OceTHE OFFICIALa MAGAZINEn ogOF THE OCEANOGRAPHYra SOCIETYphy

CITATION Sayre, R.G., D.J. Wright, S.P. Breyer, K.A. Butler, K. Van Graafeiland, M.J. Costello, P.T. Harris, K.L. Goodin, J.M. Guinotte, Z. Basher, M.T. Kavanaugh, P.N. Halpin, M.E. Monaco, N. Cressie, P. Aniello, C.E. Frye, and D. Stephens. 2017. A three-​dimensional mapping of the ocean based on environmental data. Oceanography 30(1):90–103, https://doi.org/10.5670/oceanog.2017.116.

DOI https://doi.org/10.5670/oceanog.2017.116

COPYRIGHT This article has been published in Oceanography, Volume 30, Number 1, a quarterly journal of The Oceanography Society. Copyright 2017 by The Oceanography Society. All rights reserved.

USAGE Permission is granted to copy this article for use in teaching and research. Republication, systematic reproduction, or collective redistribution of any portion of this article by photocopy machine, reposting, or other means is permitted only with the approval of The Oceanography Society. Send all correspondence to: [email protected] or The Oceanography Society, PO Box 1931, Rockville, MD 20849-1931, USA.

DOWNLOADED FROM HTTP://TOS.ORG/OCEANOGRAPHY REGULAR ISSUE FEATURE

A Three-Dimensional Mapping of the Ocean Based on Environmental Data

By Roger G. Sayre, Dawn J. Wright, Sean P. Breyer, Kevin A. Butler, Keith Van Graafeiland, Mark J. Costello, Peter T. Harris, Kathleen L. Goodin, John M. Guinotte, Zeenatul Basher, Maria T. Kavanaugh, Patrick N. Halpin, Mark E. Monaco, Noel Cressie, Peter Aniello, Charles E. Frye, and Drew Stephens

90 Oceanography | Vol.30, No.1 ABSTRACT. The existence, sources, distribution, circulation, and physicochemical features that are understood to influence nature of macroscale oceanic water bodies have long been a focus of oceanographic the distribution of species. inquiry. Building on that work, this paper describes an objectively derived and globally Ecosystems are geographically iden- comprehensive set of 37 distinct volumetric region units, called ecological marine tifiable areas where the interactions units (EMUs). They are constructed on a regularly spaced ocean point-mesh grid, of organisms with their physical envi- from sea surface to seafloor, and attributed with data from the 2013 World Ocean Atlas ronments produce differences in biotic version 2. The point attribute data are the means of the decadal averages from a 57-year diversity, trophic structure, and flows climatology of six physical and chemical environment parameters (temperature, of energy and materials between living salinity, dissolved oxygen, nitrate, phosphate, and silicate). The database includes over and nonliving components of the system 52 million points that depict the global ocean in x, y, and z dimensions. The point (Odum, 1971). On land, variations in cli- data were statistically clustered to define the 37 EMUs, which represent physically mate, landform, and substrate establish and chemically distinct water volumes based on spatial variation in the six marine the environmental potential that controls environmental characteristics used. The aspatial clustering to produce the 37 EMUs primary production and species distribu- did not include point location or depth as a determinant, yet strong geographic and tions, acknowledging that evolutionary vertical separation was observed. Twenty-two of the 37 EMUs are globally or regionally history is also an important element of extensive, and account for 99% of the ocean volume, while the remaining 15 are smaller biogeography (Bailey, 1996, 2014; Kottek and shallower, and occur around coastal features. We assessed the vertical distribution et al., 2006; Holt et al., 2013). Responding of EMUs in the water column and placed them into classical depth zones representing to the GEO commission request for a epipelagic (0 m to 200 m), mesopelagic (200 m to 1,000 m), bathypelagic (1,000 m standardized, robust, and practical global to 4,000 m) and abyssopelagic (>4,000 m) layers. The mapping and characterization map of terrestrial ecosystems, a new map of the EMUs represent a new spatial framework for organizing and understanding of global ecological land units (ELUs) the physical, chemical, and ultimately biological properties and processes of oceanic was developed from an integration of water bodies. The EMUs are an initial objective partitioning of the ocean using long- climate, landform, lithology, and land term historical average data, and could be extended in the future by adding new cover (Sayre et al., 2014). We now pres- classification variables and by introducing functionality to develop time-specific EMU ent a similar environmental stratification distribution maps. The EMUs are an open-access resource, and as both a standardized approach for extending the global ecolog- geographic framework and a baseline physicochemical characterization of the oceanic ical units map into the ocean through the environment, they are intended to be useful for disturbance assessments, ecosystem delineation of global ecological marine accounting exercises, conservation priority setting, and marine protected area network units (EMUs). design, along with other research and management applications. There are notable differences between mapping terrestrial and mapping marine INTRODUCTION (Liu et al., 2007, 2015)? Fundamental ecological units. First, the terrestrial Ecosystems are a central focus of many knowledge of the types and locations of ELUs were mapped as two-dimensional current research and policy questions, global ecosystems is necessary to address (2D) entities using a raster data surface. including: (1) What are the impacts of these questions, yet that knowledge is Marine ecosystems, however, are funda- climate change on ecosystems? (2) Which generally lacking. mentally understood as both 2D (e.g., sea ecosystems are vulnerable to climate and The development of a new global eco- surface and seafloor) and three-dimen- other perturbations (e.g., invasive spe- systems map, including terrestrial, fresh- sional (3D; e.g., water column) entities cies, land and sea use)? (3) Which ecosys- water, and marine domains, was therefore (e.g., Li and Gold, 2004; Wright et al., tems should be targeted for conservation? commissioned by the intergovernmen- 2007). EMUs would ideally need to be (4) What are the economic and social val- tal Group on Earth Observations (GEO, mapped using 3D data points represent- ues of ecosystem goods and services? https://www.earthobservations.org), a ing a volumetric mesh (e.g., Heinzer and (5) What role do ecosystems play in consortium of over 100 nations seeking et al., 2012; Reygondeau et al., 2017) and global food and environmental security to advance Earth observation approaches visualized as 2D and 3D objects. Second, for addressing societal challenges related the characteristics of the physical envi- to hazards, food, water, energy, and the ronment that influence the distribution FACING PAGE. Three-dimensional visualization of the ecological marine units (EMUs) for the environment. The new global ecosys- of species and ecosystems are different Banda Sea. EMUs are depicted as bands on cyl- tems maps were to be derived from data between terrestrial and marine environ- inders, and pink colors indicate warmer EMUs, rather than from expert opinion or socio- ments. While the ELUs were identified where blue colors represent colder EMUs. On land, the global ecological land units (ELUs) of political considerations, and they were to as distinct combinations of bioclimate, Sayre et al. (2014) are shown. be based on the physical environmental landform, lithology, and vegetation, those

Oceanography | March 2017 91 elements of terrestrial ecosystem struc- and salinity relationships, more recent of global ocean water, but did not map ture do not apply as such in the ocean, quantitative water mass characterizations different regions by depth. Reygondeau with the exception of seafloor land- are multidimensional and feature the use et al. (2017) objectively subdivided the forms. The abiotic controls on the distri- of tracer distributions to identify water Mediterranean Sea into 63 biogeochem- bution of marine biota (e.g., temperature, mass origins. For example, Gebbie and ical regions in a true 3D analysis using as in Beaugrand et al., 2013), and nutri- Huybers (2011) comprehensively iden- data and biologically meaningful crite- ents, as in Longhurst, 2007) were identi- tified the surface origin of points in the ria to separate the water column verti- fied as analogs to the terrestrial environ- ocean interior at 33 depth levels using cli- cally into epipelagic, mesopelagic, bathy- mental characteristics. Third, the ocean matological and isotope ratio data and pelagic, and seafloor zones. However, is a fluid in motion, and ocean ecosys- tracer path analysis. there has not been a purely quantita- tem conditions can be influenced by pro- In addition to origin-based quantita- tive, unsupervised approach to parti- cesses far from their location via ocean tive water mass analysis, another com- tioning the entire global ocean water circulation. Marine ecosystems are there- mon approach to identifying oceanic column from aspatial statistical cluster- fore generally understood as more spa- water bodies involves subdivision of ing of global ocean data, even though tiotemporally dynamic than their terres- ocean regions or volumes based on dif- an unprecedented amount of global trial counterparts. ferences in their physical, chemical, and marine environmental data is now avail- Considerable research in quantita- biological properties (Sherman et al., able (e.g., the 2013 World Ocean Atlas of tive water mass analysis has produced a 2005; Longhurst, 2007; Spalding et al., Locarnini et al., 2013; Zweng et al., 2013; robust and standardized terminology for 2007; Reygondeau et al., 2017). While Garcia et al., 2014a, 2014b). describing water masses with respect to various marine oceanic region maps exist We approached the challenge of aggre- their origins, properties, and locations (Table 1), few are global in extent, are gating comprehensive marine environ- (Tomczak, 1999). The ocean’s hydro- representative of the entire water column mental data through statistical clustering, graphic structure has been described in three dimensions, and were derived building on the efforts of previous authors. with evolving complexity, beginning with from quantitative analysis of data. Three- For example, Harris and Whiteway the seminal work of Sverdrup (1942) and dimensional, globally comprehensive (2009) used a multivariate statistical including the early delineation of global subdivisions of the ocean are particularly method with six biophysical variables water masses by Emery and Meincke lacking. The quantitative, 3D analysis of (depth, seabed slope, sediment thickness, (1986) based on temperature and salinity Gebbie and Huybers (2010, 2011) iden- primary production, bottom-​water dis- properties. Expanding on the temperature tified seven surface water source regions solved oxygen, and bottom temperature)

TABLE 1. Existing maps of ocean regions.

