Insights into the representativeness of biodiversity assessment in large reservoir through eDNA metabarcoding

Abstract

Monitoring biodiversity on a large scale, such as in hydropower reservoirs, poses scientific challenges. Conventional methods such as passive fishing gear are prone to various biases, while the utilization of environmental DNA (eDNA) metabarcoding has been restricted. Most eDNA studies have primarily focused on replicating results from traditional methods, which themselves have limitations regarding representativeness and bias. In our study, we employed eDNA metabarcoding with three markers (12SrRNA, COI, and 16SrRNA) to evaluate the biodiversity of an 800 km2 reservoir. We utilized hydrodynamic modeling to determine water flow velocity and the water renewal ratio throughout the study area. Additionally, we conducted statistical comparisons – rarefaction curves and multivariate methods – among samples as an alternative approach to assess biodiversity representation. The eDNA identified taxa previously documented in the reservoir by traditional monitoring methods, as well as revealed 29 – nine fishes and 20 non-fish – previously unreported species. These results highlight the robustness of eDNA as a biodiversity monitoring technique. Our findings also indicated that by randomly sampling 30% of the original number of samples, we could effectively capture the same biodiversity. This approach enabled us to comprehend the reservoir's biodiversity profile and propose a straightforward, cost-effective monitoring protocol for the future based on eDNA.