Forests regulate air quality and respond to climate change by storing carbon. Assessing the driving factors of forest aboveground carbon (AGC) storage is of great importance for forest management. We assumed that different forest types would affect the relationship between species richness, stand density, individual tree size variation, and AGC. In order to test and verify it, we analyzed the inventory data of 206 fixed plots (20 m × 20 m) of Jingouling Forest Farm, taking advantage of the piecewise structural equation model (pSEM) to explore the effects of species diversity, stand structure attributes, and topography on the AGC storage in the Wangqing Forest in Jilin Province. In addition, in this study, we aimed to investigate whether the fixed factors (species diversity, stand structure attributes, and topography) influenced AGC storage more significantly than the random factor (forest type). According to the results of pSEM, the selected factors jointly explain the impact on 33% of AGC storage. The relationship between stand density and AGC is positive, and the impact of individual tree size variation on AGC storage is negative. Species richness has direct and indirect impacts on AGC storage, and the indirect impact is more significant through individual tree size variation. Both elevation and slope are significantly negatively associated with AGC storage. Forest type explains the impact on 12% of AGC storage, which means the relationship between AGC and predictors varies across forest types. The results provide a scientific basis for the protection and management decision of natural forests in northeastern China.

Based on a multiple linear regression model, random forest algorithm and generalized additive model, a stand volume model was constructed to provide a theoretical basis for sustainable management. A total of 224 fixed plots in the Jingouling forest farm, Wangqing County, Jilin Province, were used as data sources. Specifically, 157 plots were used as training data, and 77 plots were used as test data. The effects of stand structure variables, topography variables, cutting variables, diversity variables and climate variables on stand volume were analyzed. The random forest algorithm explained 95.51% of the stand volume, and the generalized additive model explained 95.45% of the stand volume. Stand structure variables and topography variables had more influence on the stand volume of spruce-fir than other variables. Among the diversity variables, the evenness index, Shannon index and Simpson index had a relatively greater impact on the stand volume. The cutting times and the intensity of the first cutting had a direct relationship with stand volume. The influence of climate variables on the stand volume was relatively small in the study area.

The stability of forest productivity can reflect the functioning of forest ecosystems. It is a crucial topic to understand the relationship between biodiversity and ecosystem functions in ecology. Although previous studies have made great progress in understanding the effects of diversity, species asynchrony, and other factors on community biomass and productivity, few studies have explored how these factors affect the temporal stability of productivity. In this study, we hypothesized that diversity, species asynchrony, and topography would directly or indirectly impact the temporal stability of productivity. To test this hypothesis, we used a multiple regression model and a piecewise structural equation model based on the inventory data over 20 years (5‐year intervals) from 1992 to 2012 at Jingouling Forest Farm in Northeast China. Our results showed that species asynchrony was the main driving factor affecting the temporal stability of productivity. Structural diversity significantly decreased community stability, while species diversity had a nonsignificant effect on it. We found the combination of a multiple regression model and a piecewise structural equation model is an effective method for evaluating the factors that influence community stability. The effect of species asynchrony is crucial for understanding the ecological mechanisms underlying the diversity–stability relationship in mixed forests.