Decision-making for ecosystem-based management: evaluating options for a krill fishery with an ecosystem dynamics model

Authors

  • G. M. Watters,

    1. Antarctic Ecosystem Research Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, 8901 La Jolla Shores Drive, La Jolla, California 92037-1023 USA
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  • S. L. Hill,

    Corresponding author
    1. British Antarctic Survey, Natural Environment Research Council, High Cross, Madingley Road, Cambridge CB3 0ET United Kingdom
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  • J. T. Hinke,

    1. Antarctic Ecosystem Research Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, 8901 La Jolla Shores Drive, La Jolla, California 92037-1023 USA
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  • J Matthews,

    1. British Antarctic Survey, Natural Environment Research Council, High Cross, Madingley Road, Cambridge CB3 0ET United Kingdom
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  • K Reid

    1. British Antarctic Survey, Natural Environment Research Council, High Cross, Madingley Road, Cambridge CB3 0ET United Kingdom
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    • Current address: CCAMLR Secretariat, P.O. Box 213, Hobart 7000, Tasmania, Australia.


  • Corresponding Editor: T. E. Essington.

Abstract

Decision-makers charged with implementing ecosystem-based management (EBM) rely on scientists to predict the consequences of decisions relating to multiple, potentially conflicting, objectives. Such predictions are inherently uncertain, and this can be a barrier to decision-making. The Convention on the Conservation of Antarctic Marine Living Resources requires managers of Southern Ocean fisheries to sustain the productivity of target stocks, the health and resilience of the ecosystem, and the performance of the fisheries themselves. The managers of the Antarctic krill fishery in the Scotia Sea and southern Drake Passage have requested advice on candidate management measures consisting of a regional catch limit and options for subdividing this among smaller areas. We developed a spatially resolved model that simulates krill–predator–fishery interactions and reproduces a plausible representation of past dynamics. We worked with experts and stakeholders to identify (1) key uncertainties affecting our ability to predict ecosystem state; (2) illustrative reference points that represent the management objectives; and (3) a clear and simple way of conveying our results to decision-makers. We developed four scenarios that bracket the key uncertainties and evaluated candidate management measures in each of these scenarios using multiple stochastic simulations. The model emphasizes uncertainty and simulates multiple ecosystem components relating to diverse objectives. We summarize the potentially complex results as estimates of the risk that each illustrative objective will not be achieved (i.e., of the state being outside the range specified by the reference point). This approach allows direct comparisons between objectives. It also demonstrates that a candid appraisal of uncertainty, in the form of risk estimates, can be an aid, rather than a barrier, to understanding and using ecosystem model predictions. Management measures that reduce coastal fishing, relative to oceanic fishing, apparently reduce risks to both the fishery and the ecosystem. However, alternative reference points could alter the perceived risks, so further stakeholder involvement is needed to identify risk metrics that appropriately represent their objectives.

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