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A Hierarchical Classification of Benthic Biodiversity and Assessment of Protected Areas in the Southern Ocean

  • Lucinda L. Douglass ,

    Affiliations Centre for Biodiversity and Conservation Science, School of Biological Sciences, The University of Queensland, Brisbane, Queensland, Australia, Centre for Conservation Geography, Sydney, New South Wales, Australia

  • Joel Turner,

    Affiliation Centre for Conservation Geography, Sydney, New South Wales, Australia

  • Hedley S. Grantham,

    Affiliations Centre for Biodiversity and Conservation Science, School of Biological Sciences, The University of Queensland, Brisbane, Queensland, Australia, Betty and Gordon Moore Centre for Science and Oceans, Conservation International, Arlington, Virginia, United States of America

  • Stefanie Kaiser,

    Affiliations Biocentre Grindel and Zoological Museum, University of Hamburg, Hamburg, Germany, German Centre for Marine Biodiversity Research, Wilhelmshaven, Germany

  • Andrew Constable,

    Affiliations Australian Antarctic Division, Department of the Environment, Australian Government, Kingston, Tasmania, Australia, Antarctic Climate and Ecosystems Cooperative Research Centre, University of Tasmania, Hobart, Tasmania, Australia

  • Rob Nicoll,

    Affiliation WWF Australia, Ultimo, New South Wales, Australia

  • Ben Raymond,

    Affiliations Australian Antarctic Division, Department of the Environment, Australian Government, Kingston, Tasmania, Australia, Antarctic Climate and Ecosystems Cooperative Research Centre, University of Tasmania, Hobart, Tasmania, Australia

  • Alexandra Post,

    Affiliation Marine and Coastal Environment Group, Geoscience Australia, Canberra, Australian Capital Territory, Australia

  • Angelika Brandt,

    Affiliation Biocentre Grindel and Zoological Museum, University of Hamburg, Hamburg, Germany

  • Daniel Beaver

    Affiliations Centre for Biodiversity and Conservation Science, School of Biological Sciences, The University of Queensland, Brisbane, Queensland, Australia, Centre for Conservation Geography, Sydney, New South Wales, Australia

A Hierarchical Classification of Benthic Biodiversity and Assessment of Protected Areas in the Southern Ocean

  • Lucinda L. Douglass, 
  • Joel Turner, 
  • Hedley S. Grantham, 
  • Stefanie Kaiser, 
  • Andrew Constable, 
  • Rob Nicoll, 
  • Ben Raymond, 
  • Alexandra Post, 
  • Angelika Brandt, 
  • Daniel Beaver


An international effort is underway to establish a representative system of marine protected areas (MPAs) in the Southern Ocean to help provide for the long-term conservation of marine biodiversity in the region. Important to this undertaking is knowledge of the distribution of benthic assemblages. Here, our aim is to identify the areas where benthic marine assemblages are likely to differ from each other in the Southern Ocean including near-shore Antarctica. We achieve this by using a hierarchical spatial classification of ecoregions, bathomes and environmental types. Ecoregions are defined according to available data on biogeographic patterns and environmental drivers on dispersal. Bathomes are identified according to depth strata defined by species distributions. Environmental types are uniquely classified according to the geomorphic features found within the bathomes in each ecoregion. We identified 23 ecoregions and nine bathomes. From a set of 28 types of geomorphic features of the seabed, 562 unique environmental types were classified for the Southern Ocean. We applied the environmental types as surrogates of different assemblages of biodiversity to assess the representativeness of existing MPAs. We found that 12 ecoregions are not represented in MPAs and that no ecoregion has their full range of environmental types represented in MPAs. Current MPA planning processes, if implemented, will substantially increase the representation of environmental types particularly within 8 ecoregions. To meet internationally agreed conservation goals, additional MPAs will be needed. To assist with this process, we identified 107 spatially restricted environmental types, which should be considered for inclusion in future MPAs. Detailed supplementary data including a spatial dataset are provided.


The high-latitude Southern Ocean, south of the Polar Front, is globally significant for conservation due to its unique species and environments. The presence of high levels of endemism shows it to be a distinct ‘realm’ within the Earth’s oceans [1][3]. The region is remote and has been altered less by human pressures than most other parts of the world [4]. However, exploitation has occurred across all its major marine ecosystems [5]. This has prompted the establishment of international agreements, in particular, the Convention on the Conservation of Antarctic Marine Living Resources (CCAMLR) to conserve the biodiversity while, among other roles, managing exploitation of marine living resources in the region [5][7].

An international effort is underway to establish a representative system of marine protected areas (MPAs) for the Southern Ocean and CCAMLR has the primary responsibility for developing this system [8], [9]. CCAMLR aims to complete this task to meet marine protection objectives set by The United Nations World Summit on Sustainable Development [8]. CCAMLR has identified the development of a broad-scale classification of the Southern Ocean and a finer-scale, classification of its biodiversity as necessary to achieve this aim [10]. Our aim is to complement this process by identifying the areas where benthic marine assemblages are likely to differ in near-shore Antarctica and the Southern Ocean. We achieve this by using a hierarchical spatial classification of the benthic environment and assessing the extent to which the existing system of MPAs encompasses the benthic diversity.

Developing a hierarchical classification of the benthic environment in the Southern Ocean

Regional classifications are important to MPA planning and aim to identify biogeographic patterns by spatially subdividing an area, such that assemblage compositions within each subdivision are expected to be homogeneous relative to adjacent regions [2], [11]. Classifications aim to separate different assemblages and, in particular, identify areas within which species that are endemic or have restricted ranges will reside. Importantly, this process also aims to identify ecological separation (poor connectivity) for species common across assemblages. For example, some widespread species show genetic differentiation suggesting that gene flow is restricted between isolated sub-populations [12][15]. Therefore, at a finer-scale, higher heterogeneity is likely to be present than is often considered in the historical biogeography in the region e.g. [16]. The focus of the classification developed here is to identify possible general patterns of assemblages and of more restricted benthic species and populations at a circumpolar scale.

Physical environmental data can be used as surrogates for biodiversity where biological data are scarce [17], [18]. Ideally, the process of subdivision would be done largely, if not entirely, on the basis of biological data. However, biological data paucity is prevalent across marine ecosystems globally leading to a necessity to use surrogates [19], [20]. On a circum-Antarctic scale, biological data are limited, particularly for the Amundsen Sea, eastern Antarctica, under ice shelves and below 1500 m [21][23]. While this paucity of biological data introduces uncertainty into regionalisation or MPA planning processes, environmental data can be used to assess the similarity or differences between regions for the purposes of these planning processes [17], [18]. In fact, additional biological data does not always greatly increase the efficiency of protected area planning [24].

When utilizing physical environmental data in this way, a classification method should be chosen that enhances the biological relevance of the results, for example by incorporating known relationships between the physical environment and biodiversity. Recent research within the Southern Ocean has primarily focussed on using statistical clustering methods to identify areas with relatively dissimilar attributes, examples include: [11], [25], [26]. This approach has particular utility when synoptic data are available on abiotic factors that are known to be drivers or correlates of biological patterns but few data are available on actual biological patterns [27]. Where the relationships between the distribution of benthos and abiotic factors can be reasonably inferred, they can be manually incorporated to delineate regions rather than relying on an unsupervised classification e.g. clustering, [11]. For instance, the continental shelf usually supports distinct species assemblages when compared to the deeper ocean [22], [28], [29]; this has also been observed on seamounts [30]. Furthermore, the utility of biogeographical classifications to MPA planning is improved by a hierarchical (nested) classification, which shows the subdivision at increasingly finer scales [2], [20]. This approach was used by Spalding et al. [2] and Last et al. [31] to identify distinct regions based on taxonomic composition and the degree to which separation might occur as a result of patterns of dispersal, life histories of taxa, and isolation.

While a circumpolar classification of the pelagic environment has been completed and adopted by CCAMLR [25], no equivalent, accepted benthic bioregionalisation currently exists [26], although there are sufficient data to develop one. Databases of molluscs, bryozoans and zooplankton are being compiled and analysed, albeit they are limited in their geographic extent [1], [32][35]. The SCAR-MarBIN database is improving data availability at a circumpolar scale [36]. The relationships between depth, geomorphology and species distributions have been investigated [28], [37][39]. Furthermore, the connectivity between populations has been explored using genetics [14], [40], [41]. The Biogeographic Atlas of the Southern Ocean, when published, will contribute to bringing together much of this research (see However at the time of writing, no synthesis of this recent research exists for the Southern Ocean.

Here we present a circumpolar hierarchical classification of the benthic environment based on Last et al., [31] and intended for general use in spatial planning and management. The classification includes the identification of ecoregions, bathomes and environmental types, the latter of which are based on geomorphic features. Similar to the Delegations of Australia and France, [17], ecoregions were delineated by accounting for recent biogeographic research, patterns of endemism and the influence of environmental drivers as potential barriers to dispersal (Fig. 1). Environmental types represent the lowest order in the hierarchy and we used these to assess the representativeness of currently designated MPAs. We also identified environmental types which have restricted distributions and are probable locations for the focus of future MPA design.

Figure 1. The framework used to classify ecosystems within the Southern Ocean.

