The Black Scabbardfish is a deep-water fish species that lives at depths greater than 700 m. In Portugal mainland, this is an important commercial resource which is exploited by longliners that operate at specific fishing grounds located off the coast. The monitoring of the population status mainly relies on the fishery data as no independent scientific surveys take place. The present work focus on modelling the spatial distribution of the BSF species relative biomass. Georeferenced data given by the location of the fishing hauls and the corresponding catches are available for a set of different vessels that belong to the longline fishing fleet. A classical geostatistical approach was applied to fit a variogram and evaluate the isotropy of the data. Then, different regression models with fixed, structured and unstructured random effects were fitted under a Bayesian framework, considering the Stochastic Partial Differential Equation (SPDE) methodology under the Integrated Nested Laplace Approximation (INLA), addressing some practical implementation issues. The models with spatial effects seemed to perform better, although some practical constraints related to the considered covariates hindered the choice.