SML and IndVal inference for Environmental Impact Assessment using bacterial and ciliate eDNA metabarcodes from fish farm sediments

Aquaculture is the fastest growing food production sector worldwide, with Atlantic salmon (Salmo salar) being among the most important farmed finfish. Because there is a tradeoff between acceptable environmental impact of aquaculture and socio-economic benefits, international regulatory systems for sustainable industrial development with minimal environmental impacts are in place worldwide. In this study, we used eDNA metabarcodes (bacteria & ciliates) from marine sediment samples to test different approaches including IndVal calculations, Supervised Machine Learning and model-based-predictions on their accuracy of determining the EQS obtained by traditional methods.