Relationship between bird species richness and limnological
variables
The redundancy analysis (RDA) identified two significant RDA axis and
34.4% of the variation in species richness of the reservoirs is
explained by environmental variation
(R2adj = 0.34375; P <
0.001). Four variables were selected as the best factors explaining the
variation in bird species richness observed: elevation, water depth, pH
and nutrients. A clear association between the occurrence of specific
bird species and several of the measured environmental variables was
detected. A strong relationship between species richness and size of
reservoir is detected but not the depth. That is bigger reservoirs did
support a higher number of bird species (see Appendix S4 in supporting
information).
The RDA indicates that a significant portion of the variation in species
composition is explained by both biological variables (BV) and
environmental variables (ENV) (R2adj =
0.18, p<0.001). If only environmental and biological variables
are used in the RDA, environmental variation explained 9.5% of the
variation in species composition
(R2adj = 0.0953, p<0.001),
whereas the biological variables (BV) explains about 8.9% of the
variation in species composition
(R2adj = 0.089, p<0.001;
Fig. 5A). Among the environmental variables, elevation, depth, pH,
transparency and TP contributed significantly to explaining the
variation in species composition in the studied reservoirs. In a partial
RDA analysis, the complete model (ENV ∪ BV) accounted for about 18% of
the variation in species composition
(R2adj = 0.1799, p<0.001;
Fig. 5A) with environmental variables
(R2adj = 0.091, p<0.001)
being most important. But significant portion of the variation in
species composition was also explained by pure biological variables
(R2adj = 0.085, p<0.01),
while shared environmental and biological variables contributed less
than 1% (Fig. 5A and see Appendix S5 in supporting information). A
large part of the variation in species composition, however, remained
unexplained (R2adj = 0.82; Fig. 5A).
Including age of reservoirs (AGE) as an independent variable in the
partial RDA did change the percentage of variation explained by
environmental variables (R2adj =
0.107, p< 0.001; Fig. 5B) and biological variables
(R2adj = 0.074, p< 0.001)
differently. It did, reduce the variation explained by biological
descriptors (BV), and the pure effect of BV is down to 7.4%
(R2adj = 0.074, p< 0.001;
Fig. 5B). The combined effect of ENV, AGE and BV
(R2adj = 0.203) was higher than that
of the effect without age. The pure effect of AGE was small but
statistically significant (R2adj =
0.023, p < 0.05; Fig. 5B) and its effect was also confounded
with BV effects (Fig. 5B; see also appendix S5 in supporting
information). Only a small fraction of the variation was shared among BV
descriptors and environmental heterogeneity
(R2adj = 0.005, p< 0.001;
Fig. 5B).
The global model, using ENV, BV, AGE and geographic location of each
reservoir (SPACE) as an independent variables in the partial RDA did not
change the percentage of variation explained by environmental variables
(R2adj = 0.107, p< 0.001;
Fig. 5B) and biological variables
(R2adj = 0.074, p< 0.001).
However, the model indicated the marginal effect of SPACE on the bird
species richness to be 5.6%. Out of these, 4% is pure effect of SPACE
and 1.6% is confounded with effect of AGE and BV variables (see
Appendix S5 in supporting information).