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    Antarctic pack ice algal distribution: Floe-scale spatial variability and predictability from physical parameters

    71756.pdf (1.354Mb)
    Access Status
    Open access
    Authors
    Meiners, K.
    Arndt, S.
    Bestley, S.
    Krumpen, T.
    Ricker, R.
    Milnes, M.
    Newbery, K.
    Freier, U.
    Jarman, Simon
    King, R.
    Proud, R.
    Kawaguchi, S.
    Meyer, B.
    Date
    2017
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Meiners, K. and Arndt, S. and Bestley, S. and Krumpen, T. and Ricker, R. and Milnes, M. and Newbery, K. et al. 2017. Antarctic pack ice algal distribution: Floe-scale spatial variability and predictability from physical parameters. Geophysical Research Letters. 44 (14): pp. 7382-7390.
    Source Title
    Geophysical Research Letters
    DOI
    10.1002/2017GL074346
    ISSN
    0094-8276
    School
    School of Molecular and Life Sciences (MLS)
    URI
    http://hdl.handle.net/20.500.11937/71513
    Collection
    • Curtin Research Publications
    Abstract

    ©2017. Commonwealth of Australia. Antarctic pack ice serves as habitat for microalgae which contribute to Southern Ocean primary production and serve as important food source for pelagic herbivores. Ice algal biomass is highly patchy and remains severely undersampled by classical methods such as spatially restricted ice coring surveys. Here we provide an unprecedented view of ice algal biomass distribution, mapped (as chlorophyll a) in a 100 m by 100 m area of a Weddell Sea pack ice floe, using under-ice irradiance measurements taken with an instrumented remotely operated vehicle. We identified significant correlations (p < 0.001) between algal biomass and concomitant in situ surface measurements of snow depth, ice thickness, and estimated sea ice freeboard levels using a statistical model. The model's explanatory power (r2 = 0.30) indicates that these parameters alone may provide a first basis for spatial prediction of ice algal biomass, but parameterization of additional determinants is needed to inform more robust upscaling efforts.

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