NAME GEOGRAPHIC BASIS SCOPE Countries’ Exclusive Economic Zones (EEZs) Global, Coastal Political (United Nations, 1982) Large Marine Ecosystems (LMEs) (Sherman et al., 2005) Global, Coastal Management areas Marine Ecoregions of the World (MEOWs) Expert-derived biogeography (realms, provinces) and Global, Coastal (Spalding et al., 2007) management units (ecoregions) Fisheries and Agricultural Organization (FAO) Major Global Rectangular fishery statistical assessment regions Fishing Areas (FAO, 2016) International Council for the Exploration of the Sea Regional Large ecosystem and fishery management areas Ecoregions (ICES, 2004) (Northeast Atlantic) International Hydrographic Organization Seas and Oceans Global Geographically named areas (IHO, 2002) Ecoregions of the Oceans and Continents (Bailey, 2014) Global Expert recommended regions Global Open Ocean and Deep Seabed (GOODS) Global, Benthic and Expert recommended regions Biogeographic Characterization (UNESCO, 2009) Pelagic Deep-Sea Provinces (Watling et al., 2013) Global, Benthic Expert-derived revision of GOODS based on literature review Biogeochemical Provinces (Longhurst, 2007) Global Satellite ocean color Expert geomorphological feature extraction using 30 arc- Seafloor Map (GSFM) (Harris et al., 2014) Global second bathymetry data Deep-Sea Seascapes Map (Harris and Whiteway, 2009) Global Multivariate analysis of seabed morphology and sediments

92 Oceanography | Vol.30, No.1 to objectively classify the entire ocean possible that as depth changes, variation data set is a compendium of data from a floor into 53,713 separate polygons com- in temperature and chemical composi- variety of ocean research and monitoring prising 11 different categories. The 11 cat- tion creates distinct ecological zones rep- programs over the past five decades. It is egories had mean polygon sizes ranging resented by different ecological commu- an authoritative 57-year climatology that from 1,000 km2 to 22,000 km2 and were nities. However, this concept has never contains over 52 million points, hereafter restricted to the seafloor. Reygondeau been objectively tested using data at a referred to as the ocean mesh. Each point et al. (2017) statistically clustered data global scale. With few exceptions (Oliver is attributed with values for temperature, from the Mediterranean Sea into dis- and Irwin, 2008; Hardman-Mountford salinity, dissolved oxygen, nitrate, phos- tinct biogeochemical regions within bio- et al., 2009; Harris and Whiteway, 2009; phate, and silicate, and all WOA values logically meaningful, predetermined Kavanaugh et al., 2014; Schoch et al., are corrected for the effect of pressure on depth zones. To our knowledge, the pres- 2014; Reygondeau et al., 2017), most each variable. The WOA has a horizon- ent study is the first to objectively clas- existing marine maps and zonation sys- tal spatial resolution of ¼° × ¼° for tem- sify the entire global ocean water col- tems are derived from supervised clas- perature and salinity, and 1° × 1° for oxy- umn simultaneously across all depths sification and thus are influenced by the gen, nitrate, phosphate, and silicate. In into discrete regions based on compre- perspectives of their authors (Costello, the vertical dimension, points are located hensive statistical clustering of physi- 2009). To test whether recognizable at variable depth intervals, ranging from cal and chemical environmental data boundaries exist vertically and horizon- 5 m increments near the surface to 100 m from all points in the World Ocean Atlas tally in the global ocean, we clustered 3D increments at depth. A total of 102 depth (WOA)-derived ocean mesh. ocean cells into groups using an unsuper- zones extend to 5,500 m. The depth inter- Most oceanography and marine biol- vised classification of physical and chem- vals are as follows: 5 m (from 0 m to ogy textbooks include diagrams that ical environmental variables. 100 m), 25 m (from 100 m to 500 m), divide the ocean into depth zones 50 m (from 500 m to 2,000 m), and 100 m (e.g., Figure 1). Shallower, sunlit depths at METHODS (from 2,000 m to 5,500 m). The deepest or near the surface are usually presented We used the complete set of variables points for which data are available do not as the photic layer, which extends to the from the 2013 World Ocean Atlas data set, necessarily represent the actual depth of general limit of light penetration (99% of version 2 (Locarnini et al., 2013; Zweng the water column because the 5,500 m incident light) at a depth of about 200 m et al., 2013; Garcia et al., 2014a, 2014b) lower limit of the WOA data is approx- (Stal, 2016). Beneath this depth, photo- as our source of physical and chemi- imately half of the maximum depth of synthesis is largely lacking (Costello and cal environmental data for defining the the ocean (Jamieson, 2011). However, Breyer, 2017). This same depth zone to ocean mesh and subsequently modeling the 5,500 m lower limit does substan- 200 m is also commonly referred to as the ecological marine units. The WOA tially exceed the mean depth (3,682.2 m) the epipelagic zone. Beneath this zone at depths commonly understood as between 200 m and 1,000 m is the meso- pelagic zone, where organismal respi- ration is higher relative to deeper areas (Costello and Breyer, 2017). Although the 1,000 m depth boundary is arbi- trarily defined, Proud et al. (2017) objec- tively subdivided the mesopelagic region into distinct subzones using organismal echolocation data, and Reygondeau et al. (2017) use flux of particulate organic car- bon to determine biologically meaning- ful boundaries for the mesopelagic layer. Deeper, darker, and colder zones are usu- ally presented as bathyl, abyssal, and hadal zones. Although depth boundaries for these regions are largely arbitrary and can vary from text to text, they are meant to describe the biogeochemical varia- FIGURE 1. Traditional oceanographic notions of vertical zonation in the ocean. Modified tion that is correlated with depth. It is from Pinet (2009).

Oceanography | March 2017 93 of the ocean as reported in the review by We constructed an ocean point mesh as downscaling of coarser-resolution data Charette and Smith (2010). The WOA a 3D spatial data structure that holds the such as global climate model (GCM) data are water-column variables, but sea- WOA data in its highest available spatial data to finer-resolution​​ data is a com- floor geomorphology may also be signif- resolution of ¼° × ¼° (~27 km × 27 km at mon practice in global change modeling icant in influencing both these variables the equator) in the horizontal dimension. (Hall, 2014), and it is also the basis for and species ecology. The first global digi- While the temperature and salinity data pansharpening of multi­resolution imag- tal map of seafloor geomorphic features is are available at this resolution, the other ery (Vivone et al., 2015). now available (Harris et al., 2014). four variables (dissolved oxygen, nitrate, The data matrix can be conceptualized Temporally, the WOA archive is avail- phosphate, and silicate) have a coarser as columnar stacks of cells whose cen- able in seasonal, annual, and decadal res- native resolution (1° × 1°) and were there- troids define the point mesh (Figure 2). olutions. Seasonal data are not available fore downscaled to the ¼° resolution to In areas where the deepest (5,500 m) for all points in the mesh, many of which reconcile all data to a common work- WOA data points did not reach the sea- may not have been visited regularly over ing horizontal resolution. This down- floor, the bottom of the mesh was sim- the 57-year period. Moreover, data from sampling was accomplished by subdivid- ply extended downward to the seafloor polar regions, typically collected only ing the 1° × 1° by depth-interval rectan- for visualization, without interpolating during warmer summer months when gular box cuboid into sixteen ¼° × ¼° additional data points. The mesh spac- access to ice-bound regions is easier, may by depth-interval cuboids and assign- ing matched the WOA data matrix and under-report true salinity values. Decadal ing the original attribute values of the allowed for the structuring and sym- values of the WOA represent the average parent cuboid’s centroid to the cen- bolization of data as columnar volumes of the annual mean values for the param- ter points of all of the ¼° subdivisions. (or other shapes) that can be queried by eters, themselves derived from the sea- In this piecewise-constant re-meshing, ranges of values, and can be spatially ana- sonal data. We used the 57-year record of we assume that the attributes of the par- lyzed via proximity algorithms and multi- the parameters, which are provided in the ent cuboid are uniform throughout the variate statistical clustering. We first con- WOA database as archival means, derived cuboid’s volume. This is similar to the structed an “empty” ocean mesh using from the decadal averages. The modeled universal assumption in vector-based the 52,487,233 WOA point locations, EMUs therefore represent average distri- GIS that the attributes of a vector poly- and then attached the WOA attribute butions of the volumetric regions over the gon are uniform throughout the poly- data to those points. The water column past 57 years. gon’s extent. Statistical and nonstatistical was bounded at the top by the sea sur- face, and at the bottom by the seafloor, as defined in the geomorphic map of Harris et al. (2014). The set of cells intersecting or nearest to the global shoreline (or ice masses) defined the horizontal extent of the water column. We statistically clustered the points in the mesh in order to identify environ- mentally distinct regions in the water col- umn. The clustering was blind to both the depth of the point and the thickness of the depth interval at that point’s verti- cal position in the water column. The “big data” nature of the clustering of the entire ocean volume required sophisticated spa- FIGURE 2. A vertical column of the ocean tial data processing and functionality. mesh framework (illustrative and not to scale), produced from World Ocean Atlas 2013 data The clustering was implemented using extracted into a set of 52,487,233 points SAS software (©2015 SAS Institute Inc; at ¼° × ¼° (~27 km × 27 km at the equator) SAS and all other SAS Institute Inc. prod- horizontal resolution and variable depth z ranging from 5 m intervals near the sur- uct or service names are registered trade- face to 100 m intervals near the deep sea- marks or trademarks of SAS Institute floor. After constructing the mesh, points Inc., Cary, NC, USA). The ArcGIS plat- were attributed with 57-year average val- ues for temperature, salinity, dissolved form was utilized for subsequent geo- oxygen, nitrate, phosphate, and silicate. spatial assessment and visualization of

