The DOI matches its Crossref record; 98 of the 101 references it cites were found in a registry or an index.
Metabarcoding vs. morphological identification to assess diatom diversity in environmental studies
Abstract
Diatoms are frequently used for water quality assessments; however, identification to species level is difficult, time‐consuming and needs in‐depth knowledge of the organisms under investigation, as nonhomoplastic species‐specific morphological characters are scarce. We here investigate how identification methods based on DNA (metabarcoding using NGS platforms) perform in comparison to morphological diatom identification and propose a workflow to optimize diatom fresh water quality assessments. Diatom diversity at seven different sites along the course of the river system Odra and Lusatian Neisse from the source to the mouth is analysed with DNA and morphological methods, which are compared. The NGS technology almost always leads to a higher number of identified taxa (270 via NGS vs. 103 by light microscopy LM), whose presence could subsequently be verified by LM. The sequence‐based approach allows for a much more graduated insight into the taxonomic diversity of the environmental samples. Taxa retrieval varies considerably throughout the river system, depending on species occurrences and the taxonomic depth of the reference databases. Mostly rare taxa from oligotrophic parts of the river systems are less well represented in the reference database used. A workflow for DNA‐based NGS diatom identification is presented. 28 000 diatom sequences were evaluated. Our findings provide evidence that metabarcoding of diatoms via NGS sequencing of the V4 region (18S) has a great potential for water quality assessments and could complement and maybe even improve the identification via light microscopy.
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“Several studies show that DNA-based assessments are superior to morphological assessments in freshwater ecosystems (e.g., [14,25,33])”
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Cites
Basic local alignment search tool
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669
The neighbor-joining method: a new method for reconstructing phylogenetic trees.
Saitou, N., & Nei, M. (1987) · Molecular Biology and Evolution · doi:10.1093/oxfordjournals.molbev.a040454 · Cited by 59,959
MUSCLE: multiple sequence alignment with high accuracy and high throughput
Edgar, R. C. (2004) · Nucleic Acids Research · doi:10.1093/nar/gkh340 · Cited by 44,662
Tamura, K., Peterson, D., Peterson, N., Stecher, G., Nei, M., & Kumar, S. (2011) · Molecular Biology and Evolution · doi:10.1093/molbev/msr121 · Cited by 40,207
Kimura, M. (1980) · Journal of Molecular Evolution · doi:10.1007/bf01731581 · Cited by 29,778
MRBAYES: Bayesian inference of phylogenetic trees.
Huelsenbeck, J. P., & Ronquist, F. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.8.754 · Cited by 21,937
Search and clustering orders of magnitude faster than BLAST
Edgar, R. C. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq461 · Cited by 21,098
Biological identifications through DNA barcodes
Hebert, P. D. N., Cywinska, A., Ball, S. L., & deWaard, J. R. (2003) · Proceedings of the Royal Society of London. Series B: Biological Sciences · doi:10.1098/rspb.2002.2218 · Cited by 13,376
Klindworth, A., Pruesse, E., Schweer, T., Peplies, J., Quast, C., Horn, M., & Glöckner, F. O. (2013) · Nucleic Acids Research · doi:10.1093/nar/gks808 · Cited by 8,850
bold : The Barcode of Life Data System (http://www.barcodinglife.org)
RATNASINGHAM, S., & HEBERT, P. D. N. (2007) · Molecular Ecology Notes · doi:10.1111/j.1471-8286.2007.01678.x · Cited by 6,376
Research works printed but not linked: 6
- 10.1016/S0022-2836(05)80360-2 · DOI · Paper page
- Archibald REM, 1984, Diatom illustrations—an appeal, Bacillaria, 7, 173
- Hofmann G, 2011, Diatomeen im Süßwasser‐Benthos von Mitteleuropa
- JahnR(1984)The structure of the frustule and its variability in clones and populations.Proceedings of the 8th International Diatom Symposium 1984.191–204.
