Abstract
Extractive activities in the ocean are expanding into the vast, poorly studied deep sea, with the consequence that environmental management decisions must be made for data-poor seafloor regions. Habitat classification can support marine spatial planning and inform decision-making processes in such areas. We present a regional, top–down, broad-scale, seafloor-habitat classification for the Clarion-Clipperton Fracture Zone (CCZ), an area targeted for future polymetallic nodule mining in abyssal waters in the equatorial Pacific Ocean. Our classification uses non-hierarchical, k-medoids clustering to combine environmental correlates of faunal distributions in the region. The classification uses topographic variables, particulate organic carbon flux to the seafloor, and is the first to use nodule abundance as a habitat variable. Twenty-four habitat classes are identified, with large expanses of abyssal plain and smaller classes with varying topography, food supply, and substrata. We then assess habitat representativity of the current network of protected areas (called Areas of Particular Environmental Interest) in the CCZ. Several habitat classes with high nodule abundance are common in mining exploration claims, but currently receive little to no protection in APEIs. There are several large unmanaged areas containing high nodule abundance on the periphery of the CCZ, as well as smaller unmanaged areas within the central CCZ, that could be considered for protection from mining to improve habitat representativity and safeguard regional biodiversity.
Highlights
Human activities and climate change have been shown to significantly impact the deep sea (Glover and Smith, 2003; Ramirez-Llodra et al, 2011), and human influence has been recorded in even the deepest part of the ocean, the Mariana Trench (Chiba et al, 2018)
We present a broad-scale habitat map of the Clipperton Fracture Zone (CCZ) Environmental Management Plan (EMP) area using a top– down habitat classification system of environmental surrogates developed through non-hierarchical cluster analysis
The clustering iteration with the highest average silhouette width (ASW) and Calinski-Harabasz index (CH) values were chosen for the habitat classification, and this decision was supported by literature and expert review (Table 1)
Summary
Human activities and climate change have been shown to significantly impact the deep sea (Glover and Smith, 2003; Ramirez-Llodra et al, 2011), and human influence has been recorded in even the deepest part of the ocean, the Mariana Trench (Chiba et al, 2018). Ecosystem-based management is a best-practice approach to manage sustainably human activities in marine ecosystems, and is implemented through a number of different management measures, including Marine Spatial Planning (MSP). MSP is an area-based planning approach that allocates space in the marine environment to different users based on ecological, economic and social objectives, in order to balance demands for economic development with the need for environmental protection (Ehler and Douvere, 2007). In order to achieve ecological goals in a specific area, such as to conserve biodiversity, MSP may involve the allocation of protected areas where certain human activities are limited. Whereas MPAs were historically established on an ad hoc and individual basis (United Nations Environment Programme World Conservation Monitoring Centre, 2008; Toropova et al, 2010), best practices focus on establishing networks of protected areas to advance protection (Dudley, 2008; Johnson et al, 2014)
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