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NirS-type denitrifying bacteria in aerobic water layers of two drinking water reservoirs: Insights into the abundance, community diversity and co-existence model
Abstract:The nirS-type denitrifying bacterial community is the main drivers of the nitrogen loss process in drinking water reservoir ecosystems. The temporal patterns in nirS gene abundance and nirS-type denitrifying bacterial community harbored in aerobic water layers of drinking water reservoirs have not been studied well. In this study, quantitative polymerase chain reaction (qPCR) and Illumina Miseq sequencing were employed to explore the nirS gene abundance and denitrifying bacterial community structure in two drinking water reservoirs. The overall results showed that the water quality parameters in two reservoirs had obvious differences. The qPCR results suggested that nirS gene abundance ranged from (2.61 ± 0.12) × 105 to (3.68 ± 0.16) × 105 copies/mL and (3.01 ± 0.12) × 105 to (5.36 ± 0.31) × 105 copies/mL in Jinpen and Lijiahe reservoirs, respectively. The sequencing results revealed that Paracoccus sp., Azoarcus sp., Dechloromonas sp. and Thauera sp. were the dominant genera observed. At species level, Cupriavidus necator, Dechloromonas sp. R-28400, Paracoccus denitrificans and Pseudomonas stutzeri accounted for more proportions in two reservoirs. More importantly, the co-occurrence network analysis demonstrated that Paracoccus sp. R-24615 and Staphylococcus sp. N23 were the keystone species observed in Jinpen and Lijiahe reservoirs, respectively. Redundancy analysis indicated that water quality (particularly turbidity, water temperature, pH and Chlorophyll a) and sampling time had significant influence on the nirS-type denitrifying bacterial community in both reservoirs. These results will shed new lights on exploring the dynamics of nirS-type denitrifying bacteria in aerobic water layers of drinking water reservoirs.
Keywords:Corresponding author    Drinking water reservoirs  Aerobic water layers  Denitrifying bacterial community  Co-occurrence network model
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