SPYGEN, Le Bourget du Lac, France.
Pole R&D ECLA, Le Bourget-du-Lac, France.
PLoS One. 2022 Aug 12;17(8):e0272660. doi: 10.1371/journal.pone.0272660. eCollection 2022.
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.
环境 DNA (eDNA) 宏条形码技术正在彻底改变水生生物多样性的监测。eDNA 的使用具有实现非侵入性、经济高效、省时高效和高灵敏度监测鱼类群落的潜力。尽管 eDNA 宏条形码能够描述鱼类群落已经得到认可，但仍需要研究努力来更好地评估 eDNA 信号的空间和时间变异性，并最终为 eDNA 监测设计最佳采样策略。在这种情况下，我们在一年的时间里每 6 周对三个不同的湖泊（一个大坝水库、一个浅水富营养化湖泊和一个深水贫营养化湖泊）进行采样。我们对每个湖泊进行了四种类型的采样（在左岸、右岸和最深区域的沿岸剖面采集地表水综合样本，以及在靠近湖底的较深层进行点采样），以探索 1 年内湖泊尺度上 eDNA 信号的空间变异性。应用宏条形码方法分析了 92 个 eDNA 样本，以获得鱼类物种名录，并将其与传统的鱼类监测方法（标准化刺网采样）进行比较。一些已知存在于这些湖泊中的物种仅通过 eDNA 检测到，证实了该技术比刺网捕鱼更敏感。eDNA 信号在空间上存在差异，沿岸样本的物种比其他样本更丰富。此外，对于经常混合的湖泊，深水采样似乎不相关，因为 eDNA 信号均匀分布。这些结果还表明 eDNA 信号存在明显的时间变异性，这似乎与物种物候学有关，大多数物种在春季产卵期在岸边被检测到，但在冬季，洄游鱼类和白鲑科鱼类的繁殖期也有一个检测高峰。这些结果有助于我们了解湖泊中 eDNA 的时空分布，并为我们提供关于在湖泊中何处以及何时采样 eDNA 进行鱼类监测的方法建议。
