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IP 107
Agenda Item:
CEP 7e
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New Zealand
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English
Bioregionalisation and Spatial Ecosystem
Processes in the Ross Sea Region
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Bioregionalisation and Spatial Ecosystem Processes
in the Ross Sea Region
Information Paper submitted by New Zealand
(lead author B. Sharp, Ministry of Fisheries, PO Box 1020, Wellington, New Zealand;
based on contributions from all workshop participants, see Appendix 1)
Abstract
Since 2005, CCAMLR has progressed plans to implement spatial management for purposes
of marine conservation (i.e. Marine Protected Area networks). In 2008 CCAMLR utilized a
circumpolar-scale ‘bioregionalisation’ (marine environment classification) to identify
priority areas for potential MPA designation in the CCAMLR area, and encouraged Member
states to progress spatial management planning in particular regions of interest, using
bioregionalisation at a smaller regional scale and also ‘systematic conservation planning’ to
identify particular areas of high value for conservation. This process most recently
culminated in the designation of an MPA near the South Orkney Islands, the first such
designation on the high seas within the CCAMLR area.
New Zealand has been an active contributor to the CCAMLR spatial management planning
process, and has declared its interest in progressing spatial marine protection in the Ross Sea
region. To this end, in June 2009 New Zealand hosted a Ross Sea Region Bioregionalisation
and Spatial Ecosystem Processes expert workshop, attended by international experts.
Outputs from the workshop include a fine-scale benthic/demersal bioregionalisation of the
Ross Sea region, a fine-scale pelagic bioregionalisation of the Ross Sea region, and an
agreed list of spatially bounded ecosystem processes of particular importance in the regional
ecosystem, and which may be amenable to protection using spatial management tools.
The purpose of this paper is to describe the 2009 Ross Sea region Bioregionalisation and
Spatial Ecosystem Processes expert workshop, and to present the workshop outputs to the
wider Antarctic science and marine management community. These outputs are intended to
guide ongoing efforts by New Zealand and other CCAMLR Members and the ATCM to
design and implement a representative and effective marine spatial protection and
management network for the Ross Sea region. An earlier version of this paper was
submitted to CCAMLR in October 2009 as SC-XXIV-BG-25.
Introduction
Background - Spatial Management in the CCAMLR context
Discussions to progress the implementation of spatial management within CCAMLR have
been ongoing since at least 2000 and have seen considerable progress in recent years
(CCAMLR XXVII paragraph 7.2). In 2005 the CCAMLR MPA workshop suggested the
designation of MPA’s in the CCAMLR area should be considered to protect the following
kinds of areas, to ultimately safeguard the integrity of Antarctic marine ecosystems,
consistent with CCAMLR’s ecosystem focus as described in Article II (see SC-CAMLRXXIV, paragraphs 3.54 to 3.56):
i.
representative areas;
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ii.
iii.
scientific areas to assist with distinguishing between the effects of harvesting and
other activities from natural ecosystem changes as well as providing opportunities
for understanding of the Antarctic marine ecosystem without interference; and
areas potentially vulnerable to impacts by human activities, to mitigate those
impacts and/or ensure the sustainability of the rational use of marine living
resources.
A further objective was considered to be:
iv.
the protection of spatially predictable features (such as upwellings and fronts) that
are critical to the function of local ecosystems.
The identification of priority areas to deliver on conservation objectives requires different
kinds of scientific analyses and tools. Objective (i) in particular (and to a lesser extent
objective (ii)), imply the need for a marine environment classification, or
‘bioregionalisation’, by which environmentally similar habitats are grouped to inform
management purposes, including demonstrating the representativeness of a subsequently
designed spatial management network. In contrast objective (iv) requires use of existing
knowledge of dynamic processes by which marine ecosystems operate, and of particular
areas likely to be of disproportionate importance to the continued healthy functioning of
particular ecosystems. Expert workshops were convened within CCAMLR to progress these
objectives. The 2006 Bioregionalisation of the Southern Ocean workshop (Grant et al. 2006)
and the 2007 CCAMLR Bioregionalisation workshop (SC-CAMLR-XXVI/11) assembled
many of the necessary analyses at a circumpolar scale, and encouraged Member countries to
conduct similar analyses on a regional scale (paragraph 167). Consistent with these outputs,
in 2008 WG-EMM recognized eleven priority areas likely to be of particular ecological
importance at a circumpolar scale, and recommended the following (SC-CAMLR XXVII/3,
paragraph 3.77):
The Working Group agreed that it should, as a priority, initiate a process to develop
representative systems of MPAs across the priority areas identified in Figure 12.
