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

Environmental variables such as food supply, nodule abundance, sediment characteristics, and water chemistry may influence abyssal seafloor communities and ecosystem functions at scales from meters to thousands of kilometers. Thus, knowledge of environmental variables is necessary to understand drivers of organismal distributions and community structure, and for selection of proxies for regional variations in community structure, biodiversity, and ecosystem functions. In October 2019, the Deep CCZ Biodiversity Synthesis Workshop was conducted to (i) compile recent seafloor ecosystem data from the Clarion-Clipperton Zone (CCZ), (ii) synthesize patterns of seafloor biodiversity, ecosystem functions, and potential environmental drivers across the CCZ, and (iii) assess the representativity of no-mining areas (Areas of Particular Environmental Interest, APEIs) for subregions and areas in the CCZ targeted for polymetallic nodule mining. Here we provide a compilation and summary of water column and seafloor environmental data throughout the CCZ used in the Synthesis Workshop and in many of the papers in this special volume. Bottom-water variables were relatively homogenous throughout the region while nodule abundance, sediment characteristics, seafloor topography, and particulate organic carbon flux varied across CCZ subregions and between some individual subregions and their corresponding APEIs. This suggests that additional APEIs may be needed to protect the full range of habitats and biodiversity within the CCZ.


INTRODUCTION Environmental Conditions in the Deep Sea
Contrary to popular perception, the abyssal seafloor exhibits substantial variability in habitat structure and key environmental variables across a broad range of scales, with corresponding heterogeneity in benthic habitat structure and biodiversity (e.g., Lutz et al., 2007;Wei et al., 2010;Danovaro et al., 2014;Zeppilli et al., 2016;Smith et al., 2020). Commercial exploitation of deep-sea resources, including fishing, seabed mining, and deep-sea hydrocarbon drilling, have the potential to damage deep-sea ecosystems over large spatial and temporal scales (e.g., Borowski and Thiel, 1998;Glover and Smith, 2003;Jones et al., 2017;Weaver et al., 2018;Washburn et al., 2019;Smith et al., 2020). To predict and manage the impacts of human activities in the deep sea, detailed understanding of baseline ecosystem conditions is essential. This requires assessment of the natural variability of environmental drivers and biological communities in space and time.
The International Seabed Authority (ISA) has issued 16 polymetallic nodule exploration contracts within the Clarion and Clipperton Fracture Zones (CCZ), each with an area of up to 75,000 km 2 for a total contract area of ∼1.25 million km 2 (isa.org.jm/deep-seabed-minerals-contractors). Polymetallic nodule areas of greatest economic interest occur in the Pacific Ocean, between the CCZ (Hein et al., 2013). Pacific nodules generally occur between ocean depths of 3,500-6,500 m, where low deposition rates and weak currents allow manganese and iron minerals to precipitate from bottom waters and/or from sediment pore waters, generally yielding nodule growth rates of ∼1-12 mm per million years (Hein et al., 2013). To protect regional biodiversity and ecosystem functions in the face of nodule mining, a system of nine "no-mining" areas, called Areas of Particular Environmental Interest (APEIs) have been established within the CCZ region. The APEI network was originally designed to protect the full range of deep-sea habitats in the CCZ, capturing differences in seafloor community structure and function driven by food availability (e.g., north-south and east-west gradients in seafloor particulate organic carbon flux), nodule abundance, and the occurrence of seamounts (ISA, 2008;Wedding et al., 2013).

CCZ Data Synthesis Exploring APEI Representativeness
The Deep CCZ Biodiversity Synthesis Workshop held at Friday Harbor Laboratories on October 1-4, 2019 (ISA, 2019) was organized by the DeepCCZ Project (led by University of Hawaii) and ISA and included experts specializing in microbes, meiofauna, macrofauna, megafauna, ecosystem-functions and habitat-mapping to address questions on biodiversity, biogeography, genetic connectivity, ecosystem functions, and habitat modeling throughout the region. Prior to this workshop, available physiographic, nodule-resource, sediment, bottom-water, flux, climate-change, and biogeographic data for the CCZ were obtained, harmonized and compiled into a data report. This report supported analyses of environmental heterogeneity across the CCZ, and helped evaluate drivers of biodiversity, community structure, and ecosystem functions at a regional scale (ISA, 2019, see other papers in this special volume). The environmental data were also used to support habitat mapping throughout the CCZ (McQuaid et al., 2020). Workshop participants' examined correlations between environmental variables and spatial patterns of benthic communities to help identify ecological drivers, and to evaluate the representativity of APEIs for nearby contractor areas. This paper is a compilation and summary of the data provided to participants in the Deep CCZ Biodiversity Synthesis Workshop. This compilation may help to identify environmental proxies to predict environmental and ecological patterns in poorly sampled areas of the CCZ, as well as in other deep-sea regions.

Study Area
The CCZ is an approximately 6 million km 2 area between ∼115-155 • W longitude, and ∼0-20 • N latitude, containing nodule fields of interest for mining (Hein et al., 2013;Wedding et al., 2013). Locations of contract areas, reserve areas (areas reserved for mining by developing countries or the ISA itself), and APEIs were obtained from the ISA, 1 while the locations of the nine environmental subregions used in recommending APEI locations were obtained from Wedding et al. (2013) (Figure 1). Environmental subregions were created to capture the large north-south and east-west gradients in surface production and Particulate Organic Carbon (POC) flux to the seafloor in the CCZ (Lutz et al., 2007) which in turn drive changes in benthic communities (Wedding et al., 2013). The APEI system was originally designed such that each APEI would be located within, and representative of, a corresponding subregion (Wedding et al., 2013). However, when the APEI network was adopted by the ISA, some APEIs originally placed in subregions within the central CCZ were shifted to the periphery to avoid any overlaps with existing exploration and/or reserved areas (ISA, 2011;Wedding et al., 2013).

