This study evaluates bottom trawling intensity and its ecological effects in the Fehmarn Belt (southern Baltic Sea) between 2020 and 2024. The region is dominated by small-scale fishing vessels ( < 12 m length) and therefore conventional Vessel Monitoring System (VMS) data are unavailable. We compare changes in seafloor integrity caused by bottom trawling with fishing activity inferred from Automatic Identification System (AIS) tracking. Trawling intensity was quantified using a previously developed trawling index (TI) based on furrow volume derived from multibeam echosounder (MBES) data. A pronounced alteration in seafloor integrity was observed between 2021 and 2022, with furrow volumes decreasing from 5067 m 3 to 1108 m 3 over an area of 3.4 km 2 . This trend was not recorded by AIS-derived fishing activity, which showed only slight changes in vessel track counts. Spatial and temporal fluctuations in abundance, biomass and community structure were revealed through repeated macrofauna sampling. The fluctuations are likely linked to shifting trawling pressure related to the ban of a fishery targeting cod in the Western Baltic. These biological responses did not follow a linear trend but suggest a dynamic equilibrium may be reached over time. Although MBES data cannot replace AIS for tracking fishing effort, it can supplement and improve the AIS information, providing insights into physical impacts and benthic responses in regions of interest. Integrating MBES-derived indicators identifies the spatial extent of the seabed affected by bottom trawling, thereby strengthening ecological monitoring frameworks and supporting sustainable seabed management, including in marine spatial planning. • Trawling intensity from multibeam surveys improves spatial and ecological monitoring. • In Fehmarn Belt, trawl-induced furrows declined fivefold between 2021 and 2022. • Publicly available AIS data fails to reflect trends in bottom trawling activity. • Over five years, macrofauna response fluctuated non-linearly with trawling pressure. • EEA Reference grid and objective method support alignment across data platforms.
Schönke et al. (Mon,) studied this question.