The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Next‐generation monitoring of aquatic biodiversity using environmental DNA metabarcoding
Open accessAbstract
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.
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Cites
Has the Earth’s sixth mass extinction already arrived?
Barnosky, A. D., Matzke, N., Tomiya, S., Wogan, G. O. U., Swartz, B., Quental, T. B., Marshall, C., McGuire, J. L., Lindsey, E. L., Maguire, K. C., et al. (2011) · Nature · doi:10.1038/nature09678 · Cited by 4,400 · cited 1×
“It is commonly acknowledged that biodiversity is threatened by a variety of anthropogenic factors (Barnosky et al. 2011); however, the most critical issue is the sheer lack of efficient and reliable tools to document the remaining species and to assess biodiversity trends.”
BackgroundThe biodiversity of species and their rates of extinction, distribution, and protection
Pimm, S. L., Jenkins, C. N., Abell, R., Brooks, T. M., Gittleman, J. L., Joppa, L. N., Raven, P. H., Roberts, C. M., & Sexton, J. O. (2014) · Science · doi:10.1126/science.1246752 · Cited by 3,542
Biggs, J., Ewald, N., Valentini, A., Gaboriaud, C., Dejean, T., Griffiths, R. A., Foster, J., Wilkinson, J. W., Arnell, A., Brotherton, P., et al. (2015) · Biological Conservation · doi:10.1016/j.biocon.2014.11.029 · Cited by 503
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Cited by
Species Distribution Modeling for Machine Learning Practitioners: A Review
Beery, S., Cole, E., Parker, J., Perona, P., & Winner, K. (2021) · ACM SIGCAS Conference on Computing and Sustainable Societies · doi:10.1145/3460112.3471966 · Cited by 100
de Santana, C. D., Parenti, L. R., Dillman, C. B., Coddington, J. A., Bastos, D. A., Baldwin, C. C., Zuanon, J., Torrente-Vilara, G., Covain, R., Menezes, N. A., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-97128-3 · Cited by 38
Yu, Y., Shao, M., Zhou, H., Su, F., Guan, P., Ding, L., Ma, T., Zhou, B., Xu, S., Song, S., et al. (2026) · Scientific Reports · doi:10.1038/s41598-025-32964-1 · Cited by 3
Metadata
- Authors
- Valentini, A., Taberlet, P., Miaud, C., Civade, R., Herder, J., Thomsen, P. F., Bellemain, E., Besnard, A., Coissac, E., Boyer, F., Gaboriaud, C., Jean, P., Poulet, N., Roset, N., Copp, G. H., Geniez, P., Pont, D., Argillier, C., Baudoin, J., Peroux, T., Crivelli, A. J., Olivier, A., Acqueberge, M., Le Brun, M., Møller, P. R., Willerslev, E., & Dejean, T.
- Venue
- Molecular Ecology
- Publisher
- Wiley
- Published
- 2016-01-18
- Volume
- 25
- Issue
- 4
- Pages
- 929-942
- ISSN
- 0962-1083 (ISSN-L)
- 1365-294X
- OpenAlex
- W2171100005
- Cited by
- 1,252
- References
- 3
- FWCI
- 57.12
- Type
- journal article
- Language
- en
- Open access
- green
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