Predicting the Ecological Quality Status of Marine Environments from eDNA Metabarcoding Data Using Supervised Machine Learning

Monitoring
biodiversity is essential to assess the impacts of increasing
anthropogenic activities in marine environments. Traditionally, marine
biomonitoring involves the sorting and morphological identification
of benthic macro-invertebrates, which is time-consuming and taxonomic-expertise
demanding. High-throughput amplicon sequencing of environmental DNA
(eDNA metabarcoding) represents a promising alternative for benthic
monitoring. However, an important fraction of eDNA sequences remains
unassigned or belong to taxa of unknown ecology, which prevent their
use for assessing the ecological quality status. Here, we show that
supervised machine learning (SML) can be used to build robust predictive
models for benthic monitoring, regardless of the taxonomic assignment
of eDNA sequences. We tested three SML approaches to assess the environmental
impact of marine aquaculture using benthic foraminifera eDNA, a group
of unicellular eukaryotes known to be good bioindicators, as features
to infer macro-invertebrates based biotic indices. We found similar
ecological status as obtained from macro-invertebrates inventories.
We argue that SML approaches could overcome and even bypass the cost
and time-demanding morpho-taxonomic approaches in future biomonitoring.