Multi-objective optimization of forest structure for broadleaf mixed forest under different CMIP5 scenarios

Abstract

Climate change is an important global research topic and one of the most pressing issues facing the world today. It significantly impacts forest distribution, species composition, stand structure, growth, and other functions, therefore, it is crucial to simulate the dynamic changes of forest structure under future climate change and develop optimized solutions to adapt to it. Combined with the various stand structural parameters, based on the equal or unequal weights of each parameter at the stand level, to establish a dynamic stand spatial structure multi-objective optimization index (L-index) under different climate changes, and to implement broadleaf mixed forest spatial structure optimization research in Yanan Forest Farm under RCP 2.6, RCP 4.5, RCP 8.5 climate scenarios after 5 years. The results indicated that the L-index showed an increase ranging from 13.8 % to 35.7 % under equal weights in different climate scenarios. Additionally, the optimal harvesting intensity ranged from 13.2 % to 20.5 %, with the highest intensity observed in the RCP4.5 climate scenario; compared to the RCP4.5 and RCP8.5 climate scenarios, the RCP2.6 scenario shows the largest trend and magnitude of change in the indicators, suggesting that the state of stand growth is more sensitive under the RCP2.6 scenario. The L-index showed an increase ranging from 13.7 % to 34.4 % under the unequal weights in different climate scenarios, in which the optimal harvesting intensity ranged from 14.6 % to 19.8 %. More importantly, it should be noted that the values of the increases in the L-index and the relative increased proportion (RIP) were both higher than those when unequal weights were employed. Moreover, it should be highlighted that the increments in the L-index and RIP were both greater than those observed when unequal weights were applied for all three plots under different climate scenarios. Overall, the adjusted the complete mixing degree index (Mc) increased, angle index (W), dominance ratio index (U), and Hegyi competition index (CI) decreased compared to before optimizing with the equal or unequal weights of the stand structural variable, which is evident that the adjustments made to the stand’s structure have resulted in a significant improvement. Thus, the multi-objective optimization for forest spatial dynamics constructed is both reasonable and effective, and it more scientific forest management under different climatic scenarios.