A model combining landings and VMS data to estimate landings by fishing ground and harbor

2018 
Abstract At present, the assessment and management of Adriatic Sea fishery resources are based on data that do not fully account for the complex spatial patterns arising from fleet behavior and/or species’ behavior and biology, mainly because logbooks do not guarantee adequate coverage of the fishing activity exerted by the fleet. For data collection, the Adriatic Sea is divided into two management areas (namely FAO Geographical Sub-Areas–GSAs). To account for these spatial patterns while using the data available, we propose a method for estimating the monthly landings of Italian trawlers operating in the Adriatic Sea at a higher spatial resolution than the GSA. We use a stepwise approach based on the combined analysis of questionnaire-derived vessel-specific landings and the spatial activity of the vessels with respect to a set of fishing grounds. Thus, we sequentially 1) analyze the available vessel monitoring system data, 2) partition the study area into fishing grounds (the origin of the landings), 3) cross analyze vessel-specific fishing efforts with the available vessel-specific monthly landings to estimate the LPUE of each fishing ground, and 4) estimate the monthly landings (by vessel, fishing ground, and harbor) for the whole fleet and the monthly fluxes between fishing grounds (origin) and landing harbors (the destination of the landings). We apply the method to two species: the Norway lobster and the European hake. For both species, we find a few fishing grounds to be consistently more productive than others and the landings per harbor to vary greatly but with few harbors regularly receiving a significant share. In particular, the results suggest that the Pomo/Jabuka pit area represents a critical area for both species. Additional outcomes include a detailed characterization of the activity of the Adriatic bottom trawling fleet, highlighting the strengths and shortcomings of the official data available. We discuss the results in the context of the current management paradigm.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    37
    References
    16
    Citations
    NaN
    KQI
    []