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4.3 Data analysis
The structure of the fish assemblage was analyzed via
direct gradient analysis methods. Direct gradient
techniques elucidate the role of environmental
variables in shaping the assemblage structure. Direct
gradient analysis methods are useful in determining
the relative importance of measured environmental
variables, but may lead to a biased ordination of the
species data depending on the available environmental
data. Canonical Correspondence Analysis (CCA) is
becoming the most widely used method of direct
gradient analysis in ecology (Rakocinski et al., 1996).
The CCA algorithm performs better than other direct
gradient techniques when faced with certain
problematic characteristics of ecological datasets, such
as skewed species’ distributions, quantitative noise in
species’ abundance data, unusual sampling designs,
highly inter-correlated environmental variables, and
unknown species-determining factors. The software
package CANOCO version 4 (ter Braak and Smilauer,
1998) was used to perform all ordinations. Two-way
analysis of variance (ANOVA) was applied to calculate
the variation in hydrographic and biological parameters
(SPSS 18v.). Primer 6.0v was used to study fish
diversity in the KAE (Clarke and Gorley, 2006).
Author Contributions
SBN is the supervisor of the project. SBN, PRJ, OKS and VFS made the
experimental design, data analysis, manuscript preparation
.
Acknowledgements
This work forms part of the research project entitled “Ecology and fish
production potential of the Kodungallur-Azhikode back water ecosystem”
funded by the Kerala State Council for Science, Technology and Environment
(KSCSTE) and the authors are thankful for financial assistance. Authors are
thankful to The Head and Ecology Division, Department of Marine Biology,
Microbiology and Biochemistry, Cochin University of Science and
Technology for providing necessary facilities.
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