Environmental DNA for monitoring the impact of offshore wind farms on fish and invertebrate community structures
Corresponding Author
Isolde Cornelis
Animal Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food, Oostende, Belgium
Correspondence
Isolde Cornelis, Animal Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food, Jacobsenstraat 1, Oostende B-8400, Belgium.
Email: [email protected]
Search for more papers by this authorAnnelies De Backer
Animal Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food, Oostende, Belgium
Search for more papers by this authorSara Maes
Animal Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food, Oostende, Belgium
Search for more papers by this authorJoran Vanhollebeke
Animal Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food, Oostende, Belgium
Search for more papers by this authorRein Brys
Research Institute for Nature and Forest, Geraardsbergen, Belgium
Search for more papers by this authorTom Ruttink
Plant Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food, Melle, Belgium
Search for more papers by this authorKris Hostens
Animal Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food, Oostende, Belgium
Search for more papers by this authorSofie Derycke
Animal Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food, Oostende, Belgium
Biology Department, Ghent University, Ghent, Belgium
Search for more papers by this authorCorresponding Author
Isolde Cornelis
Animal Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food, Oostende, Belgium
Correspondence
Isolde Cornelis, Animal Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food, Jacobsenstraat 1, Oostende B-8400, Belgium.
Email: [email protected]
Search for more papers by this authorAnnelies De Backer
Animal Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food, Oostende, Belgium
Search for more papers by this authorSara Maes
Animal Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food, Oostende, Belgium
Search for more papers by this authorJoran Vanhollebeke
Animal Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food, Oostende, Belgium
Search for more papers by this authorRein Brys
Research Institute for Nature and Forest, Geraardsbergen, Belgium
Search for more papers by this authorTom Ruttink
Plant Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food, Melle, Belgium
Search for more papers by this authorKris Hostens
Animal Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food, Oostende, Belgium
Search for more papers by this authorSofie Derycke
Animal Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food, Oostende, Belgium
Biology Department, Ghent University, Ghent, Belgium
Search for more papers by this authorAbstract
To reach the renewable energy targets set by the European Commission, a tenfold expansion of the installed offshore wind farms is needed. Since the construction of offshore wind farms may affect local soft-sediment fauna, an efficient monitoring technique is needed to monitor the potential effects on the marine ecosystem. Here, we assess whether eDNA metabarcoding is a suitable alternative to monitor fish and epibenthos biodiversity in these difficult to access marine habitats. Water sampling and trawl surveys were conducted in parallel in 12 coastal and 18 offshore sites, the latter located inside and outside two offshore wind farms in the Belgian part of the North Sea. 12S eDNA metabarcoding retrieved 85.7% of the fish species caught in the beam trawls, whereas the COI eDNA metabarcoding only identified 31.4% of the epibenthic invertebrate species. Furthermore, the 12S marker resulted in an additional detection of 26 unique fish species, whereas the COI marker detected an additional 90 invertebrate species. Spatial patterns in alpha diversity recovered with eDNA metabarcoding were not significantly different from those observed with morphological determination. Significant differences were found in fish and invertebrate community structures between the coastal, transition and offshore zones as well as on the smaller wind farm scales, which agreed with the morphological beam trawl data. Indicator species found with morphological beam trawl monitoring for each of the three zones (coastal, transition, offshore) were also detected with 12S eDNA metabarcoding, and the latter method detected an additional 31 indicator species. Our findings show the need for adequate quality control of the obtained species lists and reveal that 12S eDNA metabarcoding analyses offers a useful survey tool for the monitoring of fish communities in offshore wind farms, but the used COI assay did not adequately capture the epibenthic communities as observed with beam trawl data.
CONFLICT OF INTEREST STATEMENT
The authors declare no conflicts of interest.
Open Research
DATA AVAILABILITY STATEMENT
All sequencing data and sample metadata have been archived on NCBI under the BioProject number PRJNA1032405 (SUB14012338 for the 12S eDNA metabarcoding data and SUB14013523 for the COI eDNA metabarcoding data). A version of the inferred biodiversity data is available at GBIF.org: DOI 10.15468/nvabg3 (12S eDNA metabarcoding data) and DOI 10.15468/tvng39 (COI eDNA metabarcoding data). This version includes all the species detected in the unrarefied and concatenated data after decontamination with Decontam instead of microDecon. All scripts used for bioinformatics, taxonomic assignments, and statistical analyses are available on GitHub (https://github.com/icornelis90/eDNA_metabarcoding_NJ2021.git) and Zenodo (DOI 10.5281/zenodo.11280892).
