In this study, we implemented environmental DNA to investigate the presence of the endangered species of narrow sawfish Anoxypristis cuspidata in Indonesia. Furthermore, many marine species are difficult to determine in the sea waters because of their rare existence based on the visual census. Rather, our findings support a growing consensus that eDNA can reliably detect fish communities across dynamic freshwater habitats.Įnvironmental DNA or eDNA is a powerful method to uncover marine organisms in the seawaters. We further demonstrated that minor variations in the recovery of all approaches would not impact on the assessment of simple ecological models of community structure and, thus, some variability between approaches should not be viewed as a serious hindrance to uptake.
In contrast, metabarcoding did not perform as well as traditional approaches in estuarine waters, although results included the novel detection of the protected sea lamprey. We demonstrated that eDNA metabarcoding consistently detected more freshwater species than traditional methods, despite extensive sampling effort using the latter. eDNA was collected at 35 sites, 14 of which were simultaneously paired with traditional fish surveys for direct comparison. Two primer sets targeting 12S and CO1 regions were used to capture fish communities across the Thames catchment, from the upper freshwaters to the mid estuary.
To address this challenge, we focused on the Thames River system, UK, which has exceptional historical fish records providing a baseline to test the accuracy of eDNA metabarcoding in recovering fish community structure across both fresh and tidal zones.
Such dynamic systems are challenging for eDNA biomonitoring due to differing eDNA transport distances in rivers and the effects of river chemistry. It is now available for researcher use at While many studies have considered the ability of eDNA to assess animal communities in lacustrine settings, fewer have considered riverine systems, particularly those spanning the environmental gradients present in large river basins. The mBRAVE platform seeks to alleviate the main informatic challenges faced by the metabarcoding research community: the storage and consistent interpretation of HTS data. mBRAVE's cloud architecture provides centralized and automated storage and compute capacity, thereby reducing the burden on individual researchers. mBRAVE integrates common analytical methods and links to the Barcode of Life Data (BOLD) System for reference datasets, presenting users with the ability to analyze large volumes of data, without requiring special technical training. MBRAVE, the Multiplex Barcode Research And Visualization Environment, is a cloud-based data storage and analytics platform with standardized pipelines and a sophisticated web interface for transforming raw high-throughput sequencing (HTS) data into biological insights. As a result of these complexities, many researchers lack the time, training, or infrastructure to work with larger datasets. Further, though the majority of the necessary tools for performing these analyses are already implemented, there is limited support for high throughput analysis due to the requirement for heavy computational capacity.
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The integration of different data types, software tools, and analytical parameters pose a barrier to scaling research. Yet consistent and reproducible interpretation of the data remains challenging. Widespread interest in the study of metabarcoding has resulted in data proliferation and the development of a multitude of powerful computational tools.
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