Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding.

Global biodiversity in freshwater and the oceans is declining at high rates. Reliable tools for assessing and monitoring aquatic biodiversity, especially for rare and secretive species, are important for efficient and timely management. Recent advances in DNA sequencing have provided a new tool for species detection from DNA present in the environment. In this study, we tested whether an environmental DNA (eDNA) metabarcoding approach, using water samples, can be used for addressing significant questions in ecology and conservation. Two key aquatic vertebrate groups were targeted: amphibians and bony fish. The reliability of this method was cautiously validated in silico, in vitro and in situ. When compared with traditional surveys or historical data, eDNA metabarcoding showed a much better detection probability overall. For amphibians, the detection probability with eDNA metabarcoding was 0.97 (CI = 0.90-0.99) vs. 0.58 (CI = 0.50-0.63) for traditional surveys. For fish, in 89% of the studied sites, the number of taxa detected using the eDNA metabarcoding approach was higher or identical to the number detected using traditional methods. We argue that the proposed DNA-based approach has the potential to become the next-generation tool for ecological studies and standardized biodiversity monitoring in a wide range of aquatic ecosystems.

Spatio-temporal variability of eDNA signal and its implication for fish monitoring in lakes.

Environmental DNA (eDNA) metabarcoding is revolutionizing the monitoring of aquatic biodiversity. The use of eDNA has the potential to enable non-invasive, cost-effective, time-efficient and high-sensitivity monitoring of fish assemblages. Although the capacity of eDNA metabarcoding to describe fish assemblages is recognised, research efforts are still needed to better assess the spatial and temporal variability of the eDNA signal and to ultimately design an optimal sampling strategy for eDNA monitoring. In this context, we sampled three different lakes (a dam reservoir, a shallow eutrophic lake and a deep oligotrophic lake) every 6 weeks for 1 year. We performed four types of sampling for each lake (integrative sampling of sub-surface water along transects on the left shore, the right shore and above the deepest zone, and point sampling in deeper layers near the lake bottom) to explore the spatial variability of the eDNA signal at the lake scale over a period of 1 year. A metabarcoding approach was applied to analyse the 92 eDNA samples in order to obtain fish species inventories which were compared with traditional fish monitoring methods (standardized gillnet samplings). Several species known to be present in these lakes were only detected by eDNA, confirming the higher sensitivity of this technique in comparison with gillnetting. The eDNA signal varied spatially, with shoreline samples being richer in species than the other samples. Furthermore, deep-water samplings appeared to be non-relevant for regularly mixed lakes, where the eDNA signal was homogeneously distributed. These results also demonstrate a clear temporal variability of the eDNA signal that seems to be related to species phenology, with most of the species detected in spring during the spawning period on shores, but also a peak of detection in winter for salmonid and coregonid species during their reproduction period. These results contribute to our understanding of the spatio-temporal distribution of eDNA in lakes and allow us to provide methodological recommendations regarding where and when to sample eDNA for fish monitoring in lakes.