Fig 1.
Map of South Australia, showing the distribution of major land-uses and the sampling sites.
Circles represent study sites; heavy black lines represent catchment area upstream of study sites; grey lines represent coastline and state borders; thin black lines represent boundaries to NMR regions. Land-use South Australia layer was sourced from Australian Natural Resources Data Library and their classifications were based on the Australian Land-use and Management (ALUM) classification.
Fig 2.
Unconstrained ordination plots of macroinvertebrates in autumn and spring.
Unconstrained (semi-strong hybrid MDS) ordination plots of macroinvertebrates (individual sites across years) based on Bray-Curtis similarity of 4th root abundance data in autumn and spring. Sites names with “Ck” and “R” represent creeks and rivers respectively. The lines connecting the dots represent trajectories of assemblage structure across the years. Square symbols indicate the start of the trajectory and the arrow head indicates the end of the trajectory. The scale represents dissimilarity of sites.
Table 1.
Results of permutational multivariate analysis of variance (PERMANOVA).
df represents degrees of freedom. Bold numbers indicate significant P-values.
Table 2.
RELATE results (ρ and P-value) reported for seriation of macroinvertebrate composition at each site for each season.
Sites names with “Ck” and “R” represent creeks and rivers respectively. ρ signifies Spearman’s correlations in the seriation test; if |0.8 ≤ ρ ≤ 1.0|, then there is a clear trend in the trajectories of the community composition [28].
Table 3.
Results of general linear models for the relationships of the biodiversity indices to site, season (site nested within season) and year (site nested within year).
Table 4.
Results from a distance-based linear model (DistLM) for the 13 sites in autumn and spring.
Variables are listed in order of contribution to explaining variation in the community composition. % variation represents explained variation attributable to each variable added to the model. Abbreviations for predictor variables are listed in S1 Table.
Fig 3.
Distance-based redundancy analysis (dbRDA) of macroinvertebrate samples in autumn.
Distance-based redundancy analysis (dbRDA) of macroinvertebrate samples in autumn, overlaid with normalised predictor variables (based on distLM analysis in Table 4). Abbreviations for predictor variables are listed in S1 Table.
Fig 4.
Distance-based redundancy analysis (dbRDA) of macroinvertebrate samples in spring.
Distance-based redundancy analysis (dbRDA) of macroinvertebrate samples in spring, overlaid with normalised predictor variables (based on distLM analysis in Table 4). Abbreviations for predictor variables are listed in S1 Table.
Table 5.
Results of general linear models for the relationships of the biodiversity indices to geographic, environmental and land-use predictor variables in autumn and spring.
S.E. represents the standard error of the coefficients. Bold numbers indicate significant P-values. *indicates trending P-values. Abbreviations for predictor variables are listed in S1 Table.
Table 6.
Macroinvertebrates indicated by BVSTEP as associated with gradients of specific predictor variables in autumn and spring.
Numbers written in the cells are Spearman’s correlation values between the taxon and gradient of that predictor variable. Predictor variables for both seasons are arranged in the order in which the most influential variables in each season appears as indicated by DistLM (Table 4) appear. Abbreviations for predictor variables are listed in S1 Table.