Bio-colonisation affects the ageing of materials and the behaviour of offshore structures. Mooring systems and umbilicals belong to the family of slender bodies which are components sensitive to bio-colonisation because of a change of dynamic behaviour due to shape, roughness and mass modifications. However, this stochastic process in time and space is hard to predict. The purpose is then twofold: first, to provide a stochastic spatial model of the bio-colonisation on a mooring line; second, to show that in some defined environmental conditions, such as low wave height, low wind and current velocities, the monitoring of mooring lines tension can help to assess and reduce uncertainty on this model. Therefore, a comprehensive stochastic modelling based on mussels colonisation was carried out using on-site videotapes, experimental campaigns and expert knowledge. We studied the efficiency of a virtual sensing network using this model and a conditional entropy metric. It is first shown that the spatial model fits well with experimental data, and second that a denser medium accuracy sensor network is to be preferred to a single high accuracy fairlead sensor to reduce the uncertainty on the model parameters. It is then worth updating bio-colonisation on mooring lines during the life-time of a floating wind turbine.