Ecoregions were defined on the basis of important environmental drivers and their potential to prevent dispersal. Biogeographic patterns identified in the literature were also incorporated. A hierarchical approach was then applied within each ecoregion, nesting the two main habitat types (geomorphic features within bathomes) to identify environmental types.

Environmental drivers

Environmental drivers are the physico-chemical processes and other factors that set the habitat conditions and influence the distribution and abundance of taxa, including their connectivity between similar habitats. Reduced connectivity can give rise to divergent evolution of similar taxa. Two major environmental drivers are depth and geomorphology [28], [29], [42][44]. Other important drivers include seabed temperature, icebergs and sea ice coverage, sea-surface productivity and ocean currents [45][48].


The Southern Ocean contains a smaller proportion of depth-restricted species than other oceans, probably due to the presence of a deeper shelf, deep-water formation (Antarctic Bottom Water), lack of thermocline and the intermittent glaciations of the shelf, creating less difference between environments on the shelf and the deep sea [49][51]. Wide depth ranges in Antarctic species have been typically associated with survival in deep-sea refuges e.g. [49]. That is, sub-populations of eurybathic shelf species may have endured past glaciations on the slope or deeper, which could have then served as a potential source for shelf (re-) colonization [28], [49], [52]. Thatje et al. [50] suggested, that life on the Antarctic continental slope during the last glacial maximum may have been extremely difficult due to turbidity currents cascading down the slope. Sedimentation processes would not have occurred everywhere though, and were probably more pronounced along canyons compared to adjacent flanks and plateaus leaving some scope for slope fauna to survive [28]. Limited depth distributions of shelf species, on the other hand, could serve as evidence for survival through glaciations in ice-free refuges on the continental shelf [53].

Many benthic species do have restricted depth ranges and depth-related factors can be strong barriers to dispersal of benthic species [37,38,54,55,56 and see ‘Barriers to dispersal’ section]. For instance, the octopus genus Paraledone, the echinoid genus Sterechinus, the ostracod genus Macroscapha and the amphipod genus Eurythenes show niche separation by depth [40], [57][59]. This depth partitioning gives rise to different assemblage structures in different depths [60], the ranges of which can be characterized as bathomes [31]. Furthermore, molecular studies show widely distributed species often represent species complexes, with each individual species having a much more restricted range size. For example, isopods: [56], [61], [62]; crinoids: [13]; and ophiuroids: [55].

Geomorphic features.

Geomorphic features are a classification of the seabed based on its surface morphology, and have been shown to be significant for delineating benthic habitats [63]. The features used here are meso-scale habitat features related to the sedimentary habitat along with the near-bottom conditions of the ocean environment [31]. Major habitat characteristics include the availability of hard-rock surfaces and the erosion or deposition of sediment along with their physical attributes [42], [64]. For example, Antarctic shelf depressions eroded during glacial maxima now have low currents and fine sediments, providing appropriate habitats for mobile deposit feeder and infaunal communities [46], [64], [65]. The species rich coral-sponge communities found within some shelf-cutting canyons are postulated to be due to an abundance of food contained in channelled water sourced from productive shelf regions [43], [66]. Elsewhere, canyons are shown to alter current flow causing upwelling and mixing which are attributed to enhanced local biological systems [67]. Seamounts have also been shown to be important for biodiversity and can have high endemism levels [30], [68][70]. Hydrothermal vents are shown to contain distinct species and unique community structures [71]. Research in the Weddell Sea shows a clear distinction in faunas of the shelf, slope and abyss [28], [53]. However, this may not be the case in all regions and for all species. For example, such patterns have not been found to exist within the Scotia Sea [28].

Seabed temperature.

Seabed temperature has a major influence on the ecology of Antarctic benthic fauna; its spatial and temporal variability contributes to determining the biogeography of those fauna; and the composition of assemblages [27], [45]. Seabed temperature is suspected to constrain the migration of benthic fauna and may lead to genetic variation and eventual speciation [27]. This hypothesis is supported by recent research on the Antarctic sea spider, Nymphon australe and the octopus genus, Pareledone [12], [72]. Temperature can be biologically meaningful even where the spatial and temporal variation is narrow [73].


Ice regimes are a key structuring element in the ecology of the Antarctic benthos [48], [74], [75]. Icebergs scour the shelf seabed, impacting the distribution of benthic habitats and the diversity of assemblages [39], [42], [76]. Areas under glacier tongues and fast ice are deprived of inorganic sedimentation leading to dominance of sessile suspension feeders [76]. Benthic habitat conditions can also be affected by the influence of ice on light availability, water currents, temperature, biogeochemistry, algal growth and productivity [42], [48], [77][80]. These conditions may be modified depending on the concentration and duration of the sea ice [78], [81].


Sea surface productivity provides a vital food source to the benthos [82] and the inter-annual reliability and magnitude of productivity blooms are likely drivers of total productivity in the benthic system [75]. Phytoplankton blooms are highest (i) where frontal activity has created an upwelling of nutrient-rich water, (ii) down-stream from iron-rich landforms and (iii) within the ice-melt zones and polynyas [83][87]. Polynyas are regions of reduced ice concentration surrounded by heavy pack ice. Feeding especially within the near shore benthos can be seasonal [88][90]. Many benthic ecological processes though, appear not to be coupled with the summer sea surface productivity bloom due to the presence of persistent sediment food banks and the influence of bottom circulation on sediment distribution [46], [75]. However, interaction between the sea surface and benthos can occur through sedimentation of organic matter from the surface [75], and through trophic transfer from herbivores at the surface to delivery to the benthos [91]. For example, in the latter case, salps feed on phytoplankton then vertically migrate to the demersal zone where they form the near-exclusive prey species of benthic octocorallian polyps [47]. In the former case, macrobenthic biomass in muddy sediments of the deep Antarctic shelf is correlated with regional primary production and sea-ice duration [75]. Similarly, organic matter is transferred within sinking water generated from rejected brine during ice formation and is suspected to act as an important source of food for benthic species creating locations of high species richness in deep-sea areas traversed by this organically laden bottom water [22], [51].

Barriers to dispersal

The geographic distribution of a species and genetic connectivity between subpopulations is linked to the ability of individuals to disperse away from their parents. This ability will be constrained by dispersal barriers including the distance between suitable habitats, geomorphic barriers and steep environmental clines. Long-distance dispersal in benthic organisms is confined to passive oceanic transport of adults or via turbidites (i.e. rafting), and transport during pelagic larval stages [92].

Habitats may support different species assemblages where the distance between suitable habitats is greater than the distance over which a species can disperse. While the dispersal distances for most species in the Southern Ocean are unknown [93], it is likely that the dispersal range for many brooding species will be restricted; brooding species protect and feed their offspring without a pelagic larval stage [50]. Within the Southern Ocean, benthic communities have more species with either lecithotrophic (non-feeding) larval development or brooding than species with planktotrophic (feeding) larvae [50]. While the strong, circumpolar currents of the Southern Ocean may assist the dispersal of some species with pelagic larval stages [94], [95], predation, parasitism and starvation will constrain the probability of moving great distances, especially for lecithotrophic larvae which have a limited food source [94], [96]. Furthermore, strong genetic variation has been shown in some widely dispersive species suggesting higher heterogeneity at a finer-scale [12][14].

Geomorphic barriers, steep ecoclines and ocean currents can also cause ecological and genetic differentiation. For example, differences in seabed temperature across relatively short distances can cause genetic differentiation within a species [12]. Similarly, genetic variation can occur through the inhibition of adult migration by geomorphic features [97]. Ocean current and frontal systems, depth-related factors as well as habitat fragmentation can also be strong physical barriers to gene flow [14], [81], [94], [95], [98], indicating that the ecological dynamics of populations can be spatially restricted. Examples include studies comparing shelf dwelling benthic diversity between the Antarctic Peninsula and Scotia Arc [14], [55]. Changes in oceanographic conditions are also important barriers to dispersal in the open ocean [14], [29], [94].

MPA Assessment

A gap analysis can identify where additional MPAs are required to ensure that all ecosystems and biodiversity are represented within a system of MPAs [99]. There are very few MPAs within the Southern Ocean. The largest is south of the South Orkney Islands [100], [101]. Other MPAs include those within the Australian, French, Norwegian and South African territories and some small areas protected under the Environmental Protocol to the Antarctic Treaty (Madrid Protocol).

We use the fine-scale (<100’s of km’s) environmental types as surrogates for different assemblages of biodiversity to assess the representativeness of existing MPAs. A system of MPAs that captures each environmental type is assumed to also capture the range of biodiversity these habitats represent. We also identify those environmental types that have distributions restricted by geographic area and location. The environmental types within these restricted locations are not necessarily more important than others ecologically. However, they tend to be the focus of MPA selection since there are limited spatial options for protecting the biodiversity for which they are a surrogate.

Materials and Methods

The region that is managed by CCAMLR defined the study area. The northern boundary of this region is a line approximating the location of the Polar Front [25]. The southern boundary was defined as the northern edge of the permanent ice shelf of the Antarctic continent.