- Swofford DL, 2002, Phylogenetic Analysis Using Parsimony (*and Other Methods)
- ZimmermannJ JahnR GemeinholzerB(2010)Diatom barcoding: water monitoring using molecular tools. 2nd Conference of the European Consortium for the Barcode of Life 38.
Cited by
Sorted by citations
Items 301–323 of 323
Cruz-Cano, R. I., Kolb, M., Saldaña‐Vázquez, R. A., Bretón‐Deval, L., Cruz‐Cano, N. B., & Aldama-Cervantes, A. (2024) · Figshare · doi:10.6084/m9.figshare.26681077
Cruz-Cano, R. I., Kolb, M., Saldaña‐Vázquez, R. A., Bretón‐Deval, L., Cruz‐Cano, N. B., & Aldama-Cervantes, A. (2024) · Figshare · doi:10.6084/m9.figshare.26681095
Cruz-Cano, R. I., Kolb, M., Saldaña‐Vázquez, R. A., Bretón‐Deval, L., Cruz‐Cano, N. B., & Aldama-Cervantes, A. (2024) · Figshare · doi:10.6084/m9.figshare.26681104
Cruz-Cano, R. I., Kolb, M., Saldaña‐Vázquez, R. A., Bretón‐Deval, L., Cruz‐Cano, N. B., & Aldama-Cervantes, A. (2024) · Figshare · doi:10.6084/m9.figshare.26681092
Cruz-Cano, R. I., Kolb, M., Saldaña‐Vázquez, R. A., Bretón‐Deval, L., Cruz‐Cano, N. B., & Aldama-Cervantes, A. (2024) · Figshare · doi:10.6084/m9.figshare.26681110
Cruz-Cano, R. I., Kolb, M., Saldaña‐Vázquez, R. A., Bretón‐Deval, L., Cruz‐Cano, N. B., & Aldama-Cervantes, A. (2024) · Figshare · doi:10.6084/m9.figshare.26681089
Cruz-Cano, R. I., Kolb, M., Saldaña‐Vázquez, R. A., Bretón‐Deval, L., Cruz‐Cano, N. B., & Aldama-Cervantes, A. (2024) · Figshare · doi:10.6084/m9.figshare.26681083
Tropical coastal seas are overlooked hotspots of Pseudo-nitzschia diversity
Niu, B., Liang, C., Yue, F., Wu, S., Li, Y., & Lü, S. (2026) · Harmful Algae · doi:10.1016/j.hal.2026.103063
Zhao, Z., Li, Z. P., He, X., Yang, J., & Xia, J. (2026) · Frontiers in Marine Science · doi:10.3389/fmars.2025.1690596
Evaluation of nanopore-based diatom metabarcoding in tropical freshwater ecosystems
Castillejo, P., Lobo, E. A., Heinrich, C. G., Perdiguero, P., Frandsen, P. B., Ríos‐Touma, B., & Ballesteros, I. (2026) · Hydrobiologia · doi:10.1007/s10750-026-06119-w
Environmental DNA reveals protist biodiversity in mangrove sediments of Java and Madura, Indonesia
Falah, M. H. A., Soeprobowati, T. R., Hadiyanto, H., Rahim, A., Jumari, J., Khotimperwati, L., Cahyani, N. K. D., Wojewódka-Przybył, M., Kruszewski, Ł., & Rydelek, P. (2025) · Biodiversitas Journal of Biological Diversity · doi:10.13057/biodiv/d261035
Characteristics of fatty acid profiles of marine diatoms
Krivova, Z. V., Kezlya, E. M., Kulikovskiy, M. S., & Maltsev, Y. (2025) · Issues of modern algology (Вопросы современной альгологии) · doi:10.33624/2311-0147-2025-1(37)-377-379
Functional Diversity of Marine Protists in Coastal Ecosystems
Pierre Ramond, B. (2018) · theses.fr (ABES) · doi:10.70675/d56d01f7z5a55z435czaab9ze083ea411e51
Algae classification algorithm based on chlorophyll fluorescence dynamics
Wang, J., Kong, X., Ma, H. L., Zhang, J., Liu, Y., & Zhao, Y. (2026) · Intelligent Marine Technology and Systems · doi:10.1007/s44295-026-00100-y