Therefore, Members were encouraged to use appropriate methodologies to further
this work, using, inter alia, bioregionalisation and/or systematic conservation
planning.
SC-CAMLR subsequently endorsed this advice (SC-CAMLR XXVII, paragraph 3.55), and
in 2009 reiterated its commitment to the achievement of a representative system of MPAs
within CCAMLR by 2012 (SC-CAMLR XXVIII, paragraphs 3.14-3.15).
CCAMLR endorsement of both bioregionalisation and the systematic conservation planning
(SCP) approaches for the design of MPA networks provides a clear directive by which
Members can proceed with MPA planning on a regional basis. Bioregionalisation assembles
spatial biological and environmental information and summarises that data to most
effectively approximate those biological patterns (e.g. species distributions) deemed most
relevant for the design of marine protection (see Grant et al. 2006). SCP provides a
comprehensive and transparent framework by which all relevant and available spatial data
(including layers representing biological, environmental, and economic/ management
patterns) are analyzed to design an optimal spatial management solution that achieves
multiple conservation purposes simultaneously (Margules & Pressey 2000). The
endorsement of both approaches implies a 2-phase process; i.e. the outputs of
bioregionalisation provide necessary inputs to SCP. This 2-phase approach has been
successfully utilised in the South African Prince Edward Island MPA network design process
(Lombard et al. 2007) and in the designation of an MPA near the South Orkney Islands, the
first such formal designation on the high seas within the CCAMLR area (SC-CCAMLRXXVIII, paragraphs 3.16-3.19).
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Spatial Management in the CEP context
Areas formally designated for special protection or management under the Antarctic Treaty
(ASPAs and ASMAs) have to date been either exclusively terrestrial or focused on terrestrial
sites with protection extended to cover a very small adjacent area of the marine environment.
Nonetheless many of the criteria guiding ASPA or ASMA designation are consistent with or
complementary to those identified by CCAMLR to guide MPA network design. Annex V to
the Protocol on Environmental Protection to the Antarctic Treaty (Article 3) identifies the
following guidelines to identify candidate areas for protection:
a) areas kept inviolate from human interference so that future comparisons may be
possible with localities that have been affected by human activities;
b) representative examples of major terrestrial, including glacial and aquatic,
ecosystems and marine ecosystems;
c) areas with important or unusual assemblages of species, including major colonies of
breeding native birds or mammals;
d) the type locality or only known habitat of any species;
e) areas of interest to on-going or planned scientific research;
f) examples of outstanding geological, glaciological or geomorphological features;
g) areas of outstanding aesthetic and wilderness value;
h) sites or monuments or recognised historic value; and
i) such other areas as may be appropriate to protect the values set out in paragraph 1
above
Specifically, representative habitats or ecosystems are identified as a priority for protection
by both CCAMLR (i, above) and the CEP (b, above). Further, CCAMLR criterion (ii)
corresponds to areas identified under (a) and (e) by the CEP, i.e. areas where human
activities are managed or restricted for purposes of science. Finally CCAMLR criterion (iv),
i.e. areas in which important ecosystem processes occur, will include (but will not be limited
to) colony or foraging areas for top predators, as identified by the CEP in (c).
Given the compatibility of the respective spatial management criteria identified above, and
their shared responsibility to safeguard the integrity of the Antarctic environment, it is likely
that greater harmonisation between the CEP and CCAMLR with respect to spatial
management planning would help both to achieve their mandates. Furthermore it is likely
that legal or procedural mechanisms available under either CCAMLR or the Treaty can be
usefully applied to achieve the aims of both, e.g. the use of ASPAs or ASMAs to define
spatial marine protection to meet the goals of CCAMLR.
