Coupling Spatiotemporal Community Assembly Processes to Changes in Microbial Metabolism.
Coupling Spatiotemporal Community Assembly Processes to Changes in Microbial Metabolism.
复制标题
将时空群落组装过程与微生物代谢变化相耦合。
DOI：
10.3389/fmicb.2016.01949
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发表时间：
2016
影响因子：
5.2
通讯作者：
Stegen JC
中科院分区：
文献类型：
--
作者：
Graham EB;Crump AR;Resch CT;Fansler S;Arntzen E;Kennedy DW;Fredrickson JK;Stegen JC
群落组装过程会导致物种丰度发生变化，从而影响生态系统的碳和养分循环，但我们对组装的理解在很大程度上仍然与生态系统层面的功能分离。在这里，我们研究了潜流微生物群落中微生物代谢随空间和时间的变化之间的关系。我们通过季节性和每小时的水文波动对两种栖息地类型（即附着和浮游）进行配对采样，并使用零模型和时间明确的多元统计数据。我们证明，沉积物和孔隙水物理化学施加的多种选择压力综合起来，在不同的生境类型的不同时间尺度上产生微生物群落组成的变化。这些成分的变化反映了β变形菌门和奇古菌门与生态选择和微生物代谢季节变化之间的对比关联。我们根据我们的结果提出了一个概念模型，其中当振荡选择压力与暂时稳定的选择压力相反时，新陈代谢会增加。我们的概念模型与经历多重选择压力的宏观和微生物系统相关，并提供了将群落组装过程同化到生态系统水平功能预测的途径。
Community assembly processes generate shifts in species abundances that influence ecosystem cycling of carbon and nutrients, yet our understanding of assembly remains largely separate from ecosystem-level functioning. Here, we investigate relationships between assembly and changes in microbial metabolism across space and time in hyporheic microbial communities. We pair sampling of two habitat types (i.e., attached and planktonic) through seasonal and sub-hourly hydrologic fluctuation with null modeling and temporally explicit multivariate statistics. We demonstrate that multiple selective pressures—imposed by sediment and porewater physicochemistry—integrate to generate changes in microbial community composition at distinct timescales among habitat types. These changes in composition are reflective of contrasting associations of Betaproteobacteria and Thaumarchaeota with ecological selection and with seasonal changes in microbial metabolism. We present a conceptual model based on our results in which metabolism increases when oscillating selective pressures oppose temporally stable selective pressures. Our conceptual model is pertinent to both macrobial and microbial systems experiencing multiple selective pressures and presents an avenue for assimilating community assembly processes into predictions of ecosystem-level functioning.