Department of Agricultural and Biological Engineering, University of Illinois at Urbana–Champaign, Urbana, IL, USA.
Microb Ecol. 2014 Feb;67(2):265-72. doi: 10.1007/s00248-013-0286-0.
Denitrifying biofilters can remove agricultural nitrates from subsurface drainage, reducing nitrate pollution that contributes to coastal hypoxic zones. The performance and reliability of natural and engineered systems dependent upon microbially mediated processes, such as the denitrifying biofilters, can be affected by the spatial structure of their microbial communities. Furthermore, our understanding of the relationship between microbial community composition and function is influenced by the spatial distribution of samples.In this study we characterized the spatial structure of bacterial communities in a denitrifying biofilter in central Illinois. Bacterial communities were assessed using automated ribosomal intergenic spacer analysis for bacteria and terminal restriction fragment length polymorphism of nosZ for denitrifying bacteria.Non-metric multidimensional scaling and analysis of similarity (ANOSIM) analyses indicated that bacteria showed statistically significant spatial structure by depth and transect,while denitrifying bacteria did not exhibit significant spatial structure. For determination of spatial patterns, we developed a package of automated functions for the R statistical environment that allows directional analysis of microbial community composition data using either ANOSIM or Mantel statistics.Applying this package to the biofilter data, the flow path correlation range for the bacterial community was 6.4 m at the shallower, periodically in undated depth and 10.7 m at the deeper, continually submerged depth. These spatial structures suggest a strong influence of hydrology on the microbial community composition in these denitrifying biofilters. Understanding such spatial structure can also guide optimal sample collection strategies for microbial community analyses.
反硝化生物滤器可以从地下排水中去除农业硝酸盐,减少导致沿海缺氧区的硝酸盐污染。依赖微生物介导过程的自然和工程系统的性能和可靠性,例如反硝化生物滤器,可能会受到其微生物群落空间结构的影响。此外,我们对微生物群落组成和功能之间关系的理解受到样本空间分布的影响。在这项研究中,我们描述了伊利诺伊州中部反硝化生物滤器中细菌群落的空间结构。使用细菌的自动核糖体基因间隔区分析和反硝化细菌的末端限制性片段长度多态性评估细菌群落。非度量多维尺度分析和相似性分析(ANOSIM)分析表明,细菌在深度和横截面上表现出统计学上显著的空间结构,而反硝化细菌没有表现出显著的空间结构。为了确定空间模式,我们为 R 统计环境开发了一套自动功能包,允许使用 ANOSIM 或 Mantel 统计数据对微生物群落组成数据进行定向分析。将该软件包应用于生物滤器数据,细菌群落的流路相关范围在较浅、定期未淹没的深度为 6.4 m,在较深、持续淹没的深度为 10.7 m。这些空间结构表明水力学对这些反硝化生物滤器中微生物群落组成有很强的影响。了解这种空间结构还可以为微生物群落分析指导最佳样本收集策略。