How do soil microbes exert impact on soil respiration and its temperature sensitivity?

Abstract

Understanding how soil microorganisms influence the direction and magnitude of soil carbon feedback to global warming is vital to predict future climate change. Although microbial activities are major contributors to soil respiration (R S ) and its temperature sensitivity (Q 10 ), the mechanisms underpinning microbial influence on R S and Q 10 remain unclear. Coupling variation partitioning analysis (VPA), correlation analysis and multiple stepwise linear regression analysis, we illustrate that bacteria mainly affect R S and its temperature sensitivity (Q 10 ) by shifting bacterial community composition (denoted by principal coordinates analysis). We also found that soil water content (SWC) and available nutrient (AN) were the factor key to changing bacterial community composition (P < 0.05). Co-occurrence network demonstrated that Mod 0 ecological cluster composed of copiotrophic taxa groups was significantly associated with R S and Q 10 (P < 0.01, R > 0.5), including Proteobacteria, Actinobacteria, and Bacteroidetes. Illuminating the mechanisms underpinning the influence of soil microbes on R S and Q 10 values is fundamental to understanding mechanistic soil-climate carbon cycles.

Soil temperature, microbial biomass and enzyme activity are the critical factors affecting soil respiration in different soil layers in Ziwuling Mountains, China

“…When the T S is low, the activities of some critical enzymes that control R S decrease, resulting in a lower R S rate. On the monthly scale, with increasing T S , the R S rate starts to increase in April, reaches a maximum value in July, and then gradually decreases to a lower value in October, which is consistent with the results of Tong et al (2021) . Our results differ from the results of Wen et al (2018) .…”

Cyanobacterial‐ and moss‐forming biocrusts consistently decrease the temperature sensitivity of microbial respiration along a continental precipitation gradient

“…Negative (Wang et al, 2018) or no (Ding et al, 2016) association of Q 10 with F:B ratio have been reported. The negative relationship between RII Q 10 and RII F:B reflects the adaptation strategies of fungi and bacteria to temperature (Dell et al, 2014; Tong et al, 2021; Wang et al, 2021). Fungi generally have a greater negative heat capacity than bacteria and thus exhibit a higher Q 10 (Alster et al, 2016; Wang et al, 2018).…”

Dependence of cumulative CO2 emission and microbial diversity on the wetting intensity in drying-rewetting cycles in agriculture soil on the Loess Plateau

“…The modularity values of the bacterial and fungal cooccurrence network that were greater than 0.4 for all DRW treatments, except for the 4-cycle DRW treatment for bacteria, indicated the modular structures of both bacterial and fungal networks (Jiao et al, 2021). The species within the same modulars were reported to have similar ecological niches (Tong et al, 2021). In addition, the bacterial cooccurrence network was more complex (i.e., more nodes and edges and larger average degree) and had more diverse niches (higher modularity) in the 4-cycle DRW treatment, whereas the co-occurrence network for fungi showed the opposite trend, which did not support our second hypothesis.…”