ABSTRACT
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.
Subject(s)
Rainforest , Soil , Soil/chemistry , Carbon , Soil Microbiology , Forests , Nitrogen/analysisABSTRACT
The spatial distribution of plant, soil, and microbial carbon pools, along with their intricate interactions, presents a great challenge for the current carbon cycle research. However, it is not clear what are the characteristics of the spatial variability of these carbon pools, particularly their cross-scale relationships. We investigated the cross-scale spatial variability of microbial necromass carbon (MNC), soil organic carbon (SOC) and plant biomass (PB), as well as their correlation in a tropical montane rainforest using multifractal analysis. The results showed multifractal spatial variations of MNC, SOC, and PB, demonstrating their adherence to power-law scaling. MNC, especially low MNC, exhibited stronger spatial heterogeneity and weaker evenness compared with SOC and PB. The cross-scale correlation between MNC and SOC was stronger than their correlations at the measurement scale. Furthermore, the cross-scale spatial variability of MNC and SOC exhibited stronger and more stable correlations than those with PB. Additionally, this research suggests that when SOC and PB are both low, it is advisable for reforestations to potentiate MNC formation, whereas when both SOC and PB are high some thinning can be advisable to favour MNC formation. Thus, these results support the utilization of management measures such as reforestation or thinning as nature-based solutions to regulate carbon sequestration capacity of tropical forests by affecting the correlations among various carbon pools.
Subject(s)
Carbon Sequestration , Rainforest , Carbon , Soil , ForestsABSTRACT
Identifying the complexity of diversity pattern of various taxa within a community is a challenge for ecologist. Scaling law is one of the suitable ways to detecting the complex ecological structure. In this study, we explored the scaling laws of soil fauna diversity pattern along an altitudinal gradient by multifractal analysis, and compared the difference of multifractal spectra between the litter and the soil layers. Consistent with results from plant communities in previous studies, there was power law scaling law for soil fauna diversity, i.e., richness, the exponential of Shannon's Diversity Index, and the inverse Simpson's Diversity Index. Moreover, power law scaling law also existed for the richness changes of different relative abundance species in both litter and soil layers. Although multifractal characteristics existed for both litter layer and soil layer of soil fauna diversity, the fractal structure of the diversity in the litter layer was more even than that in the soil layer, and the scaling properties of dominant and rare species showed different patterns in multifractal spectra between litter layer and soil layer. In conclusion, there were power law scaling laws for soil fauna diversity which had high richness and abundance along the altitudinal gradient, which would help us uncovering the spatial distribution mechanism of belowground biodiversity.
Subject(s)
Biodiversity , Soil , Beijing , China , Ecosystem , PlantsABSTRACT
Identifying interspecific associations is very important for understanding the community assembly process. However, most methods provide only an average association and assume that the association strength does not vary along the environmental gradient or with time. The scale effects are generally ignored. We integrated the idea of wavelet and network topological analysis to provide a novel way to detect nonrandom species associations across scales and along gradients using continuous or presence-absence ecological data. We first used a simulated species distribution data set to illustrate how the wavelet correlation analysis builds an association matrix and demonstrates its statistical robustness. Then, we applied the wavelet correlation network to a presence-absence data set of soil invertebrates. We found that the associations of invertebrates varied along an altitudinal gradient. We conclude by discussing several possible extensions of this method, such as predicting community assembly, utility in the temporal dimension, and the shifting effects of highly connected species within a community. The combination of the multiscale decomposition of wavelet and network topology analysis has great potential for fostering an understanding of the assembly and succession of communities, as well as predicting their responses to future climate change across spatial or temporal scales.