Table 1 provides an overview of the circumpolar datasets used in this study, their spatial and temporal resolutions along with their data sources. Additional regional information on seabed currents was also geo-referenced including: [102][105]. The spatial resolutions of the environmental data sets varied but were generally around 10 km with the exception of the seafloor temperature. However, only the depth and geomorphic data were directly utilized to define the environmental types, with the remaining data used to elucidate general environmental patterns. Thus, the effects of these scale mismatches were minimal.

The datasets were not modified except for sea-surface chlorophyll a. For this dataset we used a time series to identify areas of consistently high and low productivity. For each available season , the mean summer concentration in each grid cell was transformed to an anomaly by subtracting the seasonal mean for the entire study area (using log-transformed values):To distinguish areas of intermittent high productivity from those of consistent but moderate to high productivity, these anomalies were further transformed giving an index of the consistency of productivity from season to season [17]:Where and are the mean and standard deviation of calculated over all available seasons .

We also developed two datasets of proposed and existing MPAs. The MPAs proposed from MPA planning analyses current at September 2013 were; 1) The seven MPAs proposed from a study of Eastern Antarctica south of 60°S and between 30°E and 150°E [17] and; 2) The MPAs proposed from a study of the Ross Sea region south of 60oS and between 150oE and 150oW [106]. Existing MPAs that have been designated or acknowledged by CCAMLR were included. These MPAs included; 1) The South Orkney Islands Southern Shelf MPA within the high seas [100]; 2) The Kerguelen and Crozet Islands within the French EEZ [107]; 3) The Prince Edward and Marion Islands within the South African EEZ [108]; 4) The Heard and MacDonald Islands within the Australian EEZ; 5) A twelve nautical mile buffer around Bouvet Island for the Bouvetøya Nature Reserve [109]; 6) One nautical mile exclusion zones around Vulnerable Marine Ecosystems notified in accordance with CCAMLR conservation measures that regulate bottom fishing (specifically measures 22-06 and 22-07) [110][112] and; 7) Marine areas listed as designated under the Environmental Protocol to the Antarctic Treaty and which have management plans (available at approved by CCAMLR in accordance with Antarctic Treaty Consultative Meeting decision 9 in 2005 [113], [114].


Broad spatial boundaries, termed benthic ecoregions, were identified according to the primary environmental drivers and dispersal barriers that drive the distribution of Southern Ocean benthic biodiversity. Within these ecoregions, biodiversity patterns are further driven by finer-scale spatial processes. Therefore, we applied a hierarchical framework based on Last et al. [31] to identify each environmental type. Bathomes (broad-scale depth classes) were nested within ecoregions, and geomorphic features were nested within bathomes. Each environmental type is therefore a unique combination of geomorphic feature, bathome and ecoregion. The nesting of geomorphic features within bathomes avoids the potential for false within-class homogeneity that can arise if such features are not modified by depth [115]. The analysis was performed using both manual and automated processing within a geographic information system and is further described in the following six sections. Further detail of the analysis is provided as supplementary information including ecoregion boundary descriptions (Text S1), the spatial coverage of each level of the classification (Table S1) and a description of the methods used to generate geomorphic features (Text S2).

Benthic ecoregions.

The primary environmental drivers used to identify benthic ecoregions were depth, geomorphology, seabed temperature, sea ice concentration and chlorophyll-a as a proxy for productivity. The three major types of barriers to dispersal we used were; 1) the distance between potential habitat types (i.e. bathomes and geomorphic features); 2) large geomorphic barriers and; 3) presence of steep ecoclines in temperature, ice or productivity. The influence of both frontal and seabed currents were considered as either dispersal barriers or assisting connectivity between habitats (as further described in Text S1).

We initially delineated the distinct environments of the continental shelf and slope from the deeper ocean and then applied bathomes based on depth-species relationship studies to further account for the depth structuring of benthic assemblages. The 4000 m bathymetric contour was the most appropriate to generally separate the geomorphic features associated with the shelf and slope of the Antarctic continent from the geomorphic features associated with the abyssal ocean. Exceptions to this contour included parts of the East Indian Abyssal, Pacific-Antarctic Ridge, Kerguelen and Pacific Basin ecoregions which were adjusted to better capture geomorphology. Where the structural slope of the Oates ecoregion extends past the 4000 m contour in the Indian Ocean sector, the boundary of the structural slope was used in preference to the 4000 m contour. The boundary between the Kerguelen ecoregion and the Antarctic continent was defined by the southern limit of the contourite drift (i.e. a geomorphic feature), the slope of the broader Kerguelen Plateau (i.e. including Banzare and Elan Banks) and the canyon at 77.5oE, 61.5oS. Similar to Spalding et al [2] we included the seamounts and the seamount chain near to 120oW within the Amundsen ecoregion.

Where possible, previously defined ecoregions were reviewed and incorporated especially: [1], [2], [17], [25], [32], [33]. These studies have focussed primarily on the Antarctic continental shelf and slope where more data are available. The previously defined boundaries were upheld if the ecoregion contained levels of endemism greater than 10% as reported by Linse et al. [32] or Griffiths et al. [1]. In each case, these boundaries were adjusted to better reflect the distribution of bathomes and geomorphic features. For instance, geomorphic features can act as barriers to dispersal for some benthic species leading to genetic distinctions [97]. Therefore, if deemed more appropriate, features such as canyons were used to replace the previously defined boundaries. Also, where a previously defined boundary divided a feature such as a canyon, it was redrawn to capture the entire feature. Within the deeper ocean, ecoregion boundaries were defined by considering the effect of structuring agents on patterns of biodiversity.

For areas without previously defined ecoregions, we initially identified where areas of similar habitat types (predominantly bathomes and geomorphic features) were separated by more than 200 km. This distance is based on the general global maximum of 200 km for dispersal distance across multiple marine species even where uncertainly is accounted for [116], [117]. Spatial separation is assumed to increase the likelihood of areas of the same habitat type supporting genetically dissimilar species due to isolation and therefore contributes to the separation of ecoregions. All data layers were then used to corroborate or refute the validity of these regions defined by the separation of similar types of areas by the minimum distance. For instance, where large geomorphic barriers, including ridges, could contribute to separating areas of deeper habitats this would be seen as an ecoregion boundary between these two areas of habitat. Similarly, rapid changes in seabed temperature, sea ice, persistent productivity and ocean currents were also considered as signifying potential barriers to dispersal or where an ecoregion boundary could be most meaningful ecologically.


Bathomes are broad-scale depth classes. They were derived by dividing the region into different depth divisions based on bathymetry data which are frequently updated from satellite altimetry and ship depth soundings [17], [118], [119]. The boundaries were based on the depths at which there are expected to be rapid transitions in assemblage composition. We identified those depths based on available depth-species relationship studies within the Southern Ocean. Depth-species relationship studies are summarised in Table 2.

Table 2. Benthic bathomes and the ecological and biological events used to define them.

Geomorphic features.

We identified geomorphic features based on O’Brien et al. [64] (see Table 1 and Table 3). This dataset, however, did not cover all areas within the study region due to the absence of data in areas of persistent ice cover. All areas not covered were located on the Antarctic continental shelf and we classified them as ‘Shelf’. We also modified some features. ‘Fracture zone cliffs’ and ‘Canyon axes’ had been mapped as lines by O’Brien et al [64]. We converted ‘Fracture zone cliffs’ to areas through interpretation of the bathymetry contours with contours located close together considered part of the cliff. ‘Canyon axes’ were converted to areas by applying a 5 km buffer around the axes to capture both the canyon and its surrounding zone of influence. We deemed a 5 km buffer to be the most appropriate distance to ensure connectivity within dendritic canyon systems and discontinuity between single channelled canyon systems. The geomorphic features classified by O’Brien et al. [64] as ‘Wave affected banks’ are identical to their ‘Shelf banks’, but at depths of less than 200 m. This depth modification of geomorphology is captured by our nested hierarchical analysis and therefore these features were combined and renamed ‘Banks’. The ‘Coastal terrane’ of South Georgia Island was reclassed as ‘Island coastal terrane’ to be consistent with other island areas. The Antarctic ice shelf including areas under floating ice tongues termed ‘ice shelf cavities’ were excluded.

Table 3. The geomorphic features of the Southern Ocean classified according to the attributes of the seabed surface substratum.

Nesting data layers.

Bathomes were nested within ecoregions, and geomorphic features were nested within bathomes to identify environmental types except for the following seven geomorphic features. Four geomorphic features ‘Shelf’, ‘Cliff’, ‘Coastal terrane’ and ‘Island coastal terrane’ were not further modified by depth. Seamounts and Seamount ridges were classed by only their shallowest bathome. Canyons were classed by whether they commenced on the shelf or slope. The environmental types were reviewed to minimise the potential for false heterogeneity. Environmental types with a very small area or restricted distribution were checked and dissolved into the surrounding environmental type if deemed invalid. For example, small patches of shallow rugose ocean floor within the South Atlantic ecoregion initially appeared as a restricted or rare environmental type. Upon review, this environmental type was found to be a processing artefact caused by the presence of adjacent seamounts. It was therefore eliminated by reclassing it as the most appropriate surrounding environmental type.

Representation of MPAs.

We condensed the geomorphic features into four broad classes (Table 3). The distributions of existing MPAs and environmental types were overlaid. For each geomorphic class within each ecoregion that contains an existing MPA, we calculated; 1) the proportion of area within an MPA and, 2) the proportion of the number of environmental types with at least part of their area within an MPA. The total number of environmental types and seafloor area of each geomorphic class was also calculated.

