The PCA performed with average values of the environmental conditions
explained 33.26% of variation in fish diversity in the first axis,
18.79% in the second axis, and a total of 52.05% across both axes
(Appendix 3). The pattern found by the ranking was: (i) conductivity,
pH, dissolved oxygen and channel width positively related to the first
axis; (ii) turbidity and water velocity positively to the second axis;
and (iii) channel depth negatively to the second axis (Appendix 3). The
PCA performed with both average values and standard deviation of
environmental conditions explained 21.02% of the variance in the first
axis and 16.66% in the second one (Appendix 3). The pattern found by
the ranking was: (i) standard deviation of the channel width, standard
deviation and average turbidity and average water velocity positively
related to the first axis; (ii) standard deviation and average depth of
the channel and the standard deviation of pH, conductivity and dissolved
oxygen negatively to the first axis; and (iii) average channel width,
pH, dissolved oxygen and conductivity and the standard deviation of
water temperature positively to the second axis (Appendix 3).
Best models of richness and beta diversity of fish species included
average and standard deviation of the local conditions and the spatial
eigenvalues maps performed from Local W (Appendix 3). The richness model
had a high r-squared value (r² = 0.623), with 56.30% of variance
explained by environmental conditions, 6% by spatial maps and 0.3% by
interactions between niche and neutral effects (Table 3). Beta diversity
had an even higher r-square value (r² = 0.758), with 64.40% of variance
explained by environmental conditions, 6.70% by spatial maps, and
4.80% by interactions between environmental and spatial processes
(Table 3).
Table 3. Models of linear regression between the axis of PCA
performed with the averages and standard deviation of the environmental
conditions and the beta diversity and richness of the Cerrado stream
fish community. r2 - Correlation coefficient; F -Fisher‘s F; p - Type
one error probability; AIC - Information criteria of Akaike; Δ AIC -
Akaike variation; CN - Condition Number; Moran’s I - Autocorrelation
index of Moran for variable; Res Moran’s I - Autocorrelation index of
Moran for residual; A.B - Environmental component; A:B - Shared
Component; B.A - Spatial Component;1-(A+B) - Residual.