) in water and soil in different agricultural types, as well as within water-soil interfaces in small rural watersheds in Southwest China. The results revealed that the most abundant ARGs in water and soil were consistent, but different in subtypes, and anthropogenic activities affect the transport of ARGs between water and soils. Livestock wastewater discharges influenced the diversity and abundance of ARGs in water, while in soil it is planting type and fertilizer management, and thus interfered with the co-occurrence patterns between bacteria and ARGs. Co-occurrence analysis revealed that Proteobacteria, Actinobacteria, and Bacteroidetes were the predominant ARG hosts in water and soil, but soil exhibited a more intricate ARG-bacterial association. Overall, this study provides integrated profiles of ARGs in water and soil influenced by anthropogenic activities at the small watershed scale in a typical rural area and provides a baseline for comparisons of ARGs. [Display omitted] • Livestock cultivation altered the distribution of ARGs in water. • Planting and fertilization altered the distribution of ARGs in soil. • Anthropogenic activities affect the spread of ARGs within the watershed. • ARG-bacterial co-occurrence patterns in soil are more complex. • Proteobacteria, Actinobacteria, and Bacteroidetes were the key potential ARG hosts. [ABSTRACT FROM AUTHOR]

Reliable detection and attribution of changes in nitrogen (N) runoff from croplands are essential for designing efficient, sustainable N management strategies for future. Despite the recognition that excess N runoff poses a risk of aquatic eutrophication, large-scale, spatially detailed N runoff trends and their drivers remain poorly understood in China. Based on data comprising 535 site-years from 100 sites across China's croplands, we developed a data-driven upscaling model and a new simplified attribution approach to detect and attribute N runoff trends during the period of 1990–2012. Our results show that N runoff has increased by 46% for rice paddy fields and 31% for upland areas since 1990. However, we acknowledge that the upscaling model is subject to large uncertainties (20% and 40% as coefficient of variation of N runoff, respectively). At national scale, increased fertilizer application was identified as the most likely driver of the N runoff trend, while decreased irrigation levels offset to some extent the impact of fertilization increases. In southern China, the increasing trend of upland N runoff can be attributed to the growth in N runoff rates. Our results suggested that increased SOM led to the N runoff rate growth for uplands, but led to a decline for rice paddy fields. In combination, these results imply that improving management approaches for both N fertilizer use and irrigation is urgently required for mitigating agricultural N runoff in China. [ABSTRACT FROM AUTHOR]