Rainwash eDNA metabarcoding of invertebrates. It’s raining species: Rainwash eDNA metabarcoding as a minimally invasive method to assess tree canopy invertebrate diversity

Forest canopies are a highly diverse ecosystem, but despite several decades of intense research, there remain substantial gaps in our knowledge of their biodiversity and ecological interactions. One fundamental challenge in canopy research is the limited accessibility of the ecosystem. Consequently, previous studies have relied on the application of either highly invasive methods such as chemical knockdown, or on time-consuming and expensive setups such as canopy walkways or cranes. Therefore, time- and cost-efficient, ideally minimally invasive yet comprehensive applications are required to help close this knowledge gap. High-throughput metabarcoding of environmental DNA (eDNA) collected from water, soil, or air provides a minimally invasive method for biodiversity assessment, yet its potential for canopy biodiversity monitoring has not been explored. Herein, we conducted metabarcoding of eDNA washed off the canopy via rainwater to explore its monitoring potential. We placed four 1 m2 rain samplers beneath the canopies of four different tree taxa prior to a major rain event, filtered eDNA from the collected rainwater, and performed cytochrome c oxidase I (COI) metabarcoding to profile the invertebrate community. Additionally, we collected and identified all specimens present in the rainwater for verification. 50 invertebrate species were detected by eDNA metabarcoding, of which 43 were not physically present in the water sample, thus likely representing true canopy biodiversity signals. Furthermore, we observed distinct species occurrence patterns corresponding to the four tree taxa, suggesting that ecological patterns such as host specificity can be assessed using the method. In conclusion, our study provides a proof of principle that rainwash eDNA metabarcoding offers a minimally invasive and comprehensive complementary method for tree canopy diversity monitoring.