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Soil organic carbon and soil structure are driving microbial abundance and community composition across the arid and semi-arid grasslands in northern China

机译:土壤有机碳和土壤结构正在推动中国北方干旱和半干旱草原的微生物丰富度和群落组成

摘要

Microbial biogeography through the study of the assembly rules of microbes has the potential to yield ecological information that is generalizable for a wide range of microorganisms. Here we performed a large-scale field investigation of the distribution patterns of microbes across the arid and semi-arid grassland ecosystems covering an area of 690,000 km(2) in northern China. Soil microbial abundance and community composition were examined through quantifying microbial phospholipid fatty acids (PLFAs) at fifty sampling sites along environmental gradients. A multi-model inference analysis identified soil organic carbon (SOC) as a key driving factor for microbial biomass and quantified its effect. Structural equation models (SEM) were further fitted to the data to provide a better mechanistic resolution of direct and indirect pathways that connected PLFAs and environmental variables. The SEM analysis also supported that SOC was the main positive predictor of microbial biomass, while MAT served as the main negative factor via an indirect pathway. To visualize complex relationships between microbial community and environmental variables we engaged in a redundancy analysis. The result showed that PLFA profiles could be largely explained by the soil variables including soil structure (PSD) and pH. Overall, our report through the analysis of an unprecedented amount of primary data yields unique insights into the relative importance of abiotic factors in shaping microbial communities at large scales. (C) 2014 Elsevier Ltd. All rights reserved.
机译:通过研究微生物的组装规则,微生物的生物地理学具有产生生态信息的潜力,该信息可广泛适用于各种微生物。在这里,我们对覆盖中国北方69万平方公里(2)的干旱和半干旱草原生态系统中微生物的分布模式进行了大规模的现场调查。通过沿环境梯度对五十个采样点的微生物磷脂脂肪酸(PLFA)进行定量,可以检查土壤微生物的丰度和群落组成。多模型推理分析确定了土壤有机碳(SOC)是微生物生物量的关键驱动因素,并对其影响进行了量化。将结构方程模型(SEM)进一步拟合到数据中,以提供将PLFA和环境变量联系起来的直接和间接途径的更好的机械分辨率。 SEM分析还支持SOC是微生物生物量的主要正向预测因子,而MAT通过间接途径充当主要负向因子。为了可视化微生物群落与环境变量之间的复杂关系,我们进行了冗余分析。结果表明,PLFA概况可以很大程度上由包括土壤结构(PSD)和pH在内的土壤变量来解释。总体而言,我们的报告通过对前所未有数量的原始数据的分析得出了关于非生物因子在大规模塑造微生物群落中相对重要性的独特见解。 (C)2014 Elsevier Ltd.保留所有权利。

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