Ross Sea Region Bioregionalisation and Spatial Ecosystem Processes expert workshop
In 2008 CCAMLR identified eleven areas at a circumpolar scale as priority areas for further
efforts to develop marine protection at a regional scale. Two of these eleven areas are in the
general Ross Sea region. Consistent with the CCAMLR mandate to progress marine spatial
management planning on a regional level, and in general following the South African Prince
Edward Islands example, New Zealand in 2009 initiated a Ross Sea region marine spatial
protection and management planning process. As part of Phase 1 of this process, in June
2009 New Zealand held a Ross Sea Region Bioregionalisation and Spatial Ecosystem
Processes expert workshop (hereafter RSR workshop). The workshop was jointly sponsored
by the New Zealand government and interested non-government community, including the
Ministry of Fisheries, the Ministry of Foreign Affairs and Trade, WWF-New Zealand, the
Foundation for Research Science and Technology, and Antarctica New Zealand, with
support in kind from the UK government. The workshop was attended by international
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experts with relevant knowledge of and experience in the Ross Sea region, in a range of
relevant disciplines, including physical oceanography, marine climatology, marine
biodiversity, benthic ecology, pelagic ecology, ecosystem modelling, MPA design, GIS,
Antarctic fisheries, and specialist taxonomic knowledge for a range of relevant taxa. See
Appendix 1 for a full list of workshop participants.
The RSR workshop was tasked with producing the following list of outputs:
1. A fine-scale benthic/demersal bioregionalisation of the Ross Sea region;
2. A fine-scale pelagic bioregionalisation of the Ross Sea region; and
3. A list/map of important ecosystem processes that may be amenable to protection
using spatial management tools.
Spatial definition of the Ross Sea region
For purposes of the bioregionalisation and spatial management planning process, the Ross
Sea Region is defined as 150°E–150°W, and 60°S–80°S (excluding areas under permanent
ice). This encompasses all of CCAMLR management area 88.1, SSRU 88.2A and SSRU
88.2B. Spatially contiguous environmental data layers were prepared in advance of the
workshop corresponding to these boundaries; subsequent outputs are similarly bounded.
Scale considerations
The relationship between organisms and their environment is inevitably scale-dependent;
different organisms integrate and respond to environmental variables at different spatial and
temporal scales, and spatial associations that are strong at one scale may be weak or nonexistent at smaller or larger scales. Furthermore even where associations are known and
valid, effective spatial classification for management purposes is still constrained by data
availability and the ability of managers to implement an effective management response. It
is important then that the outputs of spatial environment classifications be constrained to
spatial scales at which they are:
i)
biologically valid (i.e. the modelled association between the organism and
its environment is real at that scale);
ii)
based on environmental variables for which data is available and accurate
(i.e. the spatial environmental layers used to predict biological distributions
can be trusted at that scale); and
iii)
useful for management, (i.e. the depicted spatial pattern is at a scale at which
managers can effectively respond in ways that address the underlying
management concern).
The Ross Sea region as defined above is considered an appropriate scale at which to conduct
fine-scale bioregionalisation consistent with the advice of SC-CCAMLR. The Ross Sea
region is recognized as a coherent ecological unit due to the uniqueness of the Ross Sea shelf
and water circulation patterns associated with the Ross Sea Gyre that define northern waters
in the area as distinct from adjacent waters. Fish stock assessments and presumed patterns of
movement for major fish species (e.g. Hanchet et al. 2009) tend to reinforce this impression.
This area encompasses the whole of the exploratory fishery for Antarctic toothfish
(Dissostichus mawsoni) in Subareas 88.1 and 88.2; for practical reasons it is useful that
bioregionalisation and subsequent spatial management planning be conducted on a similar
scale.
Output 1: Benthic Bioregionalisation
Previous CCAMLR bioregionalisation workshops recognised that the pelagic and benthic
environments were likely to be highly decoupled over much of the CCAMLR area, and that
biological patterns in the pelagic vs. benthic environments will be affected by different suites
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of environmental drivers, such that separate benthic and pelagic bioregionalisations are
required (SC-CAMLR XXVI/11, paragraph 18). The bioregionalisation map arising from
those workshops and approved by CCAMLR for use at the circumpolar scale represents the
pelagic environment only. Attempts in previous workshops to generate a credible benthic
bioregionalisation at the circumpolar scale were hampered by the unavailability of
appropriate environmental data layers representing key drivers of benthic biological pattern,
e.g. substrate and sea-floor water body characteristics (e.g. temperature, salinity, and sea
floor current speed). Acquisition and appropriate pre-processing of the necessary data layers
to complete benthic bioregionalisation at a regional scale was identified as a high priority by
the CCAMLR Bioregionalisation workshop (SC-CAMLR-XXVI/11, paragraphs 145-146,
167).