Environmental Data
Data sets for environmental variables compiled for and summarized in the ISA Deep CCZ workshop data report, using measured or modeled values, were collected from online resources and direct solicitations from scientists. All datasets were compiled in ArcGIS 10.7. For display purposes, the values for each variable were divided into ∼5-10 bins to illustrate heterogeneity observed throughout the CCZ. To interpolate point data throughout the CCZ, Empirical Bayesian Kriging was performed in ArcGIS using the empirical transformation and Bessel semi-variogram. All data sets in this study are based on interpolated values, with those not based on satellite data likely interpolated from a limited number of data points. Data sets were analyzed to estimate means, standard deviations, minimum and maximum values for each subregion and APEI (Figure 1).

Physiographic Data
Seafloor depth data were obtained from ETOPO1 for the grid calculated at 1 arc-minute intervals (Amante and Eakins, 2009;NOAA National Geophysical Data Center, 2009) 2 and extracted at 0.5-degree intervals. Means and standard deviations of depths within each subregion and APEI were calculated. Some depth points in a given region, subregion, or APEI may represent seamounts vs. abyssal seafloor; however, owing to the large number of data points and relatively few seamounts, this would be unlikely to skew mean depths. 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.

Resource Data
Most data for nodule abundance (kg/m 2 ), as well as percent cobalt, nickel, manganese, and copper content of nodules, were obtained from ISA Technical Study No. 6 (ISA, 2012). Nodule abundance data were available between 5.5-19 • N and 119-160 • W, Co-content data between 5.5-17.5 • N and 114-160 • W, and other nodule content data between 3-19 • N and 115-160 • W. Additional unpublished data for all nodule variables, compiled in 2018 were from the area delimited by 2.5-21 • N and 112-160 • W, excluding contractor areas (see data in Supplementary Material: Dr. C. Morgan, unpublished data). Finally, nodule data were unavailable from the northern 5% of the NE subregion, the western 11% of the NW subregion, and southern 1% of SW subregion as well as several contractor regions in the eastern CE and SE subregions (Figure 1 and Supplementary Figures 5-9). All nodule data were in the form of point values in a 0.5-degree grid. For mapping, values of nodule variables were interpolated as described above.

Sediment Data
Sediment type, calcium-carbonate content, and biogenic-silica content were obtained from GeoMapApp, 3 and were based on sediment-type analyses of Dutkiewicz et al. (2015), the sediment calcium-carbonate compilation of Archer (2003), and the sediment biogenic-silica (opal) content compilation of Archer (1999). Raw data for sediment type were not available with specific spatial coordinates, so summaries were made based on the figures created in GeoMapApp. Raw sediment calcium carbonate and biogenic silica data were used to interpolate these variables across the entire region, and data were extracted from these interpolations at 1-degree intervals. Sediment totalorganic-carbon data were obtained from Jahnke (1996) who digitized Total Organic Carbon (TOC) from Premuzic et al. (1982) at a 2-degree scale. Sediment accumulation-rate data were obtained from Jahnke (1996), who digitized accumulation rates from Cwienk (1986) at a 2-degree scale. Sediment thickness data were obtained from NOAA's National Centers of Environmental Information 4 (Straume et al., 2019). Sediment thickness data extracted at 1-degree intervals were used for calculations of summary statistics.

Water-Column Data
Bottom-water temperature, salinity, dissolved oxygen concentration, nitrate, phosphate, and silicate data were obtained from NOAA's Ocean Climate Laboratory Team in the World Ocean Atlas 2018 (WOA18). 5 The deepest value for each point below 3,000 m was used as representative of "bottom water, " although data were only available to a depth of 5,500 m. Bottom-water temperature  and salinity  data were averaged from 2005 to 2017 and analyzed at 15-arc-minute intervals. Bottom-water concentrations of oxygen, nitrate, phosphate, and silicate were averaged from 1960 to 2017 and calculations of summary statistics were made at 1-degree intervals . Bottom-water pH was obtained from Sweetman et al. (2017) who used the program CO2SYS and inorganic CO 2 , alkalinity, temperature, salinity, and pressure data from the Global Ocean Data Analysis Project and WOA 2013 average from 1998 to 2010 to estimate pH. Calculations on pH data were performed at 0.5-degree intervals. Concentrations of particulate matter in bottom water (1 value 10-15 mab) and nepheloid layer thickness were obtained from Gardner et al. (2018). The nepheloid layer thickness was defined as the distance between the seafloor and sampling depth with a particulate concentration >20 µg/L, with sampling intervals of ∼30 m. Analyses of bottom-water particulate matter concentration and nepheloid-layer thickness data were performed at 1-degree intervals. Bottom-water calcite saturation  was obtained from Levin et al. (2020) at a 0.5-degree scale, and calcite saturation horizon depth was estimated using maps from Yool et al. (2013).
Net Primary Production (NPP) data were obtained from Ocean Productivity website. 6 This website provides NPP based on three models: (1) Behrenfeld and Falkowski (1997), the Vertically Generalized Production Model (VGPM), (2) Behrenfeld et al. (2005), the Carbon-based Productivity Model (CBPM), and (3) Silsbe et al. (2016), the Carbon, Absorption, and Fluorescence Euphotic-resolving Model (CAFE). These models all used MODerate resolution Imaging and Spectroradiometer (MODIS) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data as well as euphotic zone depth estimates from a model by Morel and Berthon (1989). The NPP data used here represent daily measurements averaged from 1998 to 2010 and 2010 to 2017. NPP data for the 1998-2010 time interval were used to match the interval of analyses performed previously to calculate POC flux using models from Lutz et al. (2007) et al. (2017), while NPP data for the 2010-2017 time interval were used to compare to NPP from the 1998-2010 dataset to examine temporal differences in patterns of surface production throughout the CCZ.

