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Aquatic Bacterial Communities Associated With Land Use and Environmental Factors in Agricultural Landscapes Using a Metabarcoding Approach

机译:利用元条形码技术与农业景观中土地利用和环境因素相关的水生细菌群落

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摘要

This study applied a 16S rRNA gene metabarcoding approach to characterize bacterial community compositional and functional attributes for surface water samples collected within, primarily, agriculturally dominated watersheds in Ontario and Québec, Canada. Compositional heterogeneity was best explained by stream order, season, and watercourse discharge. Generally, community diversity was higher at agriculturally dominated lower order streams, compared to larger stream order systems such as small to large rivers. However, during times of lower relative water flow and cumulative 2-day rainfall, modestly higher relative diversity was found in the larger watercourses. Bacterial community assemblages were more sensitive to environmental/land use changes in the smaller watercourses, relative to small-to-large river systems, where the proximity of the sampled water column to bacteria reservoirs in the sediments and adjacent terrestrial environment was greater. Stream discharge was the environmental variable most significantly correlated (all positive) with bacterial functional groups, such as C/N cycling and plant pathogens. Comparison of the community structural similarity via network analyses helped to discriminate sources of bacteria in freshwater derived from, for example, wastewater treatment plant effluent and intensity and type of agricultural land uses (e.g., intensive swine production vs. dairy dominated cash/livestock cropping systems). When using metabarcoding approaches, bacterial community composition and coexisting pattern rather than individual taxonomic lineages, were better indicators of environmental/land use conditions (e.g., upstream land use) and bacterial sources in watershed settings. Overall, monitoring changes and differences in aquatic microbial communities at regional and local watershed scales has promise for enhancing environmental footprinting and for better understanding nutrient cycling and ecological function of aquatic systems impacted by a multitude of stressors and land uses.
机译:这项研究应用了16S rRNA基因元条形码技术来表征主要在加拿大安大略省和魁北克省农业支配流域内收集的地表水样品的细菌群落组成和功能属性。用流序,季节和河道流量可以最好地解释成分的异质性。通常,与农业河流为主的低阶河流相比,较大的河流阶次系统(例如大小河流)的群落多样性更高。但是,在相对水流量较低和累积两天降雨的时期,较大河道中的相对多样性有所增加。相对于从小到大的河流系统,细菌群落集合对较小河道中的环境/土地利用变化更为敏感,在较小规模的河流系统中,采样水柱与沉积物和邻近陆地环境中的细菌库的距离更大。溪流排放是与细菌功能组(例如C / N循环和植物病原体)最显着相关(全部为正)的环境变量。通过网络分析比较社区的结构相似性,有助于区分淡水中的细菌来源,例如废水处理厂的废水,农业用地的强度和类型(例如集约化养猪与以乳业为主的现金/畜牧系统) )。当使用元条形码方法时,细菌群落组成和共存模式而不是单个分类谱系是环境/土地利用条件(例如上游土地利用)和流域环境中细菌来源的更好指标。总体而言,在区域和地方流域尺度上监测水生微生物群落的变化和差异,有望增强环境足迹,并更好地了解受多种压力源和土地利用影响的水生系统的养分循环和生态功能。

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