Abstract
The growing number of offshore wind farms being constructed in the North Sea raises important questions about their potential ecological impact on marine communities. Monitoring these effects requires tools that can accurately describe biodiversity, not just in terms of species presence, but also their relative abundance. Environmental DNA analysis is a promising approach for this purpose, but the quantitative application of this method remains challenging due to technical biases introduced during DNA amplification.
In this study, we estimated the amplification efficiencies of 16 commonly found fish species in the Danish North Sea and applied a statistical model to correct for these biases. Water samples were collected at various distances from the Horns Rev 2 offshore wind farm, from both hard (scour protection) and soft sediment habitats. Community differences were analysed using multivariate statistics.
Using the model to correct the metabarcoding reads improved the explanatory power of the analyses and clarified group differences. For example, herring and other clupeid species exhibited higher amplification efficiencies, whereas cod and wrasses showed lower efficiencies. Community composition differed between the interior and exterior of the wind farm, as well as between different types of substrates. Notably, sandeel occurrence increased near the turbines, while sand goby detection decreased. Despite being a native species, Atlantic cod were not detected in large numbers.
This study demonstrates that using a model to correct data improves its ecological value. This approach can improve biodiversity assessments at offshore wind farms, and it has potential applications in conservation monitoring and fisheries management. However, further development is required, including more extensive testing, to improve precision and expand its use in marine environments.