Combining HPLC pigment markers and ecological similarity indices to assess phytoplankton community structure: An environmental tool for eutrophication?

  • Sherrard N
  • Nimmo M
  • Llewellyn C
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Abstract

A new tool combining the use of HPLC pigment markers with ecological similarity indices, to provide a fast and effective environmental tool for monitoring water quality, was investigated. HPLC pigment concentrations from a 4-year time series data set (2000-2003) from the western English Channel were used to calculate six similarity indices to assess changes in phytoplankton community structure. Indices obtained from HPLC data were compared with those produced from the corresponding phytoplankton biomass derived from microscopic analysis. Multivariate RELATE analyses (P < 0.001) suggested that there was little loss of information when species biomass data were grouped into higher taxonomic levels, thereby providing justification for the use of pigment markers. To test the proposed methodology, product moment (r) and Spearman's rank (rs) correlation analyses were performed. Highly significant correlations (P < 0.01, Canberra Metric: P < 0.05) between percentage similarities derived from phytoplankton class biomass and pigment markers were found for all indices when data were pooled (n = 104). A Mann-Whitney U-test suggested that indices were more sensitive to changes in relative biomass than pigment concentrations; however, pigment data also produced statistically significant findings (P < 0.05). Combining HPLC pigment markers and ecological similarity indices to assess phytoplankton community structure has advantages over microscopy in that it is rapid, reproducible and better suited to large-scale environmental monitoring. We present a basic protocol for implementing this new tool using a suite of similarity indices into environmental monitoring of phytoplankton communities. © 2005 Elsevier B.V. All rights reserved.

Author-supplied keywords

  • Eutrophication
  • HPLC pigment markers
  • Phytoplankton C biomass
  • Similarity indices

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