Restricted environmental types.

We specifically identified those environmental types which had a restricted spatial distribution. We assumed environmental types to have restricted distribution if they were either small in number (3 or less) or are predominantly limited to a particular discrete location without replication within an ecoregion. Since our focus was identifying areas where additional MPAs are required to fill large gaps in representation, restricted environments were not identified within regions where proposed MPAs have been identified through fine-scale MPA planning processes. Therefore, the region east of 30oE to 150oW and south of 60oS was excluded from the restricted environments analysis. Instead, the proposed MPAs within these regions were compiled and incorporated [17], [106].

Results and Discussion


We identified 23 benthic ecoregions and 9 bathomes (Fig. 2, Table 2). A general description of the ecoregions is provided (Table 4). Existing geomorphology mapping has identified 28 geomorphic feature types (Table 3, [64]). When these three datasets were combined within the hierarchical framework, 562 environmental types were identified (Fig. 3 and Table S1). The spatial dataset is available online (see

Figure 2. The benthic ecoregions, restricted environments and marine protected areas identified within the Southern Ocean.

An environmental type is a unique combination of an ecoregion, bathome and geomorphic feature. Existing marine protected areas and regions where planning processes are underway to propose future representation, were identified. Where large gaps in existing and proposed representation were found, the locations of geographically restricted environmental types were identified. These restricted environments indicate areas of potential future marine protected area selection since there are limited spatial options for protecting the biodiversity for which these environments are a surrogate.

Figure 3. Environmental types and the bio-physical data used to drive the classification.

A) The 562 environmental types (in colour) and ecoregion outlines (refer to figure 2 for names) broadly reflect the underlying data used within the classification B) Bathomes derived from bathymetry and species-depth relationships C) Geomorphic features; Abyssal Plain (1), Bank (2), Canyon (3), Cliff (4), Coastal (rugged) Terrane (5), Contourite Drift (6), Cross Shelf Valley (7), Fracture Zone (8), Island Arc (9), Island Coastal Terrane (10), Lower Slope (11), Marginal Ridge (12), Marginal Plateau (13), Mid-Ocean Ridge Rift Valley (14), Ocean Trough (15), Plateau (16), Plateau Slope (17), Ridge (18), Rugose Ocean Floor (19), Seamount Ridges (20), Seamount (21), Shelf (22), Shelf Deep (Depressions) (23), Structural Slope Region (24), Trench (25), Trough Mouth Fans (26), Upper (Continental) Slope (27), Volcano (28) D) Seabed temperature E) Duration of the year where more than 85% of the region is covered by sea ice F) High positive and negative values indicate areas of consistently high and low summer productivity respectively. Values approaching zero indicate areas that vary greatly between years.

Table 4. Benthic ecoregions and their features (see Fig.2 for the location of each ecoregion).

The ecoregion boundaries reflect many of the broad patterns of the underlying data used for the analysis (Fig. 3). Generally, ecoregion boundaries most closely resemble depth, geomorphology and seabed temperature with productivity and ice cover tending to contain stronger within ecoregion variation (e.g. around polynyas or persistent productivity hotspots, Fig. 3). The Southern Ocean is dominated by deeper environments with over 85% of the seabed deeper than 2000 m (Fig. 3). This is also reflected in the geomorphology where over 75% of the Southern Ocean is made up of three geomorphic types, Rugose Ocean Floor, Abyssal Plain and Lower Slope (Fig. 3). The Atlantic basin, the largest ecoregion, is dominated by depths greater than 4500 m.

Within the deeper environments of the Southern Ocean, the primary drivers of the classification were the distribution of rare and mostly isolated shallow habitats often associated with seamounts, seamount ridges, plateaus and islands. Also important was the presence of mid-ocean ridges and large plateaus that separate deeper habitats. On the shelf and slope, where survey effort is higher, biological data on species distributions (in the form of previous biogeographical research) were the key drivers. Therefore, our results broadly reflect that of Spalding et al. [2], the Delegations of Australia and France, [17] and Clarke et al. [33] with some boundaries adjusted to more closely reflect the distribution of bathomes and geomorphic features.

In contrast to most other recent benthic classifications in the region examples include: [2], [17], [32], the results of this study encompass the deeper environments of the Southern Ocean as well as the continental shelf and islands. While the Global Oceans and Deep Seabed (GOODS) biogeographic classification provides a global scale classification, the only previous benthic classification to include these deeper environments at a circum-Antarctic scale was exploratory and incomplete [26]. This exploratory classification identified 20 regions using the statistical relationships between three physical datasets of depth, slope and seabed temperature. The classification presented here incorporates known or inferred relationships between the benthic biology and the physical environment and their influence on barriers to dispersal. The seamount classification provided by Clark et al. [120] provides a biologically meaningful classification of seamounts and we also consider two of the four criteria used (i.e. summit depth and seamount proximity). However, the Clark et al. [120] classification incorporates seamounts i.e. [121] identified using a technique we found to overestimate the number of seamounts and therefore preferred the hand drawn seamounts identified by O’Brien et al, [64]. For the continental shelf and islands, this work provides a physical underpinning and whole of Southern Ocean context for the patterns in biodiversity found by Clarke et al. [33], the Delegations of Australia and France, [17] and Spalding et al. [2]. In addition to the identification of the regions themselves, we also offer a classification of the seabed habitats contained within each ecoregion.

Environmental types were mostly identified by nesting together ecoregions, bathomes and geomorphic features to avoid the potential for false within-class homogeneity [115]. However, there were some exceptions. To add utility to MPA planning, we reflect the variety of three geomorphic features by classifying seamounts and seamount ridges by only their shallowest bathome and canyons by whether they commence on the shelf or slope. When an MPA is placed to protect the benthos of integral physical features (e.g. those likely to contain vulnerable marine ecosystems) that have a high level of connectivity among the habitats they contain, such as seamounts, seamount ridges or canyons, the feature should be included as a whole and therefore the internal bathome changes become less relevant to spatial MPA planning [122]. The coastal terrane of some islands and the Antarctic continent represent areas adjacent to rugged coastlines with highly irregular depth structuring which could not be fully resolved with the depth data used and were therefore not further classed by bathomes [64]. Future MPA planning within regions where finer-scale bathymetry is available should further class these environmental types by the bathomes they contain and could be guided by the framework we provide. A level 2 nesting (see Fig. 1) provides a broader spatial management unit. For instance, a level 2b nesting of geomorphic features within ecoregions identifies 210 types. However, caution should be used when not further nesting geomorphic features within bathomes (i.e. level 3 of Fig. 1) since this can result in false within-class homogeneity [115]. Some environmental types are described within the ‘restricted environmental types’ section below.

According to the advice of the Science Committee to CCAMLR, we treat the benthic and pelagic environments separately [26], [123]. The assumption is that these environments are decoupled due to the extreme depths and complex topography and oceanography of the Southern Ocean [75]. However, there is increasing evidence of interaction, and coupling may be favoured by intense seasonality and quick and efficient cycling through the pelagic food web [47], [75], [81], [91], [124]. We partially account for this interaction by considering sea surface productivity, the duration of ice cover and frontal systems within the analysis.

The classification we provide aims to capture the larger scale patterns by incorporating a combination of physical and biological data and also taking into consideration probable barriers to dispersal. As new data become available they can be used to refine the classification. Increasing knowledge, especially of cryptic species (i.e. species that are morphologically similar but differ genetically) seems most likely to lead to adjustment of the boundaries and the identification of further within-region heterogeneity, and not to a fundamentally different view of the distribution of habitats. Biogeographic classifications aim to identify general patterns within complex and diverse systems and therefore false heterogeneity for some species is inevitable. However, for the purposes of MPA planning, incorporating the general geographic and depth distribution patterns will increase the chance of representing the range of species habitat requirements within the final MPA system. The ecoregions and the environmental types can be used to consider the representativeness of biodiversity in a system of MPAs across the whole of the Southern Ocean.

Assessment of Representativeness

We found the level of existing representation for each environmental type across geomorphic classes is low (figure 4). None of the 23 ecoregions contains a system of MPAs that is representative of the benthic biodiversity they contain, with 12 ecoregions having no MPAs. Of the remaining 11 ecoregions, none includes a system of MPAs that sample each of the environmental types present in the ecoregion. The three ecoregions which perform the best are 1) Del Cano where South Africa declared the Prince Edward Islands MPA in 2012, 2) the South Orkney Islands, where CCAMLR established its first entirely high seas MPA in 2009 and 3) the Kerguelen Plateau where Australia and France have established MPAs. For some geomorphic classes, the number of environmental types in MPAs especially within the Ross Sea, Oates, Deep Kerguelen and Antarctic Peninsula ecoregions, is greater than 20% (figure 4). However, the area of seabed in MPAs for these geomorphic classes is less than 1% suggesting that only a small portion of each environmental type is represented in MPAs.

Figure 4. Marine protected areas and their spatial coverage of ecoregions and representation of environmental types.

