Shao Siya, Wu Jianghua, He Hongxing, Roulet Nigel
Department of Geography, McGill University, Canada.
Environment and Sustainability, School of Science and the Environment, Memorial University of Newfoundland, Canada.
Sci Total Environ. 2022 Feb 1;806(Pt 3):151223. doi: 10.1016/j.scitotenv.2021.151223. Epub 2021 Oct 27.
Peatlands store a large amount of organic carbon and are vulnerable to climate change and human disturbances. However, ecosystem-scale peatland models often do not explicitly simulate the decrease in peat substrate quality, i.e., decomposability or the dynamics of decomposers during peat decomposition, which are key controls in determining peat carbon's response to a changing environment. In this paper, we incorporated the tracking of each year's litter input (a cohort) and controls of microbial processes into the McGill Wetland Model (MWMmic) to address this discrepancy. Three major modifications were made: (1) the simple acrotelm-catotelm decomposition model in MWM was changed into a time-aggregated cohort model, to track the decrease in peat quality with decomposition age; (2) microbial dynamics: growth, respiration and death were incorporated into the model and decomposition rates are regulated by microbial biomass; and (3) vertical and horizontal transport of the dissolved organic carbon (DOC) were added and used to regulate the growth of microbial biomass. MWMmic was evaluated against measurements from the Mer Bleue peatland, a raised ombrotrophic bog located in southern Ontario, Canada. The model was able to replicate microbial and DOC dynamics, while at the same time reproduce the ecosystem-level CO and DOC fluxes. Sensitivity analysis with MWMmic showed increased peatland resilience to perturbations compared to the original MWM, because of the tracking of peat substrate quality. The analysis revealed the most important parameters in the model to be microbial carbon use efficiency (CUE) and turnover rate. Simulated microbial adaptation with those two physiological parameters less sensitive to disturbances leads to a significantly larger peat C loss in response to warming and water table drawdown. Thus, the rarely explored peatland microbial physiological traits merit further research. This work paves the way for further model development to examine important microbial controls on peatland's biogeochemical cycling.
泥炭地储存着大量有机碳，且易受气候变化和人类干扰影响。然而，生态系统尺度的泥炭地模型通常未明确模拟泥炭底物质量的下降，即泥炭分解过程中的可分解性或分解者动态，而这些是决定泥炭碳对变化环境响应的关键控制因素。在本文中，我们将每年凋落物输入（一个群组）的追踪以及微生物过程的控制纳入麦吉尔湿地模型（MWMmic），以解决这一差异。进行了三项主要修改：（1）将MWM中简单的高位泥炭-低位泥炭分解模型改为时间聚合群组模型，以追踪泥炭质量随分解年龄的下降；（2）微生物动态：将生长、呼吸和死亡纳入模型，分解速率由微生物生物量调节；（3）添加了溶解有机碳（DOC）的垂直和水平传输，并用于调节微生物生物量的生长。MWMmic根据加拿大安大略省南部一个高位雨养泥炭沼泽——梅尔布卢泥炭地的测量数据进行了评估。该模型能够复制微生物和DOC动态，同时再现生态系统水平的CO和DOC通量。对MWMmic的敏感性分析表明，与原始MWM相比，由于对泥炭底物质量的追踪，泥炭地对扰动的恢复力增强。分析揭示模型中最重要的参数是微生物碳利用效率（CUE）和周转率。用这两个对干扰不太敏感的生理参数模拟微生物适应，会导致泥炭地因变暖和地下水位下降而产生显著更大的碳损失。因此，鲜有研究的泥炭地微生物生理特性值得进一步研究。这项工作为进一步的模型开发铺平了道路，以研究泥炭地生物地球化学循环中重要的微生物控制因素。
