A dynamic quantitative approach for predicting the shape of phytoplankton profiles in the ocean

Richardson, A. J., Silulwane, N. F., Mitchell-Innes, B. A. and Shillington, F. A. (2003) A dynamic quantitative approach for predicting the shape of phytoplankton profiles in the ocean. Progress In Oceanography, 59 2-3: 301-319.


Author Richardson, A. J.
Silulwane, N. F.
Mitchell-Innes, B. A.
Shillington, F. A.
Title A dynamic quantitative approach for predicting the shape of phytoplankton profiles in the ocean
Journal name Progress In Oceanography  (ERA 2012 Listed)    (ERA 2010 Rank A)   Check publisher's open access policy
Publication date 2003-10
Sub-type Article
Year available 2003
DOI 10.1016/j.pocean.2003.07.003
Volume number 59
Issue number 2-3
ISSN 0079-6611
Start page 301
End page 319
Total pages 19
Place of publication Oxford
Publisher Pergamon/Elsevier Science
Language eng
Subject 230116 Numerical Analysis
270000 Biological Sciences
04 Earth Sciences
0405 Oceanography
Abstract Estimation of primary production over large areas of the ocean requires information on the shape of phytoplankton profiles. In this study we develop a generic quantitative approach to describe the continuous variation of profile shape within a region. We illustrate this approach by application to the dynamic southern Benguela upwelling system. First, we describe profile shape by fitting a four-parameter shifted Gaussian model. We then use a model-building approach to relate each parameter to a suite of environmental variables that are either known for each point of the ocean in time and space (depth of the water column, season, and area) or are easily obtained from remote sensing (sea surface temperature and surface chlorophyll a). As these variables are highly correlated and non-linearly related to profile shape, we use generalised additive models to visualise the non-linear relationships between each parameter and all environmental variables simultaneously. These relationships are then parameterised using generalised linear models to obtain a predictive equation for each profile parameter. Relationships identified made intuitive sense in terms of the evolution of phytoplankton blooms in upwelling systems. We found strong predictive relationships for the depth of maximum chlorophyll (r(2) = 0.70) and the total chlorophyll in the peak (r(2) = 0.74), the two most important parameters for estimating primary production. Predictive relationships were weaker for the width of the peak (r(2) = 0.21) and the background chlorophyll (r(2) = 0.15). The predictive equations identified can be applied on a pixel-by-pixel basis to concurrent sea surface temperature and ocean colour images to estimate profile shape, and can be imbedded within local algorithms to provide regional primary production estimates. This approach can easily be applied to other biogeo-chemical provinces. (C) 2003 Elsevier Ltd. All rights reserved.
Keyword Phytoplankton
Chlorophyll profiles
Primary production
Biogeochemical provinces
Remote sensing
Generalised modelling
South Africa
Southern Benguela
Satellite Chlorophyll
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Unknown

 
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