Supporting Information
Filename | Description |
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edn3575-sup-0001-FiguresS1-S13.docxWord 2007 document , 1.6 MB |
Figures S1–S13. |
edn3575-sup-0002-DataS1.xlsxExcel 2007 spreadsheet , 123.6 KB |
Data S1. |
edn3575-sup-0003-AppendixS1.docxWord 2007 document , 419.3 KB |
Appendix S1. |
edn3575-sup-0004-AppendixS2.docxWord 2007 document , 120.6 KB |
Appendix S2. |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
REFERENCES
- Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990). Basic local alignment search tool. Journal of Molecular Biology, 215, 403–410. https://doi.org/10.1016/S0022-2836(05)80360-2
- Andrews, S. (2010). FastQC: a quality control tool for high throughput sequence data [WWW Document]. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/
- Andruszkiewicz Allan, E., Zhang, W. G., Lavery, C. A., & Govindarajan, A. F. (2021). Environmental DNA shedding and decay rates from diverse animal forms and thermal regimes. Environmental DNA, 3, 492–514. https://doi.org/10.1002/edn3.141
10.1002/edn3.141 Google Scholar
- Andruszkiewicz, E. A., Starks, H. A., Chavez, F. P., Sassoubre, L. M., Block, B. A., & Boehm, A. B. (2017). Biomonitoring of marine vertebrates in Monterey Bay using eDNA metabarcoding. PLoS One, 12, e0176343. https://doi.org/10.1371/journal.pone.0176343
- Ashley, M. C., Mangi, S. C., & Rodwell, L. D. (2014). The potential of offshore windfarms to act as marine protected areas—A systematic review of current evidence. Marine Policy, 45, 301–309. https://doi.org/10.1016/j.marpol.2013.09.002
- Beng, K. C., & Corlett, R. T. (2020). Applications of environmental DNA (eDNA) in ecology and conservation: Opportunities, challenges and prospects. Biodiversity and Conservation, 29, 2089–2121. https://doi.org/10.1007/s10531-020-01980-0
- Bohmann, K., Evans, A., Gilbert, M. T. P., Carvalho, G. R., Creer, S., Knapp, M., Yu, D. W., & de Bruyn, M. (2014). Environmental DNA for wildlife biology and biodiversity monitoring. Trends in Ecology & Evolution, 29, 358–367. https://doi.org/10.1016/j.tree.2014.04.003
- Buyse, J., Hostens, K., Degraer, S., & de Backer, A. (2022). Offshore wind farms affect the spatial distribution pattern of plaice Pleuronectes platessa at both the turbine and wind farm scale. ICES Journal of Marine Science, 79, 1777–1786. https://doi.org/10.1093/icesjms/fsac107
- Bylemans, J., Gleeson, D. M., Hardy, C. M., & Furlan, E. (2018). Toward an ecoregion scale evaluation of eDNA metabarcoding primers: A case study for the freshwater fish biodiversity of the Murray-Darling Basin (Australia). Ecology and Evolution, 8, 8697–8712. https://doi.org/10.1002/ece3.4387
- Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A., & Holmes, S. P. (2016). DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods, 13, 581–583. https://doi.org/10.1038/nmeth.3869
- Chen, H. (2022). VennDiagram: Generate high-resolution Venn and Euler Plots.
- Coates, D., Vanaverbeke, J., Rabaut, M., & Vincx, M. (2011). Soft-sediment macrobenthos around offshore wind turbines in the Belgian part of the North Sea reveals a clear shift in species composition. In S. Degraer, R. Brabant, & B. Rumes (Eds.), Offshore wind farms in the Belgian part of the North Sea: Selected findings from the baseline and targeted monitoring (pp. 47–63). Royal Belgian Institute of Natural Sciences: Management Unit of the North Sea Mathematical Models.
