Pasar al contenido principal

    Development of methods for evaluating the management of benthic impacts from longline fishing using spatially explicit production models

    Solicitar acceso a documento de reunión
    Número de documento:
    A. Dunn, S.J. Parker and S. Mormede (New Zealand)
    Presentado por:
    Aprobado por:
    Admin Admin

    An important management objective for CCAMLR in the high seas region of the Antarctic is to develop appropriate methods of monitoring and managing impacts of bottom fisheries on vulnerable marine ecosystems (VMEs). We describe a spatially explicit production model that can be used to investigate a range of simple scenarios for simulating the effect and management of benthic impacts from longline fishing effort. Simulations were carried out under a range of productivity assumptions, impact, and spatial scale, with and without management by areal closures. Further, the management action simulated considered a range of areal closure radii and bycatch trigger thresholds. We conclude that spatially explicit production models can provide a useful tool for the investigation of impacts of fishing effort on benthic organisms. They have the advantage that they are relatively simple to construct, run, and interpret. In general, the results of the simulations suggested that management action of areal closures in the Ross Sea region are likely to result in an improved outcome over scenarios where there was no management action, but that the size of effects under the plausible models was often very small. We also note that further work on these simulations are required — including investigating how changes in the distribution of future fishing may result in alternative impacts or how different assumptions of the underlying distributions of benthic organisms may influence the results. However, as the size of these impacts in the scenarios tested were small, we recommend that research be focused, at least in the short term, to provide observational or experimental data necessary to constrain important model parameters, to reduce uncertainty and provide more plausible scenarios.