Work underway at present in CCAMLR on the interaction of VMEs (Vulnerable Marine
Ecosystems) with bottom fishing activities would benefit from information indicative of the
spatial distributions of benthic invertebrate taxa or communities. At present benthic
distributions are largely unknown. Bioregionalisation may provide a tool by which VMEs
can be integrated into a larger spatial management context, e.g. by providing a basis for
testing spatial associations of VME taxa with particular habitat types (i.e. bioregionalisation
cluster groups), for designing/ demonstrating representativeness in a network of spatially
managed areas with respect to benthic habitats, and for defining biologically meaningful
strata within which the results of impact assessments of the likely effect of fishing activities
on VMEs can be summarized.
Available evidence suggests that the spatial distributions of benthic communities in the Ross
Sea region will be more strongly driven by patterns of variation in sea-floor habitat
parameters (e.g. water body characteristics and benthic substrate) than by water-column
characteristics and links with the pelagic environment (e.g. ice cover affecting primary
productivity; Barry et al. 2003). Links with the pelagic environment are especially complex
due to highly variable horizontal transport mechanisms affecting nutrient deposition at
smaller scales (Thrush et al. 2006, Cummings et al. 2006, Smith et al. 2003). Preparations
for the benthic bioregionalisation output of the RSR workshop therefore included special
effort to acquire and prepare data layers representing variables thought to be of particular
relevance to benthic community composition, e.g. hydrographic model outputs representing
water body characteristics and currents at the ocean floor (see below).
Output 2: Pelagic Bioregionalisation
The circumpolar pelagic bioregionalisation approved by CCAMLR classifies the pelagic
environment on the basis of large-scale water body characteristics and depth, represented by
the following four variables: i) bathymetry; ii) sea surface temperature; iii) nitrate
concentration; and, iv) silicate concentration (Grant et al. 2006). Notable for its absence is
any representation of dynamic ice behaviour and associated patterns of primary production.
It is widely recognized that dynamic ice behaviour is a main driver of spatial patterns
affecting primary production and assimilation into higher trophic levels in Southern Ocean
systems (e.g. Smith et al. 2007). To characterise Southern Ocean ecosystems both Knox
(2007) and Marchant and Murphy (1994) describe a conceptual pelagic zonation defined
primarily by ice dynamics and consisting of three major zones, i.e. the Northern/ Ice-Free
Zone, the Intermediate/ Seasonal Pack Ice Zone, and the Southern/ Permanent Pack Ice or
Fast Ice Zone. Grant et al. (2006) recognised the same limitation of the primary CCAMLR
bioregionalisation, and generated an additional ‘secondary’ classification including both ice
and chl-a concentration layers, but its accuracy was degraded by limitations of the
underlying data layers and the inherent difficulty of modelling a hierarchically scaledependent system using a single flat classification algorithm (see below).
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In preparation for the RSR workshop New Zealand took steps to overcome these difficulties
by preparing a large number of data layers representing various aspects of dynamic ice
behaviour, by preparing spatial masks to allow a hierarchically nested classification
approach, and by securing hydrographic model outputs representing water body
characteristics at considerably higher spatial resolution than was available at the circumpolar
scale (see below).
Output 3: Ecosystem Processes
Bioregionalisation is a powerful tool for discerning dominant environmental patterns and
classifying areas to guarantee the representativeness of spatial management or MPA
networks. However the 2007 CCAMLR Bioregionalisation workshop recognised that
bioregionalisation alone is not adequate for delivering another objective of MPAs -- i.e. the
protection of spatially predictable features or processes critical to the function of local
ecosystems -- and recommended a parallel process by which functionally critical ecosystem
processes are identified using expert knowledge of particularly important areas at a local or
regional scale (SC-CAMLR-XXVI/11, paragraphs 157-164; Table 1). The same concern is
echoed by scientists proposing the ecological basis for a pelagic zonation, i.e. Marchant and
Murphy (1994) described ‘zone-independent regions’ as follows:
Although the major zones described above characterize much of the
southern ocean production, there are regions which do not fit these
categories. These are areas where enhanced production can occur
associated with fronts (Lutjeharms et al. 1985), upwelling (Grindley &
David 1985) islands (Allanson et al. 1985) shelf regions and eddy effects…
Zone-independent regions are of major importance in the Southern
Ocean…. Many of the predator colonies are associated with these regions,
indicating their importance as areas where energy transfer to higher trophic
levels can occur.
The RSR workshop included equal emphasis on the production of the benthic and pelagic
bioregionalisations and the identification of ‘spatial ecosystem processes’ as parallel outputs.
