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Marine protected areas (MPAs) and associated management measures are being implemented to conserve marine benthic species. To make effective decisions, marine managers need data on the distributions of species of interest and anthropogenic pressures, but also the potential connectivity between habitat patches.To explore how predictive modelling can be utilized in such a process, a model was developed to predict the distribution of northern sea fan, Swiftia pallida, habitat around parts of Scotland, Northern Ireland, and Ireland.The predicted distribution of suitable habitat was compared with spatial data on mobile bottom‐contacting fishing activity, and the location of MPAs designated for benthic features, and the management measures within MPAs that restrict mobile bottom‐contacting fishing activity, which is the main pressure‐causing activity that S. pallida are sensitive to.Over 20% of predicted suitable habitat is within MPAs, over 10% is within MPA management measures and over 48% is within areas that have experienced no bottom contacting‐fishing activity, which is significantly higher than the equivalent values for the study region as a whole. However, patches were identified that potentially experience above average levels of fishing activity and remain unmanaged, including within MPAs designated for associated features. The analysis also highlighted patches that could be candidates for monitoring recovery and the locations of unknown populations.For each patch of suitable habitat, the number of other patches within 22 km, a previously published estimate of dispersal distance for S. pallida, was used as a proxy for connectedness. Connectedness was estimated to be greatest for patches towards the centre and west of the study region.The results indicate how the outputs of predictive distribution models can be used in conjunction with other data to prioritize areas for surveys and identify locations where effective management may facilitate conservation or the recovery of benthic species. [ABSTRACT FROM AUTHOR]