- Collins, R. A., Bakker, J., Wangensteen, O. S., Soto, A. Z., Corrigan, L., Sims, D. W., Genner, M. J., & Mariani, S. (2019). Non-specific amplification compromises environmental DNA metabarcoding with COI. Methods in Ecology and Evolution, 10, 1985–2001. https://doi.org/10.1111/2041-210X.13276
- Collins, R. A., Trauzzi, G., Maltby, K. M., Gibson, T. I., Ratcliffe, F. C., Hallam, J., Rainbird, S., Maclaine, J., Henderson, P. A., Sims, D. W., Mariani, S., & Genner, M. J. (2021). Meta-Fish-Lib: A generalised, dynamic DNA reference library pipeline for metabarcoding of fishes. Journal of Fish Biology, 99, 1446–1454. https://doi.org/10.1111/jfb.14852
- Crane, L. C., Goldstein, J. S., Thomas, D. W., Rexroth, K. S., & Watts, A. W. (2021). Effects of life stage on eDNA detection of the invasive European green crab (Carcinus maenas) in estuarine systems. Ecological Indicators, 124, 107412. https://doi.org/10.1016/j.ecolind.2021.107412
- De Backer, A., Buyse, J., & Hostens, K. (2020). A decade of soft sediment epibenthos and fish monitoring at the Belgian offshore wind farm area. In S. Degraer, R. Brabant, B. Rumes, & L. Vigin (Eds.), Environmental impacts of offshore wind farms in the Belgian part of the North Sea: Empirical evidence inspiring priority monitoring, research and management. Memoirs on the Marine Environment (pp. 79–113). Royal Belgian Institute of Natural Sciences, OD Natural Environment, Marine Ecology and Management.
- De Backer, A., Van Hoey, G., Wittoeck, J., & Hostens, K. (2022). Describing epibenthos and demersal fish communities in the Belgian part of the North Sea in view of future offshore wind farm monitoring. In S. Degraer, R. Brabant, B. Rumes, & L. Vigin (Eds.), 2022. Environmental impacts of offshore wind farms in the Belgian part of the North Sea: Getting ready for offshore wind farm expansion in the North Sea. Memoirs on the Marine Environment (pp. 19–36). Royal Belgian Institute of Natural Sciences, OD Natural Environment, Marine Ecology and Management.
- De Cáceres, M., Legendre, P., & Moretti, M. (2010). Improving indicator species analysis by combining groups of sites. Oikos, 119, 1674–1684. https://doi.org/10.1111/j.1600-0706.2010.18334.x
- S. Degraer, R. Brabant, B. Rumes, & L. Vigin (Eds.). (2022). Environmental impacts of offshore wind farms in the Belgian part of the North Sea: Getting ready for offshore wind farm expansion in the North Sea. Memoirs on the Marine Environment (pp. 19–36). Royal Belgian Institute of Natural Sciences, OD NaturaGleasonl Environment, Marine Ecology and Management.
- Degraer, S., Carey, D. A., Coolen, J. W. P., Hutchison, Z. L., Kerckhof, F., Rumes, B., & Vanaverbeke, J. (2020). Offshore wind farm artificial reefs affect ecosystem structure and functioning: A synthesis. Oceanography, 33(4), 48–57.
- Derycke, S., Cornelis, I., Doorenspleet, K., Hablützel, P., Heyndrickx, H., Nijland, R., Uhlir, C., & Backer, D. (2023). eDNA-based monitoring of the marine environment. https://www.geans.eu/sites/geans.eu/files/managed/WP7_pilot%20report_Geans_report4.pdf
- Derycke, S., Maes, S., Van den Bulcke, L., Vanhollebeke, J., Wittoeck, J., Hillewaert, H., Ampe, B., Haegeman, A., Hostens, K., & De Backer, A. (2021). Detection of macrobenthos species with metabarcoding is consistent in bulk DNA but dependent on body size and sclerotization in eDNA from the ethanol preservative. Frontiers in Marine Science, 8, 637858.
- Elbrecht, V., Vamos, E. E., Meissner, K., Aroviita, J., & Leese, F. (2017). Assessing strengths and weaknesses of DNA metabarcoding-based macroinvertebrate identification for routine stream monitoring. Methods in Ecology and Evolution, 8, 1265–1275. https://doi.org/10.1111/2041-210X.12789
- European Commission. (2020). Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions: An EU Strategy to harness the potential of offshore renewable energy for a climate neutral future. COM(2020) 741.
