2.6. Statistical Analyses
Distribution of seagrass and environmental variables were explored using
multi-panel scatterplots and rank correlations to evaluate outliers,
correlations, and skewness. Strong co-linearity (± 0.7 - 0.9) was
identified between shoot and leaf densities, leaf length and width, and
temperature and pH. These variables were excluded from multivariate
analyses to avoid confounding the true degree of variability (Lipovetsky
and Conklin 2001).
Diversity indices were assessed visually using histograms and q-q plots.
Variances in the data were tested for normality (Shapiro-Wilk’s test)
and homogeneity (Levene’s Test) and natural log transformed where
necessary to meet assumptions for parametric analyses. Two-way factorial
ANOVAs were used to test for main and interactive effects of site
(random factor: five levels) and season (fixed factor: four levels) on
each index: abundance (N ), richness (d) and diversity (H’)
followed by Tukey’s HSD tests. Univariate analyses were performed using
Sigma Stat (IBM SPSS Statistics 29).
Species abundance across seasons (fixed) and sites (random factor nested
within season) was compared using multivariate data testing in PRIMER 6
(Clarke & Gorley, 2006). Abundance was considered an adequate metric on
which to assess the effects of seagrass structure, epiphyte biomass and
environmental factors since epifauna were largely comprised of grazers
whose densities were important in controlling epiphytes (Hovel et al.,
2002; Vonk et al., 2010). Data were 4th-root
transformed to down-weight the influence of large variances. Visual
assessments of relationships between variables in ordination space were
made using non-metric multi-dimensional scaling (MDS) and cluster
dendograms based on Bray-Curtis similarity matrices. Non-parametric
multivariate analyses of variance were explored using PERMANOVA
(Permutational Multivariate Analysis of Variance) (Anderson et al.
2008). Within group differences as well as comparisons of species
composition between groups (i.e., beta diversity, Anderson et al. 2006)
were tested using Permutational Analysis of Multivariate Dispersions
(PERMDISP (9999 permutations)). The relative contribution of individual
taxa to the similarity among sites across seasons was evaluated using
SIMPER (similarity percentage breakdown).
To determine the environmental factors (temperature, salinity,
turbidity, pH, oxygen, chlorophyll a and exposure) and seagrass
metrics (shoot density, leaf length, aboveground biomass, algal epiphyte
biomass) that best explained epifaunal variability, a distance-based
linear model (DistLM, selection procedure ‘best’ and criteria ‘AIC’) was
used. DistLM calculates the proportion of variability contributed by
each factor using multiple regression modelling to partition variation
according to selected predictor variables (Anderson et al. 2008). After
all possible model combinations were explored, the five best models that
explain variability based on smallest AIC values and adjusted
R2 derivatives are presented.
To partition the net effects of environmental variables (exogenous) and
seagrass structure (endogenous) on epifaunal abundance (endogenous) into
direct and indirect effects, maximum-likelihood estimated structural
equation models (SEM) were created in AMOS (IBM SPSS Amos version 26
Graphics). Structural equation modelling is a multivariate analysis
framework that encompasses techniques derived from factor and path
analyses (Grace, 2009). This framework enables the examination of both
direct and indirect relationships between observed and unobserved
(latent) variables. These relationships are represented by paths that
indicate the statistical dependency, and the associated parameters
specify the magnitude of the effect (direct or indirect) of independent
variables on dependent variables (observed or latent).
A full model was specified based on theory and hypothesised
relationships that (i) exogenous environmental variables: temperature,
salinity, pH and oxygen would positively or negatively influence
endogenous variables: seagrass biomass, leaf length/width, shoot
density, epiphyte biomass, chlorophyll a (chl a ) and
epifaunal abundance; while (ii) turbidity and exposure would negatively
influence all endogenous variables; and (iii) seagrass density, leaf
length/width, epiphytes and chl a would positively influence
epifaunal abundance. Stepwise model selection was performed in which
variables with non-significant paths i.e. regression coefficientsp > 0.05 were removed until all remaining paths were
significant (p < 0.05) (Grace, 2009). Model fit was
assessed using the chi-square value and the root mean square error of
approximation (RMSEA) as measures of goodness-of-fit and evaluated by
ensuring observed and predicted covariance matrices were aligned. Models
were then adjusted to produce a low chi-square value with a
corresponding p value > 0.05 denoting observed and
fitted models were not significantly different, and an RMSEA <
0.08 indicating acceptable fit (Grace, 2009). The final model output
presents standardized coefficients to compare the strength of direct and
indirect effects.