A) The proportion of the seafloor and the proportion of environmental types included either partly or wholly within existing MPAs are displayed within four broad geomorphic classes. These classes are further described in Table 3 and are represented within the plots from top to bottom as Abyss (white), Slope (light grey), Oceanic Shallow (dark grey) and Continental Shelf (black). B) The total number of environmental types and seafloor area for each geomorphic class are also shown. The incomplete bars within PAR and AB have values of 2.95 E6 km2 and 6.59 E6 km2 respectively. The ecoregion code names are: SOI (South Orkneys Islands), SA (South Atlantic), RS (Ross Sea), PAR (Pacific-Antarctic Ridge), O (Oates), K-KP (Kerguelen-Kerguelen Plateau Subregion), K-DK (Kerguelen-Deep Kerguelen Subregion), DC (Del Cano), AP (Antarctic Peninsula), AB (Atlantic Basin), A (Amundsen).

We have provided an assessment of progress toward a representative system of MPAs. The primary aim of the analysis described here is to identify important gaps in the protected area system, particularly related to ecoregions and environmental types. We have not identified how the biodiversity within a region needs to be adequately included in MPAs in order to achieve viability of the protected biodiversity; requirements will vary between different ecoregions and environmental types.

There are a number of existing conservation planning processes directed towards resolving some of these gaps in the existing system of MPAs (Fig. 2), including Eastern Antarctica [17] and the Ross Sea for example: [106], [125]. These proposals will help fill the gaps for achieving a Southern Ocean system of MPAs. Analyses such as the one we present here could be used to help assess progress towards a representative system of MPAs covering each of the ecoregions and which areas may be most suitable for filling gaps. An accounting procedure to help assess the adequacy of MPAs would improve future assessments.

Restricted environmental types.

In this study we contribute to Southern Ocean MPA planning processes by identifying environmental types with a restricted spatial distribution and within ecoregions largely lacking any existing or proposed representation in MPAs. We identify 107 restricted environmental types (Fig. 2). The distribution of restricted environments is driven by rare environmental units that arise when a component of the hierarchical classification is scarce. The island arc, a geomorphic feature within the Southern Sandwich Islands ecoregion, is the only habitat of its kind in the Southern Ocean. Therefore the ecosystems associated with the particular environments created where a ridge of volcanic islands is formed adjacent to a subduction zone can only be protected in this location. Other restricted environments arise from an uncommon combination of geomorphic features and bathomes. There are many seamounts within the Pacific basin ecoregion however there are only two, the Belgica Guyot and the Lecointe Guyot, that have very shallow mounts of less than 200 m depth. Furthermore, there are only five discrete locations within the CCAMLR region where these shallow seamount environmental types occur.

Maud Rise is another example of a biologically important region containing restricted environmental types. At Maud Rise, oceanography, sediment characteristics and sea ice processes have been linked to high biodiversity throughout all trophic levels from pelagic predators to benthic species [30]. Maud Rise is an underwater plateau and slope region with two associated seamounts within the Atlantic Basin ecoregion. Four of the six environmental types of Maud Rise are restricted. These include: 1) the only seamount in the ecoregion with a mount in the –1500 to –2000 m bathome; 2) the only seamount ridge within the ecoregion with a mount in the –1000 to –1500 m bathome and; 3) the only plateau in the ecoregion with two environmental types resulting from plateau areas within the –2000 to –3000 m bathome and –3000 to –4500 m bathome. The Maud Rise seamount is suspected to contribute to the most recurring open-ocean polynya in the Southern Ocean which forms over Maud Rise [30], [126]. These polynyas drive convection which influences interaction between surface productivity, the benthos and the upwelling of nutrients all of which may contribute to the rich and prospering food web in the region [30].


The international effort overseen by CCAMLR to establish a representative network of MPAs within the Southern Ocean has already made a significant step in declaring the world’s first MPA within the high seas located outside national jurisdiction. The efforts to achieve the conservation commitments made by CCAMLR and parties to the Antarctic Treaty sets a precedent for conservation within other high seas regions of the global ocean [101]. However the current levels of representation clearly contain significant gaps that future conservation planning efforts need to address. The environmental types presented here are an improvement on previous statistical classifications because, rather than opting for a method that identifies statistically dissimilar areas within the data, we have identified environmental types using known and probable relationships between biogeographic patterns, environmental drivers, and knowledge of dispersal of benthic taxa. The classification of environmental types allows the gaps in the MPA network to be quantified. It also provides a new circum-Antarctic map of environmental types that can be used to support spatial management aimed at conserving benthic biodiversity across the entire Southern Ocean.

Supporting Information

Table S1.

Spatial coverage of ecoregions, geomorphic features and environmental types within the Southern Ocean.


Text S1.

Detailed description of ecoregion boundaries.


Text S2.

Outline of methods used to identify geomorphic features.



The authors thank Alan Williams, Ben Sharp, Brendan Brooke, Falk Huettmann, Grant Humphries, Hugh Possingham, Katrin Linse, Louise Emmerson, Matthew Pinkerton, Mark Spalding, Nic Bax, Peter Last, Philippe Koubbi, Piers Dunstan, Rudy Kloser, Steven Rintoul, Susie Grant, Vincent Lyne, Zhi Huang and two anonymous reviewers for their advice and comment and Bruno Danis, Philip E O’Brien, Rowan Romeyn, Steven Rintoul and Serguei Sokolov for their data provision.

Author Contributions

Conceived and designed the experiments: DB LLD. Performed the experiments: DB LLD JT AC. Analyzed the data: DB LLD JT AC BR. Contributed reagents/materials/analysis tools: AP SK BR AB. Wrote the paper: LLD HSG DB SK AC BR RN.


