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JOURNAL OF PLANKTON RESEARCH | VOLUME 18 | NUMBER 7 | PAGES 1225-1238 | 1996
© Oxford University Press

research-article

Discrimination of marine phytoplankton species through the statistical analysis of their flow cytometric signatures

M.R. Carr, G.A. Tarran and P.H. Burkill

Plymouth Marine Laboratory Prospect Place, West Hoe, Plymouth PL1 3DH, UK

Received on October 23, 1995; accepted on February 26, 1996 Flow cytometry is a research technique for the rapid analysis of phytoplankton abundance and distribution in marine waters. Although the technique is inherently much faster than optical microscopy for counting phytoplankton, its capability for analysing individual taxa is restricted, due largely to the lack of suitable data analysis protocols. These protocols, which use univariate and bivariate plots, can typically differentiate a maximum of three or four phytoplankton taxa under laboratory conditions. We present here two multivariate statistical techniques, quadratic discriminant analysis (QDA) and canonical variate analysis (CVA), used to identify 32 species of phytoplankton from flow cytometric data. CVA was shown to be a useful graphical technique for analysing and displaying data, while QDA was successful at discriminating over two-thirds of the phytoplankton species, with classification rates >70%. QDA was also shown to be more than two orders of magnitude faster than conventional flow cytometric analyses for discriminating and enumerating phytoplankton species. We discuss the potential of multivariate analysis. and ways of developing these techniques for the detailed analysis of complex natural phytoplankton populations in the marine environment.


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 Appl. Environ. Microbiol.Home page
M. F. Wilkins, L. Boddy, C. W. Morris, and R. R. Jonker
Identification of Phytoplankton from Flow Cytometry Data by Using Radial Basis Function Neural Networks
Appl. Envir. Microbiol., October 1, 1999; 65(10): 4404 - 4410.
[Abstract] [Full Text]


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