Community diversity and microbiota composition
Overall, 7,463,182 raw sequence reads were obtained from 104 water and
sediment samples. After quality filtering, 1,450,613 clean reads were
obtained and then clustered into 1352 ASVs. To determine whether the
sampling depth was sufficient to give an overview of the microbiota,
rarefaction curves were generated
for the number of ASVs per
individual or species (Fig. S1). The alpha diversity indices of
microbiota in water and sediment are presented in
Fig. S2.
Taking
into account the richness, Shannon, Simpson, Pielou, Chao, and ACE
indices, microbial alpha diversity was higher in Hanjiang River than in
the Weihe River and Qinling tributaries. In Weihe River, all the alpha
diversity indices in autumn were lower than those in spring.
The microbiota in water samples comprised 14 bacterial phyla and 73
bacterial families. Proteobacteria, Bacteroidetes, Actinobacteria, and
Firmicutes were identified as the
dominant phyla, accounting for >98% of all sequence reads.
In addition, there were 16 dominant families (including Comamonadaceae,
Flavobacteriaceae, Cytophagaceae, Moraxellaceae, and
Enterobacteriaceae), constituting >84% of all sequence
reads (Fig. S4a). The microbiota in sediment samples comprised 14
bacterial phyla and 75 bacterial families. Proteobacteria, Bacteroidetes
Firmicutes, Planctomycetes, Actinobacteria, Acidobacteria, and
Cyanobacteria were identified as the dominant phyla, amounting to
>96% of all sequence reads. In addition, there were 23
dominant families (including Comamonadaceae, Flavobacteriaceae,
Xanthomonadaceae, Rhodobacteraceae, Rhodocyclaceae, and
Sphingomonadaceae), constituted >81% of all sequence
reads. A notable finding was that large numbers of Acinetobacterspp. occurred at the W9, W10, and W11 sites, which mainly fell under
agricultural and urbanized land uses in spring (Fig. S4).
The
ASVs
from the water and sediment samples were clustered into distinct groups
between autumn and spring
following UPGMA clustering based
on Bray-Curtis distances (Fig. S4). The ASVs from water samples of most
sites in Weihe River were clustered into one group in autumn. The
cluster distances of ASVs in Qinling tributaries and Hanjiang River were
close. Similar results for ASV clusters were obtained for sediment
samples (Fig. S4b). NMDS ordination results showed significant
differences in the compositions of microbial communities between
different river areas (water in autumn: ANOSIM R = 0.265,P = 0.001, stress = 0.093; water in
spring: ANOSIM R = 0.243,P = 0.001, stress = 0.104; sediment in autumn: ANOSIMR = 0.294, P = 0.001, stress = 0.145; sediment in
spring: ANOSIM R = 0.362, P = 0.001, stress =
0.122). The UPGMA tree and NMDS clustering results of microbiota in
water samples were consistent with the PCoA results of land-use types in
Weihe River, Qinling River, and Hanjiang tributaries (Fig. 3, Fig.
2a).