  1. 1. Griffiths H, Barnes D, Linse K (2009) Towards a generalized biogeography of the Southern Ocean benthos. Journal of Biogeography 36: 162–177.
  2. 2. Spalding M, Fox H, Allen G, Davidson N, Ferdana Z, et al. (2007) Marine ecoregions of the world: a bioregionalization of coastal and shelf areas. Bioscience 57: 573–583.
  3. 3. Kaiser S, Brandão SN, Brix S, Barnes DK, Bowden DA, et al. (2013) Patterns, processes and vulnerability of Southern Ocean benthos: a decadal leap in knowledge and understanding. Marine Biology 160: 2295–2317.
  4. 4. Halpern BS, Walbridge S, Selkoe KA, Kappel CV, Micheli F, et al. (2008) A global map of human impact on marine ecosystems. Science 319: 948.
  5. 5. Kock K (2007) Antarctic Marine Living Resources–exploitation and its management in the Southern Ocean. Antarctic Science 19: 231–238.
  6. 6. Agnew DJ (1997) Review–The CCAMLR Ecosystem Monitoring Programme. Antarctic Science 9: 235–242.
  7. 7. Constable A, de la Mare W, Agnew D, Everson I, Miller D (2000) Managing fisheries to conserve the Antarctic marine ecosystem: practical implementation of the Convention on the Conservation of Antarctic Marine Living Resources (CCAMLR). ICES Journal of Marine Science 57: 778.
  8. 8. SC-CAMLR (2009) Scientific Committee for the Conservation of Antarctic Marine Living Resources: Report of the twenty-eighth meeting 2009 26–30 October; North Hobart, Tasmania Australia.
  9. 9. SC-CAMLR (2009) Report of the Joint SC-CAMLR–CEP Workshop (Baltimore, USA, 3 and 4 April 2009) Science Committee for the Conservation of Antarctic Marine Living Resources (SC-CAMLR-XXVIII/6).
  10. 10. SC-CAMLR (2005) Report of the Scientific Committee workshop on Marine Protected Areas held at Silver Spring, MD, USA, 29 August to 1 September 2005. Annex 7 to the Convention for the Conservation of Antarctic Living Marine Resources.
  11. 11. Harris P, Whiteway T (2009) High seas marine protected areas: Benthic environmental conservation priorities from a GIS analysis of global ocean biophysical data. Ocean & Coastal Management 52: 22–38.
  12. 12. Arango C, Soler Membrives A, Miller K (2011) Genetic differentiation in the Circum-Antarctic sea spider Nymphon australe (Pycnogonida; Nymphonidae). Deep Sea Research Part II: Topical Studies in Oceanography 58: 212–219.
  13. 13. Wilson N, Hunter R, Lockhart S, Halanych K (2007) Multiple lineages and absence of panmixia in the “circumpolar” crinoid Promachocrinus kerguelensis from the Atlantic sector of Antarctica. Marine Biology 152: 895–904.
  14. 14. Hoffman J, Peck L, Linse K, Clarke A (2010) Strong Population Genetic Structure in a Broadcast-Spawning Antarctic Marine Invertebrate. Journal of Heredity 102: 55–66.
  15. 15. Hemery L, Eléaume M, Roussel V, Améziane N, Gallut C, et al. (2012) Comprehensive sampling reveals circumpolarity and sympatry in seven mitochondrial lineages of the Southern Ocean crinoid species Promachocrinus kerguelensis (Echinodermata). Molecular Ecology 21: 2502–2518.
  16. 16. Longhurst A (1998) Ecological geography of the sea. San Diego: Academic Press.
  17. 17. Delegations of Australia and France (2011) Proposal for a representative system of Marine Protected Areas (RSMPA) in the East Antarctica planning domain. Submitted to the scientific committee to the Commission for the Conservation of Antarctic Marine Living Resources by the Delegations of Australia and France, SC-CCAMLR-XXX/11.
  18. 18. Pressey RL, Bottrill MC (2009) Approaches to landscape-and seascape-scale conservation planning: convergence, contrasts and challenges. Oryx 43: 464–475.
  19. 19. McArthur M, Brooke B, Przeslawski R, Ryan D, Lucieer V, et al. (2010) On the use of abiotic surrogates to describe marine benthic biodiversity. Estuarine, Coastal and Shelf Science 88: 21–32.
  20. 20. Huang Z, Brooke BP, Harris PT (2010) A new approach to mapping marine benthic habitats using physical environmental data. Continental Shelf Research 31: S4–S16.
  21. 21. Griffiths H (2010) Antarctic Marine Biodiversity-What Do We Know About the Distribution of Life in the Southern Ocean? PLoS ONE 5: e11683.
  22. 22. Brandt A, Gooday A, Brandão S, Brix S, Brökeland W, et al. (2007) First insights into the biodiversity and biogeography of the Southern Ocean deep sea. Nature 447: 307–311.
  23. 23. Gutt J, Barnes D, Lockhart SJ, van de Putte A (2013) Antarctic macrobenthic communities: A compilation of circumpolar information. Nature Conservation 4: 1–13.
  24. 24. Grantham HS, Moilanen A, Wilson KA, Pressey RL, Rebelo TG, et al. (2008) Diminishing return on investment for biodiversity data in conservation planning. Conservation Letters 1: 190–198.
  25. 25. Grant S, Constable A, Raymond B, Doust S (2006) Bioregionalisation of the Southern Ocean: report of experts workshop, Hobart, September 2006: WWF Australia.
  26. 26. Penhale P, Grant S (2007) Commission of the Conservation of Antarctic Marine Living Resources (CCAMLR) Workshop on Bioregionalisation of the Southern Ocean (Brussels, Belgium, 13 to 17 August 2007) Annex 9: Report of the Workshop Executive Summary.
  27. 27. Clarke A, Griffiths H, Barnes D, Meredith M, Grant S (2009) Spatial variation in seabed temperatures in the Southern Ocean: Implications for benthic ecology and biogeography. Journal of Geophysical Research 114: G03003.
  28. 28. Kaiser S, Griffiths H, Barnes D, Brandão S, Brandt A, et al. (2011) Is there a distinct continental slope fauna in the Antarctic? Deep Sea Research Part II: Topical Studies in Oceanography 58: 91–104.
  29. 29. Koubbi P, Moteki M, Duhamel G, Goarant A, Hulley PA, et al. (2011) Ecoregionalisation of myctophid fish in the Indian sector of the Southern Ocean: Results from generalized dissimilarity models. Deep Sea Research Part II: Topical Studies in Oceanography 58: 170–180.
  30. 30. Brandt A, Bathmann U, Brix S, Cisewski B, Flores H, et al. (2011) Maud Rise – a snapshot through the water column. Deep Sea Research Part II: Topical Studies in Oceanography 58: 1962–1982.
  31. 31. Last P, Lyne V, Williams A, Davies C, Butler A, et al. (2010) A hierarchical framework for classifying seabed biodiversity with application to planning and managing Australia’s marine biological resources. Biological Conservation 143: 1675–1686.
  32. 32. Linse K, Griffiths H, Barnes D, Clarke A (2006) Biodiversity and biogeography of Antarctic and sub-Antarctic mollusca. Deep Sea Research Part II: Topical Studies in Oceanography 53: 985–1008.
  33. 33. Clarke A, Griffiths H, Linse K, Barnes D, Crame J (2007) How well do we know the Antarctic marine fauna? A preliminary study of macroecological and biogeographical patterns in Southern Ocean gastropod and bivalve molluscs. Diversity and Distributions 13: 620–632.
  34. 34. McLeod DJ, Hosie GW, Kitchener JA, Takahashi KT, Hunt BPV (2010) Zooplankton Atlas of the Southern Ocean: The SCAR SO-CPR Survey (1991–2008). Polar Science 4: 353–385.
  35. 35. Barnes D, Griffiths H (2008) Biodiversity and biogeography of southern temperate and polar bryozoans. Global Ecology and Biogeography 17: 84–99.
  36. 36. Danis B, Griffiths H (2009) Polar science: bid for freely accessible biodiversity archive. Nature 458: 830–830.
  37. 37. Brandt A, Linse K, Schüller M (2009) Bathymetric distribution patterns of Southern Ocean macrofaunal taxa: Bivalvia, Gastropoda, Isopoda and Polychaeta. Deep Sea Research Part I: Oceanographic Research Papers 56: 2013–2025.
  38. 38. Duhamel G, Hautecoeur M (2009) Biomass, abundance and distribution of fish in the Kerguelen Islands EEZ (CCAMLR statistical division 58.5.1). CCAMLR Science 16: 1–32.
  39. 39. Koubbi P, Ozouf-Costaz C, Goarant A, Moteki M, Hulley P-A, et al. (2010) Estimating the biodiversity of the East Antarctic shelf and oceanic zone for ecoregionalisation: Example of the ichthyofauna of the CEAMARC (Collaborative East Antarctic Marine Census) CAML surveys. Polar Science 4: 115–133.
  40. 40. Díaz A, Féral JP, David B, Saucède T, Poulin E (2011) Evolutionary pathways among shallow and deep-sea echinoids of the genus Sterechinus in the Southern Ocean. Deep Sea Research Part II: Topical Studies in Oceanography 58: 205–211.
  41. 41. González-Wevar CA, David B, Poulin E (2011) Phylogeography and demographic inference in Nacella (Patinigera) concinna (Strebel, 1908) in the western Antarctic Peninsula. Deep Sea Research Part II: Topical Studies in Oceanography 58: 220–229.
  42. 42. Beaman RJ, Harris PT (2005) Bioregionalization of the George V Shelf, East Antarctica. Continental Shelf Research 25: 1657–1691.
  43. 43. Post A, O’Brien P, Beaman R, Riddle M, De Santis L (2010) Physical controls on deep water coral communities on the George V Land slope, East Antarctica. Antarctic Science 22: 371–378.
  44. 44. Barry JP, Grebmeier JM, Smith J, Dunbar RB (2003) Oceanographic versus seafloor-habitat control of benthic megafaunal communities in the SW Ross Sea, Antarctica. Antarctic Research Series 78: 327–354.
  45. 45. Hall S, Thatje S (2011) Temperature-driven biogeography of the deep-sea family Lithodidae (Crustacea: Decapoda: Anomura) in the Southern Ocean. Polar Biology 34: 363–370.
  46. 46. Gutt J (2007) Antarctic macro-zoobenthic communities: a review and an ecological classification. Antarctic Science 19: 165–182.