94 Oceanography | Vol.30, No.1 the clusters. We utilized a k-means clus- six variables was executed in repeated sustained decline in the pseudo-F curve tering algorithm to identify the physi- sequential runs, where the number of (Figure 3), which we interpreted as a cal and chemical structure of the water clusters produced was incremented by point where additional clustering is less column. The k-means algorithm deter- one with each run, starting with two likely to reduce the within-​cluster vari- mines k centroids in the data and clusters clusters, and ending with 100 clus- ation. Thus, the 37-cluster solution was points by assigning them to the nearest ters. The optimum cluster number was the basis for our partitioning of the water centroid. Of hundreds of clustering algo- determined by inspection of the behav- column, and resulted in the 37 EMUs rithms available, the k-means approach is ior of the pseudo F-statistic (Calinski described below. the most widely used due to its simplic- and Harabasz, 1974; Milligan and Following the depth-blind statisti- ity, versatility, extensibility, data handling Cooper, 1985) across the iterations. The cal clustering, basic descriptive statistics ability, and generally robust performance pseudo-F statistic is the ratio of between-​ (mean, minimum, maximum, and stan- (Jain, 2010), although it is sensitive to ini- cluster variance to within-​cluster vari- dard deviation) were produced for the six tial placement of cluster centers (Celebi ance. Larger values of pseudo-F indicate deterministic parameters (temperature, et al., 2013). While concurrent implemen- “tight” (i.e., low within-​cluster variance) salinity, dissolved oxygen, nitrate, phos- tation and integration of complementary and “well separated” (i.e., high between-​ phate, and silicate) for each EMU. The clustering approaches has been advocated cluster variance) clusters. A plot of this unit-middle depth for each EMU was for ocean partitioning (Oliver et al., 2004; statistic against the number of clusters also calculated as the median depth of all Reygondeau et al., 2017), this multi- should show local peaks of the pseudo-F the points allocated into an EMU. algorithm approach was outside the value at potential cluster-​number optima. We then labeled the clusters using the scope of our globally comprehensive and We did not extend the clustering beyond naming criteria (Table 2) of the Coastal data intensive analysis. 100 clusters because there was a clear and Marine Ecosystem Classification Our statistical approach was proto- overall decline in pseudo-F values as the Standard (CMECS), a Federal Geographic typed on a subset (97,329 points) of the number of clusters increased. Local peaks Data Committee standard for the United global point mesh representing the ocean representing relatively high pseudo-F val- States (FGDC, 2012). The CMECS labels volume off the US West Coast out to the ues were found at 17, 28, 37, and 50 clus- for the EMUs begin with their depth Exclusive Economic Zone (EEZ). The ters (Figure 3). We explored summary zone assignments based on their median successful identification of known hydro- statistics and the horizontal and verti- depths, followed by a concatenation of graphic features (e.g., the Mendocino cal spatial distributions at each of the the CMECS descriptors for tempera- Ridge and Fracture Zone off the northern four local peaks. At 37 clusters, a strong ture, salinity, and dissolved oxygen. The California coast) in the prototype exer- peak was observed prior to a relatively CMECS framework does not include cise provided initial assurances that the clustering approach would be sensitive to environmental gradients, and that scal- 45 ing up to global clustering was warranted. 40 We therefore implemented the clustering 35 globally on all cells (>52 million points), 30 ) with all variables included. 6 All the WOA variables were standard- 25 ized to a mean of zero and a standard 20

deviation of one to establish a common Pseudo-F (x 10 basis for comparison between variables 15 of disparate units and value ranges and 10 to promote relative equal weightings 5 of the inputs to the clustering (Milligan 0 and Cooper, 1988). After standardiza- 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 tion, a Pearson’s correlation analysis of Cluster # the six inputs was implemented to iden- FIGURE 3. A plot of the pseudo-F statistic (y axis) against the requested num- ber of clusters (x axis) in successive iterations from 2 to 100 clusters, incre- tify colinearity among variables. To mented by one for each successive iteration. The red vertical line at 37 clus- determine the optimal number of clus- ters shows a strong peak prior to a relatively sustained decline in the curve of ters that would best represent the col- the pseudo-F statistic, which we interpret as a stopping-point where additional clustering does not significantly reduce within-cluster heterogeneity (Calinski lective variation in the input data, clus- and Harabasz, 1974; Milligan and Cooper, 1985). We therefore chose the tering of all the WOA points with all 37-cluster solution to represent the number and distributions of global EMUs.

Oceanography | March 2017 95 TABLE 2. Depth and physicochemical properties (column 1) and corresponding depth zones standard names and value ranges for and regime units of the Coastal and Marine Ecosystem Classification Standard (CMECS; FGDC, 2012; column 2) and of Ecological Marine Units (EMUs; column 3). CMECS regimes do not exist nitrate, phosphate, and silicate. For these for nitrate, phosphate, and silicate. They were therefore adopted from EMU terms developed three variables, labels corresponding to for these three variables, based on assessment and classification of the approximately 52 mil- high, medium, and low nutrient concen- lion observations for each nutrient into three relative classes (high, medium, and low). trations in a relative sense were deter- mined from assessment of the observed DEPTH (m) CMECS MARINE OCEANIC EMU WATER WATER COLUMN LAYER COLUMN LAYER distribution of values. These three nutri- ent descriptors were added to the four 0 to <200 Epipelagic Shallow CMECS descriptors for a total of seven 200 to <1,000 Mesopelagic Moderate Depth descriptors in the CMECS names. The 1,000 to <4,000 Bathypelagic Deep sequence of presentation of the descrip- ≥4,000 Abyssopelagic Very Deep tors used in the CMECS names is: depth, temperature, salinity, dissolved oxygen, TEMPERATURE CMECS TEMPERATURE REGIME EMU TEMPERATURE (°C) REGIME nitrate, phosphate, and silicate. While 20 to <30 Warm to Very Warm Warm depth was not used as a clustering vari- 10 to <20 Moderate to Cool Cool able, it is a key descriptor in the CMECS 5 to <10 Cold Cold classification and an important variable for considering ocean depth zones, and 0 to <5 Very Cold Very Cold was therefore included in the CMECS ≤0 Frozen/Superchilled Superchilled labeling. As an illustrative example of

