2.4 Data statistics
According to Scott & Helfman
(2001) and Liu et al. (2019), we identified native and native-invasive
fishes for each fish species collected in this study. Specially, the
native species with high degrees of endemism are typically adapted to
cool, clear, lotic conditions in mountain streams, whereas the
native-invasive species occurring mainly in large streams would be
potential invaders of degraded headwater streams, which are highly
adapted to warm, nutrient- and sediment-rich conditions (Scott &
Helfman, 2001; Dala-Corte et al., 2019; Liu et al., 2019). The latest
research has suggested that habitat degradation following human-induced
disturbances may facilitate the native invasion of downstream fish fauna
to headwater streams (Dala-Corte et al., 2019). From the fish data, the
frequency occurrence (FO ) and relative abundance (RA ) were
calculated for each species as FO i =
100(S i/S ) % andRA i = 100(N i/N ) %,
where S i and S are the numbers of the
samplings of which species i was collected and of the total
samples, N i and N are the individual
numbers of species i and of total fish species, respectively.
Meanwhile, based on the number of species and number of samples (the
reference sites and treatment sites), the species accumulation curves
were constructed in R software (R Core Team, 2018) utilizing the
“vegan ” package (Appendix S2).
Taxonomic similarity of fish assemblages was quantified by using the
Bray-Curtis similarity index in this study. Specially, site ×species matrices were created separately for both the
impoundments (Im ) and free-flowing segments (Fr ) based the
abundance data (Figure 2a). Then, the taxonomic similarity was
separately calculated separately for each habitat (impoundments:Im.TS ; free-flowing segments: Fr.TS ). Changes in pairwise
taxonomic similarity (taxonomic ΔCS) were calculated between the
impoundments and free-flowing segments, as follows: taxonomic ΔCS =Im.TS – Fr.TS (Figure 2a). For functional similarity, we
first obtained the community-weighted mean of trait values per site
(CWM) for both the impoundments and free-flowing segments using
abundance data. The site × trait matrices (CWM)
were created by multiplying the site × speciesmatrix and species × trait matrix for each habitat
type (Figure 2b). The functional similarity was separately calculated
for the impoundment assemblages (Im.FS ) and assemblages sampled
in the free-flowing segments (Fr.FS ), according to the Euclidean
distance (Dala-Corte et al., 2019; Daga et al., 2020). Following the
same approach used for the taxonomic analysis, changes in pairwise
functional similarity (functional ΔCS) were calculated as follows:
functional ΔCS = Im.FS – Fr.FS (Figure 2b). Positive
values for ΔCS indicated taxonomic or functional homogenization, whereas
negative values indicated differentiation (Baiser & Lokweed, 2011; Pool
& Olden, 2012; Daga et al., 2020). All the calculations were performed
in R software utilizing the “FD ” package (Laliberté et al.,
2014) and the “vegan ”
package (Oksanen et al., 2013).
Using the log10 (x+1) transformed fish-abundance data,
the one-way analysis of similarity (ANOSIM) was used to identify the
discrete spatial variation in fish assemblages between the two habitat
types. And the contribution of each species to differences among
assemblage groups was assessed using the similarity percentages
(SIMPER). These analyses were conducted in Primer 7.0
software. Paired samples t-tests
were used to test the between-habitat (i.e., impoundments and
free-flowing segments) differences in the mean
taxonomic and functional similarity
of fish assemblages.
Meanwhile, both the principal component analysis (PCA) and permutational
multivariate analysis of variance (PERMANOVA) to test the differences in
local habitat between the impoundments and free-flowing segments based
on the standardized environmental variables. We further used the
permutational analysis of multivariate dispersions (PERMDISP; Anderson,
2006) to test the differences in the environmental heterogeneity between
the two habitat types. Using analysis of variance (ANOVA) F-statistics,
PERMDISP compares among-group differences in the distance from
observations to their group centroid, and the significance was tested
via permutations of least-squares residuals (Heino et al., 2013). The
Mantel permutation test was used to test the relationships between
changes in taxonomic and functional similarity (taxonomic ΔCS and
functional ΔCS) for abundance-based data. The significance of each
Pearson correlation was calculated using the Mantel test with 9,999
permutations (Nekola & White, 1999). Differences in the observed
percentages
of each quadrant (i.e., homogenization or differentiation) were tested
by Chi-square test with null hypothesis that the proportions were the
same. The above analyses were performed in R software (R Core Team,
2018) utilizing the “vegan ” package (Oksanen et al., 2013).