Differences in spatial patterns and driving factors of biomass carbon density between natural coniferous and broad-leaved forests in mountainous terrain, eastern Loess Plateau of China

Abstract 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.