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Worldwide reduction of carbon emissions is needed to help reduce the effects of climate change. Twenty-seven member states of the European Union have committed to reduce emissions by 55% of 1990 levels by 20301. To achieve this, an unprecedented installation of offshore marine renewable energy devices (wind, wave, tidal, solar) and cable networks is required2. To date, offshore wind energy is the largest marine renewable energy provider, currently producing globally 35 GW with an increase to 70 GW expected by 20253 and a potential increase worldwide to 1000 GW expected by 20504. Europe has the majority of offshore wind farms (OWFs) with a capacity of 28 GW5, which corresponds to 5,795 grid-connected wind turbines across 123 OWFs and 12 countries5. Marine biodiversity and their associated ecosystems are increasingly being affected by anthropogenic pressures, such as the growing number of artificial structures6,7, eutrophication, fisheries and climate change8–10. The introduction of man-made structures can potentially have both positive and negative effects on marine ecosystems11– 14. Soft-bottom communities are altered close to artificial structures15–17, while a significant amount of marine growth colonises the artificial hard structures18,19. To assess the effects of man-made structures on the benthic community, most environmental impact assessment data collection studies have been conducted over small spatial and temporal scales20 such as single turbines or single OWFs and associated infrastructure15,21,22. Some countries have coordinated programmes to standardise data collection methods on soft sediments (e.g., Germany23, Belgium24, the Baltic Sea25), and there are existing methods to study macrofauna on natural hard substrates such as rocky bottoms26. However, there are no internationally agreed methods, metrics or databases for the data collection, which is critical for understanding the effects of artificial structures on marine ecosystems. Data are disparate owing to differences in data diversity, regarding (i) sampling devices and methods, (ii) sample analysis (e.g., variables, taxonomic resolution), (iii) data storage and management, as well as (iv) continuously changing taxonomy. This results in a lack of consistent data with regards to offshore artificial structures and benthos. Thus, investigation of large-scale benthic effects requires merging data from different sources, which is challenging (time consuming, costly, difficult) or even not possible19. Taken together, the available data are underutilised. A few attempts have been made to collect and analyse biodiversity data from different substrates (wind turbines, oil and gas platforms, surrounding soft sediments and rocky reefs) in a single region19,27,28. Ecosystem-based management requires a deep understanding of the effects of artificial structures over large spatial and temporal scales that exceed budgets, timeframes and jurisdictional borders. Data sharing through the creation of an integrated database can provide multiple benefits for science, industry, and policy. It could be used for large-scale research studies examining the aforementioned effects and facilitate ecosystem-based management. Furthermore, the creation of a centralised dataset could enable answering scientific questions regarding stepping stone effects beyond the scale of individual OWFs, platforms or countries29,30. Industry could exploit this dataset for environment-friendly planning, predicting effects of new activities at offshore locations. Finally, sharing such data is crucial in developing fact-based scientific advice for decommissioning decisions for various stakeholders. This paper presents the first data collection ‘Biodiversity Information of benthic Species at ARtificial structures’ (BISAR). BISAR contains data on benthic macrofauna collected in environmental impact studies, scientific projects and species inventories conducted at 17 artificial offshore structures in the North Sea between 2003 and 2019. The structures include OWFs, oil and gas platforms, a research platform and a geogenic reef to compare natural and artificial reef communities. BISAR includes data from soft and hard substrate studies (34 artificial structures), allowing comparisons of changes in both habitat types. This data collection currently contains data from a total of 3864 samples with 890 taxa. BISAR is the first data product containing harmonised and quality-checked international data on benthos from substrates influenced by artificial structures in the North Sea. Various stakeholders (e.g., industry, public authorities, research) will profit from the BISAR data collection as the greatest challenge in an era of blue growth is to get access to data from various sources

Given the global offshore wind farm (OWF) proliferation, we investigated the impact of OWFs on the marine food web. Using linear inverse modelling (LIM), we compared theOWF food web with two softsediment food webs nearby. Novel in situ data on species biomass and their isotopic composition were combined with literature data to construct food webs.Our findings highlight the prominent role of hard-substrate species on turbine foundations as organic material inputs for the food web. Hard substrate species account for approximately 26% of food source uptake from the water column and increase carbon deposition on the surrounding seafloor by ~10%. OWFs facilitate a novel food web with a higher productivity than expected based on standing biomass alone, as a result of numerous interactions between a diverse species community. Our study underscores profound effects of OWFs on marine ecosystems, suggesting the need for further research into their ecological impacts.

