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Marine Ecology Progress Series

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MEPS 536:107-121 (2015)  -  DOI: https://doi.org/10.3354/meps11396

Algorithms to estimate Antarctic sea ice algal biomass from under-ice irradiance spectra at regional scales

Jessica Melbourne-Thomas1,2,*, Klaus M. Meiners1,2, C. J. Mundy3, Christina Schallenberg4, Katherine L. Tattersall5, Gerhard S. Dieckmann6

1Australian Antarctic Division, Department of the Environment, 203 Channel Highway, Kingston, Tasmania 7050, Australia
2Antarctic Climate & Ecosystems Cooperative Research Centre, University of Tasmania, Hobart, Tasmania 7001, Australia
3Centre for Earth Observation Science, Clayton H. Riddell Faculty of Environment, Earth, and Resources, University of Manitoba, Winnipeg, Manitoba R3T 2N2, Canada
4School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia V8P 5C2, Canada
5Integrated Marine Observing System, University of Tasmania, Hobart, Tasmania 7001, Australia
6Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, 27570 Bremerhaven, Germany
*Corresponding author:

ABSTRACT: The presence of algal pigments in sea ice alters under-ice irradiance spectra, and the relationship between these variables can be used as a non-invasive means for estimating ice-associated algal biomass on ecologically relevant spatial and temporal scales. While the influence of snow cover and ice algal biomass on spectra transmitted through the snow-ice matrix has been examined for the Arctic, it has not been tested for Antarctic sea ice at regional scales. We used paired measurements of sea ice core chl a concentrations and hyperspectral-transmitted under-ice irradiances from 59 sites sampled off East Antarctica and in the Weddell Sea to develop algorithms for estimating algal biomass in Antarctic pack ice. We compared 4 approaches that have been used in various bio-optical studies for marine systems: normalised difference indices, ratios of spectral irradiance, scaled band area and empirical orthogonal functions. The percentage of variance explained by these models ranged from 38 to 79%, with the best-performing approach being normalised difference indices. Given the low concentrations of integrated chl a observed in our study compared with previous studies, our statistical models performed surprisingly well in explaining variability in these concentrations. Our findings provide a basis for future work to develop methods for non-invasive time series measurements and medium- to large-scale spatial mapping of Antarctic ice algal biomass using instrumented underwater vehicles.

KEY WORDS: Sea ice algae · Chl a · Bio-optics · Normalised difference index · Weddell Sea · East Antarctica

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Cite this article as: Melbourne-Thomas J, Meiners KM, Mundy CJ, Schallenberg C, Tattersall KL, Dieckmann GS (2015) Algorithms to estimate Antarctic sea ice algal biomass from under-ice irradiance spectra at regional scales. Mar Ecol Prog Ser 536:107-121. https://doi.org/10.3354/meps11396

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