Taken together these three outputs aim to adequately describe the most important spatial
patterns and features of the Ross Sea region ecosystem. In isolation any one of these outputs
provides an incomplete picture. It is New Zealand’s intention that these three outputs be
used simultaneously to inform the systematic design of an MPA network in Phase 2 of a
Ross Sea region marine spatial protection and management planning process.
Methods
Environmental data layers
Approximately 60 environmental data layers were made available to the RSR workshop as
detailed below. These included the initial set of environmental data layers utilised in the
circumpolar bioregionalisation of the Southern Ocean (Grant et al. 2006; Pinkerton et al.
2007, 2008) and new layers acquired or generated specifically for the RSR workshop. See
Table 1. Layers representing new environmental variables were chosen to characterize the
average or long-term physical and chemical oceanographic properties attributable to each
pixel, and their inter-annual variability, with an emphasis on variables representing those
properties deemed most likely to exert influence on biological distributions. Environmental
data layers were largely derived from satellite observations and data climatologies from
research vessel sampling, and from oceanographic models. A polar stereographic grid was
used, using the Clark 1866 ellipsoid (Clark 1866), with grid sizes of nominal 4 km. Front
positions in the study area are taken from Orsi et al. (1995), and are (from north): Antarctic
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Polar Front (APF), Southern Antarctic Circumpolar Current Front (SACCF), and the
Southern Boundary of the Antarctic Circumpolar Current (SB-ACC).
Where layers included small amounts of missing data, pixels with missing values were filled
in using an average of the 10 closest valid data points. Layers including larger areas of
missing data were not used.
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Bathymetric data layers
Data from the GEBCO Digital Atlas (2003) were used to calculate the bathy data layer
representing average water depth for the pixel (IOC, IHO and BODC, 2003). It is known that
this dataset is in error over the seamounts to the north of the study region, with insufficient
representation of relatively shallow features. Slope was computed using ESRI’s formula for
the gradient of a spatial grid (Burrough & McDonnell 1998). Smoothed bathymetry was
obtained using a median over a sliding 2°x2° window. The bathyanom layer (bathymetric
anomaly) was calculated as the difference between the original bathymetry data and the
smoothed data (Burrough & McDonnell 1998).
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Satellite-derived upper ocean chlorophyll
Ocean colour remote sensing was used to measure surface chlorophyll-a concentration (chla) as a proxy for primary production in the water column. Low incident light prevents data
being collected by ocean colour satellite sensors in the Ross Sea between approximately
April and October each year. In the absence of light, primary production ceases, and
phytoplankton concentrations are assumed to rapidly fall to trace concentrations. Note
however that ocean colour satellite measurements of surface chl-a are not available under
cloudy conditions, or where sea ice is present. Primary production is therefore poorly
observed in some parts of the study area at some times of the year, and values in different
pixels represent averages calculated over a different number of observed years. SeaWiFS
chl-a data were used for the period September 1997–end 2007 (Hooker et al. 1992), and
MODIS data were used for 2008 when SeaWiFS was not available (NASA 2008).
Three data layers were provided to the workshop, as follows: (1) The chlamean layer is the
log mean of all available data out of 10 years, 1997–2007; (2) The chlamax layer is the
log10-transformed maximum annual value for each season (July–June) calculated from
monthly composite values; (3) The chlamaxsd layer is the standard deviation of the log10transformed maximum chl-a concentration.
It is important to note that chl-a concentrations are an imperfect proxy for primary
productivity because chl-a concentration is a static property measurable at a specific point in
time, whereas productivity is a dynamic property integrated over a period of time. One
consequence is that primary productivity estimates based on chl-a concentrations will be
confounded by variable levels of herbivory; i.e. where secondary productivity (zooplankton
herbivory) is high chl-a may be consumed as rapidly as it is produced, skewing the primary
productivity estimates downward relative to areas where herbivory is lower.
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Insolation (Photosynthetically Available Radiation)
A layer representing annual average insolation (broadband photosynthetically available
radiation, PAR) was generated as a possible indicator for potential primary production, using
the equation of Kirk (1994) neglecting the effect of the atmosphere and clouds. Insolation
calculated in this way is purely a function of latitude, increasing with distance from the pole.