SALINITY CMECS SALINITY REGIME EMU SALINITY the CMECS nomenclature, an EMU (dimensionless) REGIME cluster might be named Bathypelagic, >30 Euhaline to Hyperhaline Normal Salinity Very Cold, Euhaline, Severely Hypoxic, 18 to 30 Lower to Upper Polyhaline Low Salinity High Nitrate, Medium Phosphate, and <18 Oligohaline to Mesohaline Very Low Salinity Medium Silicate. We then developed a separate and par- DISSOLVED CMECS OXYGEN REGIME EMU OXYGEN allel label, the EMU name (Table 2), to OXYGEN (ml/L) REGIME simplify the CMECS terminology. The ≥8 Highly Oxic to Very Oxic High Oxygen EMU equivalent of the CMECS clus- 4 to <8 Oxic Moderate Oxygen ter described above is Deep, Very Cold, 2 to <4 Hypoxic Low Oxygen Normal Salinity, Very Low Oxygen, High 0.1 to <2 Severely Hypoxic Very Low Oxygen Nitrate, Medium Phosphate, and Medium <0.1 Anoxic No Oxygen Silicate. The CMECS and simplified EMU names reflect the properties of the EMU, NITRATE CMECS NITROGEN REGIME EMU NITROGEN not its location in the ocean. Naming (µM) (adopted from next column) REGIME the EMUs based on their chemical and >30 High Nitrate High Nitrate physical properties is both accurate and 10 to 30 Medium Nitrate Medium Nitrate “classification neutral” in the sense that <10 Low Nitrate Low Nitrate the label is purely descriptive in a compo- sitional sense (Sayre et al., 2014). PHOSPHATE CMECS PHOSPHATE REGIME EMU PHOSPHATE (µM) (adopted from next column) REGIME Finally, in addition to the CMECS and EMU compositional names, we >5 High Phosphate High Phosphate also developed a set of EMU volumet- 2.5 to 5 Medium Phosphate Medium Phosphate ric region names that describe both their <2.5 Low Phosphate Low Phosphate geographic distributions in the ocean and their vertical positions in the water col- SILICATE CMECS SILICATE REGIME EMU SILICATE (µM) (adopted from next column) REGIME umn. In the field of quantitative water >100 High Silicate High Silicate mass analysis, water masses are generally 50 to 100 Medium Silicate Medium Silicate associated with or defined by their for- <50 Low Silicate Low Silicate mation regions (Tomczak, 1999; Emery, 2001) or surface water sources (Gebbie and Huybers, 2010, 2011). Because we

96 Oceanography | Vol.30, No.1 have not identified the source geogra- volumetric regions, see Appendix 1 the vertical distribution of the EMUs phies for our EMUs, we avoid calling (available in the online supplementary with respect to the CMECS boundar- them water masses herein. We instead materials). Maps of the EMUs, along ies for epipelagic (0 m to 200 m), meso- describe the total volumetric distribu- with descriptive statistics on their envi- pelagic (200 m to 1,000 m), bathypelagic tion of an EMU as a volumetric region ronmental characteristics, are found in (1,000 m to 4000 m), and abyssopelagic rather than as a water mass. Each EMU Appendix 2 (also available in the online (>4,000 m) zones. The number labels in volumetric region name contains both supplementary materials). each EMU represent the EMU number a geographic descriptor and a CMECS The EMUs are presented in a num- for cross-referencing to the EMU names depth zone class (epipelagic, meso- ber of 2D slices at several depths in and maps in Appendices 1 and 2. These pelagic, bathypelagic, and abyssopelagic) Figure 4. A plot of EMU area against EMU number labels have been placed based on the median depth of the EMU. depth, shown in Figure 5, characterizes vertically at a depth corresponding to the Examples of EMU volumetric regions the vertical position of the EMUs in the median unit-middle of the EMU. include Antarctic and Subantarctic water column and the depth at which The global maps of the EMUs listed in Bathypelagic, Mediterranean and Red the maximum and minimum horizon- Appendix 2 show the maximum global Seas Mesopelagic, and Arctic and tal distributions occur can be easily visu- horizontal extent of the clusters look- Labrador Sea Epipelagic. ally interpreted. This graph also shows ing vertically from above, as well as the

RESULTS The strength of the relationship between the standardized variables and the result- ing EMU configurations was either strong (>90 to <95% confidence) or statistically significant (≥ 95% confidence) for each of the six input variables: temperature (R2 = 0.95), salinity (R2 = 0.97), dissolved oxygen (R2 = 0.91), nitrate (R2 = 0.96), phosphate (R2 = 0.96), and silicate (R2 = 0.94). A strong correlation (>.8) among the three nutrient inputs (nitrate, phosphate, silicate) was observed, sug- gesting that they may have had a slightly disproportionate influence on the cluster- ing. However, we did not remove any of the nutrient variables and then re-cluster in order to ensure that regional variation in any of the six input variables could influ- ence the clustering outcome. The ratio of between-cluster variance to within- ​ cluster variance (R2/(1 − R2) was: tem- perature, 17.47; salinity, 12.89; dissolved oxygen, 10.72; nitrate, 24.42; phosphate, 22.11; and silicate, 15.12. These results indicate that the six parameters contrib- uted strongly and approximately equally to the identification of the clusters. Clustering the entire set of points in FIGURE 4. The global distribution of EMUs at eight depth intervals. EMUs represent physically and the global mesh yielded 37 mutually chemically distinct volumetric regions based on combined temperature, salinity, oxygen, and nutri- ent gradients. While a total of 37 EMUs were statistically determined, a number of them are small, exclusive clusters (EMUs) that are volu- localized, and shallow, and are not discernible in these depth-layer maps. Black indicates regions metric regions of relative compositional shallower than the depth at that layer. Major hydrographic features like Northern and Southern homogeneity. For a listing of EMUs Hemisphere gyre systems and coastal upwelling-based westward flow of water from western con- tinental margins are evident, particularly at shallower depths (upper left and right panels). Colors described using CMECS and EMU ter- reflect mean EMU temperatures, with pink colors representing warmer EMUs and blue colors rep- minology, and the names of the EMU resenting colder EMUs.

Oceanography | March 2017 97 thickness of the EMU at any location. of the water column from sea surface to open ocean into large volumetric regions, Interacting visually with true volumes in seafloor at any user-selected surface loca- the ¼° horizontal spatial resolution of 3D, especially with such a large resource, tion, and it returns the values for the the ocean mesh may not be sufficient to is a software challenge, but it is possible physical and chemical properties of the completely resolve the finer resolution, with commercially available virtual globe- points in the column. It also identifies the ecologically meaningful coastal systems. based visualization software, as shown in EMUs associated with any point in the We therefore consider these very small, Figure 6. Although the EMUs are continu- vertical profile and provides the EMUs’ yet statistically derived coastal clusters as ous data surfaces, Figure 6 shows that they descriptive statistics. likely indicators of coastal and estuarine are more easily visualized as stacked bands EMUs that need to be further clarified. on horizontally separated cylinders. DISCUSSION Latitudinal distribution patterns The size and complexity of the EMU EMU Geographic Distributions of EMUs are observed (Figure 4 and resource, a de facto example of big data Twenty-two of the EMUs are large, with Appendix 2), with EMUs occurring in with over 52 million attributed points essentially global or large regional dis- latitudinal biomes that include polar in three dimensions, potentially pres- tributions, while the 15 others are small, and subpolar regions (e.g., EMUs 14, ents barriers to its efficient use. To help shallow, and coastal, and collectively rep- 19, 23, 25 and 31), temperate regions mitigate this challenge, we developed resent only about 1% of the ocean vol- (e.g., EMUs 3, 8, and 37), subtropi- an open-access web application, EMU ume. They generally have lower salini- cal and tropical regions (e.g., EMUs 11, Explorer (http://livingatlas.arcgis.com/ ties than the other EMUs, and are found 24, 26, 33), and equatorial regions emu) that permits real-time query and where mixing of fresh and saline waters is (e.g., EMUs 10, 18). Bimodal latitudinal visualization of both the points and the occurring (e.g., the Baltic Sea and north- distributions (Northern and Southern EMUs by anyone with Internet access. ern, ice-occurring regions). While suit- Hemispheres) are observed (e.g., EMUs 8, The application shows the vertical profile able for global-scale stratification of the 11, 21, and 24). Some EMUs are associ- ated with physiographic features, such as Surface Area (106 km2) the Mendocino Ridge and Fracture Zone, 0 100200 300400 situated near the southern boundary of 0 5 FIGURE 5. EMU distributions by 25 18 21 24 EMU 30. Parts of some EMUs are located 23 depth. The two-dimensional global 3530 31 area (km2) at any depth is shown in the discharge regions of major rivers 11 for the 22 EMUs that comprise like the Amazon and Congo (EMUs 18 19 99% of the ocean volume. The hor- and 24). The Mediterranean and Red Seas

EPIPELAGIC izontal boundary lines separat- 8 ing the depth zone classes are as were clustered into a single unit (EMU 9), described in the Coastal and Marine consistent with Longhurst’s (2007) identi- Ecosystem Classification Standard 200 26 9 (CMECS), the Federal Geographic fication of a single biogeochemical prov- Data Committee (FGDC) stan- ince for the Mediterranean. Reygondeau 10 400 dard for the United States (FGDC, et al. (2017), however, objectively iden- 2012). The EMU number labels tified 63 three-dimensional, manage- 600 (see Appendices 1 and 2 in the online supplementary materials for MESOPELAGIC ment-appropriate subdivisions of the 800 33 names, maps, and descriptions of Mediterranean, maintaining that while the EMUs) are placed at the median 1,000 37 unit-middle depth for each EMU. global analyses are useful for macro- 1,200 29 Although the EMUs are not uni- scale comparisons of ocean regions, local Depth (m) 1,400 formly distributed into the CMECS management strategies and policies will 36 depth zones, strong vertical sepa- 1,600 3 ration is evident, with many small require appropriately scaled geographic 1,800 EMUs in the upper water column assessment and accounting units. 2,000 and fewer larger EMUs in the mid- One very large, deep, circumglobal dle and lower water columns. Pink

