Marine biomonitoring: predicting biotic indices from eDNA metabarcoding

We investigated the possibility of using supervised machine learning (SML) algorithms to build predictive models from eDNA metabarcoding data targeting groups that are not commonly used for benthic monitoring. We tested our approach on benthic foraminifera, a group of unicellular eukaryotes known to be sensitive to organic enrichment associated with marine aquaculture