- Fraija-Fernández, N., Bouquieaux, M.-C., Rey, A., Mendibil, I., Cotano, U., Irigoien, X., Santos, M., & Rodríguez-Ezpeleta, N. (2020). Marine water environmental DNA metabarcoding provides a comprehensive fish diversity assessment and reveals spatial patterns in a large oceanic area. Ecology and Evolution, 10, 7560–7584. https://doi.org/10.1002/ece3.6482
- Gehri, R. R., Larson, W. A., Gruenthal, K., Sard, N. M., & Shi, Y. (2021). eDNA metabarcoding outperforms traditional fisheries sampling and reveals fine-scale heterogeneity in a temperate freshwater lake. Environmental DNA, 3, 912–929. https://doi.org/10.1002/edn3.197
10.1002/edn3.197 Google Scholar
- Gleason, J. E., Elbrecht, V., Braukmann, T. W. A., Hanner, R. H., & Cottenie, K. (2021). Assessment of stream macroinvertebrate communities with eDNA is not congruent with tissue-based metabarcoding. Molecular Ecology, 30, 3239–3251. https://doi.org/10.1111/mec.15597
- Gold, Z., Choi, E. S., Kacev, D., Frable, B. W., Burton, R., Goodwin, K., Thompson, A., & Barber, P. (2020). FishCARD: Fish 12S California current specific reference database for enhanced metabarcoding efforts. https://doi.org/10.22541/au.159136805.55528691
10.22541/au.159136805.55528691 Google Scholar
- Gold, Z., Curd, E. E., Goodwin, K. D., Choi, E. S., Frable, B. W., Thompson, A. R., Walker, H. J., Jr., Burton, R. S., Kacev, D., Martz, L. D., & Barber, P. H. (2021). Improving metabarcoding taxonomic assignment: A case study of fishes in a large marine ecosystem. Molecular Ecology Resources, 21, 2546–2564. https://doi.org/10.1111/1755-0998.13450
- Gold, Z., Sprague, J., Kushner, D. J., Marin, E. Z., & Barber, P. H. (2021). eDNA metabarcoding as a biomonitoring tool for marine protected areas. PLoS One, 16, e0238557. https://doi.org/10.1371/journal.pone.0238557
- Guri, G., Westgaard, J.-I., Yoccoz, N., Wangensteen, O. S., Præbel, K., Ray, J. L., Kelly, R. P., Shelton, A. O., Hanebrekke, T., & Johansen, E. T. (2023). Maximizing sampling efficiency to detect differences in fish community composition using environmental dna metabarcoding in subarctic fjords. Environmental DNA, 6, e409. https://doi.org/10.1002/edn3.409
10.1002/edn3.409 Google Scholar
- Handley, S. J., Willis, T. J., Cole, R. G., Bradley, A., Cairney, D. J., Brown, S. N., & Carter, M. E. (2014). The importance of benchmarking habitat structure and composition for understanding the extent of fishing impacts in soft sediment ecosystems. Journal of Sea Research, 86, 58–68. https://doi.org/10.1016/j.seares.2013.11.005
- Hestetun, J. T., Ray, J. L., Murvoll, K. M., Kjølhamar, A., & Dahlgren, T. G. (2023). Environmental DNA reveals spatial patterns of fish and plankton diversity at a floating offshore wind farm. Environmental DNA, 5(6), 1289–1306. https://doi.org/10.1002/edn3.450
- Hinz, S., Coston-Guarini, J., Marnane, M., & Guarini, J.-M. (2022). Evaluating eDNA for use within marine environmental impact assessments. Journal of Marine Science and Engineering, 10, 375. https://doi.org/10.3390/jmse10030375
- Holman, L. E., Chng, Y., & Rius, M. (2022). How does eDNA decay affect metabarcoding experiments? Environ. DNA, 4, 108–116. https://doi.org/10.1002/edn3.201
- Jeunen, G.-J., Knapp, M., Spencer, H. G., Lamare, M. D., Taylor, H. R., Stat, M., Bunce, M., & Gemmell, N. J. (2019). Environmental DNA (eDNA) metabarcoding reveals strong discrimination among diverse marine habitats connected by water movement. Molecular Ecology Resources, 19, 426–438. https://doi.org/10.1111/1755-0998.12982
- Kelly, R. P., Shelton, A. O., & Gallego, R. (2019). Understanding PCR processes to draw meaningful conclusions from environmental DNA studies. Scientific Reports, 9(1), 12133. https://doi.org/10.1038/s41598-019-48546-x
- Kerckhof, F., & Houziaux, J.-S. (2003). Biodiversity of the Belgian marine areas. Fauna Van Belg. Faune Belg.