14 THYPELAGIC cluster (EMU 13) is observed in the 13 BA colors indicate warmer EMUs, and blue colors indicate colder EMUs. Pacific and Indian Oceans but is all but 3,000 absent in the Atlantic, consistent with the recognition of a Circumpolar Deep 4,000 Water (CDW) mass by Emery and Meincke (1986). Likewise, EMU 29 is - similar to the Arctic Deep Water (ADW) 5,000 and North Atlantic Deep Water (NADW) ABYSSO PELAGIC 5,500 units of those authors. Although some

98 Oceanography | Vol.30, No.1 of the EMUs are similar to water masses not include bimodal distributions of regions of ocean waters. The boundar- described by Emery and Meincke, in provinces in both Northern and Southern ies separating these seven source regions other instances there is less resemblance, Hemispheres as was obtained with some are also present in the surface-occurring which is to be expected, given that the EMUs (e.g., EMU 8, Subantarctic, North EMUs. It is evident that an aggregation of EMUs were derived from six composi- Atlantic, and North Pacific Epipelagic). EMUs into the Gebbie and Huybers for- tional properties rather than from the Overall, considerable visual correspon- mation regions would be very “clean,” temperature- and salinity-derived units dence is observed between Longhurst’s resulting in minimal splitting of EMUs of Emery and Meincke. BGCPs and the surface-occurring across formation region boundaries. The surface-occurring EMUs (upper EMUs, and a more quantitative compar- Neither the Large Marine Ecosystems left panel of Figure 4) can be compared ison is merited. of Sherman et al. (2005) nor the Marine with the surface-derived biogeochemical Another set of ocean-surface regions Ecoregions of the World of Spalding provinces (BGCPs) of Longhurst (2007). that can be compared to the surface- et al. (2007) address open-ocean pelagic As mentioned above, the Mediterranean occurring EMUs is that of Gebbie and ecosystems, so comparisons between Sea was identified as a single unit (EMU 9) Huybers (2011), who identified seven them and the geographic extent of the without subdivision in both classifica- global surface water masses: Antarctic, EMUs are not feasible. The interpretive, tions. The Mediterranean and Red Seas North Atlantic, Subantarctic, North expert-derived subdivisions of the Global EMU was placed in the mesopelagic class Pacific, Arctic, Mediterranean, and Open Ocean and Deep Seabed (GOODS) because its median unit-middle depth Tropics, representing the formation Biogeographic Classification (UNESCO, (302 m) is between 200 m and 1,000 m, but its vertical distribution is throughout the water column. Other similarities between the EMUs and Longhurst’s BGCPs are apparent. Both classification systems identify obvious latitudinal banding sep- arating the Antarctic, Subantarctic, and Southern Hemisphere tropics and equa- torial regions. EMU 18 (North Pacific Subtropical and Equatorial Indian Epipelagic) closely approximates the dis- tributions of Longhurst’s North Pacific Equatorial Countercurrent Province and Pacific Equatorial Divergence Province. Several of Longhurst’s provinces in the Arctic and Subarctic regions (e.g., Boreal Polar Province, Atlantic Arctic Province, Atlantic Subarctic Province, North Pacific Epicontinental Sea Province) cor- respond visually with EMUs 5 (Arctic Epipelagic), 23 (Arctic and Labrador Sea Epipelagic), and 30 (North Pacific and Beaufort Sea Epipelagic). The latitudinal demarcation between Longhurst’s two Indian Ocean provinces (Indian South Subtropical Gyre Province and Indian Monsoon Gyres Province) is a strong lat- itudinal delineation between EMUs 11 (Northern Subtropical and Southern FIGURE 6. Example of the visualization approach taken to represent the EMUs in three dimensions, Subtropical Epipelagic), 18 (North mapped over space. The region shown is off the eastern coast of Japan. Although the EMUs are Pacific Subtropical and Equatorial Indian mapped as a continuous surface, representing them in three dimensions is facilitated by the use of Epipelagic), and 24 (Tropical Pacific, stacked cylinders, where each color band on a cylinder is an EMU. In the coastal zone, EMUs are single or few, and shallow, whereas offshore there are more and deeper EMUs. Surface tempera- Tropical Indian, and Equatorial Atlantic ture gradients are also apparent between the pink (warmer) EMUs in the south, and the blue (colder) Epipelagic). The Longhurst system does EMUs in the north.

Oceanography | March 2017 99 2009) are similarly difficult to compare currently classified into CMECS depth 30 realms were obtained from 2D clus- with the quantitative, statistically derived zones using standardized criteria, an tering of occurrence records represent- EMUs presented here. attempt to statistically separate EMUs ing over 65,000 species from the Ocean into depth classes and associated quanti- Biogeographic Information System EMU Depth Distributions tative determination of the depth bound- (OBIS, http://www.iobis.org) database, The number of EMUs is highest at or aries for those groupings appears war- and they reflect global patterns of spe- near the surface, and decreases with ranted. In addition, the EMUs should cies endemicity. Initially, we note corre- depth (Figures 4 and 5). Although we be assessed for their depth-based rela- spondence between realms and EMUs used the CMECS criteria and the median tionship to physical properties like light (e.g., realms 2, 5, 7, and 30) in some areas, unit-middle value to classify the EMUs attenuation limits (Stal, 2016) and biolog- but in other cases a relationship is less into epipelagic, mesopelagic, bathype- ically mediated phenomena like respira- apparent (e.g., realms 18, 21, and 22). In lagic, or abyssopelagic zones, Figure 5 tion (Costello and Breyer, 2017) and car- another global assessment of 11,567 spe- shows that those depth class assign- bon flux (Reygondeau et al., 2017). We cies occurrences representing 13 tax- ments, while informative, are also imper- suggest that the depth boundaries of the onomic groups, Tittensor et al. (2010) fect. Many EMUs are distributed across vertical zones and the environmental fac- identified concentrations of coastal and depth zone boundaries, with some hav- tors controlling the vertical separation of open-ocean species in the eastern Pacific ing most of their distribution in one zone, the pelagic ocean need additional analy- and in mid-latitudinal belts, respectively. but with vertical extensions into upper or sis and further clarification. The mapping of the EMUs will allow lower depth zones as well. EMUs vary improved characterization of the hor- considerably in water column position, Biogeography and EMUs izontal and vertical distributions and thickness, and horizontal area at varying Biogeographic regions are delineated the chemical and physical natures of depths. No EMUs were classified as abys- from an analysis of species distribu- species-rich regions. sal as there were no EMUs with a median tion data. The number and types of tax- unit-​middle depth >4,000 m. However, onomic groups represented (Fontaine Limitations and Future Work the distributions of many EMUs extend et al., 2015), as well as the relative focus We recognize limitations in our work beyond the 4,000 m bathypelagic/​ on endemism (Briggs and Bowen, 2012), related to both temporal scaling dimen- abyssopelagic boundary. Acknowledging can vary widely. Although not quan- sions and parameters selected for the some overlap, the EMUs appear to be bet- titatively assessed, we made a prelimi- clustering. The WOA data offer several ter separated, visually, at depths approx- nary visual comparison (Figure 7) of the native temporal resolutions (seasonal, imately corresponding to water column EMUs’ spatial distributions and the dis- annual, and decadal) that we did not positions from 0 m to 200 m (upper tributions of 30 marine biogeographic exploit. As our aim was to use long-term water column), 200 m to 2,000 m (mid- realms developed from statistical clus- historical average values for the point dle water column), and >2,000 m (lower tering of species-distribution data, based locations, we used the 57-year mean val- water column). Although the EMUs are on recent work of author Costello. The ues for the six parameters to map EMU

6 FIGURE 7. Relationship between 6 8 surface-occurring EMU distri- 4 butions (colors) and marine bio- geographic realms (numbered, 3 7 outlined polygons), from recent 18 work of author Costello. Spatial 5 congruence between biogeo- 9 graphic realms and surface-​ 9 29 20 11 14 occurring EMUs is apparent for some realms (e.g., 5, 7, 26, 30) 12 but not for others (e.g., 18, 21, 22). 22 23 13 21 17 19 16 15 10 27 25 26 28 24 28 30

