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    Marine classification: lessons from the New Zealand experience

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    B. Sharp, M. Pinkerton and J. Leathwick (New Zealand)
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    New Zealand has a considerable body of experience creating spatial classifications of the marine environment, and applying them for management. We assert that recent innovations in multivariate statistical modeling have made possible the combined use of spatially comprehensive environmental data and discontinuous biological data to generate rigorous, objective, data-driven classifications of the Southern Ocean sensitive to ecologically important contrasts, consistent with CCAMLR’s ecosystem management mandate. This paper considers a range of methodological options for data-driven marine classification, and reviews the results of three New Zealand classifications to draw methodological and practical lessons relevant to CCAMLR’s Bioregionalisation of the Southern Ocean.
    We offer the following explicit recommendations to the CCAMLR Bioregionalisation process: 1) Use biological data; 2) Model species individually; 3) Generate a classification based on abundance, not presence-absence; 4) Use the most powerful statistical methods available, such as BRT and GDM; 5) Use a hierarchical algorithm; 6) Focus on an environment or community of interest; 7) Include information representing uncertainty.
    We also highlight some of the inherent limitations of all attempts to represent spatially and temporally dynamic ecosystems using static representations such as produced by marine classifications. We identify important ecosystem processes that may not be captured by even the best classifications, and warn against uncritical or misinformed application of marine classifications in the management stage. Finally we highlight some practical steps to make marine classifications more ‘management-friendly’.