Cross-scale spatial variability and associations of carbon pools provide insight into regulating carbon sequestration in tropical montane rainforests

Spatial variation and controls of soil microbial necromass carbon in a tropical montane rainforest

Soil microbial necromass carbon is an important component of the soil organic carbon (SOC) pool which helps to improve soil fertility and texture. However, the spatial pattern and variation mechanisms of fungal- and bacterial-derived necromass carbon at local scales in tropical rainforests are uncertain. This study showed that microbial necromass carbon and its proportion in SOC in tropical montane rainforest exhibited large spatial variation and significant autocorrelation, with significant high-high and low-low clustering patterns. Microbial necromass carbon accounted for approximately one-third of SOC, and the fungal-derived microbial necromass carbon and its proportion in SOC were, on average, approximately five times greater than those of bacterial-derived necromass. Structural equation models indicated that soil properties (SOC, total nitrogen, total phosphorus) and topographic features (elevation, convexity, and aspect) had significant positive effects on microbial necromass carbon concentrations, but negative effects on its proportions in SOC (especially the carbon:nitrogen ratio). Plant biomass also had significant negative effects on the proportion of microbial necromass carbon in SOC, but was not correlated with its concentration. The different spatial variation mechanisms of microbial necromass carbon and their proportions in SOC are possibly related to a slower accumulation rate of microbial necromass carbon than of plant-derived organic carbon. Geographic spatial correlations can significantly improve the microbial necromass carbon model fit, and low sampling resolution may lead to large uncertainties in estimating soil carbon dynamics at specific sites. Our work will be valuable for understanding microbial necromass carbon variation in tropical forests and soil carbon prediction model construction with microbial participation.