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Mapping soil microbial residence time at the global scale
Global Change Biology ( IF 10.8 ) Pub Date : 2021-09-06 , DOI: 10.1111/gcb.15864
Liyuan He 1 , Xiaofeng Xu 1
Affiliation  

Soil microbes are the fundamental engine for carbon (C) cycling. Microbial residence time (MRT) therefore determines the mineralization of soil organic C, releasing C as heterotrophic respiration and contributing substantially to the C efflux in terrestrial ecosystems. We took use of a comprehensive dataset (2627 data points) and calculated the MRT based on the basal respiration and microbial biomass C. Large variations in MRT were found among biomes, with the largest MRT in boreal forests and grasslands and smallest in natural wetlands. Biogeographic patterns of MRT were found along climate variables (temperature and precipitation), vegetation variables (root C density and net primary productivity), and edaphic factors (soil texture, pH, topsoil porosity, soil C, and total nitrogen). Among environmental factors, edaphic properties dominate the MRT variations. We further mapped the MRT at the global scale with an empirical model. The simulated and observed MRT were highly consistent at plot- (R2= .86), site- (R2 = .88), and biome- (R2 = .99) levels. The global average of MRT was estimated to be 38 (±5) days. A clear latitudinal biogeographic pattern was found for MRT with lower values in tropical regions and higher values in the Arctic. The biome- and global-level estimates of MRT serve as valuable data for parameterizing and benchmarking microbial models.

中文翻译:

在全球范围内绘制土壤微生物停留时间图

土壤微生物是碳 (C) 循环的基本引擎。因此,微生物停留时间 (MRT) 决定了土壤有机碳的矿化,以异养呼吸的形式释放 C,并对陆地生态系统中的 C 外排做出重大贡献。我们使用了一个综合数据集(2627 个数据点),并根据基础呼吸和微生物生物量 C 计算了 MRT。在生物群落中发现 MRT 存在很大差异,其中最大的 MRT 在北方森林和草原中,而在天然湿地中最小。MRT 的生物地理模式随气候变量(温度和降水)、植被变量(根 C 密度和净初级生产力)和土壤因素(土壤质地、pH、表土孔隙度、土壤 C 和总氮)被发现。在环境因素中,土壤特性在 MRT 变化中占主导地位。我们使用经验模型进一步在全球范围内绘制了 MRT。模拟和观察到的 MRT 在情节 - (R 2 = .86)、 站点- ( R 2 = .88) 和生物群落- ( R 2  = .99) 水平。MRT 的全球平均值估计为 38 (±5) 天。发现 MRT 具有明显的纬度生物地理模式,热带地区的值较低,北极地区的值较高。MRT 的生物群落和全球水平估计可作为参数化和基准微生物模型的宝贵数据。
更新日期:2021-11-12
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