The effects of land use patterns on carbon and nitrogen cycling microbe in a river of the upper reaches of the Yellow River
In this study,the Qingshui River,a typical river in arid and semi-arid areas of the upper reaches of the Yellow River,was investigated.The metagenomic technology was used to reveal the impact of land use on carbon and nitrogen cycling microorganisms in the water bodies.The findings revealed the water quality was less polluted in forest reaches,the average concentrations of NH4+-N,NO3--N,TN,and COD were 0.19,1.35,1.66,and 21.92mg/L,respectively.Conversely,the NH4+-N(4.23mg/L)and COD(48.8mg/L)concentration elevated in the water body in urban and rural reaches,while NO3--N(21.62mg/L)and TN(31.59mg/L)contents increased in agricultural reaches.The relative abundance of the nitrification genes amoAB,nxrAB and the carbon cycling genes sucCD,IDH13,mdh,porAB,and gap2 was higher in forested reaches,while the relative abundance of the denitrification genes napAB,nirKS,and nosZ was higher in urban and rural area reaches,and in agricultural area reaches.The dominant microbial species of carbon and nitrogen functional were higher in forested reaches and urban and rural reaches with 20 species than in agricultural reaches with 15species.The microbial network showed that the number of correlation edges was less than 35 in urban and rural reaches and agricultural reaches,and more than 35 in forested reaches,thus the microbial network structures were notably more complex in forested reaches.In urban and rural reaches,Paracoccus and Sphingomonas were significantly negatively correlated with NH4+-N concentration,while Thalassiosira were significantly positively correlated with NO3--N concentration.In agricultural reaches,Leptothrix and Methylocystis were significantly negatively correlated with NH4+-N and COD contents;while Acidovorax,Flavobacterium,and Hydrogenophaga were significantly negatively correlated with NO3--N concentration.
upper reaches of the Yellow Riverriverland usecarbon-nitrogen cyclemicroorganisms
万方数据
维普