100 Oceanography | Vol.30, No.1 extents and locations, and this approach communities (Sherman, 2014; Wallace 2016), but we have not documented the has been successful in mapping ocean et al., 2014; Thresher et al., 2015). Finally, relationship between environmental vari- regions. However, it prohibits assess- variables associated with ocean currents, ation and species diversity. As a first step, ment of temporal variability and trends. such as flow direction and magnitude, we are currently undertaking a more Recent work shows that it would be pos- may substantially influence EMU char- quantitative assessment of this relation- sible to construct temporally sequenced acteristics and distributions. We plan to ship between physically and chemically EMU distribution maps (e.g., Oliver and pursue the addition of these attributes to distinct regions in the ocean and spe- Irwin, 2008; Reygondeau et al., 2013; the ocean mesh and to study their effects cies biogeography, the results of which Kavanaugh et al., 2014). We now have a on EMU distributions in future statistical will facilitate a deeper understanding of framework for that assessment and are clustering analyses. the true ecological nature of the EMUs. planning the development of seasonal, We also plan to enrich the EMU For example, we are exploring the cross-​ annual, and decadal characterizations of resource by combining the EMU data indexing of OBIS species records and oceanic water masses. However, the com- with other data layers. This will result in EMUs, and we expect that subsequent putational requirements for six variables the creation of new 2D layers for the sea versions of EMUs will not only contain increase by orders of magnitude when surface and the seafloor. For example, species records as attributes but may also contemplating temporal variations for for the seafloor, we have combined the change geographically to be more reflec- over 52 million points. This is currently a bottom-occurring EMUs with the sea- tive of marine organism distributions. big-data challenge (Gallagher et al., 2015; floor physiographic regions and features Finally, we recognize the opportu- Alder and Hostetler, 2015; Coro et al., of Harris et al. (2014) in order to evalu- nity to use finer-resolution data to cre- 2016; Wright, 2016), but as spatial pro- ate the influence of seafloor geomorphol- ate a more refined mapping of EMUs at cessing technologies evolve, these kinds ogy on the water-column structure above regional and local scales, as was demon- of analyses will be rendered less compu- it. We also have combined a 13-year aver- strated by Reygondeau et al. (2017). tationally intense than they are at present. age ocean color value data set (chloro- Moreover, we plan to elucidate the coastal The clustering of oceanic data to derive phyll a from the NASA Aqua-MODIS and estuarine units in greater detail in an EMUs was based on the six variables in sensor) to our surface-occurring EMUs, independent development of a set of eco- the WOA data set. The addition of other and plankton abundance characteris- logical coastal units (ECUs), which will variables would likely influence the oce- tics are now available for the surface data be undertaken along the entire global anic partitioning we present here. The points and the surface-occurring EMUs. shoreline. We are working on the devel- inclusion of data on particulate organic We intend to continue adding associa- opment of a new global shoreline vector carbon (POC), carbonate contents, and tive attributes from other globally avail- extracted from satellite imagery (30 m ocean current patterns might influence able resources, and we are exploring the spatial resolution) and attributed with the clustering results. POC plays a crucial relationship between EMUs and estab- environmental characteristics as the spa- role in the marine and global carbon cycle lished temporally dynamic climatological tial framework for the planned develop- and is a primary component of oceano- classifications (Oliver and Irwin, 2008; ment of a global set of ECUs. graphic food webs (Buesseler et al., 2007). Kavanaugh et al., 2016). POC flux was one of the parameters used While we are calling these 37 volu- CONCLUSIONS to subdivide the Mediterranean Sea into metric regions EMUs, we acknowledge The present EMU mapping effort is an 63 biogeochemical regions (Reygondeau that their true ecological character has objective partitioning of the global ocean et al., 2017), where it was used to quan- not yet been established. Their deriva- into environmentally distinct volumet- titatively separate the mesopelagic layer tion from entirely physicochemical data, ric region units using an aspatial cluster- from the bathypelagic layer. Variability and their similarity to widely recognized ing exercise where clusters were not con- in the vertical flux of POC is import- global surface waters, lends validation to strained into particular ocean regions ant for understanding the main path- the EMUs as physically and chemically and the cluster sites were selected by ways by which organic carbon is formed distinct volumetric regions. We call them homogeneity in physical and chemical in ocean surface waters via photosynthe- ecological in the general sense that depth, parameters only, blind to both depth and sis and then transferred to the deep ocean temperature, salinity, oxygen, and nutri- location. We have developed a new clas- where it may be sequestered (Lutz et al., ents are known to be important in struc- sification scheme for 37 compositionally 2007). Similarly, the carbonate chemis- turing biotic distributions (Longhurst, varying marine volumetric regions. Their try of the ocean is ecologically import- 2007; Oliver and Irwin, 2008), and properties are listed using both standard- ant, as the persistence of ocean acidifi- because microbial processes shape nutri- ized CMECS descriptors and new EMU cation is likely to have implications for ent and oxygen distributions through- terminology. EMU volumetric region many surface and pelagic ecosystems and out the water column (Kavanaugh et al., names are compiled by combining a

