African Journal of
Biotechnology

  • Abbreviation: Afr. J. Biotechnol.
  • Language: English
  • ISSN: 1684-5315
  • DOI: 10.5897/AJB
  • Start Year: 2002
  • Published Articles: 12481

Full Length Research Paper

Phytoplankton community characteristics of the icebound season and its relationship to the environmental variables in the Zhalong Wetland, China

LI Jing1,2, QI Peishi1*, MA Yun2 and ZHOU Hao2
1School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin 150090, China. 2Heilongjiang Provincial Research Institute of Environment Sciences, Harbin 150056, China.
Email: [email protected]

  •  Accepted: 03 June 2011
  •  Published: 25 July 2011

Abstract

The taxonomic structure and spatial variability of phytoplankton abundance in the icebound season was obtained from the Zhalong Wetland. A total of 109 taxa were identified in all samples, 92 taxa occurring in at least two samples or the percentages over 1% in at least one sample were utilized in further study. The algal population of the Zhalong Wetland was extremely scarce under the ice-cover condition, with a mean density of 3.11×10ind./L (ranged from 1.11×106 ind./L to 1.99×108×ind./L). Principle component analysis (PCA) displayed two major groups in environmental variables, that is, (1) ion and organic matters; (2) physical characters and non-organic matters. The relationship between phytoplankton community and environmental variables was analyzed from the inhalant and core region of the Zhalong Wetland. The detrended correspondence analysis (DCA) examined all sites positioned in the range of the DCA biplot and the largest gradient length (13.7 standard deviation units) evoked a strong unimodal response modal. Canonical correspondence analysis (CCA) with forward selection and a Monte Carlo permutation test revealed that nitrate (NO3-N) and TN mostly affected the distribution of algae.

 

Key words: Phytoplankton, icebound season, canonical correspondence analysis (CCA), Zhalong Wetland.

Abbreviation

PCA, Principle component analysis; DCA, detrended correspondence analysis; CCA, canonical correspondencs analysis.