Climate-Change Data
Projected changes in bottom-water temperature, oxygen, pH, POC flux, and calcite saturation (from 1951-2000 to 2081-2100), were obtained from Levin et al. (2020). All projections are based on the Representative Concentration Pathway (RCP) 8.5 climate-change scenario, and projected values (2081-2100) were subtracted from historical values  to get change over time. Model results were mapped at a 0.5-degree resolution using bilinear interpolation.

Analyses
Data for all compiled environmental variables were extracted for each CCZ subregion as well as for each APEI at the spatial resolution provided in the data sources as indicated above. If the data sources were only provided as spatial rasters, then data were extracted at 1-degree intervals for our analyses. The mean, minimum, maximum, and range of values within a subregion or APEI for each variable were calculated using R version 4.0. The identification of APEIs and CCZ subregions are given in Figure 1.

Physiographic Data Depth
Abyssal seafloor depths at 0.5-degree resolution increase from east to west and south to north within the CCZ (Table 1 and Supplementary Figure 1), yielding shallowest average abyssal depths (∼4,300-4,500 m) in the southeast and central-east near the fracture zones, and deepest average depths (∼5,400 m) in the northwest (south of Hawaii). The central CCZ has the smallest range in abyssal seafloor depths (∼4,650-5,350 m) while the northern areas near, the Clarion Fracture Zone, have the largest range (∼3,900-5,800 m), although this range could include seamounts. The APEIs have similar depths to their designated subregions, except for APEIs 5 and 6, which are substantially shallower than their subregions (CC and CE, respectively). The greatest average depths are found in APEI 1 (∼5,400 m) ( Table 1 and Supplementary Figure 1). No APEI has a depth range, at 0.5-degree resolution, greater than 1,000 m.

Seamounts
Based on bathymetry at 30 arc-second resolution (Yesson et al., 2011), there were 347 seamounts (elevation >1,000 m) within the CCZ. These were distributed primarily around the fracture zones and at the eastern and western ends of the CCZ, including the peripheral exploration claim areas and APEIs, with the western CCZ having the highest abundance (  Figure 2).
Average seamount summit water depth is shallowest in the east and deepest in the north, corresponding to patterns in seafloor depth ( Table 1 and Supplementary Figure 3). The central CCZ contained seamounts of similar summit water depths while the average depth of seamounts in the north ranged from 3,000 to 4,000 m. The average height (from base to summit) of seamounts was highest in the NW and CE (∼2,000 m). The tallest seamounts were >3,500 m tall and found in the north and east (Supplementary Table 1 and Supplementary Figure 4). Most regions had a wide range of seamount heights. Average seamount area was smallest in the central CCZ and largest in the south. The seamounts with smallest areas were in the north, resulting in larger ranges of seamount size here (Supplementary Table 1 and Supplementary Figure 5).
Average seamount summit water depth was lowest in APEIs 6 and 8 (∼2,600 m) and highest in APEI 1 (∼4,300 m) ( Table 1 and Supplementary Figure 3). APEI 6 had at least one seamount that reaches 500 m depth, which was much shallower than any seamounts found throughout the rest of the APEI system (Supplementary Table 1). The average height of seamounts was lowest in APEI 5 (∼1,100 m) and highest in APEI 3 (∼1,700 m) ( Table 1

Knolls
Based on bathymetry at 30 arc-second resolution (Yesson et al., 2011), knolls, with heights of 200-1,000 m above the general seafloor, were much more widely distributed than seamounts, and were common around the periphery of the CCZ. The SC region had the fewest number of knolls, while the most were found in the north and west (Table 1 and Supplementary Figure 6). Subregions with the deepest and shallowest knolls mirrored average depth patterns (Supplementary Figure 7). Knolls were, on average, highest in the NE (∼ 600 m) and lowest in the CE (∼400 m) ( Table 1 and Supplementary Figure 8). Average knoll area was highest in the SW (∼700 m 2 ) and lowest in the NW (∼600 m 2 ) ( Table 1 and Supplementary Figure 9). In the APEI system, there were fewer knolls in the southern APEIs and more in the west (Table 1 and Supplementary Figure 6). Average knoll summit depth was lowest in APEI 6 (∼3,500 m) and highest in APEI 1 (∼5,100 m) ( Table 1 and Supplementary Figure 7). Average knoll height was lowest in APEI 9 (∼400 m) and highest in APEI 3 (∼600 m) ( Table 1 and Supplementary Figure 8). Average knoll area was lowest in APEI 7 (∼500 m 2 ) and highest in APEI 6 (∼700 m 2 ) ( Table 1 and Supplementary Figure 9).