DNA metabarcoding of diatoms: A next-generation tool for aquatic biodiversity and bioassessment
Abubaker, B., Stenger‐Kovács, C., Rimet, F., & Tapolczai, K. (2026) · Metabarcoding and Metagenomics · doi:10.3897/mbmg.10.168096
Li, Y., Huang, X., Du, H., Ding, X., Gong, Z., Lim, P. T., Leaw, C. P., & Chen, N. (2026) · Harmful Algae · doi:10.1016/j.hal.2026.103166
Cross-Domain Evaluation of Modern Deep Learning Architectures for Microscopic Diatom Classification
Yurtsever, U. (2026) · IEEE Access · doi:10.1109/access.2026.3729554
Obertegger, U., Cappelletti, C., Blanco, S., Pindo, M., Bertoli, M., Pizzul, E., Pastorino, P., Prearo, M., & Ciutti, F. (2026) · Ecologies · doi:10.3390/ecologies7040111
Lawlor, K. C., Kléparski, L., Warwick-Dugdale, J., Widdicombe, C. E., & Cunliffe, M. (2026) · Harmful Algae · doi:10.1016/j.hal.2026.103240
Zhou, Y., Liu, Y., Cong, B., 陈健华, & Deng, J. (2026) · iScience · doi:10.1016/j.isci.2026.117909
Tseplik, N. D., Kezlya, E. M., Glushchenko, A., Krivova, Z. V., Maltsev, Y., Genkal, S., Hai, D. N., & Kulikovskiy, M. S. (2026) · Phycology · doi:10.3390/phycology6040115
Wu, D., Wang, S., Lubembe, S. I., Zou, C.-J., Xie, P.-F., Ullah, N., Wu, J.-B., Zhang, Y.-M., Wang, T., Zhang, Y., et al. (2026) · Inland Waters · doi:10.1080/20442041.2026.2758283
Metadata
- Authors
- Zimmermann, J., Glöckner, G., Jahn, R., Enke, N., & Gemeinholzer, B.
- Affiliations
- Justus-Liebig-Universität Gießen (DE)
- University of Cologne (DE)
- Botanic Garden and Botanical Museum Berlin (DE)
- Corresponding author
- Zimmermann, Jonas
- Venue
- Molecular Ecology Resources
- Publisher
- Wiley
- Published
- 2014-10-20
- Volume
- 15
- Issue
- 3
- Pages
- 526-542
- ISSN
- 1755-098X (ISSN-L)
- 1755-0998
- OpenAlex
- W2089848025
- Cited by
- 317
- References
- 101
- Citations by year
- 2026: 17 · 2025: 23 · 2024: 39 · 2023: 24 · 2022: 21 · 2021: 23 · 2020: 41 · 2019: 26 · 2018: 55 · 2017: 25 · 2016: 14 · 2015: 7 · 2014: 1
- FWCI
- 13.58
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Molecular Ecology Resources (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Deutsche Forschungsgemeinschaft (GE 12 42/11-1)
- Topics
- Physical Sciences › Environmental Science › Ecology › Environmental DNA in Biodiversity Studies
- Physical Sciences › Environmental Science › Ecology › Microbial Community Ecology and Physiology
- Life Sciences › Agricultural and Biological Sciences › Ecology, Evolution, Behavior and Systematics › Diatoms and Algae Research
- Keywords
- diatoms, metabarcoding, morphological identification, species identification, water quality assessment, reference database, rare taxa, Oder River
- MeSH terms
- Algorithms, Cytological Techniques, Fresh Water, Microscopy, Molecular Sequence Data, Genetic Variation, Diatoms, Sequence Analysis, DNA, Biodiversity, Rivers, Workflow, DNA Barcoding, Taxonomic, Water Quality
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