A Multi-Scale Ecological Approach to Assessing Antimicrobial Resistance in a Freshwater Fish

Abstract Antimicrobial resistance (AMR) genes are increasingly recognized as an emerging environmental contaminant. Yet, the ecological mechanisms shaping their distribution across natural landscapes remain poorly understood. Here, we quantified AMR gene abundances in microbial communities sampled from wild fish from eight freshwater lakes on Vancouver Island and paired these gene-level measurements with fine-scale limnological and land-use data. Using droplet digital PCR, field surveys, and an iterative spatial forecasting framework that integrates Random Forest models with regression kriging, we explored how watershed-scale processes relate to variation in AMR genes across lakes. Our analyses reveal potential associations between elevated AMR gene levels, changes in water quality, deforestation, and geographic proximity to salmon aquaculture. By integrating data across biological and spatial scales, from genes within microbial communities to lake-level conditions and landscape patterns, this study illustrates the value of combining quantitative molecular measurements with geospatial modeling to identify environmental factors that may promote antimicrobial resistance in natural systems. Our approach provides a proof-of-concept and a general predictive framework for generating hypotheses and informing future monitoring efforts aimed at understanding, managing, and forecasting environmental reservoirs of resistance. Significance Antimicrobial resistance (AMR) genes are ancient components of environmental microbiomes. Yet, the mechanisms that generate modern hotspots of resistance across natural landscapes remain unclear. Here, we reveal how watershed-scale environmental change, including water quality metrics linked with deforestation and proximity to salmon aquaculture, predicts elevated AMR gene levels in the microbiomes of wild fish populations. By combining quantitative droplet digital PCR with ecological data and geospatial modeling, we move beyond isolated surveillance data to identify ecological mechanisms that promote antimicrobial resistance in freshwater ecosystems. This integrative approach provides mechanistic insight into why certain habitats, and the organisms within them, become reservoirs of resistance while others do not. Our findings highlight the importance of ecological context in understanding resistance evolution and offer a predictive tool for informing proactive monitoring and management strategies. Competing Interest Statement The authors have declared no competing interest.