New insights in interpolating fishing tracks from VMS data for different métiers

The Vessel Monitoring by satellite System (VMS) is a powerful tool in fishery management, since it allows for high resolution analyses of fishing activity and quantitative evaluations of fishing effort at both spatial and temporal scale. Given that the main VMS limit is represented by the temporal resolution (generally 2 h) of signals, a series of approach has been developed to interpolate vessels positions. The newest and most powerful method in this framework is based on cubic Hermite splines (cHs), which have been efficiently tested against the conventional straight line interpolation over a dataset representing fishing activity by beam trawl. However, this method has never been applied on other different gears and/or métiers. Here we propose a new approach (CRm), which is a modification of the Catmull–Rom algorithm (CR). This new method takes into account for the different aspects involved in vessel navigation, such as the combined actions of human control and drift by sea current and wind (if present). The drift component is not observed, but is estimated within the method, using the VMS data. This method has been developed in order to model the behaviour of vessels that operate using different gear types. The CRm method was compared to the cHs method, using three reference datasets (each containing VMS signals at intervals of 20 min) corresponding to three different métiers largely used in Mediterranean fisheries: bottom otter trawl for demersal species (OTB), trammel nets for demersal species (GTR), and purse seine for small pelagic fish (PS), which differ each other for the dynamic aspects connected to the use of fishing gears, and represent an archetype of the three groups actually used to classify fishing gears (namely towed, active and passive). The comparison was carried out both analyzing the error affecting interpolation of single tracks and converting the interpolated tracks into gridded data to be used for computation of ecological indicators of fishing pressure. All the results coherently evidences that the CRm algorithm performs better in interpolating trawl tracks (OTB) and that, moreover, it is able to capture the complex behaviour characterizing the trajectories of vessels performing the other two inspected métiers (GTR and PS). Finally, CRm allows a better estimation of fishing effort, as measured by ecological indicators. These findings support the idea that the conceptual formulation of CRm method is appropriate to model whatever fi