Dragonflies of Nee Soon swamp forest 
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Qualitative surveys 
Additional species not otherwise recorded during the quantitative surveys were 
included to compile a full species list. Species spotted during reconnaissance trips were 
also added to provide a comprehensive updated inventory for Nee Soon freshwater 
swamp forest. 
Data analysis 
All data were analysed using Palaeontological Statistics (PAST 3.15) (Hammer, 2017). 
Three of the 36 sites were not included in the analysis due to incomplete environmental 
data. Habitat parameter data were transformed either by square root or by log (x+1). 
The community count data was log (x+1) transformed before analysis. 
The Shannon-Weaver Index (H’) was used as a measurement of species diversity. 
The index is calculated as , where p. is the proportion of individuals found of species 
i and n is the total number of species. Species Richness (R) refers to the number of 
species found at each site. 
Ward’s hierarchical clustering using Bray-Curtis dissimilarities was carried out 
according to abundance and species composition of the odonate assemblage to examine 
if there were any natural groupings in the data. The results of the cluster analysis were 
overlaid onto other multivariate analysis plots to indicate graphical representation of 
groupings in the community. The significance of the groupings was then tested using 
Non-parametric Multivariate Analysis of Variance (PERMANOVA). 
Detrended correspondence analysis (DCA) was carried out to show the 
distribution of odonate communities across all sites. DCA is a multivariate statistical 
technique widely used by ecologists to find the main factors or gradients in the 
large, species-rich but usually sparse data matrices that typify ecological community 
data. Canonical correspondence analysis (CCA) (Legendre & Legendre, 1998) was 
performed to determine correlations between odonata abundance and environmental 
parameters. CCA is correspondence analysis of a site/species matrix where each site has 
given values of environmental variables. The ordination axes are linear combinations 
of the environmental variables. CCA is thus an example of direct gradient analysis, 
where the gradient in environmental variables is known a priori and the species 
abundances are considered to be a response to this gradient (Hammer, 2017). The CCA 
model significance was tested using a Monte Carlo permutation test (1000 iterations). 
Environmental variables were filtered through a Multivariate Liner Regression model 
where each individual environmental variable was treated as one independent variable 
and the raw scope of the two DCA Axes (DCA 1 & DCA 2) were loaded as dependent 
variables. Results of the regression indicate how relevant the given environmental 
variable is in driving the overall gradient order of site and species. Selection of the 
variables was then based on the statistical results of R square and p value (F-test). 
Principal components analysis (PCA) was applied to analysis of the 23 
environmental variables. The results were used to summarise environmental 
conditions. The significant axes were determined by eigenvalues which are expected 
to be above a random model (Broken Stick) curve (Jackson, 1993; Hammer, 2017). 
These significant axes (in this case the first four, which explained 63% of the variation 
