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.

Evaluating the performance of multifunctionality is a necessary foundation for forest multifunctionality management. This study aims to comprehensively adopt multiple methods to construct a multifunctional evaluation system for natural spruce–fir forests and explore the impact of thinning intensity on the multifunctional management effect of spruce–fir. This article combines subjective and objective evaluation methods and selects three methods to construct an evaluation system: the Analytic Hierarchy Process, the combined entropy weight method, and the CRITIC method. The results showed that the consistency of the three evaluation methods is good, and according to the score based on the evaluation results, the multifunctional performance of the sample plot with a thinning intensity of 20% (average score of AHP method is 75.5; EWM is 91; CRITIC is 96.5) is significantly better than that of the sample plot with a thinning intensity of 40% (AHP is 65.3; EWM is 51; CRITIC is 48), both of which were significantly better than those of the untreated sample plot (AHP is 12.7; EWM is 18.7; CRITIC is 17.3). A coupling relationship model between multifunctional values and different functions, as well as a coupling relationship model between different functions and various indicators, were constructed based on the evaluation system. Finally, the forest stand with the highest multifunctional comprehensive value was selected as the reference for the target structure to construct the target structure system, which is convenient for actual management. This study found that there is a nurturing intensity (20%) that can best utilize the multiple functions of forests, which has practical significance for promoting forest multifunctionality in forest management. In addition, this study scientifically constructed and compared several evaluation systems for the multifunctional performance of forests, laying a certain foundation for forest multifunctional evaluation and future forest multifunctional management.