4.5 Article

Comparison of two methods to derive the size-structure of natural populations of phytoplankton

Journal

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.dsr.2013.11.007

Keywords

Phytoplankton; Pigment; Filtration; Size; Chlorophyll

Categories

Funding

  1. Changing Earth Science Network initiative
  2. STSE program of the European Space Agency (ESA)
  3. UK National Centre for Earth Observation
  4. Ocean Colour Climate Change Initiative of ESA
  5. UK Natural Environment Research Council National Capability
  6. NERC [pml010008, pml010007, earth010003, nceo020006] Funding Source: UKRI
  7. Natural Environment Research Council [pml010007, nceo020006, earth010003, pml010008] Funding Source: researchfish

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Various methods have been proposed to estimate the size structure of phytoplankton in situ, each exhibiting limitations and advantages. Two common approaches are size-fractionated filtration (SFF) and analysis of pigments derived from High Performance Liquid Chromatography (HPLC), and yet these two techniques have rarely been compared. In this paper, size-fractionated chlorophylls for pico- (< 2 pm), nano- (2-20 mu m) and micro-phytoplankton (> 20 mu m) were estimated independently from concurrent measurements of HPLC and SFF data collected along Atlantic Meridional Transect cruises. Three methods for estimating size-fractionated chlorophyll from HPLC data were tested. Size-fractionated chlorophylls estimated from HPLC and SFF data were significantly correlated, with HPLC data explaining between 40 and 88% of the variability in the SFF data. However, there were significant biases between the two methods, with HPLC methods overestimating nanoplankton chlorophyll and underestimating picoplankton chlorophyll when compared with SFF. Uncertainty in both HPLC and SFF data makes it difficult to ascertain which is more reliable. Our results highlight the importance of using multiple methods when determining the size-structure of phytoplankton in situ, to reduce uncertainty and facilitate interpretation of data. (C) 2013 Elsevier Ltd. All rights reserved.

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