Slope
High resolution multibeam bathymetry indicates that the seafloor of the CCZ is characterized by ridges and valleys varying in depth by at least 500 m (e.g., Amon et al., 2016), likely resulting from horst and graben structures formed during seafloor spreading (Johnson, 1972;Macdonald et al., 1996). However, these features are poorly resolved in the 0.5-degree bathymetric data available across the CCZ (Supplementary Figure 1). At the 0.5-degree scale, average seafloor slope is generally between 1 and 2 degrees for CCZ subregions and APEIs ( Table 1). The highest and most variable slopes are found in the CC and NE (ranging ∼12-13 degrees) (Supplementary Table 1

Nodule abundance
Mean nodule abundance in the CCZ at a resolution of 0.5 degrees is highly variable, with some areas completely devoid of nodules while others have nodule densities >18 kg/m 2 (ISA, 2012; Supplementary Table 1 and Supplementary Figure 11). Nodule abundance varies over scales of <1 to 100 km apparently related to differences in sedimentation rates, organic matter content, concentration of leachable manganese, and oxygen penetration depth (Mewes et al., 2014). Mean nodule abundances were highest in the CE subregion (∼7.5 kg/m 2 ) and lowest in the southern subregions (∼1.5-3 kg/m 2 ). APEIs 1, 2, 3, 7, 8, and 9 contained lower mean abundance of nodules than their corresponding CCZ subregions (<2 kg/m 2 less) ( Table 1 and Supplementary Figure 11). APEIs 5 and 6, which were relocated from within their subregions by the ISA (Wedding et al., 2013), have substantially lower mean nodule abundances than their respective subregions (CC and CE; >3 kg/m 2 less). No APEI contained areas with nodule abundances >9 kg/m 2 (Supplementary Table 1). It should be emphasized that these values are from modeled data with a resolution of 0.5 degrees. Core sampling and photograph surveys indicate substantial finerscale variations in nodule abundance than captured here (e.g., Peukert et al., 2018;Simon-Lledó et al., 2019a).

Nodule cobalt content
Mean nodule cobalt content at a resolution of 0.5 degrees was highest in the north (∼0.25%) and lowest in the south (∼0.15%). Cobalt values were very similar between all subregions and APEIs (within 0.02%) except for the CC, where cobalt content was 0.05% higher than in its corresponding APEI (Table 1 and Supplementary Figure 12). Minimum cobalt content was lower and maximum content higher in every subregion vs. nearest APEI (differing by as much as 0.1%) except for APEI 6 (Supplementary Table 1 and Supplementary Figure 12), suggesting that there is variability in nodule composition within CCZ subregions not captured in the APEI system.

Nodule nickel content
In contrast to cobalt, mean nodule nickel content at a resolution of 0.5 degrees was lowest in the north (∼1.1%) and higher in the central and west (∼1.2-1.3%) ( Table 1 and Supplementary  Figure 13). Nickel content was generally similar between CCZ subregions and corresponding APEIs (within 0.01%), but several subregions had lower minimum and higher maximum nickel values (Supplementary Table 1 and Supplementary Figure 13).

Nodule copper content
Mean nodule copper content at a resolution of 0.5 degrees increased from north to south (∼0.3-0.4%) and from west to east (∼0.1%). Copper content was also generally similar between CCZ subregions and corresponding APEIs (within 0.05%, although there is a difference of ∼0.25% between CE and APEI 6) ( Table 1 and Supplementary Figure 14); however, subregions in the south and west had maximum copper content ∼0.1-0.2% higher than any areas found in the APEI system (Supplementary Table 1 and Supplementary Figure 4).

Nodule manganese content
Mean nodule manganese content at a resolution of 0.5 degrees increased from north to south (∼5-6% increase) and from west to east (∼4-5%), following the same trend as copper.
Manganese content was generally similar (i.e., within 1%) between CCZ subregions and corresponding APEIs; however, in the central CCZ, where exploration contract areas are concentrated, maximum manganese values were several percent higher in subregions than in the nearest APEIs (Table 1 and Supplementary Figure 15). Likewise, minimum manganese values were often much lower in subregions than nearby APEIs (as much as 10% between SW and APEI 7), suggesting that there is variation in nodule characteristics and, potentially, habitat structure not captured by the APEI system (Supplementary Table 1 and Supplementary Figure 15).

Seafloor Sediment Data Sediment type
According to Dutkiewicz et al. (2015), most of the northern and central CCZ is dominated by siliciclastic clay while the southern CCZ is dominated by biogenic calcareous oozes (Supplementary Figure 16). The NW and CW subregions have large areas of radiolarian ooze while much of the SC is comprised of finegrained calcareous sediment. APEIs generally mirror regional sediment patterns, although much of APEI 6 is indicated to include gravel and coarser siliciclastic material; the only location in the CCZ indicated to have this sediment type (Dutkiewicz et al., 2015 ; Supplementary Figure 16). However, direct sampling and extensive seafloor photography in APEI 6 primarily found brown-clay sediments (Menendez et al., 2019;Simon-Lledó et al., 2019a).

Sediment total organic carbon content
Total organic carbon content at a 2-degree resolution appeared to be below 0.3% in sediment throughout the CCZ, including within APEIs, with the north having lower TOC than the south (Jahnke, 1996, Plate 2).

Sediment calcium carbonate content
Mean calcium-carbonate content at a 1-degree resolution was lowest in the northern (<1-2%) and highest in the southern CCZ (as high as >75%) (Supplementary Figure 17). This is likely related partially to depth, because many of the northern areas tend to be deeper than the calcite compensation depth in the region (∼4,500 m) (Van Andel, 1975). Overlying productivity and sinking calcium carbonate flux most likely also play a role, since areas deeper than 4,500 m in the southern subregions still have moderate calcium carbonate content (10-20%). Sediment calcium-carbonate content varied between several CCZ subregions and corresponding APEIs by as much as 20% (Table 1 and Supplementary Figure 17).

Sediment biogenic silica content
Mean sediment biogenic silica content at a 1-degree resolution appeared to increase from north (3-5%) to south, up to 25-30% in the SW subregion (Table 1 and Supplementary Figure 18). Because of the extremely low number of values collected, including several APEIs with no biogenic silica values, other patterns and comparisons between subregions and APEIs are not appropriate.

