Microbial biomass carbon (MBC), a crucial soil labile carbon fraction, is the most active component of the soil organic carbon (SOC) that regulates bio-geochemical processes in terrestrial ecosystems. Some studies in the literature ignore the effect of microbial population growth on carbon decomposition rates. In reality, we might expect that the decomposition rate should be related to the population of microbes in the soil and have a positive relationship with the size of the microbial biomass pool. In this study, we explore the effect of microbial population growth on the accuracy of modelling soil carbon sequestration by developing and comparing two soil carbon models that consider a carrying capacity and limit to the growth of the microbial pool. We apply our models to three datasets, two small and one large datasets, and we select the best model in terms of having the best predictive performance through two model selection methods. Through this analysis we reveal that commonly used complex soil carbon models can over-fit in the presence of both small and large time-series datasets, and our simpler model can produce more accurate predictions. We conclude that considering the microbial population growth in a soil carbon model improves the accuracy of a model in the presence of a large dataset.
翻译:微生物生物量碳(MBC)是管理陆地生态系统生物地球化学过程的土壤有机碳(SOC)中最活跃的组成部分。文献中的一些研究忽略了微生物人口增长对碳分解率的影响。在现实中,我们可能期望分解率应该与土壤微生物种群有关,并与微生物生物量库的规模有着积极的关系。在这项研究中,我们探讨微生物人口增长对土壤碳固存建模型准确性的影响,方法是开发和比较两种土壤碳模型,这些模型考虑承载能力和微生物集合生长的限度。我们把模型应用于三个数据集,两个小的和一个大的数据集,我们选择最佳模型,通过两种模型选择最佳的预测性能。我们通过这一分析发现,常用的复杂土壤碳模型在小型和大型时间序列数据集的存在中都可能过度适用,而我们比较简单的模型可以产生更准确的预测。我们的结论是,考虑到土壤模型中的微生物数量增长,可以提高土壤碳模型的准确性。