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Satellite-derived sea surface temperature
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The following representations of sea-surface temperature (SST) on a 1° grid were derived
from OIV2 satellite data, accessing all data for the years 1991–2006 (Reynolds & Smith,
1994): (1) Annual mean sea surface temperature (sst) was calculated as means of all monthly
satellite data composites; (2) Annual mean summer sea surface temperature (sstsum) was
calculated as means of monthly satellite data composites over the months December–
February; (3) The sstsumsd layer gives the standard deviation of the summer SST values
between years as an indication of interannual variability in SST in a given location.
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Satellite-derived indicators of frontal features
Sea surface height (SSH) from satellite altimetry is normally used to show positions of fronts
and other features of the circulation field. However sea surface height data was not used in
the RSR due to unacceptable levels of missing data south of c. 40°S (see Pinkerton et al.
2007). The gradient of sea surface temperature (sstgrad) was used instead to give an
indication of upper ocean current structure; this gradient was found to have a reasonable
correlation with SSH gradient
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Sea ice dynamics
Sea ice is a dominant forcing function in the ecology of the Ross Sea (e.g., Knox 2007 and
references therein; Thomas & Dieckmann 2002), Sea ice in the Ross Sea expands from late
February onwards, and retreats from late October. As in other parts of the Antarctic, the
advance of the sea ice in the Ross Sea begins near the Antarctic continent. At its maximum
extent, sea ice extends from the Ross Sea shelf to the southern boundary of the ACC. North
of the boundary, the sea is too warm for sea ice to persist. Unlike other Antarctic sectors,
retreat of sea ice en mass in the Ross Sea sector starts near the Antarctic continent, with the
formation of the Ross Sea polynya in front of the Ross Ice Shelf (Zwally et al. 1985), and the
Terra Nova Bay polynya (Kutz & Bromwich 1985). Polynyas are regions of long-lived open
water within the sea ice cover. There are two main forms; latent heat and sensible heat
polynyas (Wadhams 2000). Sensible heat polynyas are formed where warm water from
below prevents ice formation; these are rarely found in Antarctica. Latent heat polynyas are
formed where ice is continually removed by winds or ocean currents. These are also usually
associated with glacial barriers which prevent downwind ice from entering the polynya, and
strong winds flowing either off an ice shelf or off the coast. In the Ross Sea, strong winds off
the Antarctic land mass lead to the break up of the ice sheet and the formation of polynyas in
front of the Ross Ice Shelf, and at points along the western Ross Sea coast. Polynyas in the
Ross Sea are significant to a number of middle and higher trophic level organisms (e.g.,
seabirds, seals, whales, benthos), since they form a connection between the water and air.
The formation of polynyas next to the permanent ice shelf also has a strong influence on
light, nutrient and trace element concentrations in the water, and on the degree of vertical
mixing in the water column.
Satellite ice data used in the preparation of data layers were obtained from the National Snow
and Ice Data Center (NSIDC) at the Earth Observing System Data and Information System
(EOSDIS) Distributed Active Archive Center, University of Colorado, Boulder, US. Sea ice
concentrations were derived from the Nimbus-7 Scanning Multichannel Microwave
Radiometer (SMMR) and the Defense Meteorological Satellite Program's (DMSP) DMSPF8, -F11 and -F13, Special Sensor Microwave/Imager (SSM/I), using the NASA group
algorithm (Cavalieri et al. 1990, updated 2007). Data are gridded onto the SSM/I polar
stereographic grid (25 x 25 km). Here, we used SMMR-SSM/I satellite ice data extending
from the 1979/80 to 2006/07 seasons, an NSIDC season being 1 July to 30 June the
following year.
A large number of ice data layers were assembled to represent different aspects of the
dynamic sea ice regime in the Ross Sea region. First, a measure of sea ice duration through
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the entire year was obtained using daily satellite sea ice data files to calculate the fraction of
the year for which a given pixel was covered with <15% cover (i.e. ice15mean, the mean ice
free proportion of the year). An indication of interannual variability in sea ice cover was
represented as the SD of this value for a given pixel (ice15sd). Next, 12 layers were
assembled showing the average ice concentration by month (designated ice_jan, ice_feb,
etc.).