Sediment thickness
Mean sediment thickness at a 1-degree resolution was highest in the SW (∼350 m) and lowest in the northeast (∼100 m), decreasing northward and eastward. Average thickness was generally similar between CCZ subregions and APEIs, although APEIs 4 and 5 differed from their respective subregions (CW and CC) ( Table 1 and Supplementary Figure 19).

Sediment accumulation rate
Sediment accumulation rates at a 2-degree resolution appeared to be below 0.5 g/cm 2 /1,000 year in nearly all areas of the CCZ seafloor, with the north and central areas having lower sedimentation rates than the south and west (Jahnke, 1996).

Bottom-water temperature
At a resolution of 0.25 degrees, mean bottom-water temperature was nearly uniform throughout the CCZ. Nowhere did temperatures fall below 1.3 or rise above 2 • C ( Table 1 and Supplementary Figure 20). In the west and CE, temperatures varied by ∼0.5-0.6 • C, while within all other subregions and all APEIs, temperature variations did not exceed 0.3 • C (Supplementary Table 1 and Supplementary Figure 20).

Bottom-water salinity
At a resolution of 0.25 degrees, variations in bottom-water salinity were small throughout the CCZ subregions and APEIs, ranging from 34.68 to 34.70 (Table 1 and Supplementary  Figure 21).

Bottom-water dissolved-oxygen concentration
At a resolution of 1 degree, mean bottom-water oxygen concentrations were lowest in the SE (∼3.6 mL/kg) and highest in the NW (∼4.1 mL/L), decreasing eastward (by ∼0.4 µmol/L) and southward (by ∼0.2 µmol/L) ( Table 1 and Supplementary  Figure 22). Oxygen concentrations did not fall below 3.2 mL/L throughout the CCZ while maximum oxygen concentrations did not exceed 4.3 mL/L. In the NE CCZ, oxygen concentrations varied by ∼1 mL/L while other areas had less variability. The APEIs had similar oxygen concentrations to adjacent subregions, although oxygen ranges were usually smaller (Supplementary Table 1 and Supplementary Figure 22). Oxygen concentrations seemed to follow similar patterns to seafloor POC flux and organic carbon content, with increases in seafloor POC flux likely resulting in decreases in oxygen due to greater biological oxygen demand. No seafloor areas (bottom waters) in the CCZ had oxygen levels below 3 mL/L, i.e., they remained well above thresholds considered stressful for deep-sea biota (Levin, 2003).

Bottom-water nitrate
At a resolution of 1 degree, mean bottom-water nitrate concentrations were highest in the SE (∼36 µmol/kg) and lowest in the NW (∼34 µmol/kg), with slight increases moving south (increasing ∼0.5 µmol/kg) and east (∼1 µmol/kg). Average nitrate concentrations in APEIs were similar to their respective subregions (generally differing by less than 0.5 µmol/kg) ( Table 1 and Supplementary Figure 23). Minimum nitrate concentrations in APEIs were somewhat higher than their respective subregions, although all values were within 2 µmol/kg (Supplementary Table 1 and Supplementary Figure 23).

Bottom-water phosphate
Mean bottom-water phosphate concentrations at a 1-degree resolution were relatively uniform throughout the CCZ, with all average values near 2.4 µmol/kg (Table 1 and Supplementary  Figure 24). The NE and CE varied by 0.5 µmol/kg, twice the variability of any other area (Supplementary Table 1 and Supplementary Figure 24).

Bottom-water silicate
Mean bottom-water silicate concentrations at a 1-degree resolution were highest in the SE (∼157 µmol/kg) and lowest in the NW (∼133 µmol/kg), generally increasing from north to south and west to east. While CCZ subregions and corresponding APEIs had similar average silicate concentrations (with differences generally less than 4 µmol/kg, except between CE and APEI 6), APEIs often had higher minimum values (by ∼5-10 µmol/kg) ( Table 1 and Supplementary Figure 25). APEI 6 had higher concentrations than anywhere else, and minimum values (∼152 µmol/kg) here were higher than maximums found in most other areas (Supplementary Table 1 and Supplementary  Figure 25).

Bottom-water pH
Mean bottom-water pH at a 0.5-degree resolution was nearly constant throughout the CCZ and APEI system, with average, minimum, and maximum pH falling between 7.9 and 7.98 (Table 1 and Supplementary Figure 26).

Bottom-water particulate matter
Bottom-water particulate matter concentrations at a 1-degree resolution were extremely low in all regions of the CCZ, with means, minimums, and maximums all <10 µg/L, which is below the detectable limit for the methods ( Table 1, Supplementary  Table 1 and Supplementary Figure 27). These very low values indicate little or no sediment resuspension in the region (Gardner et al., 2018).

"Strong" Nepheloid-Layer Thickness
There was no evidence of "strong" nepheloid layers in the CCZ, at a 1-degree resolution, with mean, minimum, and maximum thicknesses all <50 m, i.e., below the detectable limit for the methods used ( Table 1, Supplementary Table 1 and Supplementary Figure 28). These low values provide no evidence of "strong" nepheloid layers in the region (Gardner et al., 2018).

Calcite saturation
Bottom waters throughout the region were undersaturated with calcite, with mean calcite saturation at a 0.5-degree resolution lowest in the NC (∼64%) and highest in the CE (∼69%), increasing from north to south (by ∼2-3%) and west to east (by ∼2-3%) ( Table 1 and Supplementary Figure 29). Calcite saturation did not fall below 60% throughout the CCZ while it exceeded 70% in areas throughout the south and east (Supplementary Table 1 and Supplementary Figure 29). Saturation levels differed by <2% between each subregion and its representative APEI and varied ∼10% within several subregions.

Calcite saturation horizon
Calcite saturation horizons appear to be above 3 km depth in all areas throughout the CCZ .

