Aim: Given the paucity of data on the distribution of habitats and species for most marine species, particularly those that are rare and in need of protection, there is a need to model species distributions. Using the fan mussel, Atrina fragilis (Pennant 1777), as our case species, the aim of the study was to predict new areas of occurrence for A. fragilis, estimate the extent of potentially suitable areas, determine the proportion of these areas that are included in the recently designated nature conservation MPAs off the west of Scotland and identify possible environmental drivers in the distribution of A. fragilis. Location: West coast of Scotland, UK. Methods: Using a point process framework, we modelled presence-only data, including historic records. A quadrat survey employing digital still photography was conducted in areas of high and low suitability to verify the model, and subsequently, a targeted survey was undertaken in areas predicted as highly suitable by the models using towed video cameras. Results: Five environmental variables were of prime importance in explaining the distribution of A. fragilis. The results from the verification survey support model performance. Atrina fragilis was found in 80% of the targeted transects undertaken. Approximately 14% of the total area predicted as suitable for A. fragilis occurred within recently designated marine protected areas (MPAs) indicating considerable potential for recolonization given suitable protection. Main conclusions: The verified model suggests that limited presence-only and historical records of rare species can perform well within a SDM framework allowing the identification of further suitable areas. The prominence of bathymetric ruggedness in the models is unexpected, given the understood ecological niche for A. fragilis and pinnids in general, but is consistent with the fact that seabed topography can offer protection from fishing pressure. These results will inform restoration objectives of the MPA network.
- Benthic habitats
- Ecological niche modelling
- Fishing impacts
- Historic records
- Inhomogeneous point process models
- Rare species
- Species distribution modelling