Institute for Environmental Genomics, University of Oklahoma, Norman, OK, USA.
Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK, USA.
ISME J. 2019 Dec;13(12):2901-2915. doi: 10.1038/s41396-019-0485-x. Epub 2019 Aug 5.
The susceptibility of soil organic carbon (SOC) in tundra to microbial decomposition under warmer climate scenarios potentially threatens a massive positive feedback to climate change, but the underlying mechanisms of stable SOC decomposition remain elusive. Herein, Alaskan tundra soils from three depths (a fibric O horizon with litter and course roots, an O horizon with decomposing litter and roots, and a mineral-organic mix, laying just above the permafrost) were incubated. Resulting respiration data were assimilated into a 3-pool model to derive decomposition kinetic parameters for fast, slow, and passive SOC pools. Bacterial, archaeal, and fungal taxa and microbial functional genes were profiled throughout the 3-year incubation. Correlation analyses and a Random Forest approach revealed associations between model parameters and microbial community profiles, taxa, and traits. There were more associations between the microbial community data and the SOC decomposition parameters of slow and passive SOC pools than those of the fast SOC pool. Also, microbial community profiles were better predictors of model parameters in deeper soils, which had higher mineral contents and relatively greater quantities of old SOC than in surface soils. Overall, our analyses revealed the functional potential of microbial communities to decompose tundra SOC through a suite of specialized genes and taxa. These results portray divergent strategies by which microbial communities access SOC pools across varying depths, lending mechanistic insights into the vulnerability of what is considered stable SOC in tundra regions.
在气候变暖的情景下，土壤有机碳 (SOC) 更容易受到微生物分解，这可能对气候变化产生巨大的正反馈，但稳定 SOC 分解的潜在机制仍不清楚。本研究中，对来自阿拉斯加三种不同深度（富含有机质的 O 层，包含凋落物和粗根；O 层，含有分解中的凋落物和根；以及位于永冻层之上的矿物质-有机物混合层）的冻原生态土壤进行了培养。将得到的呼吸数据纳入到 3 库模型中，以获得快速、慢速和惰性 SOC 库的分解动力学参数。在整个 3 年的培养过程中，对细菌、古菌和真菌分类群以及微生物功能基因进行了分析。相关性分析和随机森林方法揭示了模型参数与微生物群落特征、分类群和特征之间的关联。在慢速和惰性 SOC 库的模型参数与微生物群落数据之间存在更多关联，而在快速 SOC 库中则较少。此外，微生物群落特征在深层土壤中对模型参数的预测能力更强，因为这些土壤的矿物质含量较高，且相对而言老 SOC 的含量也更高。总的来说，我们的分析揭示了微生物群落通过一系列专门的基因和分类群来分解冻原生态 SOC 的功能潜力。这些结果描绘了微生物群落通过不同策略获取不同深度 SOC 库的情况，为了解冻原生态区被认为是稳定的 SOC 的脆弱性提供了机制上的见解。
