Background: Understanding the spatial pattern and driving factors of forest carbon density in mountainous terrain is of great importance for monitoring forest carbon in support of sustainable forest management for mitigating climate change. Methods: We collected the forest inventory data in 2015 in Shanxi Province, eastern Loess Plateau of China, to explore the spatial pattern and driving factors of biomass carbon density (BCD) for natural and planted coniferous forests using Anselin Local Moran's I, Local Getis-Ord G* and semivariogram analyses, and multi-group structural equation modeling, respectively. Results: The result of spatial pattern of BCDs for natural forests showed that the BCD was generally higher in the north but lower in the south of Shanxi. The spatial pattern for planted forests was substantially different from that for natural forests. The results of multi-group SEM suggested that elevation (or temperature as the alternative factor of elevation) and stand age were important driving factors of BCD for these two forest types. Compared with other factors, the effects of latitude and elevation on BCD showed much greater difference between these two forest types. The difference in indirect effect of latitude (mainly through affecting elevation and stand age) between natural and planted forests was to some extent a reflection of the difference between the spatial patterns of BCDs for natural and planted forests in Shanxi. Conclusions: The natural coniferous forests had a higher biomass carbon density, a stronger spatial dependency of biomass carbon density relative to planted coniferous forests in Shanxi. Elevation was the most important driving factor, and the effect on biomass carbon density was stronger for natural than planted coniferous forests. Besides, latitude presented only indirect effect on it for the two forest types.

Mountain forests, accounting for 84.95% of the total forest area, are the most important part of the natural vegetation in China. An assessment of the factors affecting the carbon capture capacity of mountain forests is very crucial to realizing the nation’s goal of capping carbon-emissions growth by 2030. Based on the 9th national forest inventory data in the eastern Loess Plateau of China, which is mountainous terrain, we characterized the spatial pattern of biomass carbon density (BCD) for natural coniferous and broad-leaved forests using Local Getis-ord G* and proposed an integrative framework to evaluate the direct and indirect effects of stand, geographical and climatic factors on BCD for the two types of forests using structural equation modeling. The results showed that there was no significant difference between the mean BCDs of the natural coniferous and broad-leaved forests. Compared with broad-leaved forests, the hot spots of BCDs at the 1% significance level for coniferous forests were located in areas with higher average latitude, higher average elevation, lower mean temperature, or lower mean precipitation. Stand age and elevation were important driving factors, which had stronger effects for the coniferous forests than broad-leaved forests. Among all driving factors, age had the strongest total effect for the two forests types. No significant difference was detected in BCDs between natural coniferous and broad-leaved forests. Spatial patterns of BCDs were different between the two forests types. Stand age and elevation were important driving factors, which had stronger effects for the coniferous forests than broad-leaved forests.

Background Mountain forests in China are an integral part of the country’s natural vegetation. Understanding the spatial variability and control mechanisms for biomass carbon density of mountain forests is necessary to make full use of the carbon sequestration potential for climate change mitigation. Based on the 9th national forest inventory data in Shanxi Province, which is mountainous terrain, eastern Loess Plateau of China, we characterized the spatial pattern of biomass carbon density for natural coniferous and broad-leaved forests using Local Getis-ord G* and proposed an integrative framework to evaluate the direct and indirect effects of stand, geographical and climatic factors on biomass carbon density for the two types of forests using structural equation modeling. Results There was no significant difference between the mean biomass carbon densities of the natural coniferous and broad-leaved forests. The number of spots with a spatial autocorrelation accounted for 51.6% of all plots of the natural forests. Compared with the broad-leaved forests, the hot spots at the 1% significance level for the coniferous forests were distributed in areas with higher latitude, higher elevation, lower temperature, and lower precipitation. Geographical factors affected biomass carbon density positively and indirectly, via the stand and climatic factors, with larger effects for the natural coniferous than broad-leaved forests. Latitude and elevation are the most crucial driving factors for coniferous forests, but stand age and forest coverage are for broad-leaved forests. Climatic factors had weaker effects than other factors, with negative effects of temperature for coniferous and no effects for broad-leaved forests. Conclusions The effects of stand, geographical and climatic factors on biomass carbon density are different between natural coniferous and broad-leaved forests, respectively. Employing the integrative framework can improve the prediction of the impact of stand, geographical and climatic factors on natural forests in mountainous areas.

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