Using habitat classification and large-scale spatial planning model to identify environmental areas: A case study in the area of the Northwest Pacific Ocean

The deep sea refers to the parts of the oceans with a depth of more than 200 m, and it comprises the largest habitat on earth with the most abundant and diverse marine life. Human activities, such as deep-sea mining, fishing, and shipping, can have severe impacts on deep-sea species and habitats. With the development of the related equipment and technology, interest in deep-sea mining continues to rise; therefore, protecting the unique and fragile deep-sea marine ecosystem has become a global focus. Given the fragility of the deep-sea habitats and the growing demand for deep-sea resources, the United Nations established the International Seabed Authority (the ISA) in 1972, which represents everyone worldwide, to manage the mineral resources in the international seabed area (also known as "the Area") while fulfilling environmental protection obligations. The most important objective of the regional environmental management plan (REMP) in the Area initiated by the ISA is to balance deep-sea mining and environment protection, and the ISA has considered numerous types of area-based man-agement tools (ABMTs) in the development of the REMPs (Table 1). Using the different ABMTs, several key environmental areas or sites have been identified in various regions, such as the areas of particular environ-mental interest (APEIs), sites in need of protection (SINPs), or areas in need of protection (AINPs). Additionally, deep-sea mining is prohibited in these sites/areas to avoid its irrecoverable impact on the environment in the Area. The designation these environmental areas or sites can improve the function and resilience of the ecosystem and avoid the unrecoverable impact of deep-sea mining. In this study, we selected the international areas of the Northwest Pacific Ocean for a case study. In order to protect representative species and habitats, such as cold-water corals and seamounts, we utilized environmental factors to classify the study area into eight types of habitats. We then used the appropriate criteria to identify candidate AINPs in two scenarios with a large-scale spatial planning model and analyzed the differences in the protected proportion of the eight types of habitats. The results indicate that identifying the candidate AINPs can reach the protection target of approximately 30% of the area, and each representative species and habitat can be protected. The results obtained in this study can provide technical references for the implementation of REMPs and the designation of AINPs in the study area.