Climate change and forest management are recognized as pivotal factors influencing forest ecosystem services and thus multifunctionality. However, the magnitude and the relative importance of climate change and forest management effects on the multifunctionality remain unclear, especially for natural mixed forests. In this study, our objective is to address this gap by utilizing simulations of climate-sensitive transition matrix growth models based on national forest inventory plot data. We evaluated the effects of seven management scenarios (combinations of various cutting methods and intensities) on the future provision of ecosystem services and multifunctionality in mixed conifer-broad-leaved forests in northeastern China, under four climate scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5, and constant climate). Provisioning, regulating, cultural, and supporting services were described by timber production, carbon storage, carbon sequestration, tree species diversity, deadwood volume, and the number of large living trees. Our findings indicated that timber production was significantly influenced by management scenarios, while tree species diversity, deadwood volume, and large living trees were impacted by both climate and management separately. Carbon storage and sequestration were notably influenced by both management and the interaction of climate and management. These findings emphasized the profound impact of forest management on ecosystem services, outweighing that of climate scenarios alone. We found no single management scenario maximized all six ecosystem service indicators. The upper story thinning by 5% intensity with 5-year interval (UST5) management strategy emerged with the highest multifunctionality, surpassing the lowest values by more than 20% across all climate scenarios. In conclusion, our results underlined the potential of climate-sensitive transition matrix growth models as a decision support tool and provided recommendations for long-term strategies for multifunctional forest management under future climate change context. Ecosystem services and multifunctionality of forests could be enhanced by implementing appropriate management measures amidst a changing climate.

Crown width (CW) is one of the most important tree metrics, but obtaining CW data is laborious and time-consuming, particularly in natural forests. The Deep Learning (DL) algorithm has been proposed as an alternative to traditional regression, but its performance in predicting CW in natural mixed forests is unclear. The aims of this study were to develop DL models for predicting tree CW of natural spruce-fir-broadleaf mixed forests in north-eastern China, to analyse the contribution of tree size, tree species, site quality, stand structure, and competition to tree CW prediction, and to compare DL models with nonlinear mixed effects (NLME) models for their reliability. An amount of total 10, 086 individual trees in 192 subplots were employed in this study. The results indicated that all deep neural network (DNN) models were free of overfitting and statistically stable within 10-fold cross-validation, and the best DNN model could explain 69% of the CW variation with no significant heteroskedasticity. In addition to diameter at breast height, stand structure, tree species, and competition showed significant effects on CW. The NLME model (R² ​= ​0.63) outperformed the DNN model (R² ​= ​0.54) in predicting CW when the six input variables were consistent, but the results were the opposite when the DNN model (R² ​= ​0.69) included all 22 input variables. These results demonstrated the great potential of DL in tree CW prediction.