Open Access
| Issue |
EPJ Web Conf.
Volume 344, 2025
AI-Integrated Physics, Technology, and Engineering Conference (AIPTEC 2025)
|
|
|---|---|---|
| Article Number | 01018 | |
| Number of page(s) | 5 | |
| Section | AI-Integrated Physics, Technology, and Engineering | |
| DOI | https://doi.org/10.1051/epjconf/202534401018 | |
| Published online | 22 December 2025 | |
- A. Djurhuus, C.J. Closek, R.P. Kelly, K.J. Pitz, R.P. Michisaki, H.A. Starks, K.R. Walz, E.A. Andruszkiewicz, E. Olesin, K. Hubbard, E. Montes, D. Otis, F. Muller-Karger, F. P. Chavez, A. B. Boehm, M. Breitbart, Environmental DNA reveals seasonal shifts and potential interconnectivity across the marine tree of life. Nat. Commun. 11, 254 (2020). https://doi.org/10.1038/s41467-019-14105-1 [Google Scholar]
- C.C. Gaonkar, L. Campbell, A full‐length 18S ribosomal DNA metabarcoding approach for determining protist community diversity using Nanopore sequencing. Ecol. Evol. 14(4), e11232 (2024). https://doi.org/10.1002/ece3.11232 [Google Scholar]
- C.I. Adams, G.J. Jeunen, H. Cross, H.R. Taylor, A. Bagnaro, K. Currie, C. Hepburn, N.J. Gemmell, L. Urban, F. Baltar, M. Stat, Environmental DNA metabarcoding describes biodiversity across marine gradients. ICES J. Mar. Sci. 80(4), 953-971. (2023). https://doi.org/10.1093/icesjms/fsad017 [Google Scholar]
- C.L. Hurd, P. J. Harrison, K. Bischof, C.S. Lobban, Seaweed ecology and physiology. (Cambridge University Press, 2014) [Google Scholar]
- D. Minardi, D. Ryder, J. Del Campo, V. Garcia Fonseca, R. Kerr, S. Mortensen, A. Pallavicini, D. Bass, Improved high throughput protocol for targeting eukaryotic symbionts in metazoan and eDNA samples. Mol. Ecol. Resour. 22(2), 664-678 (2022). https://doi.org/10.1111/1755-0998.13509 [Google Scholar]
- M.M. Littler, D.S. Littler, Models of tropical reef biogenesis: the contribution of algae. (Smithsonian Institution Press, 1984) [Google Scholar]
- E. Bolyen, J.R. Rideout, M.R. Dillon, N.A. Bokulich, C.C. Abnet, G.A. Al-Ghalith, H. Alexander, E.J. Alm, M. Arumugam, F. Asnicar, Y. Bai, Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37(8), 852-857 (2019). https://doi.org/10.1038/s41587-019-0209-9 [Google Scholar]
- E.E. Curd, Z. Gold, G.S. Kandlikar, J. Gomer, M. Ogden, T. O’Connell, L. Pipes, T.M. Schweizer, L. Rabichow, M. Lin, B. Shi, Anacapa Toolkit: An environmental DNA toolkit for processing multilocus metabarcode datasets. Methods Ecol. Evol. 10(9), 1469-1475 (2019). https://doi.org/10.1111/2041-210X.13214 [Google Scholar]
- F. Leliaert, D.R. Smith, H. Moreau, M.D. Herron, H. Verbruggen, C.F. Delwiche, O. De Clerck, Phylogeny and molecular evolution of the green algae. Crit. Rev. Plant Sci. 31(1), 1-46 (2012). https://doi.org/10.1080/07352689.2011.615705 [Google Scholar]
- H. Madduppa, N.K.D. Cahyani, A.W. Anggoro, B. Subhan, E. Jefri, L.M.I. Sani, D. Arafat, N. Akbar, and D.G. Bengen, eDNA metabarcoding illuminates species diversity and composition of three phyla (chordata, mollusca and echinodermata) across Indonesian coral reefs. Biodivers. Conserv. 30(11), 3087-3114 (2021). https://doi.org/10.1007/s10531-021-02237-0 [Google Scholar]
- J. Choi, J.S. Park, Comparative analyses of the V4 and V9 regions of 18S rDNA for the extant eukaryotic community using the Illumina platform. Scientific Reports, 10(1), 6519 (2020). https://doi.org/10.1038/s41598-020-63561-z [Google Scholar]
- K. Deiner, H.M. Bik, E. Mächler, M. Seymour, A. Lacoursière-Roussel, F. Altermatt, S. Creer, I. Bista, D.M. Lodge, N. De Vere, M.E. Pfrender, Environmental DNA metabarcoding: Transforming how we survey animal and plant communities. Mol. Ecol. 26(21), 5872–5895 (2017). https://doi.org/10.1111/mec.14350 [Google Scholar]
- M.A. Latz, V. Grujcic, S. Brugel, J. Lycken, U. John, B. Karlson, A. Andersson, A.F. Andersson, Short‐and long‐read metabarcoding of the eukaryotic rRNA operon: evaluation of primers and comparison to shotgun metagenomics sequencing. Mol. Ecol. Resour. 22(6), 2304-2318 (2022). https://doi.org/10.1111/1755-0998.13623 [Google Scholar]
