Where IndVal is the Indicator Value of species i in site
cluster j , Aij , is a measure of
specificity, Nindividualsij is the mean number of
individuals of species i across sites of group j, while
Nindividualsi. is the sum of the mean numbers of
individuals of species i over all groups. B, is a measure
of fidelity, Nsitesij is the number of sites in
cluster j where species i is present, while
Nsites.j is the total number of sites in that
cluster. Bij is maximum when species i is
present in all objects of cluster j. Indval greater than 50%
were regarded as criteria to determine indicator species.
Five index of macroinvertebrate community namely species richness,
abundance, biomass, Shannon’s diversity, and Pielou’s evenness were also
determined in this study.
Statistical analysis
Prior to analysis, the macroinvertebrate abundance data was
log-transformed Hellinger-transformed for SOM and Redundancy analysis
(RDA), respectively. With exception of pH, all the environmental
variables were log-transformed to satisfy the normality and variance
assumption before doing PCA and RDA analysis. To analyze whether the
classification of macroinvertebrate community was affected by
environmental variables, Principal component analyses (PCA) was
conducted to test the variation of environmental variables in each
group, and the correlation between environmental variables was
evaluated. Kruskal-Wallis test was performed to determine the important
variables affecting the classification of macroinvertebrate community.
The relationship between environmental variables and macroinvertebrate
species composition were evaluated through RDA using rda function in the
vegan package (Oksanen et al., 2013) of the R (version 3.6.3)
statistical software (Team, 2019). Variance inflation factors (VIF) was
used to test multicollinearity among environmental variables. Stepwise
forward selection (Monte Carlo test with 999 permutations) was used to
determine the environmental variables significantly correlated with the
macroinvertebrate species. The statistical significance of
species-environment correlations for the ordination axes were also
determined based on 999 Monte Carlo permutation tests, and the
eigenvalues of the first 2 axes were used to measure their importance
(Ter Braak & Verdonschot, 1995). Spearman correlation analysis was used
to evaluate the response of these community index to environmental
variables. The two-tailed Student’s t test (T-test) was used to
test for significance (P < 0.05), while P -values
were adjusted using the multiple comparisons test (Benjamini &
Hochberg, 1995). SOM was conducted using the ANN Toolbox
on Matlab software, R2010b (The MathWorks Inc., Natick, MA, USA).
Shannon’s diversity and Pielou’s evenness were calculated in Primer
(ver. E-v5) (Clarke & Gorley, 2001). K -means clustering analysis
were performed in the vegan package (Oksanen et al., 2013) of the R
(version 3.6.3) statistical software (Team, 2019). IndVal, PCA, ANOVA,
and spearman correlation analysis were performed in the labdsv package
(Roberts & Roberts, 2016), ade4 package (Dray & Siberchicot, 2020),
agricolae package (de Mendiburu & de Mendiburu, 2019), and psych
package (Revelle & Revelle, 2015) of R statistical
software.
Results
A
total of 44 macroinvertebrate taxa were collected and identified from
all the sampling points during the study period which included 23
aquatic insects, 10 gastropods, 4 bivalves, 4 oligochaetes, 2 leeches
and 1 crustacean (see Appendix S1in Table S2). Simple structure index
(SSI) showed that when the neurons in the SOM output layer were divided
into five groups, the clustering quality was the highest (Fig. 2a). SOM
revealed both spatial and seasonal variation in the classification of
macroinvertebrate community (Fig. 2b). Most of the sampling points in
the autumn were grouped in Group I in the left-top area of the map.
Group V located at the right-bottom area of the map mainly included the
upper reaches of Lianhuan lake measured in spring and summer. All
seasonal sampling points of Habuta Lake, which was farther away from
other lakes, were grouped in Group II in the left-bottom area of the
map. Most seasonal sampling points in the south-central part of Lianhuan
Lake were grouped in Group III in the top area of the map. Most seasonal
sampling points in the eastern part of Lianhuan Lake, which was closer
to Durbote County, were grouped in Group IV in the right-top area of the
map.
Variations of environmental variables, including WT, COND, pH, DO, TP,
NH4-N, NO3-N, and CODMnin water in all seasons across the Lianhuan Lake are summarized in Fig.
