Improving Whole Biodiversity Monitoring and Discovery With Environmental DNA Metagenomics

Abstract

Environmental DNA (eDNA) metagenomics sequences all DNA molecules present in environmental samples and has the potential of identifying virtually any organism from which they are derived. However, due to unacceptable levels of false positives and negatives, this approach is underexplored as a tool for biodiversity monitoring across the tree of life, particularly for non-microscopic eukaryotes. We present SeqIDist, a framework that combines multilocus BLAST matches against several reference databases followed by an analysis of sequence identity distribution patterns to disentangle false positives while revealing new biodiversity and increasing the accuracy of metagenomic approaches. We tested SeqIDist on an eDNA metagenomic dataset from a riverine site and compared the results to those obtained with an eDNA metabarcoding approach for benchmarking purposes. We start by characterising the biological community (~2000 taxa) across the tree of life at low taxonomic levels and show that eDNA metagenomics has a higher sensitivity than eDNA metabarcoding in discovering new diversity. We show that limited representation of whole genome sequences in reference databases can lead to false positives. For non-microscopic eukaryotes, eDNA metagenomic data often consist of a few sparse, anonymous sequences scattered across the genome, making metagenome assembly methods unfeasible. Finally, we infer eDNA source and residency time using read length distributions as a measure of decay status. The higher accuracy of SeqIDist opens the discussion of the potential of eDNA metagenomics for archived samples and its implementation in long-term biodiversity monitoring at a planetary scale.