Mixed fisheries refer to fishing activities that catch more than one species simultaneously, and a species may be fished using different gear. A trawl fishery shares these features to exploit multiple species simultaneously, with diverse fishing gear and strategies. The situation becomes more complex when interactions among fleet dynamics, fishing activities, and fishery resources are involved and influence each other. Information regarding the operational patterns may be hidden in a set of long-term big data. This study aims to investigate the fishery structure and fleet dynamics of trawl fisheries in Taiwan for spatial planning and management, based on a long-term dataset from a management system that collects information by using voyage data recorders (VDR) and dockside observers. We applied a two-step data mining process with a clustering algorithm to classify the main groups of fishery resources and then identified 18 catch métiers based on catch composition. The target species, operation pattern, and fishing season were determined for each métier, and associated with the relevant fishery resources and the fishing gear used. Additionally, fishing effects on target species were estimated using information on fishing grounds and trajectories from VDR. The métier-based approach was successfully applied to define the six major fishery resources targeted by trawlers. We examined the key features of fishing activity associated with catch composition and spatial-temporal fishing metrics, which could be used to provide suggestions for the spatial planning and management of the mixed trawl fishery in the offshore waters of Taiwan.
Artificial reefs: a double-edged fishery management tool
