|Developing methods for mapping pelagic longline fishing effort using Vessel Monitoring System data (VMS) in the Azores|
Glogovac, B. (2011). Developing methods for mapping pelagic longline fishing effort using Vessel Monitoring System data (VMS) in the Azores. MSc Thesis. University of the Azores: Ponta Delgada. 32 pp.
The need to accurately quantify fishing effort has increased in recent years as fisheries have expanded and many fish stocks and non-target species are threatened with collapse. Vessel monitoring system (VMS) stands for a powerful tool for fisheries control and enforcement that can be also used to provide description of fishing activity, regarding its spatial and temporal distribution. Here we compiled an estimate of fishing effort for to pelagic longline fishery, targeting mainly swordfish and pelagic sharks, in the Azores. Quantification methods vary greatly among fisheries, but methods that are based on information on gear use and spatial distribution offer the best approaches to representing fishing effort on a broad scale. The method for mapping fishing effort was developed. Beside the speed criteria for fishing detection, angle variations and distance per fishing leg were implemented to identify true fishing events. The proposed method processed VMS data, by removing erroneous and duplicated VMS records, identifying fishing trips and distinguishing fishing from non-fishing events, using several fishing indicators. Our estimates were based on observer data and vessel monitoring system (VMS). The Bayesian method will be used to assess the probabilities of successful true fishing detection, with regard to different combinations of values for speed, angle and distance per fishing leg. The fishing effort were estimated in time and space, by the number of lines and by summation of the line lengths. The main outputs of this project were a database of pelagic fishing effort for the international vessels in the Azores and layers with the outcomes of the analyses. This approach can be accessible for similar VMS data sets, thus bringing a new outlook of potential applications.