Statistical analysis
To detect significant differences in single metrics between the two
sampling campaigns we applied the Wilcoxon Signed Test for paired
samples. To highlight possible correlations between biological metrics
(such as taxa richness, biodiversity etc.) and indices derived from RHS
(i.e., HQA, HMS, and LRD) we applied the Spearman test. For community
analyses, we ran an NMDS on taxa composition and performed the PERMANOVA
test to detect possible significant differences between years. To test
biotic homogenization in both taxonomic diatom and macroinvertebrate
communities, we performed the test of homogeneity for multivariate
dispersion (Anderson, 2006). These analyses were based on the
Bray-Curtis dissimilarity metric for diatoms, while the Jaccard metric
for macroinvertebrates.
We followed the approach of Legendre & De Cáceres (2013) to estimate
the proportion by which each sampling site and each taxon contributed to
the overall area diversity. This approach calculates: 1) the local
contribution to ß-diversity (LCBD). LCBD is a measure of the ecological
uniqueness of the single sampling site: the higher the site uniqueness,
the higher its contribution to the total richness; 2) the species
contribution to ß-diversity (SCBD), which is a score calculated for each
species (diatoms) or taxon (macroinvertebrates). The higher is SCBD for
a single species, the higher its contribution to the variance among
sites. We calculated LCBD and SCBD Hellinger-transformed abundance data
with the “beta.div” function of the values using adespatial R package
(Legendre & De Cáceres, 2013). All the statistical analyses were
performed in R (R Core Team, 2020).
and has been used as a tool to identify sites to prioritize in
conservation efforts (Ruhı´, Datry, & Sabo, 2017).