来源：生态环境学家（2024年12月12日）
FIGURE 1| Effect of vegetation restoration on microbial carbon use efficiency (CUE) on a local scale. (a) CUE and soil organic carbon (SOC) content along with vegetation restoration. (b) Regression analysis for CUE and SOC content. The solid lines indicate the linear regression (p<0.05), and the gray areas represent the 95% confidence intervals.
FIGURE 2| Key factors affecting microbial carbon use efficiency (CUE) during vegetation restoration on a local scale. (a) Relative importance analysis predicts the importance of biotic and abiotic factors to changes in CUE. (b-g) Relationships between CUE and biotic and abiotic factors. *p<0.05, **p<0.01, ***p<0.001. Solid lines indicate linear regression (p<0.05), and gray areas represent the 95% confidence intervals. BChao1, bacterial Chao 1 index; BD, soil bulk density; BNMDS1, bacterial communities; BSha, bacterial Shannon index; EO:H, oxidative: hydrolytic enzymes ratio; FB, fine root biomass; FC, fine root carbon content; FChao1, fungal Chao 1 index; FNMDS1, fungal communities; FSha, fungal Shannon index; LB, litter biomass; LC, litter carbon content.
FIGURE 3| Drivers of microbial carbon use efficiency (CUE) on a local scale. (a) Partial least squares path modeling showing cascading relationships of ecosystem type, soil properties, plant input properties, microbial properties, and microbial CUE. The red and blue arrows indicate positive and negative effects (p<0.05), respectively. The gray arrows indicate lack of significant effects (p>0.05). The numbers on the arrows represent standardized path coefficients. The width of the arrows is proportional to the strength of the path coefficients.R2indicates the proportion of variance explained. Models were assessed by a goodness-of-fit statistic. (b) Total effects of variables on CUE. EO:H, oxidative: hydrolytic enzyme ratio; FB, fine root biomass; LB, litter biomass.
FIGURE 4| Soil microbial carbon use efficiency (CUE) during vegetation restoration according to the meta-analysis. (a) Sampling locations included field sampling and meta-analysis. (b) The relationships between CUE and soil organic carbon (SOC). The black lines indicate model fit between CUE and SOC, while the gray areas are the 95% confidence intervals of these models. (c) Partial least squares path model showing the influence of abiotic and biotic factors on CUE along a vegetation restoration. The red and blue arrows indicate positive and negative effects (p<0.05), respectively. The gray arrows indicate the lack of significant effects (p>0.05). The numbers on the arrows represent standardized path coefficients. The width of the arrows is proportional to the strength of the path coefficients.R2indicates the proportion of variance explained. Models were assessed by a goodness-of-fit statistics. (d) Total effects of variables on CUE. (e) Relationship between soil pH and CUE. MAP, mean annual precipitation; MAT, mean annual temperature; MBC, microbial biomass carbon; MBN, microbial biomass nitrogen. Map lines delineate study areas and do not necessarily depict accepted national boundaries.
FIGURE 5| Conceptual model of microbial carbon use efficiency (CUE) during natural vegetation restoration from farmland to forest. In the farmland stage, high soil pH and low plant litter prevent SOC from being easily decomposed by the priming effect. Additionally, high fungal diversity and sufficient nutrient availability lead to high CUE. In the forest stage, neutral pH and high plant litter make SOC more susceptible to decomposition through the priming effect (Wang and Kuzyakov 2024b). Concomitant low fungal diversity and severe P limitations lead to a decrease in CUE, despite the soil having a high SOC content. This conceptual diagram indicates an inconsistency between SOC content and CUE in natural ecosystems.
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