Wang Gangsheng, Mayes Melanie A, Gu Lianhong, Schadt Christopher W
Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America ; Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America.
Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America ; Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America.
PLoS One. 2014 Feb 18;9(2):e89252. doi: 10.1371/journal.pone.0089252. eCollection 2014.
Dormancy is an essential strategy for microorganisms to cope with environmental stress. However, global ecosystem models typically ignore microbial dormancy, resulting in notable model uncertainties. To facilitate the consideration of dormancy in these large-scale models, we propose a new microbial physiology component that works for a wide range of substrate availabilities. This new model is based on microbial physiological states and the major parameters are the maximum specific growth and maintenance rates of active microbes and the ratio of dormant to active maintenance rates. A major improvement of our model over extant models is that it can explain the low active microbial fractions commonly observed in undisturbed soils. Our new model shows that the exponentially-increasing respiration from substrate-induced respiration experiments can only be used to determine the maximum specific growth rate and initial active microbial biomass, while the respiration data representing both exponentially-increasing and non-exponentially-increasing phases can robustly determine a range of key parameters including the initial total live biomass, initial active fraction, the maximum specific growth and maintenance rates, and the half-saturation constant. Our new model can be incorporated into existing ecosystem models to account for dormancy in microbially-driven processes and to provide improved estimates of microbial activities.
休眠是微生物应对环境压力的一种重要策略。然而，全球生态系统模型通常忽略微生物休眠，从而导致显著的模型不确定性。为了便于在这些大规模模型中考虑休眠因素，我们提出了一种新的微生物生理学组件，它适用于广泛的底物可用性范围。这个新模型基于微生物生理状态，主要参数是活跃微生物的最大比生长速率和维持速率，以及休眠与活跃维持速率之比。我们的模型相对于现有模型的一个主要改进在于，它能够解释在未受干扰土壤中普遍观察到的低活跃微生物比例现象。我们的新模型表明，底物诱导呼吸实验中呈指数增长的呼吸作用只能用于确定最大比生长速率和初始活跃微生物生物量，而代表指数增长和非指数增长阶段的呼吸数据能够可靠地确定一系列关键参数，包括初始总活生物量、初始活跃比例、最大比生长速率和维持速率，以及半饱和常数。我们的新模型可以纳入现有的生态系统模型中，以考虑微生物驱动过程中的休眠因素，并提供对微生物活动的改进估计。