- N. Fraija-Fernández, M.C. Bouquieaux, A. Rey, I. Mendibil, U. Cotano, X. Irigoien, M. Santos, N. Rodríguez‐Ezpeleta, Marine water environmental DNA metabarcoding provides a comprehensive fish diversity assessment and reveals spatial patterns in a large oceanic area. Ecol. Evol. 10(14), 7560-7584. https://doi.org/10.1002/ece3.6482 [Google Scholar]
- N.D. Esiobu, I.M. Ezeonu, F. Nwaokorie, Principles and techniques for deoxyribonucleic acid (DNA) manipulation. In Medical Biotechnology, Biopharmaceutics, (Forensic Science and Bioinformatics (CRC Press, 2022) [Google Scholar]
- P. Fong, V.J. Paul, Coral reef algae. In Coral reefs: an ecosystem in transition (Dordrecht: Springer Netherlands,2010) [Google Scholar]
- J.F. Karisa, D.O. Obura, C.A. Chen, Spatial heterogeneity of coral reef benthic communities in Kenya. PloS one 15(8), e0237397 (2020). https://doi.org/10.1371/journal.pone.0237397 [Google Scholar]
- K.T. Brown, D. Bender-Champ, A. Kubicek, R. Van der Zande, M. Achlatis, O. Hoegh-Guldberg, S.G. Dove, The dynamics of coral-algal interactions in space and time on the southern Great Barrier Reef. Front. Mar. Sci. 5, 181 (2018). https://doi.org/10.3389/fmars.2018.00181 [Google Scholar]
- P. Iturbe-Espinoza, B.W. Brandt, M. Braster, M. Bonte, D. M. Brown, & R.J. van Spanning, Effects of DNA preservation solution and DNA extraction methods on microbial community profiling of soil. Folia Microbiol. 66(4), 597-606 (2021). https://doi.org/10.1007/s12223-021-00866-0 [Google Scholar]
- S. Bombin, A. Bombin, B. Wysor, J.M. Lopez-Bautista, Application of Environmental DNA Metabarcoding to Differentiate Algal Communities by Littoral Zonation and Detect Unreported Algal Species. Phycology 4(4), pp.605-620 (2024). https://doi.org/10.3390/phycology4040033 [Google Scholar]
- S. Fernández, L. Cartairade, E. Garcia-Vazquez, S. Planes, Metabarcoding Reveals Diversity of Potentially Toxic Algae in Papeete Port (Tahiti). Toxins 17(8), 424 (2025). https://doi.org/10.3390/toxins17080424 [Google Scholar]
- S. Hassan, H.J. MacIsaac, M. E. Cristescu, eDNA metabarcoding for biodiversity assessment: Current status and future perspectives. Biodivers. Data J. 10, e12345 (2022). https://doi.org/10.3897/BDJ.10.e12345 [Google Scholar]
- X. Han, B. Pan, X. Jin, M. Li, Y. Ding, X. Liu, The assembly mechanisms of algal community across different habitats mediated by sediment in the heavily sediment-laden Yellow River. J. Hydrol. 631, 130825 (2024). https://doi.org/10.1016/j.jhydrol.2024.130825 [Google Scholar]
- A. Rasyid, M.Y. Putra, W. Handayani, Y. Yasman, Macroalgal community structure and beta diversity in the coastal waters of East Lombok, Indonesia. Biodiversitas 26(7), 3115-3124 (2025). https://doi.org/10.13057/biodiv/d260703 [Google Scholar]
- G. Capurso, B. Carroll, K.A. Stewart, Transforming marine monitoring: Using eDNA metabarcoding to improve the monitoring of the Mediterranean Marine Protected Areas network. Mar. Policy 156, 105807 (2023). https://doi.org/10.1016/j.marpol.2023.105807 [Google Scholar]
- Z. Gold, A.R. Wall, T.M. Schweizer, N.D. Pentcheff, E.E. Curd, P.H. Barber, R.S. Meyer, R. Wayne, K. Stolzenbach, K. Prickett, J. Luedy, A manager’s guide to using eDNA metabarcoding in marine ecosystems. PeerJ 10, e14071 (2022). https://doi.org/10.7717/peerj.14071 [CrossRef] [PubMed] [Google Scholar]
- M.A. Sze, P.D. Schloss, The impact of DNA polymerase and number of rounds of amplification in PCR on 16S rRNA gene sequence data. Msphere 4(3)10-1128 (2019). https://doi.org/10.1128/msphere.00163-19 [Google Scholar]
- H. Krehenwinkel, M. Fong, S. Kennedy, E.G. Huang, S. Noriyuki, L. Cayetano, R. Gillespie, The effect of DNA degradation bias in passive sampling devices on metabarcoding studies of arthropod communities and their associated microbiota. PLoS one 13(1), e0189188 (2018). https://doi.org/10.1371/journal.pone.0189188 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