- Larson, W. A., Barry, P., Dokai, W., Maselko, J., Olson, J., & Baetscher, D. (2022). Leveraging EDNA metabarcoding to characterize nearshore fish communities in Southeast Alaska: Do habitat and tide matter? Environmental DNA, 4(4), 868–880. https://doi.org/10.1002/edn3.297
- Legendre, P., & Gallagher, E. D. (2001). Ecologically meaningful transformations for ordination of species data. Oecologia, 129, 271–280. https://doi.org/10.1007/s004420100716
- Leray, M., Knowlton, N., & Machida, R. J. (2022). MIDORI2: A collection of quality controlled, preformatted, and regularly updated reference databases for taxonomic assignment of eukaryotic mitochondrial sequences. Environmental DNA, 4, 894–907. https://doi.org/10.1002/edn3.303
- Leray, M., Yang, J. Y., Meyer, C. P., Mills, S. C., Agudelo, N., Ranwez, V., Boehm, J. T., & Machida, R. J. (2013). A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: Application for characterizing coral reef fish gut contents. Frontiers in Zoology, 10, 34. https://doi.org/10.1186/1742-9994-10-34
- Lindeboom, H. J., Kouwenhoven, H. J., Bergman, M. J. N., Bouma, S., Brasseur, S., Daan, R., Fijn, R. C., de Haan, D., Dirksen, S., van Hal, R., Lambers, R. H. R., ter Hofstede, R., Krijgsveld, K. L., Leopold, M., & Scheidat, M. (2011). Short-term ecological effects of an offshore wind farm in the Dutch coastal zone; a compilation. Environmental Research Letters, 6, 035101. https://doi.org/10.1088/1748-9326/6/3/035101
- Liu, Z., Collins, R. A., Baillie, C., Rainbird, S., Brittain, R., Griffiths, A. M., Sims, D. W., Mariani, S., & Genner, M. J. (2022). Environmental DNA captures elasmobranch diversity in a temperate marine ecosystem. Environmental DNA, 4(5), 1024–1038. https://doi.org/10.1002/edn3.294
- Martin, M. (2011). Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.Journal, 17, 10–12. https://doi.org/10.14806/ej.17.1.200
- Martinez Arbizu, P. (2020). pairwiseAdonis: Pairwise multilevel comparison using adonis. R package version 0.4.
- Massart, S., Adams, I., Al Rwahnih, M., Baeyen, S., Bilodeau, G. J., Blouin, A. G., Boonham, N., Candresse, T., Chandellier, A., De Jonghe, K., Fox, A., Gaafar, Y. Z. A., Gentit, P., Haegeman, A., Ho, W., Hurtado-Gonzales, O., Jonkers, W., Kreuze, J., Kutjnak, D., … Lebas, B. S. M. (2022). Guidelines for the reliable use of high throughput sequencing technologies to detect plant pathogens and pests. Peer Community Journal, 2, e62. https://doi.org/10.24072/pcjournal.181
10.24072/pcjournal.181 Google Scholar
- McKnight, D. T., Huerlimann, R., Bower, D. S., Schwarzkopf, L., Alford, R. A., & Zenger, K. R. (2019). microDecon: A highly accurate read-subtraction tool for the post-sequencing removal of contamination in metabarcoding studies. Environmental DNA, 1, 14–25. https://doi.org/10.1002/edn3.11
10.1002/edn3.11 Google Scholar
- McMurdie, P. J., & Holmes, S. (2013). phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One, 8, e61217. https://doi.org/10.1371/journal.pone.0061217
- Mikryukov, V. (2018). vmikk/metagMisc: v.0.0.4. metagMisc: miscellaneous functions for metagenomic analysis. R package version 0.0.4. https://doi.org/10.5281/zenodo.1172500
10.5281/zenodo.1172500 Google Scholar
- Minamoto, T., Miya, M., Sado, T., Seino, S., Doi, H., Kondoh, M., Nakamura, K., Takahara, T., Yamamoto, S., Yamanaka, H., Araki, H., Iwasaki, W., Kasai, A., Masuda, R., & Uchii, K. (2021). An illustrated manual for environmental DNA research: Water sampling guidelines and experimental protocols. Environmental DNA, 3, 8–13. https://doi.org/10.1002/edn3.121
