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[Objective] To clarify the spatial distribution characteristics of soil organic carbon (SOC) age and microbial diversity, explore the relationship of microbial diversity and network complexity with SOC age, and quantitatively assess the relative contributions of microbial diversity, network complexity, climate, vegetation, and soil properties to SOC age. [Methods] Using global soil radiocarbon (Δ14C) data and environmental variable data, we constructed nine machine learning models for predicting SOC age and selected the best-performing model. Based on global soil microbial 16S rRNA gene data and environmental variable data, microbial network analysis, multiple regression analysis, random forest models, and structural equation modeling were employed to analyze the correlation between SOC age and soil microorganisms and identify the main driving factors of SOC age. [Results] Soil microbial richness decreased with the rise in absolute latitude (P<0.001), being higher near the equator and lower at higher latitudes. Among the nine machine learning models constructed, the rule regression model showed the best prediction performance (R2=0.77, RMSE=0.84). Soil microbial richness and Shannon index were negatively correlated with absolute latitude and SOC age (P<0.001). The global soils were classified into young (44-171 a), middle-aged (172-321 a), and old (322-5 035 a) soil groups, and the network densities followed a trend of young soil group (0.400)>middle-aged soil group (0.285)>old soil group (0.125). Multiple regression analysis, random forest models, and structural equation modeling all showed that microbial network complexity explained the largest portion of SOC age variation (34%), far surpassing vegetation (10%) and climate (6%). [Conclusion] Global soil SOC age has significantly negative correlations with soil microbial diversity and network complexity. The soil with old SOC has lower microbial diversity and simpler microbial network structure. Microbial network complexity is a key factor influencing SOC age, and its impact is significantly greater than that of vegetation and climate. These results provide new insights into the driving mechanisms of SOC age and suggest that future models of SOC dynamics should fully consider the role of microbial interaction network.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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