Microbial Diversity In Copper Mine Tailings With Different Oxidized Statuses

The wastes of copper mine tailings is a typical artificial bare, which have extremely harsh conditions. Microbe is an important part of the functional group in the ecosystem. This study was about microbial diversity in copper mine tailings with different oxidized statuses (four different kinds of surface samples Y1, Y2, Y3, Y4 and four different depths samples AO, A, B, C), using 16S rDNA clone library construction and restriction fragment length polymorphism (restriction fragment length polymorphism, RFLP) analysis, rRNA as a molecular marker, while taking advantage of statistical methods, such as Canonical Correlation Analysis (CCA) and Principal Component Analysis (PCA), to analyse the influence that environmental factors had on the bacterial diversity and community composition. The results showed that:(1) There were not significant differences in physical and chemical properties among different kinds of surface samples; four kinds of surface samples with low pH value, between 2.50 and 3.11, were all typical extreme acidic environment samples. The total content of heavy metals, such as Cu, Fe, Zn, Cr, Pb, in the Y3 and Y4 were higher than those in the Y1 and Y2. The main mineral contained in the surface samples was gypsum, which was beyond 99% in the Y1 and Y2. Besides gypsum, Y3 and Y4 also contained quartz and smectite. Through the establishment of bacteria 16S rDNA clone library of four different surface samples and the use of RFLP analysis and sequencing, alignment, phylogenetic tree, it could be found that Betaproteobacteria was the most important group of bacteria in the four kinds of surface samples; Gammaproteobacteria and Alphaproteobacteria could be detected in all surface samples; some Bacteroidetes could be detected in the surface samples, except Y1; there were also some Cyanobacteria in the Y2, Y3 and some Firmicutes in the Y1, Y2; a few Actinobacteria only could be detected in the Y4.(2) There were significant differences in physical and chemical properties among different depths samples in the copper-rich region. The pH value and Total Phosphorus both had obvious positive correlation with depth, but water content and Total Nitrogen both had significant negative correlation with depth. The distribution of total content of heavy metals in different depths samples showed significant differences. That in the layer AO was lower than others obviously; The total content of Cu, Zn, Pb had the max in the layer B; with the increase of depth, the total content of Fe and Cr decreased first, then increased. There were significant differences in the distribution of minerals among different depths samples. The main mineral contained in the layer AO and A was gypsum, which was beyond 97%. The kinds of minerals contained in the layer B and C were rich, such as gypsum, quartz, feldspar, chlorite, garnet, amphibole, smectite, illite, calcite, but the content of quartz was the highest. Through the establishment of bacteria 16S rDNA clone library of four different depths samples in the copper-rich region and the use of RFLP analysis and sequencing, it could be found that Betaproteobacteria was the most important group of bacteria in most samples, but in the layer A, the most important group was Cyanobacteria. Gammaproteobacteria could be detected in each layer, and its quantity distribution proportion decreased with the increase of depth. A few Alphaproteobacteria only could be found in the layer AO and B, and Firmicutes only could be found in the layer AO and A. In additional, a few Bacteroidetes and Planctomycetes only could be detected in the layer A.(3) There were kinds of complex relationship between microbe and environmental factors. Canonical correlation analysis (CCA) found the environmental factors, especially total nitrogen, moisture content, pH, total iron, might have a major impact on microbial community composition. Monte Carlo test results showed that most environmental factors can not be independent of explaining microbial community composition. Principal component analysis (PCA) illustrated microbial community composition could be effected by the combined effects of environmental factors.