  47. 47. Gili J, Rossi S, Pagès F, Orejas C, Teixidó N, et al. (2006) A new trophic link between the pelagic and benthic systems on the Antarctic shelf. Marine Ecology Progress Series 322: 43–49.
  48. 48. Gutt J (2001) On the direct impact of ice on marine benthic communities, a review. Polar Biology 24: 553–564.
  49. 49. Brey T, Dahm C, Gorny M, Klages M, Stiller M, et al. (1996) Do Antarctic benthic invertebrates show an extended level of eurybathy? Antarctic Science 8: 3–6.
  50. 50. Thatje S, Hillenbrand C, Larter R (2005) On the origin of Antarctic marine benthic community structure. Trends in Ecology & Evolution 20: 534–540.
  51. 51. Clarke A (2003) The polar deep seas. In: Tyler PA, editor. Ecosystems of the Deep Oceans. Amsterdam: Elsevier. 239–260.
  52. 52. Brandt A (1992) Origin of Antarctic Isopoda (Crustacea, Malacostraca). Marine Biology 113: 415–423.
  53. 53. Barnes DKA, Kuklinski P (2010) Bryozoans of the Weddell Sea continental shelf, slope and abyss: did marine life colonize the Antarctic shelf from deep water, outlying islands or in situ refugia following glaciations? Journal of Biogeography 37: 1648–1656.
  54. 54. France SC, Kocher T (1996) Geographic and bathymetric patterns of mitochondrial 16S rRNA sequence divergence among deep-sea amphipods, Eurythenes gryllus. Marine Biology 126: 633–643.
  55. 55. Hunter RL, Halanych KM (2008) Evaluating connectivity in the brooding brittle star Astrotoma agassizii across the Drake Passage in the Southern Ocean. Journal of Heredity 99: 137–148.
  56. 56. Held C, Wägele JW (2005) Cryptic speciation in the giant Antarctic isopod Glyptonotus antarcticus (Isopoda, Valvifera, Chaetiliidae). Scientia marina 69: 175–181.
  57. 57. Allcock A (2005) On the confusion surrounding Pareledone charcoti (Joubin, 1905) (Cephalopoda: Octopodidae): endemic radiation in the Southern Ocean. Zoological Journal of the Linnean Society 143: 75–108.
  58. 58. Brandão SN, Sauer J, Schön I (2010) Circumantarctic distribution in Southern Ocean benthos? A genetic test using the genus Macroscapha (Crustacea, Ostracoda) as a model. Molecular Phylogenetics and Evolution 55: 1055–1069.
  59. 59. Havermans C, Sonet G, d’Acoz CdU, Nagy ZT, Martin P, et al. (2013) Genetic and Morphological Divergences in the Cosmopolitan Deep-Sea Amphipod Eurythenes gryllus Reveal a Diverse Abyss and a Bipolar Species. PLoS ONE 8: e74218.
  60. 60. Barry JP, Dayton PK (1991) Physical Heterogeneity and the Organization of Marine Communities. In: Kolasa J, Pickett STA, editors. Ecological Heterogeneity. New York: Springer-Verlag. 270–320.
  61. 61. Held C (2003) Molecular evidence for cryptic speciation within the widespread Antarctic crustacean Ceratoserolis trilobitoides (Crustacea, Isopoda). Antarctic biology in a global context: 135–139.
  62. 62. Leese F, Kop A, Wägele J-W, Held C (2008) Cryptic speciation in a benthic isopod from Patagonian and Falkland Island waters and the impact of glaciations on its population structure. Frontiers in zoology 5: 19.
  63. 63. Harris PT, Baker EK (2012) Seafloor Geomorphology as Benthic Habitat: GeoHab Atlas of seafloor geomorphic features and benthic habitats. Amsterdam: Elsevier. 936 p.
  64. 64. O’Brien PE, Post AL, Romeyn R (2009) Antarctic-wide Geomorphology as an aid to habitat mapping and locating Vulnerable Marine Ecosystems. Science Committee to the Commission of Antartctic Marine Living Resources (SC-CAMLR-XXVIII/10) Workshop on Vulnerable Marine Ecosystems. La Jolla, CA, USA 3–7th August 2009: GeoScience Australia. Conference paper: WS-VME-09/10. Available: with search ID ‘ant_seafloor_geomorph.’ Accessed: 2013 Nov.
  65. 65. Post AL, Beaman RJ, O’Brien PE, Eléaume M, Riddle MJ (2011) Community structure and benthic habitats across the George V Shelf, East Antarctica: Trends through space and time. Deep Sea Research Part II: Topical Studies in Oceanography 58: 105–118.
  66. 66. Schlacher TA, Schlacher-Hoenlinger MA, Williams A, Althaus F, Hooper JNA, et al. (2007) Richness and distribution of sponge megabenthos in continental margin canyons off southeastern Australia. Marine Ecology Progress Series 340: 73–88.
  67. 67. Harris PT, Whiteway T (2011) Global distribution of large submarine canyons: Geomorphic differences between active and passive continental margins. Marine Geology 285: 69–86.
  68. 68. Richer de Forges BR, Koslow JA, Poore G (2000) Diversity and endemism of the benthic seamount fauna in the southwest Pacific. Nature 405: 944–947.
  69. 69. Bowden DA, Schiaparelli S, Clark MR, Rickard GJ (2011) A lost world? Archaic crinoid-dominated assemblages on an Antarctic seamount. Deep Sea Research Part II: Topical Studies in Oceanography 58: 119–127.
  70. 70. Gjerde KM (2006) Ecosystems and Biodiversity in Deep Waters and High Seas. UNEO Regional Seas Report and Studies 178: 1–60.
  71. 71. Rogers AD, Tyler PA, Connelly DP, Copley JT, James R, et al. (2012) The discovery of new deep-sea hydrothermal vent communities in the Southern Ocean and implications for biogeography. Plos Biology 10: e1001234.
  72. 72. Allcock AL, Barratt I, Eléaume M, Linse K, Norman MD, et al. (2011) Cryptic speciation and the circumpolarity debate: A case study on endemic Southern Ocean octopuses using the COI barcode of life. Deep Sea Research Part II: Topical Studies in Oceanography 58: 242–249.
  73. 73. Morley S, Martin S, Bates A, Clark M, Ericson J, et al. (2012) Spatial and temporal variation in the heat tolerance limits of two abundant Southern Ocean invertebrates. Marine Ecology-Progress Series 450: 81–92.
  74. 74. Clarke A, Aronson R, Crame J, Gili J, Blake D (2004) Evolution and diversity of the benthic fauna of the Southern Ocean continental shelf. Antarctic Science 16: 559–568.
  75. 75. Smith CR, Mincks S, DeMaster DJ (2006) A synthesis of bentho-pelagic coupling on the Antarctic shelf: food banks, ecosystem inertia and global climate change. Deep Sea Research Part II: Topical Studies in Oceanography 53: 875–894.
  76. 76. Clarke A, Arntz WE (2006) An introduction to EASIZ (Ecology of the Antarctic Sea Ice Zone): An integrated programme of water column, benthos and bentho-pelagic coupling in the coastal environment of Antarctica. Deep Sea Research Part II: Topical Studies in Oceanography 53: 803–814.
  77. 77. Barry J, Dayton P (1988) Current patterns in McMurdo Sound, Antarctica and their relationship to local biotic communities. Polar Biology 8: 367–376.
  78. 78. Massom RA, Stammerjohn SE (2010) Antarctic sea ice change and variability-Physical and ecological implications. Polar Science 4: 149–186.
  79. 79. Wilson P, Ainley D, Nur N, Jacobs S, Barton K, et al. (2001) Adélie penguin population change in the pacific sector of Antarctica: relation to sea-ice extent and the Antarctic Circumpolar Current. Marine Ecology Progress Series 213: 301–309.
  80. 80. Baines PG (2007) Coastal and Regional Currents of Antarctica. In: Riffenburgh B, editor. Encyclopedia of the Antarctic. New York: Routledge. 1146.
  81. 81. Grebmeier JM, Barry JP (1991) The influence of oceanographic processes on pelagic-benthic coupling in polar regions: A benthic perspective. Journal of Marine Systems 2: 495–518.
  82. 82. Falkowski P, Barber R, Smetacek V (1998) Biogeochemical controls and feedbacks on ocean primary production. Science 281: 200.
  83. 83. Knox GA (2007) Biology of the Southern Ocean. Boca Raton, United States: CRC Press.
  84. 84. Arrigo K, van Dijken G, Bushinsky S (2008) Primary production in the Southern Ocean, 1997–2006. Journal of Geophysical Research 113: C08004.
  85. 85. Sokolov S, Rintoul S (2007) On the relationship between fronts of the Antarctic Circumpolar Current and surface chlorophyll concentrations in the Southern Ocean. Journal of Geophysical Research 112: C07030.
  86. 86. Arrigo KR, van Dijken GL (2003) Phytoplankton dynamics within 37 Antarctic coastal polynya systems. Journal of Geophysical Research 108: 3271.
  87. 87. Fichefet T, Goosse H (1999) A numerical investigation of the spring Ross Sea polynya. Geophysical Research Letters 26: 1015–1018.
  88. 88. Clarke A, Meredith MP, Wallace MI, Brandon MA, Thomas DN (2008) Seasonal and interannual variability in temperature, chlorophyll and macronutrients in northern Marguerite Bay, Antarctica. Deep Sea Research Part II: Topical Studies in Oceanography 55: 1988–2006.
  89. 89. Barnes DK, Clarke A (1995) Seasonality of feeding activity in Antarctic suspension feeders. Polar Biology 15: 335–340.
  90. 90. Brockington S, Clarke A, Chapman A (2001) Seasonality of feeding and nutritional status during the austral winter in the Antarctic sea urchin Sterechinus neumayeri. Marine Biology 139: 127–138.
  91. 91. Suhr SB, Pond D (2006) Antarctic benthic foraminifera facilitate rapid cycling of phytoplankton-derived organic carbon. Deep Sea Research Part II: Topical Studies in Oceanography 53: 895–902.
  92. 92. Bradbury IR, Snelgrove PVR (2001) Contrasting larval transport in demersal fish and benthic invertebrates: the roles of behaviour and advective processes in determining spatial pattern. Canadian Journal of Fisheries and Aquatic Sciences 58: 811–823.
  93. 93. Potthoff M, Johst K, Gutt J (2006) How to survive as a pioneer species in the Antarctic benthos: minimum dispersal distance as a function of lifetime and disturbance. Polar Biology 29: 543–551.