Net Primary Production Data Vertically Generalized Production Model (VGPM) NPP
Average, minimum, and maximum NPP estimated by the VGPM model (Behrenfeld and Falkowski, 1997) were highest in the SW (∼110-120 g C/m 2 /year) and lowest in the NW (∼65-75 g C/m 2 /year) for both time periods examined (1998-2010 and 2010-2017). NPP increased eastward (by ∼20 g C/m 2 /year) and southward (by ∼20-50 g C/m 2 /year) across the CCZ for both time periods (1998-2010 and 2010-2017). NPP was relatively high along the southern CCZ, and there was also a band of higher primary production in the CC and CE subregions ( Table 1 and Supplementary Figures 30, 31). NPP was lower in 2010-2017 than 1998-2010 across all subregions; however, relative productivity patterns across the CCZ remained similar (Table 1 and Supplementary Figures 30, 31). The minimum values of ∼55-60 g C/m 2 /year in the NW were less than half of the maximum values of ∼ 125-135 in the SW. The NE had less spatial variability in NPP than other areas. NPP in APEIs also increased southward, with the highest values in APEIs 7 and 8 (∼125 and 110 g C/m 2 /year, respectively) ( Table 1 and Supplementary  Figures 30, 31). Central and western APEIs also had higher NPP than corresponding subregions, while the opposite was true for eastern APEIs.

Carbon-Based Productivity Model (CBPM) and Carbon, Absorption, and Fluorescence Euphotic-resolving model (CAFE) NPP
Average, minimum, and maximum values of NPP estimated via CBPM and CAFE were roughly twice those estimated via the VGPM model (Behrenfeld and Falkowski, 1997). Mean NPP values for CBPM and CAFE NPP were within 5% of each other in nearly all areas, and means ranged between ∼150 and 170 g C/m 2 /year in the NE to ∼240-260 g C/m 2 /year in the SW ( Table 1 and Supplementary Figures 32-34). Patterns in NPP across the CCZ were also slightly different between the CBPM/CAFE and VGPM models, with CBPM/CAFE showing the lowest values in the NE instead of NW. Comparing time periods, VGPM NPP was always higher in 1998-2010 compared to 2010-2017 while CBPM NPP was always higher in 2010-2017 compared to 1998-2010 (Table 1 and Supplementary  Figures 30-33). However, CBPM estimates were more variable than VGPM estimates in 2010-2017 (often 2.3-2.5× greater) compared to 1998-2010 (no more than 2-2.2× greater) illustrating that temporal variability may not be captured the same way among models ( Table 1). Another confounding factor is that chlorophyll data used to estimate NPP was estimated using measurements from the SeaWiFs instrument up to the mid-2000s but by MODIS beyond that. Thus, while patterns in NPP across CCZ areas and APEIs are similar among the three different methods with the north and west areas having lower NPP and the south and east areas having higher NPP, the ranking of specific areas is not identical nor are differences among time periods proportional.

Particulate Organic Carbon Flux Data Seafloor POC flux
Average Lutz POC flux mirrored NPP (measured by VGPM, which was used as an input for POC flux calculations), with lowest mean value in the NW subregion (∼1 g C/m 2 /year) and highest mean value in the SW subregion (1.74 g C/m 2 /year) ( Table 1 and Supplementary Figure 35). The lowest average Lutz POC flux for APEIs was in APEI 1 (1.07) while the highest average values were in APEIs 7 and 8 (1.8-1.9) which conformed with patterns in CCZ subregions. The NW subregion and APEI 1 were the only areas with POC flux <1 g C/m 2 /year, while the NE, SW, and SC subregions had flux of ∼2 g C/m 2 /year in some areas (Supplementary Table 1). Variations in POC fluxes were 2-3 times larger in southern areas compared to the rest of the CCZ except for the NE. When comparing POC data from 1997 to 2004 (Lutz et al., 2007) to data from 1998 to 2010    Table 1).

Bottom-water temperature
Mean bottom-water temperature at a 0.5-degree resolution is projected to change very little by 2081-2100, with all areas throughout the CCZ seeing less than 0.05% change in absolute temperature (Table 1 and Supplementary  Figure 36). The northwest may include areas with slightly more temperature change (0.03%) than the rest of the CCZ (≤0.02%) (Supplementary Table 1 and Supplementary Figure 36).

Bottom-water oxygen concentration
Mean bottom-water oxygen at a 0.5-degree resolution is projected to change most in the northern and western CCZ, decreasing by ∼5-7% (∼0.25 mL/L), but less change will be observed in the east, decreasing by ∼ 2-3% (∼0.05 mL/L) ( Table 1 and Supplementary Figure 37). The NC and CW subregions include areas that may experience ∼3-4% (∼0.1 mL/L) greater decreases in oxygen concentrations compared to their respective APEIs (Supplementary Table 1 and Supplementary Figure 37), although all locations within the CCZ are projected to remain well above oxygen thresholds considered stressful for deep-sea communities (Levin et al., 2009).

Bottom-water pH
Bottom-water pH at a 0.5-degree resolution is projected to change very little by 2100, with the largest decreases of ∼0.2% (0.02 pH units) observed in the NW (Table 1 and Supplementary  Figure 38).

Bottom-water calcite saturation
Projected bottom-water calcite saturation changes at a 0.5-degree resolution follow the same patterns as bottom-water oxygen and pH, with the largest decreases of ∼2-4% in the north and west, and the smallest decreases in the east at <1% (Table 1 and Supplementary Figure 39). Several subregions include areas that may experience decreases in calcite saturation 1% greater than their corresponding APEIs (Supplementary Table 1 and Supplementary Figure 39). No areas will go from saturated to unsaturated states.

