Liu Miaomiao, Liu Shuang, Tang Raohan, Liu Minggao, Hu Xisheng, Lin Sen, Wu Zhilong
College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.
College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou, 350002, China.
J Environ Manage. 2025 Feb;375:124412. doi: 10.1016/j.jenvman.2025.124412. Epub 2025 Feb 4.
The conservation and restoration of forests are a crucial component of climate mitigation strategies in many countries. However, the scientific selection of priority areas for forest conservation and restoration remains a challenge. Based on the landscape indices, the forest landscape structural connectivity index was constructed based on principal component analysis; the forest landscape functional connectivity index was constructed based on the minimum cumulative resistance model. Geodetector was employed to identify the driving forces of forest landscape structural and functional connectivity in the Fujian Delta region. The patch-generating land use simulation model was then used to simulate land use changes under different shared socioeconomic pathways (SSPs) and representative concentration pathways (RCPs) scenarios from 2030 to 2050. The optimal scenario of forest development was then selected based on the forest landscape structural and functional connectivity. Finally, graph theory was used to identify priority forest conservation and restoration areas under optimal scenarios. The results indicate the following: (1) elevation (q = 0.34, P < 0.01) and nighttime light (q = 0.33, P < 0.01) are the primary drivers of structural connectivity in forested landscapes, while nighttime light (q = 0.38, P < 0.01) and gross domestic product (q = 0.28, P < 0.01) are the primary drivers of functional connectivity in forested landscapes. The joint effect of elevation and nighttime lighting (q = 0.44, P < 0.01) enhances the explanatory power of structural connectivity in forested landscapes. The joint effect of nighttime lighting and gross domestic product (q = 0.46, P < 0.01) enhances the explanatory power of functional connectivity in forested landscapes. (2) Overall, between 2020 and 2050, forest landscape structural and functional connectivity tends to increase in the SSP1-2.6 scenario and decrease in the SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios. Based on the structural and functional connectivity of the forest landscape, the optimal scenario for future development was identified as SSP1-RCP2.6. (3) The areas of forests prioritized for conservation in 2030, 2040, and 2050 are 12,470.18 km, 12,470.18 km, and 12,227.67 km, respectively. The areas of forests prioritized for restoration are 51.80 km, 103.14 km, and 390.86 km, respectively. This study identified priority forest conservation and restoration areas under SSPs-RCPs scenarios using graph theory, offering valuable insights into biodiversity conservation and the identification of locations for forest conservation and restoration planning.
