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Abstract: Internationally there is political momentum to establish networks of marine protected areas for the conservation of threatened species and habitats. Practical implementation of such networks requires an understanding of the distribution of these species and habitats. Predictive modelling provides a method by which continuous distribution maps can be produced from limited sample data. This method is particularly useful in the deep sea where a number of biological communities have been identified as vulnerable ‘habitats’, including Lophelia pertusa reefs. Recent modelling efforts have focused on predicting the distribution of this species. However the species is widely distributed where as reef habitat is not. This study uses Maxent predictive modelling to investigate whether the distribution of the species acts as a suitable proxy for the reef habitat. Models of both species and habitat distribution across Hatton Bank and George Bligh Bank are constructed using multibeam bathymetry, interpreted substrate and geomorphology layers, and derived layers of bathymetric position index (BPI), rugosity, slope and aspect. Species and reef presence records were obtained from video observations. For both models performance is fair to excellent assessed using AUC and additional threshold dependant metrics. 7.17% of the study area is predicted as highly suitable for the species presence while only 0.56% is suitable for reef presence, using the sensitivity–specificity sum maximisation approach to determine the appropriate threshold. Substrate is the most important variable in the both models followed by geomorphology in the RD model and fine scale BPI in the SD model. The difference in the distributions of reef and species suggest that mapping efforts should focus on the habitat rather than the species at fine (100m) scales. [Copyright &y& Elsevier]