Using Habitat Classification to Assess Representativity of a Protected Area Network in a Large, Data-Poor Area Targeted for Deep-Sea Mining

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.

Patterns of Macrofaunal Biodiversity Across the Clarion-Clipperton Zone: An Area Targeted for Seabed Mining

“…This result is consistent with expectations of macrofaunal food limitation in this region based on direct measurements of POC flux versus macrofaunal parameters at many different abyssal sites (Smith et al, 2008a), and with the reasonable match of Lutz POC fluxes (within 20%) with results from sediment diagenetic models (Volz et al, 2018). Thus, POC flux is likely a major driver of polychaete, tanaid, and isopod abundances across the CCZ, an important contributor to habitat quality, and an important variable to consider when setting up and evaluating APEIs across the CCZ (as in Wedding et al, 2013;McQuaid et al, 2020).…”

“…Linear Mixed-Effects Modeling, 'lmer, ' in R, was used to explore which environmental variables best explained macrofaunal abundance and taxonomic richness across individual box cores (Bates et al, 2015). Sample depth, Lutz seafloor POC flux (Lutz et al, 2007), nodule abundance (kg/m 2 ) (Morgan, 2012), bottom-water oxygen concentration, bottomwater salinity, bottom-water temperature, bottom-water nitrate, phosphate, and silicate concentrations (all downloaded from World Ocean Atlas 2018 4 ; Washburn et al, 2021), bottom slope (largest change in elevation between a cell and its eight neighbors), broad-scale bathymetric position index (BBPI; with an inner radius of 100 km and outer radius of 10000 km) and fine-scale bathymetric position index (FBPI, with an inner radius of 10 km and outer radius of 100 km) (McQuaid et al, 2020) were obtained for each box-core sample location. Since environmental data were not available for many individual locations and several studies, and to ensure data were consistent across studies, data for all environmental variables (except depth, which was provided for each sample) were extracted for each box-core location from interpolated rasters in ArcGis (see Washburn et al, 2021).…”

Environmental Heterogeneity Throughout the Clarion-Clipperton Zone and the Potential Representativity of the APEI Network

“…Seamount and knoll data at a resolution of 30 arc-seconds were obtained from Yesson et al (2011). Seamounts are defined as features rising >1,000 m above the surrounding seafloor, while knolls are defined as features rising between 200 and 1,000 m. Seafloor slope data at a resolution of 1 arc-minute were obtained from McQuaid et al (2020) who derived slope from the GEneral Bathymetric Chart of the Oceans (GEBCO) bathymetry using the Benthic Terrain Modeler extension in ArcMap 10.4 to determine the largest change in elevation between a cell and its eight nearest neighbors at a scale of 1 km 2 . Interpolated seafloor slope data were extracted at 1-degree intervals.…”

“…Sediment calciumcarbonate content (Supplementary Figure 17), biogenic silica content (Supplementary Figure 18), sediment thickness (Supplementary Figure 19), and bottom-water calcite saturation (Supplementary Figure 29) are also lower in the CC than APEI 5. Reduced nodule abundance in APEI 5 may yield important ecological differences between APEI 5 and CC (McQuaid et al, 2020).…”

“…APEI 6 is shallower (<4,200 m, Supplementary Figure 1) than most of the CCZ and has lower sediment calciumcarbonate content (<∼5%, Supplementary Figure 17), biogenic-silica content (<∼7%, Supplementary Figure 18 -15) than most of the CE subregion. While many of the sediment and bottom-water characteristic differences are likely not ecologically significant, the differences in nodule abundance/composition and seafloor POC flux may yield important ecological differences between APEI 6 and the CE subregion (Smith et al, 2008;McQuaid et al, 2020).…”