Seafloor POC flux
Mean POC Flux to the seafloor at a 0.5-degree resolution is modeled to change much more by 2100 than other variables examined, although patterns of change match the other variables examined above. The largest differences in POC flux are estimated to be in the north where flux may decrease as much as 20-25% (∼0.25-0.35 g C/m 2 /year), although central subregions may also see a decrease of 15-20% (∼0.25-0.35 g C/m 2 /year). In contrast, seafloor POC flux in the southeast is estimated to decrease by 2-3% (<0.1 g C/m 2 /year) and flux is projected to increase in APEI 9 (Table 1 and Supplementary  Figure 40). POC flux is projected to change by similar quantities between CCZ subregions and APEIs, except that APEIs 4 and 5 may be less impacted by future changes than their respective subregions, CW and CC. While the southern CCZ as a whole may be less impacted by climate change influences on seafloor POC flux, there are still locations in all 3 southern subregions that are projected to experience POC flux decreases of 10-20% (∼0.4-0.6 g C/m 2 /year). There are also areas in the SC and SE that are projected to experience POC flux increases of ∼5% (∼0.1 g C/m 2 /year) (Supplementary Table 1 and Supplementary Figure 40).

Lower-Bathyal Provinces
Seamounts in the western and central CCZ were within Bathyal Province 14 while seamounts in the eastern CCZ were within Bathyal Province 7 postulated by Watling et al. (2013) (Supplementary Figure 42). Likewise, western and central APEIs were in Bathyal Province 14 while western APEIs were in Bathyal Province 7 (Supplementary Figure 42; Watling et al., 2013).

Pelagic Provinces
The northern and central CCZ were within the North Central Pacific Gyre pelagic province with the SW in the Equatorial Pacific province and SE in the Eastern Tropical Pacific province (UNESCO, 2009). Likewise, APEIs 1, 2, 3, and 6 were in the North Central Pacific Gyre, APEIs 4, 5, and 7 were in the Equatorial Pacific, and APEIs 8 and 9 in the Eastern Tropical Pacific (Supplementary Figure 44).  Figures 36-40) occur in the northwestern corner of the NW subregion but are not captured in APEI 1.

NC Subregion vs. APEI 2
Area of Particular Environmental Interest 2 is largely similar to its NC subregion. However, APEI 2 generally has higher POC fluxes to the seafloor than anywhere in the NC (Supplementary Figure 35). The absence of very low POC flux areas in the APEI could reduce its ecological representativity for this subregion (Smith et al., 2008). APEI 2 is also estimated to be less impacted by climate change than the rest of the NC (Supplementary  Figures 36-40).

NE Subregion vs. APEI 3
Area of Particular Environmental Interest 3 is largely similar to its NE subregion. While APEI 3 appears to have less variability in many environmental variables than the NE (e.g., depth, Supplementary Figure 1; POC flux, Supplementary Figure 35), much of this range is captured by APEI 6, which is largely contained within the NE subregion (Figure 1).

CW Subregion vs. APEI 4
Area of Particular Environmental Interest 4, which is largely in the SW subregion (Figure 1), has some dissimilarities to the CW subregion it is meant to represent, including lower maximum nodule abundance, lower nodule cobalt content, and higher estimated primary production and seafloor POC flux. In addition, it does not capture the deepest abyssal areas ( Figures 30-34), and POC flux (<1.35 g C/m 2 /year) present throughout much of the CW region are also not represented in APEI 4. The differences in seafloor POC flux between CW and APEI 4 may be ecologically significant (Smith et al., 2008).

CC Subregion vs. APEI 5
Area of Particular Environmental Interest 5, which is almost entirely in the SC subregion (Figure 1), has some dissimilarities to the CC subregion it is intended to represent, including in nodule abundance/characteristics and seafloor POC flux. Relatively deep abyssal seafloor (<4,900 m, Supplementary  Figure 1), seamounts (Supplementary Figure 2) and knolls (Supplementary Figure 6) are poorly represented in APEI 5 (although seamounts are also very rare in the CC). In addition, APEI 5 has very low nodule abundance compared to the CC subregion (an average difference of ∼3.5 kg/m 2 , Supplementary Figure 11). 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).

CE Subregion vs. APEI 6
Area of Particular Environmental Interest 6, which is almost entirely in the NE subregion (Figure 1), differs in nodule abundance/characteristics, sediment/bottom-water characteristics, estimated primary production, and POC flux from the CE subregion, which it is intended to represent. 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), NPP (Supplementary Figures 30-34), and seafloor POC flux (<1.5 g C/m 2 /year; Supplementary Figure 35 Figures 13-15) 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).

SW Subregion vs. APEI 7
Area of Particular Environmental Interest 7 is largely similar to it respective SW subregion. While APEI 7 protects only a few of the seamounts and knolls in SW, many of the others are located in APEI 4 (Supplementary Figures 2, 6). APEI 7 does not capture the higher nodule abundances (Supplementary Figure 11), and lower nodule metal contents (Supplementary Figures 12-15 Figures 30-35) found in SW, but these are captured in APEI 4, which is largely in this region (Figure 1).

SC Subregion vs. APEI 8
Area of Particular Environmental Interest 8 is largely similar to its SC subregion. While some variability in nodule abundance/characteristics (Supplementary Figures 11-15) and sediment characteristics (Supplementary Figures 16-19) in the SC are not fully captured by APEI 8, they are largely present in APEI 5 which is almost entirely within this region (Figure 1).