- Miya, M., Gotoh, R. O., & Sado, T. (2020). MiFish metabarcoding: A high-throughput approach for simultaneous detection of multiple fish species from environmental DNA and other samples. Fisheries Science, 86, 939–970. https://doi.org/10.1007/s12562-020-01461-x
- Miya, M., Sato, Y., Fukunaga, T., Sado, T., Poulsen, J. Y., Sato, K., Minamoto, T., Yamamoto, S., Yamanaka, H., Araki, H., Kondoh, M., & Iwasaki, W. (2015). MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: Detection of more than 230 subtropical marine species. Royal Society Open Science, 2, 150088. https://doi.org/10.1098/rsos.150088
- Muri, D., Cristina, L. L., Handley, C. W., Bean, M. B., Harper, L. R., James, B., Li, J., Winfield, I. J., & Hänfling, B. (2023). Spatio-temporal monitoring of lake fish spawning activity using environmental DNA metabarcoding. Environmental DNA, 5(5), 849–860. https://doi.org/10.1002/edn3.343
10.1002/edn3.343 Google Scholar
- Murtagh, F., & Legendre, P. (2014). Ward's hierarchical agglomerative clustering method: Which algorithms implement Ward's criterion? Journal of Classification, 31, 274–295. https://doi.org/10.1007/s00357-014-9161-z
- Oksanen, J., Kindt, R., & Legendre, P. (2007). The Vegan package: Community ecology package 2007.
- Port, J. A., O'Donnell, J. L., Romero-Maraccini, O. C., Leary, P. R., Litvin, S. Y., Nickols, K. J., Yamahara, K. M., & Kelly, R. P. (2016). Assessing vertebrate biodiversity in a kelp forest ecosystem using environmental DNA. Molecular Ecology, 25, 527–541. https://doi.org/10.1111/mec.13481
- R Core Team. (2014). R: The R Project for Statistical Computing [WWW Document]. https://www.r-project.org/
- Raoux, A., Tecchio, S., Pezy, J.-P., Lassalle, G., Degraer, S., Wilhelmsson, D., Cachera, M., Ernande, B., Le Guen, C., Haraldsson, M., Grangeré, K., Le Loch, F., Dauvin, J.-C., & Niquil, N. (2017). Benthic and fish aggregation inside an offshore wind farm: Which effects on the trophic web functioning? Ecological Indicators, 72, 33–46. https://doi.org/10.1016/j.ecolind.2016.07.037
- Roger, A. J., Muñoz-Gómez, S. A., & Kamikawa, R. (2017). The origin and diversification of mitochondria. Current Biology, 27, R1177–R1192. https://doi.org/10.1016/j.cub.2017.09.015
- Russo, T., Maiello, G., Talarico, L., Baillie, C., Colosimo, G., D'Andrea, L., Di Maio, F., Fiorentino, F., Franceschini, S., Garofalo, G., Scannella, D., Cataudella, S., & Mariani, S. (2021). All is fish that comes to the net: Metabarcoding for rapid fisheries catch assessment. Ecological Applications, 31, e02273. https://doi.org/10.1002/eap.2273
- Schenekar, T., Schletterer, M., Lecaudey, L. A., & Weiss, S. J. (2020). Reference databases, primer choice, and assay sensitivity for environmental metabarcoding: Lessons learnt from a re-evaluation of an eDNA fish assessment in the Volga headwaters. River Research and Applications, 36, 1004–1013. https://doi.org/10.1002/rra.3610
- Siddall, M. E., Fontanella, F. M., Watson, S. C., Kvist, S., & Erséus, C. (2009). Barcoding bamboozled by bacteria: Convergence to metazoan mitochondrial primer targets by marine microbes. Systematic Biology, 58, 445–451. https://doi.org/10.1093/sysbio/syp033
- Sigsgaard, E. E., Nielsen, I. B., Carl, H., Krag, M. A., Knudsen, S. W., Xing, Y., Holm-Hansen, T. H., Møller, P. R., & Thomsen, P. F. (2017). Seawater environmental DNA reflects seasonality of a coastal fish community. Marine Biology, 6, 1–15. https://doi.org/10.1007/s00227-017-3147-4
