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Estimating spatial and seasonal phytoplankton δ13C variations in an estuarine mangrove ecosystem
Bouillon, S.; Dehairs, F.A. (2005). Estimating spatial and seasonal phytoplankton δ13C variations in an estuarine mangrove ecosystem, in: (2005). VLIZ Coll. Rep. 33-34(2003-2004). VLIZ Collected Reprints: Marine and Coastal Research in Flanders, 33-34: pp. chapter 7 [Subsequent publication]
In: (2005). VLIZ Coll. Rep. 33-34(2003-2004). VLIZ Collected Reprints: Marine and Coastal Research in Flanders, 33-34. Flanders Marine Institute (VLIZ): Oostende, more
In: VLIZ Collected Reprints: Marine and Coastal Research in Flanders. Vlaams Instituut voor de Zee: Oostende. ISSN 1376-3822, more

Also published as
  • Bouillon, S.; Dehairs, F.A. (2000). Estimating spatial and seasonal phytoplankton δ13C variations in an estuarine mangrove ecosystem. Isot. Environ. Healt. S. 36(3): 273-284, more

Available in Authors 
    VLIZ: Open Repository 98759 [ OMA ]

Keywords
    Carbon 13; Mangroves; Phytoplankton; Variance analysis; Marine

Authors  Top 
  • Bouillon, S., more
  • Dehairs, F.A., more

Abstract
    In estuarine ecosystems, large spatial and seasonal variations in δ13C values of primary producers can occur, and knowledge of these variations may be crucial when interpreting stable isotope data of higher trophic levels. Obtaining clean phytoplankton samples for isotope analysis is usually impossible in such systems, and analysis of total suspended matter is not a simple proxy for phytoplankton δ13C variations. Based on a few simple assumptions regarding the C and N content of the two end-members (terrestrial detritus and phytoplankton) and the δ13C of the terrestrial component, we here present a simple model to estimate the phytoplankton δ13C variations using an existing dataset on the δ13C and elemental (C:N) composition of suspended organic matter from an estuarine mangrove ecosystem in southeast India. These variations are related to the monthly rainfall pattern during the sampling period. It is stressed that this method estimates approximate phytoplankton δ13C values, which should not be used in e.g., mixing models. However, we propose that in cases where sufficiently large datasets are available, the described procedure could provide a valuable method to semi-quantitatively estimate the seasonal or spatial variations of the phytoplankton δ13C signal.

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