SE Subregion vs. APEI 9
Area of Particular Environmental Interest 9 has some dissimilarities with its SE subregion. In particular, shallower depths (<∼4,400 m, Supplementary Figure 1), seamounts (Supplementary Figure 2), most knolls (Supplementary Figure 6), higher nodule abundance (>4 kg/m 2 , Supplementary Figure 11), lower bottomwater oxygen concentrations (<∼3.7 ml/L, Supplementary  Figure 22), and higher bottom-water calcite saturation (>∼69%, Supplementary Figure 29) present in the eastern SE are not captured in APEI 9. While differences in bottomwater characteristics are likely not ecologically significant, the lack of seamounts and low nodule abundances in APEI 9 may yield important ecological differences with most of the SE subregion. While much of the environmental variability in the northern and southern CCZ appears to be captured in the APEI network, the central and SE CCZ may include areas with seafloor POC flux, nodule abundance, and seamount characteristics not found in the APEIs meant to represent them. To ensure adequate protection of biodiversity in these subregions, additional APEIs may be warranted. For detailed habitat mapping in the CCZ using the environmental data in this paper, as well as identification of potential areas for additional representative APEIs, see McQuaid et al. (2020).

Environmental Heterogeneity and Deep-Sea Mining
In the deep sea, proposed polymetallic nodule mining activities are likely to harm seafloor ecosystems, including unique habitats such as manganese nodules (Glover and Smith, 2003;Niner et al., 2018;Washburn et al., 2019;Smith et al., 2020). To protect regional biodiversity and ecosystem functions from the nodule-mining impacts in the CCZ, nine APEIs were designated as no-mining areas (Wedding et al., 2013). Thus, for the APEI network to serve its purpose in protecting biodiversity and ecosystem function throughout the CCZ, it must fully capture the full range of habitats and important environmental variables found in the region.
While there were statistically significant differences across the CCZ in every environmental variable examined, it is likely that many of these differences are not ecologically significant, and thus are likely less important for the APEI network to fully represent. Differences in seafloor slope, at a broad scale, (Supplementary Figure 10), sediment thickness (Supplementary Figure 19), bottom-water particulate concentrations, and nepheloid-layer thickness (Supplementary  Figures 27, 28) are small or within measurement errors and likely not ecologically significant. Variations in bottomwater temperature, salinity, oxygen concentration nitrate concentration, phosphate concentration, silicate concentration, pH, particulate matter concentration, and calcite saturation ( Table 1 and Supplementary Table 1 and Supplementary  Figures 20-29) are relatively small and may not be ecologically significant (Levin, 2003), although little work has been done to understand how differences in many of these variables affect abyssal communities.
Several studies, including many in this volume, have found that nodule abundance (Amon et al., 2016;De Smet et al., 2017;Simon-Lledó et al., 2019bWashburn et al., 2021) and POC flux to the seafloor (Smith et al., 2008;Bonifácio et al., 2020;Washburn et al., 2021) are major drivers of benthic community abundance, diversity, and composition. Both of these variables vary across the CCZ and between some CCZ subregions and their respective APEIs. Differences between APEIs and their subregions are most pronounced in the central CCZ (Table 1 and Supplementary Figures 11 and 35), where APEIs were located away from nodule-rich areas (Wedding et al., 2013). Nodules provide habitat for apparently nodule-dependent epifauna and infauna (Amon et al., 2016;Vanreusel et al., 2016;Gooday et al., 2017;Simon-Lledó et al., 2019b) as well as metal-cycling bacteria and archaea (Shulse et al., 2016;Molari et al., 2020). Thus, differences in nodule abundance and metal composition ( Table 1 and Supplementary Figures 11-15) are likely to be ecological significant. Although seafloor POC flux appears to have large impacts on benthic communities (e.g., Smith et al., 2008), there are relatively few POC flux measurements from the CCZ (Lutz et al., 2007;Kim et al., 2010Kim et al., , 2011Kim et al., , 2012. Therefore, patterns of seafloor POC flux across the CCZ are estimated from models of export through the water column (e.g., Lutz et al., 2007). Substantially more direct measurements of deep POC flux are needed to better understand food availability in space and time across the CCZ.
Seamounts also serve as important habitats for benthic and pelagic communities (Clark et al., 2010;Leitner et al., 2017;Laroche et al., 2020). Seamount and knoll habitats appear to be underrepresented in the APEIs for the central CCZ (Supplementary Figure 2). Other variables with differences that could be ecologically significant include sediment calcium carbonate and biogenic silica content ( Table 1 and Supplementary Figures 17, 18), which may influence the formation of molluscan and foraminiferan shells (Wheeler, 1992;Sen Gupta, 1999). Finally, it appears that changes in bottom-water temperature, oxygen content, and pH due to climate change will likely not be ecologically significant throughout the CCZ, while it is unclear whether changes in calcite saturation will be ( Supplementary  Figures 36-39). However, estimated climate driven changes in POC flux for time intervals 1951-2000 to 2081-2100 between APEIs and their respective subregions may be as much as ∼30% (Supplementary Figure 40), which could significantly influence abyssal benthic communities (Smith et al., 2008;Jones et al., 2013) in the deep-sea throughout the CCZ, albeit unevenly (Supplementary Figure 40).

CONCLUSION
There is significant heterogeneity in all environmental variables examined across the CCZ. Much of this variability falls along north-south and east-west gradients, as postulated by Wedding et al. (2013). Most of this environmental variability is captured by the APEI system erected by the ISA's Regional Environmental Management Plan (ISA, 2011), but some APEIs differ in some important ecological characteristics (in particular nodule abundance and seafloor POC flux) from the subregions they were intended to represent, especially the central and SE CCZ. Thus, the formation of additional APEIs or other protected areas merits consideration, particularly within the nodulerich central areas of the CCZ. Areas where additional APEIs could be erected to help protect some of these habitats are proposed in McQuaid et al. (2020).

DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.