Statistical analyses
Canonical Correspondence Analyses (CCA) were used to explore differences
in drift and stranding propensity, respectively, among the three HP
reaches and to assess their correlation with a pre-selection of
explanatory environmental variables (based on the state of knowledge,
see introduction). Drift: Qpeak,
Qpeak - Qbase, URmean,
URmax, v0, vnet,
v100, τb, Fr, T, NTU . Stranding:Qpeak,
Qpeak/Qbase, DRmean,
DRmax, v0, vnet,
v100, DAex, T, NTU . Drift and stranding
propensity, respectively, were used as response variable. Environmental
variables were log-transformed (x + 1) and then sequentially checked for
variance inflation factor (vif), while variables exceeding a vif of five
were removed prior to the CCA modeling. The final selection of vif
validated variables was based on a stepwise model selection
(permutational forward and backward) based on the Akaike information
criterion (AIC). Environmental variables that were selected in both
approaches were used for the final CCA model construction. The CCA model
was then tested for total significance, significant axes and significant
terms via PERMANOVAs. An additional factor fitting was performed to
assess statistical differences in drift and stranding propensity,
respectively, between HP reaches (Sitter vs Hasliaare vsLinth), sampling day (day 1 vs day 2; corresponding to higher
peak flow velocities at day 2, see Table 1) and HP scenario (SC 1vs SC2 vs SC3).
Non-metric multidimensional scaling (NMDS) was used to explore
differences in the benthic community compositions among the different HP
and RF reaches. Benthic densities were used to create a resemblance
matrix based on the Bray-Curtis similarity index. Permutational
multivariate analysis of variance (PERMANOVA) with a similarity
percentage (SIMPER) analysis were performed to assess for significant
differences in the benthic community composition among reaches and to
identify the taxa that most contributed to dissimilarity.
In addition to CCA and NMDS, non-parametric Mann-Whitney or
Kruskal-Wallis tests followed by pairwise post hoc tests, and linear
regression models were applied for specific analyses. CCA and NMDS
analyses were performed using the software R version 4.1.1 (R Core Team,
2021) with the package ‘vegan’ (Oksanen et al., 2019). Non-parametric
tests and linear regressions were performed using the software SPSS
Statistics version 27.0 and SigmaPlot version 12.5.