ALBERT

Description: Coastal marine ecosystems are sensitive to anthropogenic stressors and environmental change. The Chinese Government proposed an ecosystem-based marine spatial planning scheme called the “Marine Ecological Red Line” (MERL), aimed at protecting ecologically sensitive areas. The assessment and mapping of ecosystem sensitivity provide important tools for regional MERL and setting conservation priorities. In this study, an integrated framework for the comprehensive sensitivity assessment of coastal marine ecosystems was proposed. This framework had two components: endogenous sensitivity and exogenous sensitivity. A weighted summation method was used along with a GIS-based spatial analysis to calculate regional sensitivity quantitatively. Taking Xiamen Bay in China as a case study, the approach integrated spatial data on the distribution of marine habitats and multiple human activities. An index system (including 26 indicators) was established for the case study. Five areas with different levels of sensitivity were delineated. In the study region of 1281 km2, areas with extremely high and high sensitivity covered 87 km2 and 235 km2, respectively, and were hypothesized to be priority areas of conservation and regulation. Areas with the highest sensitivity were located closer to Jiulongjiang Estuary and along the coastline of Xiang’an. Areas with lower sensitivity were also mapped in Tong’an Bay, representing possible areas that could accommodate future industrial or intensive human use. Comparative analyses between endogenous sensitivity and exogenous sensitivity allowed the presence of development-protection conflict zones to be identified. The results provide an important scientific basis for MERL decisions. In addition, targeted management strategies were proposed for Xiamen Bay. This study presents an operational approach to provide relevant scientific knowledge on the process of ecosystem-based marine spatial planning, facilitating policy-making decisions in sustainable coastal and marine management.