Exploiting taxonomic information from metagenomes to infer bacterial bioindicators and environmental quality at salmon aquaculture installations

Abstract

Environmental DNA (eDNA) metabarcoding has emerged as a powerful method for assessing the environmental impacts of marine Atlantic salmon aquaculture by identifying bacterial bioindicators and inferring biotic indices. However, because this approach relies on the PCR amplification of 16S rRNA gene fragments, it may introduce errors that compromise bioindicator reliability. In contrast, metagenomic analysis which captures the complete set of genetic material directly extracted from environmental samples circumvents biases inherent to PCR amplification. We hypothesized that metagenomic data could offer superior assessments of benthic environmental impacts associated with salmon aquaculture compared to metabarcoding. To test this, we compared bacterial community structures derived from both metabarcoding and metagenomic analyses of 68 sediment samples obtained from aquaculture installation sites characterized by varying degrees of benthic impact as determined by macroinvertebrate inventories. Bacterial bioindicators were identified from each dataset, and Random Forest models were used to predict the degrees of benthic impacts. Metagenomics identified a greater number of bioindicators at both the family and individual sequence variant levels, resulting in higher predictive accuracy for impact assessments. Notably, only a few bioindicators were common to both methods, suggesting that methodological limitations and distorted abundance patterns in metabarcoding data may lead to spurious indicators. These findings highlight both the challenges and potential advantages of employing metagenomics for reliable environmental impact assessments.