  94. 94. Thornhill DJ, Mahon AR, Norenburg JL, Halanych KM (2008) Open ocean barriers to dispersal: a test case with the Antarctic Polar Front and the ribbon worm Parborlasia corrugatus (Nemertea: Lineidae). Molecular Ecology 17: 5104–5117.
  95. 95. Matschiner M, Hanel R, Salzburger W (2009) Gene flow by larval dispersal in the Antarctic notothenioid fish Gobionotothen gibberifrons. Molecular Ecology 18: 2574–2587.
  96. 96. Cowen RK, Sponaugle S (2009) Larval dispersal and marine population connectivity. Annual Review of Marine Science 1: 443–466.
  97. 97. Shaw P, Arkhipkin A, Al-Khairulla H (2004) Genetic structuring of Patagonian toothfish populations in the Southwest Atlantic Ocean: the effect of the Antarctic Polar Front and deep water troughs as barriers to genetic exchange. Molecular Ecology 13: 3293–3303.
  98. 98. White C, Selkoe KA, Watson J, Siegel DA, Zacherl DC, et al. (2010) Ocean currents help explain population genetic structure. Proceedings of the Royal Society B: Biological Sciences 277: 1685.
  99. 99. Scott JM, Davis F, Csuti B, Noss R, Butterfield B, et al. (1993) Gap analysis: A geographic approach to protection of biological diversity. Wildlife monographs 123: 3–41.
  100. 100. CCAMLR (2009) Conservation Measure 91-03 (2009): Protection of the South Orkney Islands southern shelf: Commission to the Conservation of Antarctic Marine Living Resources. Available: Accessed: 2013 May.
  101. 101. Christiansen S (2010) Background document for the high seas MPAs regional approaches and experiences: Side event at the 12th UNEP global meeting of the regional seas conventions and action plans, 20th of September 2010. WWF-International.
  102. 102. McCartney M, Donohue K (2007) A deep cyclonic gyre in the Australian-Antarctic Basin. Progress in Oceanography 75: 675–750.
  103. 103. Robertson R, Visbeck M, Gordon A, Fahrbach E (2002) Long-term temperature trends in the deep waters of the Weddell Sea. Deep Sea Research Part II: Topical Studies in Oceanography 49: 4791–4806.
  104. 104. Fukamachi Y, Rintoul SR, Church A, Aoki S, Sokolov S, et al.. (2010) Strong export of Antarctic BottomWater east of the Kerguelen plateau. DOI:10.1038/NGEO842. Nature Geoscience 3: 327–331.
  105. 105. Meijers AJS, Klocker A, Bindoff NL, Williams GD, Marsland SJ (2010) The circulation and water masses of the Antarctic shelf and continental slope between 30 and 80oE. Deep Sea Research II 57: 723–737.
  106. 106. N.Z and U.S (2012) Rev 1 Conservation Measure 91-XX (2012) Ross Sea Region Marine Protected Area. Submitted to the scientific committee to the Commission for the Conservation of Antarctic Marine Living Resources by the delegations of New Zealand and the United States. Available: Accessed: 2013 May 5.
  107. 107. MEDD (2006) Décret no 2006-1211 du 3 octobre 2006: Portant création de la réserve naturelle des Terres australes françaises: Ministère De L’écologie Et Du Développement Durable (MEDD).
  108. 108. Lombard A, Reyers B, Schonegevel L, Cooper J, Smith-Adao L, et al. (2007) Conserving pattern and process in the Southern Ocean: designing a marine protected area for the Prince Edward Islands. Antarctic Science 19: 39–54.
  109. 109. Njaastad B (2011) Personal communication 28th March. Norway: Head of environmental management section, Norwegian Polar Institute.
  110. 110. CCAMLR (2010) Conservation Measure 22-07. Interim measure for bottom fishing activities subject to Conservation Measure 22-06 encountering potential vulnerable marine ecosystems in the Convention Area. Commission for the Conservaton of Antarctic Marine Living Resources. Available: Accessed: 2013 Sep.
  111. 111. CCAMLR (2012) Conservation Measure 22-06. Bottom fishing in the Convention Area.: Commission for the Conservaton of Antarctic Marine Living Resources. Available: Accessed: 2013 Sep.
  112. 112. CCAMLR Secretariat (2013) Vulnerable Marine Ecosystem Registry. Accessed: 2013 Jul 26.
  113. 113. CCAMLR (2012) Conservation Measure 91-02: Protection of the values of Antarctic Specially Managed and Protected Areas. Commission for the Conservation of Antarctic Marine Living Resources. Available: Accessed: 2013 May 6.
  114. 114. ERA (2011) Antarctic Protected Areas. Data source: Environmental Research & Assessment. Available: Accessed: 2013 Apr 10.
  115. 115. Williams A, Bax N, Kloser R, Althaus F, Barker B, et al. (2009) Australia’s deep-water reserve network: implications of false homogeneity for classifying abiotic surrogates of biodiversity. ICES Journal of Marine Science 66: 214.
  116. 116. Palumbi S (2004) Marine reserves and ocean neighborhoods: the spatial scale of marine populations and their management. Annual Review of Environment and Resources 29: 31.
  117. 117. Halpern B, Regan H, Possingham H, McCarthy M (2006) Accounting for uncertainty in marine reserve design. Ecology Letters 9: 2–11.
  118. 118. Smith W, Sandwell D (2010) Measured and Estimated Seafloor Topography Version 13.1. World Data Center-A for Marine Geology and Geophysics. Available: Accessed: 2010 Nov.
  119. 119. Smith W, Sandwell D (1997) Global sea floor topography from satellite altimetry and ship depth soundings. Science 277: 1956.
  120. 120. Clark MR, Watling L, Rowden AA, Guinotte JM, Smith CR (2011) A global seamount classification to aid the scientific design of marine protected area networks. Ocean & Coastal Management 54: 19–36.
  121. 121. Kitchingman A, Lai S, Morato T, Pauly D (2007) How many seamounts are there and where are they located. Seamounts: Ecology, Fisheries and Conservation: 26–40.
  122. 122. UQEC (2009) Scientific Principles for Design of Marine Protected Areas in Australia: A Guidance Statement: The University of Queensland Ecology Centre. Available: Accessed: 2014 Jun. 29 p.
  123. 123. SC-CAMLR. (2010) Pre-release version: Report of the Twenty-ninth Meeting of the Scientific Committee Hobart, Australia 25–29 October 2010. Science Committee for the Conservation of Antarctic Marine Living Resources.
  124. 124. Schnack-Schiel SB, Isla E (2005) The role of zooplankton in the pelagic-benthic coupling of the Southern Ocean. Scientia marina 69.
  125. 125. Ainley DG, Ballard G, Weller J (2010) Ross Sea Bioregionalisation Part I: Validation of the 2007 CCAMLR Bioregionalization Workshop Results Towards Including the Ross Sea in a Representative Network of Marine Protected Areas in the Southern Ocean. CAMLR reference number: CCAMLR WG-EMM-10/11.
  126. 126. Martin S (2001) Polynyas. In: John HS, editor. Encyclopedia of Ocean Sciences. Oxford: Academic Press. 2241–2247.
  127. 127. Locarnini RA, Mishonov A, Antonov J, Boyer T, Garcia H, editors (2006) World Ocean Atlas 2005. Vol. 1, Temperature. Washington, D.C.: National Oceanic and Atmospheric Administration Atlas NESDIS 61, United States Government. Available: Accessed:2011 Sep. 182 p.
  128. 128. Feldman GC, McClain CR, editors (2010) SeaWiFS Reprocessing: NASA Goddard Space Flight Center. Available:
  129. 129. Spreen G, Kaleschke L, Heygster G (2008) Sea ice remote sensing using AMSR-E 89 GHz channels. J Geophys Res 113: 3481–3484.
  130. 130. Sokolov S, Rintoul SR (2009) The circumpolar structure and distribution of the Antarctic Circumpolar Current fronts. Part 1: Mean circumpolar paths. Journal of Geophysical Research-Oceans 114: 1–19.
  131. 131. Wiencke C, Roleda MY, Gruber A, Clayton MN, Bischof K (2006) Susceptibility of zoospores to UV radiation determines upper depth distribution limit of Arctic kelps: evidence through field experiments. Journal of Ecology 94: 455–463.
  132. 132. Munilla León T (2001) Synopsis of the pycnogonids from Antarctic and Subantarctic waters. Polar Biology 24: 941–945.
  133. 133. Williams R, De La Mare W (1995) Fish distribution and biomass in the Heard Island zone (Division 58.5. 2). CCAMLR Science 2: 1–20.
  134. 134. O’Brien PE, Leitchenkov G (1997) Deglaciation of Prydz Bay, East Antarctica based on echo sounder and topographic features. In: Barker PF, Cooper AK, editors. Geology and seismic stratigraphy of the Antarctic Margin, 2: American Geophysical Union, Antarctic Research Series. 109–126.
  135. 135. Pena Cantero A (2004) How rich is the deep-sea Antarctic benthic hydroid fauna? Polar Biology 27: 767–774.
  136. 136. Aldea C, Olabarria C, Troncoso JS (2008) Bathymetric zonation and diversity gradient of gastropods and bivalves in West Antarctica from the South Shetland Islands to the Bellingshausen Sea. Deep Sea Research Part I: Oceanographic Research Papers 55: 350–368.
  137. 137. David B, Choné T, Festeau A, Mooi R, De Ridder C (2003) Antarctic Echinoids. An Interactive Database on CD-ROM. Version 2.0. Dijon: Biogeosciences Publisher, University of Burgundy.
  138. 138. Kruse S, Bathmann U, Brey T (2009) Meso-and bathypelagic distribution and abundance of chaetognaths in the Atlantic sector of the Southern Ocean. Polar Biology 32: 1359–1376.
  139. 139. Brandt A, Brökeland W, Choudhury M, Brix S, Kaiser S, et al. (2007) Deep-sea isopod biodiversity, abundance and endemism in the Atlantic sector of the Southern Ocean-results from the ANDEEP I–III expeditions. Deep Sea Research II 54: 1760–1775.
  140. 140. Nicol S, Pauly T, Bindoff N, Wright S, Thiele D, et al. (2000) Ocean circulation off east Antarctica affects ecosystem structure and sea-ice extent. Nature 406: 504–507.