Antibiotics are increasingly used and released into the environment due to the rapid development of aquaculture, resulting in spread of antibiotic resistance. This study investigated the distribution and diversity of antibiotic resistance genes (ARGs) in the aquaculture waterbody of different aquaculture systems in Guangdong, as well as their associations with opportunistic pathogens. A comparative analysis of 92 samples revealed that selected ARGs were more prevalent in freshwater aquaculture systems (FAS) than in marine aquaculture systems (MAS), with higher accumulation levels of tetracycline (Tet) and sulfonamide (Sul) resistance genes observed in FAS. Notably, certain ARGs such as tetGF and mphA were more abundant in MAS compared to FAS, suggesting differing antibiotic selection pressures across the two systems. Analysis of bacterial communities revealed distinct compositions: FAS was dominated by Acidobacteria and Chloroflexi, while Actinobacteria and Bacteroidetes were more prevalent in MAS. Network analysis showed positive correlations between opportunistic pathogens and selected ARGs, suggesting that these pathogens may serve as reservoirs for resistance genes and pose potential public health risks. Bacterial community assembly in MAS was primarily driven by stochastic processes, whereas both deterministic and stochastic processes influenced community structure in FAS. Moreover, TN, NH 4 +, NO 2 -, and PO 4 3- strongly influence bacterial communities and ARGs, with greater effects in MAS. Overall, this study reveals the complex interactions among microbial diversity, ARGs, and environmental factors, providing a theoretical foundation for mitigating the spread of antibiotic resistance in aquaculture and improving ecosystem health. • ARGs are more prevalent in freshwater than in marine aquaculture systems. • Distinct bacterial communities were found between FAS and MAS environments. • Opportunistic pathogens may act as reservoirs for antibiotic resistance genes. • Community assembly in FAS is shaped by both deterministic and stochastic processes.
Zhong et al. (Wed,) studied this question.