Inferring Species Interactions From Co-occurrence Networks With Environmental DNA Metabarcoding Data in a Coastal...

A good understanding of biotic interactions is necessary to accurately predict the vulnerability of ecosystems to climate change. Recently, co-occurrence networks built from environmental DNA (eDNA) metabarcoding data have arisen as a tool to explore interspecific interactions in ecological communities exposed to different human and environmental pressures. Such networks can identify environmentally driven relationships in microbial and eukaryotic communities, but whether inferred co-occurrences robustly represent biotic interactions remains unclear. Here, we tackle this challenge and compare spatio-temporal variability in the structure and complexity of inferred co-occurrence networks and food webs, using 60 eDNA samples covering vertebrates and other eukaryotes in a North Sea coastal ecosystem. We compare topological characteristics and identify highly connected species across spatial and temporal subsets to evaluate variance in community composition and structure. We find consistent trends in topological characteristics across eDNA-derived co-occurrence networks and food webs that support some ability for the co-occurrence networks to detect real ecological processes, despite trophic interactions forming a minority of significant co-occurrences. The lack of significant trophic interactions detected in co-occurrence networks may result from ecological complexities, such as generalist predators having flexible interactions or behavioural partitioning, the inability to distinguish age class with eDNA or co-occurrences being driven by non-trophic or abiotic interactions. We find support for using eDNA-derived co-occurrence networks to infer ecological interactions, but further work is needed to assess their power to reliably detect and differentiate different interaction types and overcome methodological limitations, such as species detection uncertainties, which could influence inferred ecosystem complexity.