Motivation: Here, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database. Main Types of Variables Included: The database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years. Spatial Location and Grain: Sampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size. Time Period and Grain: The earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample-level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric. Major Taxa and Level of Measurement: The database includes any eukaryotic taxa, with a combined total of 56,400 taxa. Software Format: csv and. SQL.

The development of offshore wind farms (OWFs) in the North Sea is a crucial component for the transition to renewable energy. However, local hydrodynamics in the vicinity of OWF turbine foundations may be affected due to their interaction with tidal currents. This study investigates the impact of offshore wind turbine foundations on local hydrodynamics and stratification in the southern North Sea. We conducted a series of measurements around a single monopile in the Belgian part of the North Sea, focusing on hydrodynamics, salinity and temperature both near the surface and over the water column, and turbulent kinetic energy (TKE). Our results indicate that the foundation-induced wake significantly affects local hydrodynamics, leading to a well-defined band of colder, more saline water at the surface and warmer, less saline water near the seabed. This is quantified through the Potential Energy Anomaly (PEA), which shows a marked decrease in the wake-affected area. The wake is spatially confined, with a width of approximately 70 meters and a length of less than 400 meters downstream of the monopile. Additionally, our measurements reveal an increase in TKE within the wake, indicating enhanced turbulent mixing. This mixing reduces vertical gradients in salinity and temperature, leading to a more homogeneous water column. The findings highlight the importance of considering monopile-induced mixing in large-scale hydrodynamic and ecosystem models, as these effects can influence nutrient transport, primary production, and overall ecosystem dynamics. Furthermore, our research provides valuable data for validating and improving the models used to predict the ecological impact of OWFs.

In recent years, research into the occurrence of bats at the Dutch North Sea has shown that there is regular seasonal migration over sea. However, so far, little is known about their migration ecology, the fatality risks at offshore wind turbines, and the number of individuals migrating over sea. Since the Dutch government wants to boost the further development of wind energy production in the southern North Sea, the Ministry of Economic Affairs commissioned to Rijkswaterstaat a Wind at Sea Ecological Programme (in Dutch: Wozep). This study, as part of the Wozep-project Behaviour and Collision Risk of Bats (Bats_2), investigates how telemetry can be applied to gain insight into migratory movements of bats over land and over sea and individual bat behaviour near and in offshore wind farms. To find out whether it is wise to continue and further develop telemetry research in the context of the Wozep programme, we first identified potential telemetry methods for small bats based on a desk study and selected the most promising method for the application of telemetry. Members of the team attended an international workshop on telemetry in Lund (Sweden) to gather practical technical knowledge, gain insight in data-management standards, and increase their international network. Several field tests were conducted to test the equipment and explore the possibilities of tracking. Finally, suitable locations for bat trapping in bat boxes and for the use of a Heligoland trap were identified. There are several options to track bats with radio telemetry during their migration at the coast and over sea. However, for long-term monitoring of multiple individuals, establishing a grid of stationary receivers is the only feasible option. Eight field tests were carried out to test the performance of the technical infrastructure. Each of the tests was unique and set up to test the signal strength/detection range for a certain type of antenna or a certain transmitter/receiver constellation. We showed that detections over at least 6 km are possible, and likely more than 10 km can be achieved. Precondition is that the receiving stations must be installed at high structures (lighthouses, buildings, masts) or hills, installing them a few meters off ground level will lead to detection ranges just over one km. Furthermore we explored the possibilities of calculating movement tracks with a setup of several receivers using different estimation methods. These experiments indicated that a relatively high accuracy (of c. 100 m) can be reached estimating the location when signals of different receiving stations are combined. It is likely that the accuracy can be improved by estimating the bearings of the received signals based on the signal strength of different antennas. Even further improvement seems possible by assessing the (likely) flight route with a state-space model. There are plenty of locations with bat boxes, especially in the province of Noord-Holland, where potentially hundreds of bats can be captured during migration stopovers, though it is not known how many of these are likely to be migrants. In addition, we identified four locations where actively migrating bats can potentially be captured with an Heligoland trap. In conclusion, we are confident that telemetry can be successfully applied to study migratory movements of bats over land and over sea and individual bat behaviour near and in offshore wind farms. Joining a wildlife tracking system like Motus (Canada) in order to enlarge the data collection, is highly recommended. Motus-members can get detections from both