Metagenomic biodiversity assessment within an offshore wind farm
Abstract Environmental DNA (eDNA) analysis can be a powerful tool for monitoring biodiversity and assessing human impacts on ecosystems. In this study, we employed a genome-wide metagenomic eDNA approach to assess the marine biodiversity within and around the Horns Rev 1 offshore wind farm in the Da...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-01541-x |
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| Summary: | Abstract Environmental DNA (eDNA) analysis can be a powerful tool for monitoring biodiversity and assessing human impacts on ecosystems. In this study, we employed a genome-wide metagenomic eDNA approach to assess the marine biodiversity within and around the Horns Rev 1 offshore wind farm in the Danish North Sea. Seawater samples were collected from both within the windfarm and surrounding control sites, sequenced, and analyzed using a combination of DNA k-mer matching and alignment-based classification methods. We identified a wide range of species across the tree of life—highlighting the species richness of this marine ecosystem. Our results revealed a high degree of species diversity congruence between the wind farm and control sites. While this could suggest minimal ecological disruption of the wind farm, we cannot rule out that the influence of ocean currents and water mixing the DNA from different regions dominate the species detection. We detected bioindicator species, such as Thalassiosira, Phaeocystis and Skeletonema, which can provide insights into water quality. Our metagenomic approach also enabled us to obtain population genomics insights for species, such as the European anchovy (Engraulis encrasicolus) and the diatom Rhizosolenia setigera, and genetically confirmed the origin of the invasive Sea walnut (Mnemiopsis leidyi) in the North Sea. This study highlights the potential of genome-wide eDNA metagenomics as a framework for assessing marine biodiversity and detecting population-level genetic signals, contributing to informed and scalable ecosystem monitoring strategies. |
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| ISSN: | 2045-2322 |