4. All the environmental variables presented were statistically
significant different among the groups. Group I which principally
encompasses samples that were taken in autumn in the top-left area of
the Lianhuan Lake, was characterized by lower values of WT,
NH4-N and CODMn and high values of DO,
COND, TP, and NO3-N. Group II which include sampling
points located in Habuta lake on the bottom-left area of the Lianhuan
Lake was characterized by high WT, pH and CODMn values.
High pH values and relatively low CODMn characterized
sampling points grouped in Group III located at the top area (South) of
the map. Group IV which encompasses samples that were taken in spring
and autumn at the bottom-right area of the map and included the upper
reaches of Lianhuan lake was characterized by high values of WT, pH, and
NH4-N and low values of DO and NO3-N.
Sampling sites which were located closer to Durbote County grouped in
Group V were characterized by high WT, DO and NH4-N.
According to IndVal ≥50% criterion, a total of 29 macroinvertebrates
were found to be useful as indicator species for different groups (Table
1). However, 13 species with indicator values lower than 50%
(31.89-48.98%) was also considered to be significant and important for
particular groups. There were significant variations in the indicator
species and the number of indicator species among the five groups. Group
II had the most diverse indicator species, including one crustacean,
three
annelids,
four
molluscs,
and five aquatic insects. Group III had only two indicator species, both
of which belong to the Chironomidae. The indicator species of groups I
and IV were dominated by Chironomidae and Mollusca. Group V indicator
species were mainly characterized by Mollusca. It is worth noting that
many indicator species such as Anatopynia sp and G.
pervia , G. albus were distributed in two or more groups (see
Appendix S1 in Table S2), implying that the differences in the indicator
species between groups mainly resulted from differences in the abundance
of the taxa for each group.
The PCA using 13 environmental variables explained 44.9% of the data
variability in the first two axes (axis 1 = 23.8% of the total variance
with eigenvalues of 3.10 and axis 2= 21.1% of the total variance with
eigenvalues of 2.74). In axis 1, the most important variables which were
positive correlated were NO2-N, TP, pH, SS, Chla, and DO
while TN and WD were negatively correlated. Axis 1 fundamentally
distinguished 2 groups: Group I and Group V. With respect to
axis 2, the most important
environmental variables which were positive correlated are TP, WT,
NH4-N, and CODMn. Total phosphorus (TP),
NO3-N and COND were negatively correlated with axis 2.
Kruskal-Wallis test results indicated that pH, TP,
NO3-N, WT, DO, COND, CODMn, and
NH4-N had a significant effect on the classification of
macroinvertebrate community (Fig. 4).
The
RDA ordination of the macroinvertebrate composition with respect to
environmental variables are presented in Fig. 5. Using the function
ordistep from vegan package to conduct forward selection and screening
of the environmental variables yielded 4 variables that were significant
to the model. These variables were WT, pH, DO and Chla (Fig. 5). These 4
variables accounted for 77% of the total variance in the
macroinvertebrate species composition. The first RDA axis which
explained 45.3% of the total variability was positively correlated to
WT while the second axis which explained 32.4% variability was positive
correlated with pH. Among the strongest species-environment
associations, we found that
molluscs,
such as G. albus , R. pereger , and S. glabra were
significantly positively correlated with WT and negatively to pH and
Chla. Annelids, such as B. sowerkyi and Herpobdella sp,
were significantly positively correlated with Chla and DO, and
negatively to pH. Aquatic insects, such as Chaoborus sp,Ephemera sp, and Anatopynia sp were were significantly
positively correlated with Chla and DO,and negatively to WT.
From
the Spearman correlation analysis
(Table
2), the community index of
macroinvertebrates was significantly affected by environmental
variables. Macroinvertebrate abundance was most affected by
environmental variables and was significantly negatively correlated with
DO (R = -0.40, P =
0.01), NO2-N
(R = -0.33, P =
0.04) and Chla (R = -0.32,P = 0.04). Both species
richness and Shannon’s diversity were significantly negatively
correlated with TP (R =
-0.35, P = 0.04 and R = -0.34, P = 0.04). The
biomass of macroinvertebrates was significantly negatively correlated
with pH (R = -0.39,P = 0.01).