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

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 scenarios for simulating the effect and management of benthic impacts from longline fishing effort. Further, we update this paper with a set of simulations using a range of simple and extreme case studies to validate the underlying model and code. In general, the model simulations were carried out under a range of productivity assumptions, impact, and spatial scale, with and without management by areal closures. 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 most cases, 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 relative to scenarios where there was no management action, but that the magnitude of simulated impacts 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 influence estimated impacts or how different assumptions of the underlying distributions of benthic organisms may influence recovery or management effectiveness. However, as the magnitude of these impacts in the scenarios tested were small, we recommend that research be focused, at least in the short term, to provide the observational or experimental data necessary to constrain important model parameters, to reduce uncertainty and provide more plausible scenarios.