Oceanography | March 2017 101 geographic descriptor and a depth zone Bailey, R.G. 2014. Ecoregions – The Ecosystem technical ed., NOAA Atlas NESDIS 75, NOAA Geography of the Oceans and Continents, 2nd ed. National Centers for Environmental Information, term (Appendices 1 and 2). Springer, New York, NY, 180 pp. Silver Spring, MD, 27 pp. The aim of this work was to produce a Beaugrand, G., I. Rombouts, and R.R. Kirby. 2013. Garcia, H.E., R.A. Locarnini, T.P. Boyer, J.I. Antonov, Towards an understanding of the pattern of bio- O.K. Baranova, M.M. Zweng, J.R. Reagan, and new global characterization and detailed diversity in the oceans. Global Ecology and D.R. Johnson. 2014b. World Ocean Atlas 2013 data set of marine environments as a Biogeography 22:440–449, https://doi.org/10.1111/ version 2 (WOA13 V2), Volume 4: Dissolved geb.12009. Inorganic Nutrients (phosphate, nitrate, silicate). resource for biogeographic assessments, Briggs, J.C., and B.W. Bowen. 2012. A realignment S. Levitus, ed., and A. Mishonov, technical ed., of marine biogeographic provinces with par- NOAA Atlas NESDIS 76, NOAA National Centers impact studies, biodiversity priority set- ticular reference to fish distributions. Journal for Environmental Information, Silver Spring, MD, ting, and ecosystem accounting, man- of Biogeography 39:12–30, https://doi.org/​ 25 pp. 10.1111/j.1365-2699.2011.02613.x. Gebbie, G., and P. Huybers. 2010. Total matrix inter- agement, and research. The mapping of Buesseler, K.O., C.H. Lamborg, P.W. Boyd, P.J. Lam, comparison: A method for determining the geom- global ecological land units (ELUs), spon- T.W. Trull, R.R. Bidigare, J.K.B. Bishop, K.L. Casciotti, etry of water-mass pathways. Journal of Physical F. Dehairs, M. Elskens, and others. 2007. Revisiting Oceanography 40:1,710–1,728, https://doi.org/​ sored by the GEO commission, has now carbon flux through the ocean’s twilight zone. 10.1175/2010JPO4272.1. Science 316(5824):567–570, https://doi.org/10.1126/ Gebbie, G., and P. Huybers. 2011. How is the ocean been extended to the ocean. We parti- science.1137959. filled? Geophysical Research Letters 138, L06604, tioned the global ocean water column Calinski, T., and J. Harabasz. 1974. A dendrite https://doi.org/10.1029/2011GL046769. method for cluster analysis. Communications Hall, A. 2014. Projecting regional change. Science into 37 physically and chemically dis- in Statistics 3(1):1–27, https://doi.org/10.1080/​ 346(6216):1,461–1,462, https://doi.org/10.1126/​ tinct volumetric regions using available 03610927408827101. science.aaa0629. Celebi, M., H. Kingravi, and P. Vela. 2013. A compara- Hardman-Mountford, N.J., T. Hirata, K. Richardson, data from the 57-year World Ocean Atlas tive study of efficient initialization methods for the and J. Aiken. 2009. An objective methodology data set. The global map of EMUs is an k-means clustering algorithm. Expert Systems with for the classification of ecological pattern into Applications 40(1):210–215, https://doi.org/10.1016/​ biomes and provinces for the pelagic ocean. initial, unsupervised, statistical classifi- j.eswa.2012.07.021. Remote Sensing of Environment 112:3,341–3,352, Charette, M.A., and W.H.F. Smith. 2010. The volume https://doi.org/10.1016/j.rse.2008.02.016. cation approach to mapping ocean envi- of Earth’s ocean. Oceanography 23(2):112–114, Harris, P.T., M. Macmillan-Lawler, J. Rupp, and ronmental structure in three dimen- https://doi.org/10.5670/oceanog.2010.51. E.K. Baker. 2014. Geomorphology of the oceans. Coro, G., C. Magliozzi, A. Ellenbroek, K. Kaschner, and Marine Geology 352:4–24, https://doi.org/10.1016/​ sions. Based on this methodology, we can P. Pagano. 2016. Automatic classification of climate j.margeo.2014.01.011. re-cluster the ocean using additional or change effects on marine species distributions in Harris, P.T., and T. Whiteway. 2009. High seas 2050 using the AquaMaps model. Environmental marine protected areas: Benthic environmen- different deterministic variables if desir- and Ecological Statistics 23:155–180, tal conservation priorities from a GIS analysis of https://doi.org/10.1007/s10651-015-0333-8. global ocean biophysical data. Ocean & Coastal able, and also cluster the global ocean in Costello, M.J. 2009. Distinguishing marine hab- Management 52:22–38, https://doi.org/10.1016/​ different time intervals ranging from sea- itat classification concepts for ecological j.ocecoaman.2008.09.009. data management. Marine Ecology Progress Heinzer, T.J., M.D. Williams, E.C. Dogrul, sonal to annual to decadal to explore the Series 397:253–268, https://doi.org/10.3354/ T.N. Kadir, C.F. Brush, and F.I. Chung. 2012. temporal geographies of EMUs. The exis- meps08317. Implementation of a feature-constraint mesh gen- Costello, M.J., and S. Breyer. 2017. Ocean depths: The eration algorithm within a GIS. Computers and tence of the EMUs has the potential to mesopelagic and implications for global warming. Geosciences 49:46–52, https://doi.org/10.1016/​ Current Biology 27:113–119, https://doi.org/10.1016/​ j.cageo.2012.06.004. facilitate research on the extent to which j.cub.2016.11.042. Holt, B.G., J. Lessard, M.K. Borregaard, S.A. Fritz, environmental drivers control biotic dis- Emery, W.J. 2001. Water types and water M.B. Araújo, D. Dimitrov, P. Fabre, C.H. Graham, masses. Pp. 1,556–1,567 in Encyclopedia of G.R. Graves, K.A. Jønsson, and others. 2013. tributions. The EMU data we have cre- Ocean Sciences. J.H. Steele, S.A. Thorpe, and An update of Wallace’s zoogeographic ated allow for characterization of the K.K. Turekian, eds, Academic Press, San Diego, CA, regions of the world. Science 339(6115):74–78, 3,399 pp. https://doi.org/10.1126/science.1228282. physical and chemical environment con- Emery, W.J., and J. Meincke. 1986. Global water ICES (International Council for Exploration of the tained in marine protected areas, fishing masses: Summary and review. Oceanologica Seas). 2004. Report of the Regional Ecosystem Acta 9(4):383–391. Study Group for the North Sea. April 5–7, 2004, grounds, or other marine geographies. FAO (Food and Agriculture Organization). 2016. ICES CM 2004/ACE:06, International Council for Coordinating Working Party Handbook of Fishery Exploration of the Seas, Copenhagen, Denmark. As an open-access resource, the EMU Statistical Standards, Section H: Fishing Areas IHO (International Hydrographic Organization). data are available to scientists, managers, for Statistical Purposes. FAO Fisheries and 2002. International Hydrographic Organization Aquaculture Department, http://www.fao.org/ Publication S-23: Limits of Oceans and Seas, draft and the interested public. Future work fishery/cwp/handbook/h/en. 4th ed. International Hydrographic Organization, will enrich the EMU resource by adding FGDC (Federal Geographic Data Committee). 2012. Paris, Monaco, https://www.iho.int/mtg_docs/ Coastal and Marine Ecological Classification com_wg/S-23WG/S-23WG_Misc/Draft_2002/ additional attributes to the ocean mesh Standard Version 4.0. Report FGDC-STE-18-2012, Draft_2002.htm. Federal Geographic Data Committee Secretariat, Jain, A.K. 2010. Data clustering: 50 years and developing a finer resolution ecolog- US Geological Survey, Reston, Virginia, 343 pp. beyond k-means. Pattern Recognition ical coastal unit (ECU) product along the Fontaine, A., R. Devillers, P.R. Peres-Neto, and Letters 31(8):651–666, https://doi.org/10.1016/​ L.E. Johnson. 2015. Delineating marine eco- j.patrec.2009.09.011. global shoreline. logical units: A novel approach for deciding Jamieson, A.J. 2011. Ecology of deep oceans: which taxonomic group to use and which tax- Hadal trenches. eLS, https://doi.org/10.1002/​ SUPPLEMENTARY MATERIALS onomic resolution to choose. Diversity and 9780470015902.a0023606. Appendices 1 and 2 are available at https://doi.org/​ Distributions 21(10):1,167–1,180, https://doi.org/10.1111/ Kavanaugh, M.T., B. Hales, M. Saraceno, Y.H. Spitz, 10.5670/oceanog.2017.116. ddi.12361. A.E. White, and R.M. Letelier. 2014. Hierarchical Gallagher, J., J. Orcutt, P. Simpson, D. Wright, and dynamic seascapes: A quantitative frame- J. Pearlman, and L. Raymond. 2015. Facilitating work for scaling pelagic biogeochemistry and ecol- REFERENCES open exchange of data and information. Earth ogy. Progress in Oceanography 120:291–304, Alder, J.R., and S.W. Hostetler. 2015. Web based visu- Science Informatics 8:721–739, https://doi.org/​ https://doi.org/10.1016/j.pocean.2013.10.013. alization of large climate data sets. Environmental 10.1007/s12145-014-0202-2. Kavanaugh, M.T., M.J. Oliver, F.P. Chavez, R.M. Letelier, Modelling & Software 68:175–180, https://doi.org/​ Garcia, H.E., R.A. Locarnini, T.P. Boyer, J.I. Antonov, F.E. Muller-Karger, and S.C. Doney. 2016. 10.1016/j.envsoft.2015.02.016. O.K. Baranova, M.M. Zweng, J.R. Reagan, and Seascapes as a new vernacular for pelagic ocean Bailey, R.G. 1996. Ecosystem Geography. Springer- D.R. Johnson. 2014a. World Ocean Atlas 2013 monitoring, management and conservation. ICES Verlag, New York, NY, 204 pp. version 2 (WOA13 V2), Volume 3: Dissolved Journal of Marine Science 73(7):1,839–1,850, Oxygen, Apparent Oxygen Utilization, and Oxygen https://doi.org/10.1093/icesjms/fsw086. Saturation. S. Levitus, ed., and A. Mishonov,

