Community ecology in a bottle: Leveraging eDNA metabarcoding data to predict occupancy of co-occurring species

Abstract

Detecting environmental DNA (eDNA) of numerous organisms from the same samples has been revolutionized by metabarcoding. However, utilizing the vast amounts of data generated from metabarcoding to predict occupancy probabilities for co-occurring species is currently rare. Here, we demonstrate how metabarcoding data can be used to advance community ecology research through a case study using replicate stream water samples and Bayesian occupancy models to test hypotheses of eDNA occurrence for a native fish (brook trout, Salvelinus fontinalis), its major ectoparasite (gill lice, Salmincola edwardsii), and an introduced potential competitor (brown trout, Salmo trutta). Gill lice DNA occupancy was positively associated with brook trout biomass determined via electrofishing conducted near eDNA sampling sites, suggesting gill lice occupancy is dependent on host density. Leveraging site-specific molecular operational taxonomic units identified from metabarcoding, DNA occupancy of trout and gill lice was often positively predicted by species richness of aquatic insect orders trout commonly feed on, which are also environmental quality indicators. Thus, high-quality habitats that environmentally sensitive salmonids and their primary prey rely on may promote higher fish occupancy rates, further facilitating the spread of fish parasites. An increasing amount of community-level data is being generated from global metabarcoding efforts, and we suggest our framework could be broadly implemented to enhance understanding of factors impacting distributions of co-occurring species, reveal new ecological phenomena, and support management and conservation efforts.