Antimicrobial resistance (AMR) in aquatic environments poses a critical threat to both environmental and human health. This study presents a novel hybrid modeling framework that integrates a process-based hydrodynamic-environmental model with a data-driven approach to predict the spatiotemporal dynamics of AMR in coastal waters. Macrolide-related antimicrobial resistance genes (ARGs_Macro) were selected as representative markers. The model results were validated using data from a monthly sampling campaign conducted across Singapore’s coastal waters, yielding a mean coefficient of determination (R²) of 0.693, a Nash-Sutcliffe efficiency (NSE) of 0.589, and a root-mean-square deviation (RMSE) of 0.0257 GC/16S rRNA across 12 sampling points. Lincomycin, pH, dissolved oxygen, zinc and temperature were identified as significant influencers of ARGs_Macro. Although Lincomycin is not classified as a macrolide, it ranks as the most important driver of ARGs_Macro due to its shared resistance mechanisms with macrolides, potentially facilitating cross-resistance. The spatiotemporal model results revealed that coastal areas, particularly in the northern part of Singapore, are vulnerable to significant ARG accumulation, with monsoon seasons amplifying the spread of AMR due to hydrodynamic conditions. This study highlights the development of a robust modeling framework that provides valuable insights into the environmental drivers of AMR in coastal waters, offering a foundation for regulatory strategies and future research aimed at mitigating the risks of antimicrobial resistance in aquatic environments.

Antimicrobial resistance (AMR) is a global challenge that has impacted aquaculture and surrounding marine environments. In this study, a year-long monitoring program was implemented to evaluate AMR in two different aquaculture settings (i.e., open cage farming, recirculating aquaculture system (RAS)) and surrounding marine environment within a tropical coastal region. The objectives of this study are to (i) investigate the prevalence and co-occurrence of antibiotic-resistant bacteria (ARB), antibiotic resistance genes (ARGs), antibiotics (AB) and various associated chemical compounds at these study sites; (ii) explore the contributing factors to development and propagation of AMR in the coastal environment; and (iii) assess the AMR risks from different perspectives based on the three AMR determinants (i.e., ARB, ARGs and AB). Key findings revealed a distinct pattern of AMR across the different aquaculture settings, notably a higher prevalence of antibiotic-resistant Vibrio at RAS outfalls, suggesting a potential accumulation of microorganisms within the treatment system. Despite the relative uniform distribution of ARGs across marine sites, specific genes such as qepA, blaCTX−M and bacA, were found to be abundant in fish samples, especially from the RAS. Variations in chemical contaminant prevalence across sites highlighted possible anthropogenic impacts. Moreover, environmental and seasonal variations were found to significantly influence the distribution of ARGs and chemical compounds in the coastal waters. Hierarchical cluster analysis that was based on ARGs, chemical compounds and environmental data, categorized the sites into three distinct clusters which reflected strong association with location, seasonality and aquaculture activities. The observed weak correlations between ARGs and chemical compounds imply that low environmental concentrations may be insufficient for resistance selection. A comprehensive risk assessment using methodologies such as the multiple antibiotic resistance (MAR) index, comparative AMR risk index (CAMRI) and Risk quotient (RQ) underscored the complexity of AMR risks. This research significantly contributes to the understanding of AMR dynamics in natural aquatic systems and provides valuable insights for managing and mitigating AMR risks in coastal environments.