Selections of environmental substrates for DNA-based monitoring of multiple trophic biodiversity in a whole lake ecosystem

Abstract

Environmental DNA (eDNA) metabarcoding has become a powerful tool for effective biomonitoring and bioassessment in aquatic ecosystems. However, the effectiveness of eDNA from various substrates (e.g., water, plankton nets, and sediment) in monitoring lake biodiversity across multiple trophic levels remains underexplored. This study assessed the performance of these substrates in terms of: (1) detection precision, (2) biodiversity coverage at taxonomic, phylogenetic, and functional levels and (3) sensitivity in ecological health assessment of a lake ecosystem. Samples were collected from 11 sites in Dianchi Lake, China, and analyzed using multiple genetic loci (16S-V3, 18S-V9, COI, and mitochondrial 12S rDNA) to detect a broad range of taxa, including bacteria, cyanobacteria, eukaryotic phytoplankton, fungi, protozoa, zooplankton, macroinvertebrates, and fish. Totally, 8693 amplicon sequence variants (ASVs) were identified from 36 phyla, 765 genera, and 421 species. Firstly, water samples showed the highest detection precision (93.89 % ± 5.33 %), defined as the repeatability of sequence detection across biological replicates, followed by sediment and plankton nets for zooplankton. Secondly, a single substrate captured only 42.48 % of taxonomic and 39.72 % of phylogenetic diversity, but over 88 % of functional diversity. Furthermore, substrates explained 58.83 %±8.25 % of community composition variations, with water and plankton net samples more sensitive to spatial changes, particularly in response to land use. Overall, water samples were optimal for monitoring small organisms (e.g., cyanobacteria, eukaryotic phytoplankton, fungi and protozoa), while plankton nets excelled for zooplankton. Both water and sediment samples effectively captured fish biodiversity, but all substrates showed limited capacity for assessing macroinvertebrates. These findings provide valuable insights for optimizing eDNA-based monitoring methods and supporting better environmental management decisions.