The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Environmental DNA reveals that rivers are conveyer belts of biodiversity information
Open accessAbstract
DNA sampled from the environment (eDNA) is a useful way to uncover biodiversity patterns. By combining a conceptual model and empirical data, we test whether eDNA transported in river networks can be used as an integrative way to assess eukaryotic biodiversity for broad spatial scales and across the land–water interface. Using an eDNA metabarcode approach, we detect 296 families of eukaryotes, spanning 19 phyla across the catchment of a river. We show for a subset of these families that eDNA samples overcome spatial autocorrelation biases associated with the classical community assessments by integrating biodiversity information over space. In addition, we demonstrate that many terrestrial species are detected; thus suggesting eDNA in river water also incorporates biodiversity information across terrestrial and aquatic biomes. Environmental DNA transported in river networks offers a novel and spatially integrated way to assess the total biodiversity for whole landscapes and will transform biodiversity data acquisition in ecology.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Biodiversity hotspots for conservation priorities
Myers, N., Mittermeier, R. A., Mittermeier, C. G., da Fonseca, G. A. B., & Kent, J. (2000) · Nature · doi:10.1038/35002501 · Cited by 31,710
Global threats to human water security and river biodiversity
Vörösmarty, C. J., McIntyre, P. B., Gessner, M. O., Dudgeon, D., Prusevich, A., Green, P., Glidden, S., Bunn, S. E., Sullivan, C. A., Liermann, C. R., et al. (2010) · Nature · doi:10.1038/nature09440 · Cited by 7,582
Elbrecht, V., & Leese, F. (2015) · PLOS ONE · doi:10.1371/journal.pone.0130324 · Cited by 765
Metabarcoding vs. morphological identification to assess diatom diversity in environmental studies
Zimmermann, J., Glöckner, G., Jahn, R., Enke, N., & Gemeinholzer, B. (2015) · Molecular Ecology Resources · doi:10.1111/1755-0998.12336 · Cited by 317
Stein, E. D., Martinez, M. C., Stiles, S., Miller, P. E., & Zakharov, E. V. (2014) · PLoS ONE · doi:10.1371/journal.pone.0095525 · Cited by 152
Haase, P., Pauls, S. U., Schindehütte, K., & Sundermann, A. (2010) · Journal of the North American Benthological Society · doi:10.1899/09-183.1 · Cited by 119
Cited by
Emerging threats and persistent conservation challenges for freshwater biodiversity
Reid, A. J., Carlson, A. K., Creed, I. F., Eliason, E. J., Gell, P. A., Johnson, P. T. J., Kidd, K. A., MacCormack, T. J., Olden, J. D., Ormerod, S. J., et al. (2019) · Biological Reviews · doi:10.1111/brv.12480 · Cited by 3,645
Lateral and longitudinal fish environmental DNA distribution in dynamic riverine habitats
Thalinger, B., Kirschner, D., Pütz, Y., Moritz, C., Schwarzenberger, R., Wanzenböck, J., & Traugott, M. (2021) · Environmental DNA · doi:10.1002/edn3.171 · Cited by 66
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
- Deiner, K., Fronhofer, E. A., Mächler, E., Walser, J.-C., & Altermatt, F.
- Venue
- Nature Communications
- Published
- 2016-08-30
- Volume
- 7
- Issue
- 1
- Pages
- 12544-12544
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2951985551
- Cited by
- 673
- References
- 6
- FWCI
- 47.98
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.