Figure 1.
Map of the study area showing the water courses and reaches where fish and macroinvertebrates were collected.
Names of the main water courses are reported. See Supporting Information for site coordinates.
Table 1.
List of the measured environmental descriptors.
Table 2.
Loadings of the first two principal components (PC1 and PC2) of the PCAs on the three sets of environmental variables.
Figure 2.
Distribution of independent effects (I%) of predictor variables calculated with hierarchical partitioning of fish and macroinvertebrate taxonomic richness.
Chan PC: channel principal components; Subs PC: substratum principal components; P-c PCA: physico-chemical principal components; T: temperature; AI: Anthropogenic Index; Distance: distance from source. * denotes statistically significant variables selected by the hierarchical partitioning procedure.
Table 3.
Hierarchical partitioning of predictor variables explaining fish and macroinvertebrate richness.
Figure 3.
Partitioning of variance in taxonomic composition of macroinvertebrate and fish with partial Canonical Correspondence Analysis showing i) the unexplained variation; ii) the unique effect of environmental variables; iii) the unique effect of biotic variables and iv) the shared effect of environmental and biotic variables.
See text for more details.
Table 4.
Results of forward variable selection in CCA and partial CCA (i.e. the unique effect of environmental variables) performed on macroinvertebrate and fish occurrence matrix.
Figure 4.
Matrices of fish×reaches and macroinvertebrate×reaches sorted by the software BINMATNEST to maximise nestedness (i.e. minimise unexpected presences and absences).
Filled squares represent presence. The curved line shows isoclines of prefect nestedness. Perfect nestedness occurs where rarer species are exclusive to species-rich locations, and where species poor locations host only a subset of species found in richer locations. Arrows represent relationships between the ranking of reaches sorted to maximise nestedness and environmental variables.