Institute of Crop Science and Resource Conservation (INRES), Molecular Biology of the Rhizosphere, Nussallee 13, 53115, Bonn, Germany.
Department of Geography, University of Bonn, Meckenheimer Allee 166, 53115, Bonn, Germany.
ISME J. 2019 Aug;13(8):2031-2043. doi: 10.1038/s41396-019-0409-9. Epub 2019 Apr 5.
Microbial communities in arctic-alpine soils show biogeographic patterns related to elevation, but the effect of fine-scale heterogeneity and possibly related temperature and soil moisture regimes remains unclear. We collected soil samples from different micro-topographic positions and elevational levels in two mountain regions of the Scandes, Central Norway. Microbial community composition was characterized by 16S rRNA gene amplicon sequencing and was dependent on micro-topography and elevation. Underlying environmental drivers were identified by integration of microbial community data with a comprehensive set of site-specific long-term recorded temperature and soil moisture data. Partial least square regression analysis allowed the description of ecological response patterns and the identification of the important environmental drivers for each taxonomic group. This demonstrated for the first time that taxa responding to elevation were indeed most strongly defined by temperature, rather than by other environmental factors. Micro-topography affected taxa were primarily controlled by temperature and soil moisture. In general, 5-year datasets had higher explanatory power than 1-year datasets, indicating that the microbial community composition is dependent on long-term developments of near-ground temperature and soil moisture regimes and possesses a certain resilience, which is in agreement with an often observed delayed response in global warming studies in arctic-alpine regions.
北极高山土壤中的微生物群落表现出与海拔相关的生物地理模式，但小尺度异质性以及可能相关的温度和土壤水分条件的影响尚不清楚。我们从挪威中部斯堪的纳维亚山脉的两个地区的不同微地形位置和海拔高度采集了土壤样本。通过 16S rRNA 基因扩增子测序来描述微生物群落组成，并依赖于微地形和海拔。通过将微生物群落数据与一套全面的特定地点长期记录的温度和土壤水分数据进行整合，确定了潜在的环境驱动因素。偏最小二乘回归分析允许描述生态响应模式，并确定每个分类群的重要环境驱动因素。这首次证明了对海拔响应的分类群实际上主要受温度而不是其他环境因素的影响。受微地形影响的分类群主要受温度和土壤水分控制。总的来说，5 年数据集比 1 年数据集具有更高的解释能力，这表明微生物群落组成取决于近地面温度和土壤水分条件的长期发展，并具有一定的弹性，这与在北极高山地区进行的全球变暖研究中经常观察到的延迟响应是一致的。
