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    An approach to feedback management (FBM) of the krill fishery based on routine acoustic data collection and intermittent land-based predator studies

    Solicitar acceso a documento de reunión
    Número de documento:
    Delegations of Norway, China and Chile
    Presentado por:
    Dr Olav Rune Godø (Noruega)
    Aprobado por:
    Dr Olav Rune Godø (Noruega)

    The conservation measure regarding (or stipulating) the interim distribution of trigger level in the fishery of Antarctic krill in Subareas 48.1 through 48.4 (CM 51-07) has continuously been renewed due the CCAMLR’s inability to establish an agreed, operational feedback management (FBM) approach. As the trigger level lacks any form of relationship with the actual stock condition this approach is strictly not in line with the CCAMLR ecosystem approach to management. FBM has been considered an alternative approach for decades, but still lacks a plan that can be made operational within realistic cost and effort levels.  Our proposal outlines that acoustic data would be collected, processed and reported continuously during the fishing season as measure of the available prey field. This information can be integrated with finer-scale knowledge of top predator feeding strategies and updated through specific scientific studies at regular (multiyear) intervals. The foundations of the proposal are that acoustic monitoring of the prey field of nearby predator colonies feeds into a decision-making framework that is far less dependent on land-based effort. We consider it important to base decisions on simple, understandable and robust relationships which in turn creates trust by the stakeholders and will enable an efficient implementation of FBM. Initiating the model development and the krill fishing-predator study now will bring the SG-ASAM work and our proposed FBM framework development into a similar timeline. While the proposed framework provides a clear way forward it will take several years to implement fully due to the required development of methodology and time series data.