[Spatial Patterns and Drivers of Carbon Density in Terrestrial Ecosystems in Southwest China]

The complexity of the spatial distribution pattern of carbon density in terrestrial ecosystems in Southwest China and its influencing factors determines its key position in regional carbon cycle research and makes it a crucial point for accurate carbon sink assessment and ecosystem management. Based on the 2010s Chinese terrestrial ecosystem carbon density dataset and soil profile data from soil monographs， this study employs random forest and piecewise structural equation modeling to explore the spatial distribution characteristics and the contribution of influencing factors to the three major carbon pools （aboveground biomass carbon pool， belowground biomass carbon pool， and soil organic carbon pool） in terrestrial ecosystems in Southwest China. ① The results showed that the carbon density （C） of terrestrial ecosystems in Southwest China during the 2010s was （16.42±3.07） kg·m^-2， with a total carbon storage of 31.58 Pg. The contributions of the soil carbon pool， aboveground vegetation carbon pool， and belowground vegetation carbon pool were 73.69% （23.27 Pg）， 20.77% （6.56 Pg）， and 5.54% （1.75 Pg）， respectively. ② The vegetation carbon pool showed a spatial pattern of higher values in the south and lower values in the north， while the soil carbon pool generally had higher values in the west and lower values in the east. ③ The piecewise structural equation model indicated that climate change， vegetation cover， and soil physicochemical properties directly affected the biophysical processes of terrestrial ecosystems， while geographical environment and human activities mainly influenced carbon density indirectly by altering land use and vegetation cover. Therefore， accurately assessing the carbon storage of terrestrial ecosystems in Southwest China， clarifying their spatial distribution characteristics， and exploring the interaction mechanisms among influencing factors are vital for evaluating the carbon sink/source feedback mechanisms under climate change and predicting future carbon cycle trends.