102 Oceanography | Vol.30, No.1 Kottek, M., J. Grieser, C. Beck, B. Rudolf, and F. Rubel. Schoch, G.C., D.M. Albert, and C.S. Shanley. 2014. NOAA Atlas NESDIS 74, NOAA National Centers 2006. World map of the Köppen-Geiger cli- An estuarine habitat classification for a com- for Environmental Information, Silver Spring, mate classification updated. Meteorologische plex fjordal island archipelago. Estuaries and Maryland, 39 pp. Zietschrift 15(3):259–263. Coasts 37(1):160–176, https://doi.org/10.1007/ Li, Z., and C. Gold. 2004. Multi-dimensional geo- s12237-013-9622-3. spatial technology for geosciences. Computers Sherman, K. 2014. Adaptive management institu- ACKNOWLEDGMENTS We are grateful for the thoughtful reviews of three and Geosciences 30(4):321–323, https://doi.org/​ tions at the regional level: The case of Large U.S. Geological Survey scientists, Jonathan H. Smith, 10.1016/j.cageo.2003.09.010. Marine Ecosystems. Ocean and Coastal Page C. Valentine, and Ilsa B. Kuffner, as well as those Liu, J., T. Dietz, S.R. Carpenter, M. Alberti, C. Folke, Management 90:38–49, https://doi.org/10.1016/​ of three anonymous reviewers. Cressie’s research E. Moran, A.N. Pell, P. Deadman, T. Kratz, j.ocecoaman.2013.06.008. was partially supported by a 2015–2017 Australian J. Lubchenco, and others. 2007. Complexity Sherman, K., M. Sissenwine, V. Christensen, Research Council Discovery Project (DP150104576). of coupled human and natural systems. A. Duda, G. Hempel, C. Ibe, S. Levin, D. Lluch- Goodin’s research was partially supported by the Science 317(5844):1,513–1,516, https://doi.org/​ Belda, G. Matishov, J. McGlade, and others. Langar Foundation. Kavanaugh’s research was par- 10.1126/science.1144004. 2005. A global movement toward an ecosys- tially supported by the National Ocean Partnership Liu, J., H.A. Mooney, V. Hull, S.J. Davis, J. Gaskell, tem approach to marine resources management. Program’s Marine Sanctuaries as Sentinel Sites for T. Hertel, and J. Lubchenco, K.C. Seto, P. Gleick, Marine Ecology Progress Series 300:275–279. a Demonstration Marine Biodiversity Observation C. Kremen, and S. Li. 2015. Systems integra- Spalding, M.D., H.E. Fox, G.R. Allen, N. Davidson, Network award (NNX14AP62A). Any use of trade, tion for global sustainability. Science 347(6225), Z.A. Ferdaña, M. Finlayson, B.S. Halpern, product, or firm names is for descriptive pur- https://doi.org/10.1126/science.1258832. M.A. Jorge, A. Lombana, S.A. Lourie, and poses only and does not imply endorsement by the Locarnini, R.A., A.V. Mishonov, J.I. Antonov, T.P. Boyer, others. 2007. Marine ecoregions of the world: U.S. Government. H.E. Garcia, O.K. Baranova, M.M. Zweng, C.R. Paver, A bioregionalization of coastal and shelf areas. J.R. Reagan, D.R. Johnson, and others. 2013. World BioScience 57(7):573–583, https://doi.org/10.1641/ Ocean Atlas 2013 version 2 (WOA13 V2), Volume 1: B570707. AUTHORS Temperature. S. Levitus, ed., and A. Mishonov, Stal, L.J. 2016. The euphotic realm. Pp. 209–225 in Roger G. Sayre ([email protected]) is Senior technical ed., NOAA Atlas NESDIS 73, NOAA The Marine Microbiome. L.J. Stal and M.S. Cretoiu, Scientist for Ecosystems, U.S. Geological Survey National Centers for Environmental Information, eds, Springer International Publishing, Switzerland. (USGS), Reston, VA, USA. Dawn J. Wright is Chief Silver Spring, MD, 40 pp. Sverdrup, H.U., M.W. Johnson, and R.H. Fleming. Scientist, Sean P. Breyer is Program Manager Longhurst, A. 2007. Ecological Geography of the Sea, 1942. The Oceans: Their Physics, Chemistry and ArcGIS Content, Kevin A. Butler is Spatial Statistics 2nd ed. Academic Press, San Diego, CA, 542 pp., General Biology. Prentice-Hall, Englewood Cliffs, Product Engineer, and Keith Van Graafeiland is https://doi.org/10.1016/B978-012455521-1/50000-0. New York, 1,087 pp. Product Engineer, all at Esri, Redlands, CA, USA. Lutz, M.J., K. Caldeira, R.B. Dunbar, and Thresher, R.E., J.M. Guinotte, R.J. Matear, and Mark J. Costello is Associate Professor, Institute of M.J. Behrenfeld. 2007. Seasonal rhythms of net A.J. Hobday. 2015. Options for managing Marine Science, University of Auckland, Auckland, primary production and particulate organic car- impacts of climate change on a deep-sea com- New Zealand. Peter T. Harris is Managing Director, bon flux to depth describe the efficiency of bio- munity. Nature Climate Change 5:635–639, GRID-Arendal, Arendal, Norway. Kathleen L. Goodin logical pump in the global ocean. Journal of https://doi.org/10.1038/nclimate2611. is Chief of Staff, Conservation Science Division, Geophysical Research 112, C10011, https://doi.org/​ Tittensor, D.P., C. Mora, W. Jetz, H.K. Lotze, D. Ricard, NatureServe, Arlington, VA, USA. John M. Guinotte 10.1029/2006JC003706. E.V. Berghe, and B. Worm. 2010. Global pat- is Fish and Wildlife Biologist, U.S. Fish and Wildlife Milligan, G.W., and M.C. Cooper. 1985. An examina- terns and predictors of marine biodiver- Service, Denver, CO, USA, formerly at the Marine tion of procedures for determining the number of sity across taxa. Nature 466(7310):1,098–1,101, Conservation Institute, Seattle, WA, USA, during this clusters in a data set. Psychometrika 50(2):159–179, https://doi.org/10.1038/nature09329. study. Zeenatul Basher is Research Technologist, https://doi.org/10.1007/bf02294245. Tomczak, M. 1999. Some historical, theoretical, and USGS, Reston, VA, USA. Maria T. Kavanaugh is Milligan, G.W., and M.C. Cooper. 1988. A study of stan- applied aspects of quantitative water mass anal- Research Associate, Woods Hole Oceanographic dardization of variables in cluster analysis. Journal ysis. Journal of Marine Research 57:275–303, Institution, Woods Hole, MA, USA. Patrick N. Halpin of Classification 5:181–204, https://doi.org/10.1007/ https://doi.org/10.1357/002224099321618227. is Associate Professor of Marine Geospatial Ecology, BF01897163. UN (United Nations). 1982. Article 57: Breadth of Nicholas School of the Environment, Duke University, Odum, E.P. 1971. Fundamentals of Ecology, 3rd ed. the Exclusive Economic Zone. United Nations Durham, NC, USA. Mark E. Monaco is Acting Director, W.B. Saunders, Philadelphia, PA, 574 pp. Convention on the Law of the Sea of 10 December Center for Coastal Monitoring and Assessment, Oliver, M.J., S. Glenn, J.T. Kohut, A.J. Irwin, 1982, United Nations Secretary General, National Ocean Service, National Oceanic and O.M. Schofield, M. Moline, and W.P. Bissett. 2004. Paris, France. Atmospheric Administration, Silver Spring, MD, USA. Bioinformatic approaches for objective detec- UNESCO (United Nations Educational Scientific and Noel Cressie is Distinguished Professor, National tion of water masses on continental shelves. Cultural Organization). 2009. Global Open Oceans Institute for Applied Statistics Research Australia, Journal of Geophysical Research 109, C07S04, and Deep Seabed (GOODS): Biogeographic University of Wollongong, Wollongong, Australia. https://doi.org/10.1029/2003JC002072. Classification.UNESCO-IOC Technical Series 84, Peter Aniello is Geospatial Scientist, Sandia National Oliver, M.J., and A.J. Irwin. 2008. Objective global United Nations Educational and Scientific Council Laboratory, Albuquerque, NM, USA. Charles E. Frye ocean biogeographic provinces. Geophysical Intergovernmental Oceanographic Commission, is Chief Cartographer, and Drew Stephens is Ocean Research Letters 35, L15601, https://doi.org/​ Paris, France, 96 pp. Industry Manager, both at Esri, Redlands, CA, USA. 10.1029/2008GL034238. Vivone, G., L. Alparone, J. Channussot, M. Dalla Mura, Pinet, P.R. 2009. Invitation to Oceanography, 5th ed. A. Garzelli, G. Licciardi, R. Restaino, and L. Wald. Jones and Bartlett Publishers Inc. 2015. A critical comparison among pansharpen- ARTICLE CITATION Proud, R., M.J. Cox, and A.S. Brierley. 2017. ing algorithms. IEEE Transactions on Geoscience Sayre, R.G., D.J. Wright, S.P. Breyer, K.A. Butler, Biogeography of the global ocean’s mesopelagic and Remote Sensing 53(5):2,565–2,586, K. Van Graafeiland, M.J. Costello, P.T. Harris, zone. Current Biology 27:113–119, https://doi.org/​ https://doi.org/10.1109/TGRS.2014.2361734. K.L. Goodin, J.M. Guinotte, Z. Basher, M.T. Kavanaugh, 10.1016/j.cub.2016.11.003. Wallace, R.B., H. Baumann, J.S. Grear, R.C. Aller, and P.N. Halpin, M.E. Monaco, N. Cressie, P. Aniello, . Reygondeau, G., C. Guieu, F. Benedetti, J. Irrison, C.J. Gobler. 2014. Coastal ocean acidification: The C.E. Frye, and D. Stephens 2017. A three-​dimensional S. Ayata, S. Gasparini, and P. Koubbi. 2017. other eutrophication problem. Estuarine, Coastal, mapping of the ocean based on environmental data. Oceanography Biogeochemical regions of the Mediterranean and Shelf Science 148:1–13, https://doi.org/10.1016/​ 30(1):90–103, https://doi.org/10.5670/ Sea: An objective multidimensional and mul- j.ecss.2014.05.027. oceanog.2017.116. tivariate environmental approach. Progress in Watling, L., J. Guinotte, M.R. Clark, and C.R. Smith. Oceanography 151:138–148, https://doi.org/10.1016/​ 2013. A proposed biogeography of the deep j.pocean.2016.11.001. ocean floor. Progress in Oceanography 111:91–112, Reygondeau, G., A. Longhurst, E. Martinez, https://doi.org/10.1016/j.pocean.2012.11.003. G. Beaugrand, D. Antoine, and O. Maury. Wright, D.J., ed. 2016. Ocean Solutions, Earth 2013. Dynamic biogeochemical provinces Solutions, 2nd ed. Esri Press, Redlands, CA, 500 pp. in the global ocean. Global Biogeochemical Wright, D.J., M.J. Blongewicz, P.N. Halpin, and Cycles 27(4):1,046–1,058, https://doi.org/10.1002/ J. Breman. 2007. Arc Marine: GIS for a Blue Planet. gbc.20089. Esri Press, Redlands, CA, 202 pp. Sayre, R., J. Dangermond, C. Frye, R. Vaughan, Zweng, M.M., J.R. Reagan, J.I. Antonov, R.A. Locarnini, P. Aniello, S. Breyer, D. Cribbs, D. Hopkins, A.V. Mishonov, T.P. Boyer, H.E. Garcia, R. Nauman, W. Derrenbach­er, and others. 2014. O.K. Baranova, D.R. Johnson, D. Seidov, and A New Map of Global Ecological Land Units: M.M. Biddle. 2013. World Ocean Atlas 2013 An Ecophysiographic Stratification Approach. version 2 (WOA13 V2), Volume 2: Salinity. Association of American Geographers, S. Levitus, ed., and A. Mishonov, technical ed., Washington, DC, 46 pp.

Oceanography | March 2017 103