Incorporating environmental DNA metabarcoding for improved benthic biodiversity and habitat mapping

Abstract Complex coastal seascapes harbor high marine biodiversity from which humans derive numerous ecosystem services. Maps of benthic habitats are important tools used to inform coastal development and conservation efforts. Seafloor imagery is commonly used to collect information about the distribution of benthic organisms, but these data are often limited to low taxonomic resolutions and may systematically underrepresent local biodiversity. Recent advances in genomics enable rapid and accurate detection of taxa with high taxonomic resolution from environmental DNA (eDNA) extracted from water samples, but there are few examples of broad‐spectrum eDNA biodiversity data in nearshore benthic habitat mapping. We combined an eDNA‐based biodiversity assessment with concurrently collected high‐resolution video ground‐truth data to assess the benefit of metabarcoding data for improving benthic habitat mapping in the sub‐Arctic coastal embayment of Mortier Bay, Newfoundland and Labrador, Canada. Features derived from acoustic bathymetry and backscatter data were used to develop full‐coverage habitat and biodiversity maps using a joint species distribution‐modeling framework. The predicted taxonomic richness spatial patterns were similar between video‐only, eDNA‐only and combined datasets, suggesting diversity patterns were accurately represented by both methods. However, 226 additional taxa (72 species, 109 genera) were identified using eDNA compared to the 46 detected by video ground‐truthing. Averaged over all taxa, the video‐only model performed best in terms of discriminating presences from absences; however, we found that most sessile taxa were better predicted by the combined dataset compared to video data alone. These results highlight the limitations of imagery‐only datasets for biodiversity surveys and demonstrate the utility and limitations of metabarcoding data to improve benthic habitat and diversity maps in complex coastal habitats. This study highlights opportunities to fill gaps that could improve spatial modeling of seafloor assemblages derived from metabarcoding data, including sources and sinks of DNA in the environment and water column properties that control its dispersal.