2.3 Data analysis
Non-metric
multidimensional scaling (NMDS) ordination analysis based on the
Bray-Curtis similarity matrix (Clarke 1993), were used to classify the
spatial (stream order) and temporal (season) variations of fish
assemblage structure. Relative
abundance data for each species was log (x+1) transformed to normalize
the data and avoid skew. Bray-Curtis similarity coefficient was
calculated based on the relative abundance matrices. Rare species that
occurred in less than 3 sites were excluded from the analysis. Next,
one-way analysis of similarity (ANOSIM) was carried out to determine
whether fish assemblages changed significantly among stream orders or
seasons. Then, a similarity of percentage analysis (SIMPER) was used to
identify species that contributed most to the spatial or temporal
dissimilarities of fish assemblages. All these analyses were performed
with the PRIMER 5 software package (Clarke and Warwick 2001), including
modules “NMDS”, “SIMPER” and “ANOSIM”.
Difference in environmental factors between seasons were tested by
one-way analysis of variance (ANOVAs) using SPSS statistical programs
(Version 20.0). Relationships between fish assemblage structure and
environmental factors for each season were examined by constrained
canonical ordinations. Detrended Correspondence Analysis (DCA) were used
to determine the appropriate model for direct gradient analysis (Leps
and Smilauer 2003). As the length of gradients for the first axis was
estimated at 1.908 (<3) for wet season and 1.746 (<3) for dry season,
Redundancy Analysis (RDA) was applied in further analyses. Fish relative
abundance data were log10(x+1) transformed and the
downweighting option was used to reduce the influence of rare species.
Environmental variables that did not meet the normality assumption
(Shapiro-Wilk test, p<0.05) were transformed using natural
logarithms, and collinear environmental viable with high variation
inflation factors (VIF>20) were eliminated from further
analyses (McCune and Grace 2002). Stepwise forward selection with Monte
Carlo permutation tests (999 permutations,
p<0.05)
was used to select a parsimonious set of explanatory variables. These
analyses were performed using the software CANOCO for Windows 4.5
version (Ter Braak and Smilauer 2002).