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    Use of biological data to inform bioregionalisation of the Southern Ocean

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    M. Pinkerton, B. Sharp and J. Leathwick (New Zealand)
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    Innovative multivariate statistical modeling techniques allow modelers to generate spatially comprehensive species distribution layers from discontinuous biological data, by fitting complex and scale-dependent relationship between species abundance and available environmental data. These species-specific layers can then be used in bioregionalisation, for instance by classifying directly on biological data without the need for environmental proxies.
    We describe one method, called BRT (Boosted Regression Trees), by which we propose that CCAMLR may wish to generate species layers to inform the bioregionalisation of the Southern Ocean. We demonstrate the use of this method by generating 13 taxon-specific and aggregate data layers for pelagic zooplankton using a circumpolar dataset collected by Continuous Plankton Recorder (CPR) and twelve existing or newly derived continuous environmental data layers.
    We also describe other available data that is appropriate for this method and is likely to be important for the CCAMLR bioregionalisation process, e.g. a quantitative circumpolar krill and salp database and various top predator databases. We also describe biological distribution estimates that we have compiled from other sources, using other methods. These include 115 marine mammal species layers generated using a semi-objective Relative Environment Suitability (RES) model, and 33 avian taxa distributions collated from available literature. While perhaps not as rigorous as distributions generated using statistical methods, we argue that these data constitute best available information at present, and should not be ignored in the bioregionalisation process.
    Finally we identify potentially valuable sources of biological data that are currently unavailable but likely to become available in the near future, and advocate the use of ‘placeholder’ data layers built into the bioregionalisation process, to be replaced as better data becomes available. In this way CCAMLR can proceed using best available information at present and still incorporate improved data layers without revisiting methodological or procedural decisions such as those that will be reached by this workshop.