10.1007/s00227?017?3147?4 Google Scholar
- Stat, M., John, J., DiBattista, J. D., Newman, S. J., Bunce, M., & Harvey, E. S. (2019). Combined use of eDNA metabarcoding and video surveillance for the assessment of fish biodiversity. Conservation Biology, 33, 196–205. https://doi.org/10.1111/cobi.13183
- Stoufer, S., Demokritou, M., Buckley, D., Teska, P., & Moore, M. D. (2023). Evaluation of the ability of commercial disinfectants to degrade free nucleic acid commonly targeted using molecular diagnostics. The Journal of Hospital Infection, 133, 28–37. https://doi.org/10.1016/j.jhin.2022.12.010
- Suter, L., Polanowski, A. M., Clarke, L. J., Kitchener, J. A., & Deagle, B. E. (2021). Capturing open ocean biodiversity: Comparing environmental DNA metabarcoding to the continuous plankton recorder. Molecular Ecology, 30, 3140–3157. https://doi.org/10.1111/mec.15587
- Tagliabue, A., Matterson, K. O., Ponti, M., Turicchia, E., Abbiati, M., & Costantini, F. (2023). Sediment and bottom water eDNA metabarcoding to support coastal management. Ocean and Coastal Management, 244, 106785. https://doi.org/10.1016/j.ocecoaman.2023.106785
- Valdivia-Carrillo, T., Rocha-Olivares, A., Reyes-Bonilla, H., Domínguez-Contreras, J. F., & Munguia-Vega, A. (2021). Integrating eDNA metabarcoding and simultaneous underwater visual surveys to describe complex fish communities in a marine biodiversity hotspot. Molecular Ecology Resources, 21, 1558–1574. https://doi.org/10.1111/1755-0998.13375
- van Bleijswijk, J. D. L., Engelmann, J. C., Klunder, L., Witte, H. J., Witte, J. I. J., & van der Veer, H. W. (2020). Analysis of a coastal North Sea fish community: Comparison of aquatic environmental DNA concentrations to fish catches. Environmental DNA, 2, 429–445. https://doi.org/10.1002/edn3.67
10.1002/edn3.67 Google Scholar
- van Denderen, P. D., Hintzen, N. T., van Kooten, T., & Rijnsdorp, A. D. (2015). Temporal aggregation of bottom trawling and its implication for the impact on the benthic ecosystem. ICES Journal of Marine Science, 72, 952–961. https://doi.org/10.1093/icesjms/fsu183
- Vandendriessche, S., Derweduwen, J., & Hostens, K. (2015). Equivocal effects of offshore wind farms in Belgium on soft substrate epibenthos and fish assemblages. Hydrobiologia, 756, 19–35.
- Wang, Q., Garrity, G. M., Tiedje, J. M., & Cole, J. R. (2007). Naïve Bayesian Classifier for rapid assignment of RRNA sequences into the new bacterial taxonomy. Applied and Environmental Microbiology, 73(16), 5261–5267. https://doi.org/10.1128/AEM.00062-07
- Wickham, H. (2016). ggplot2: Elegant graphics for data analysis.
- Willassen, E., Westgaard, J.-I., Kongsrud, J. A., Hanebrekke, T., Buhl-Mortensen, P., & Holte, B. (2022). Benthic invertebrates in Svalbard fjords—When metabarcoding does not outperform traditional biodiversity assessment. PeerJ, 10, e14321. https://doi.org/10.7717/peerj.14321
- WindEurope. 2022. ‘ Wind energy in Europe: 2022 Statistics and the outlook for 2023–2027’. WindEurope. 2022. https://windeurope.org/intelligence-platform/product/wind-energy-in-europe-2022-statistics-and-the-outlook-for-2023-2027/
- Zhang, S., Zhao, J., & Yao, M. (2020). A comprehensive and comparative evaluation of primers for metabarcoding eDNA from fish. Methods in Ecology and Evolution, 11, 1609–1625. https://doi.org/10.1111/2041-210X.13485