In recognition of the particular ecological importance of dynamic ice behaviour during
particular times of year (e.g. polynya formation and early summer ice edge retreat as a site
for high primary and secondary productivity) a number of ice layers were specially designed
in an attempt to represent changing ice conditions at what was thought to be most crucial
time of year affecting biological patterns. Four layers were generated depicting the
distribution of ice at different concentrations in early summer, i.e. marginal ice (15-40% ice
concentration) and unconsolidated pack ice (40-70% ice concentration) in December and
January (ice_marginal_dec, ice_marginal_jan, ice_pack_dec, ice_pack_jan). To emphasize
rapidly changing ice conditions during the critical summer period, two layers were calculated
as the differences between the mean monthly ice concentrations i.e. November-December
change (ice_dec-nov), and December-January change (ice_jan-dec). The former potentially
gives an indication of the location of the Ross Sea polynya, and the latter the location of the
“break-out” zone between the polynya and open waters to the north of the ice barrier.
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Dissolved Nutrients
Three data layers were used depicting large-scale nutrient concentrations, from WOCE
global hydrographic data (Gouretski & Koltermann, 2004): (1) nitrate (N200); phosphate
(Ph200); and (3) silicate (Si200). Concentrations at 200 m depth were preferred over surface
nutrient values as, quantities of missing data were low for the study area, and values at this
depth are likely to be less affected by recent history of surface production at the time of
sampling.
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Ocean alkalinity and CO2
The calcium carbonate (CaCO3) cycle in the ocean is central to the control of marine pH, and
potentially impacts calcifying organisms in the Ross Sea sector such as corals and pteropods.
Measures of the state of the global ocean CaCO 3 cycle were compiled by the GLODAP
project (Key et al. 2004). Three data layers derived from this database were prepared for use
by the workshop: (1) total alkalinity (alk) in which all measurements included in the
GLODAP dataset were made by potentiometric titration using a titrator and a potentiometer
(Sabine et al. 2005); (2) total CO2 (tCO2) based on shipboard coulometric titrations (Sabine
et al. 2005); (3) potential alkalinity (palk) which corrects total alkalinity for the effects of
mixing and the small changes resulting from the decomposition of organic matter, leaving
only the influence of the calcium carbonate dissolution (Brewer & Goldman 1976; Sabine et
al. 2005). For each of these three variables, depth-resolved data were available. Two layers
were prepared in advance of the workshop: (1) _surface (the shallowest valid data value);
and (2) _bottom (the deepest valid data value). Subsequently, additional layers were
generated on an as-needed basis to represent values at particular depths thought by workshop
experts to be most relevant to represent the desired biological patterns. (e.g. tCO2_xxxx
with depth expressed in meters).
The variables alk_bottom, palk_bottom, and tCo2_bottom seem to have unrealistic looking
features north of the slope in the east of the Ross Sea. These may be artefacts of the
GLODAP gridding procedure coupled with a paucity of actual measurements in the study
region. The variables palk_surface, alk_surface and tCO2_surface only have structure on the
large scale, and there may be a paucity of actual measurements in this part of the global
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ocean from which to derive realistic fields. These observations caution against uncritical use
of these data.
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Circulation and water column structure from numerical model
Three numerical circulation models of the Ross Sea are available and have been compared
with each other and with available in situ data (Rickard et al. 2009, submitted). The results
show that these models provide a consistent and plausible picture of circulation and water
mass structure in the region. Data layers were prepared using data from the HiGEM 1.1
(Shaffrey et al. 2009) model to obtain an estimate of annual averages of the following
properties: (1) current speed (speed); (2) temperature (T-HIGEM); and (3) salinity (SHIGEM). Depth-resolved values were available for all three variables. In advance of the
workshop layers were prepared depicting surface values (_surface) and the deepest valid
depth available (_bottom). Subsequently layers were generated at intermediate depths on the
advice of workshop experts, to most effectively capture relevant biological patterns (e.g. THIGEM-xxxx, with depth expressed in meters).
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Proximity variables approximating biogeographic and behavioural effects
Automated environmental classifications assign individual locations (pixels) to cluster
groups based on average or prevailing conditions (pixel values) in that location, without
regard for the values of surrounding pixels or for proximity to other features. Environmental
classifications are therefore largely ‘blind’ to proximity factors known to effect biological
distributions, to biogeographic effects (e.g. factors affecting dispersal, colonisation, and
speciation) and to behavioural effects (e.g. factors affecting active habitat selection by
mobile organisms). Proximity layers to major features were therefore prepared as a rough
proxy for such effects. One layer represents distance to the nearest land from each pixel in
the study area (landprox). A second such layer represents distance to the continental shelf
break (defined as the 1000 m depth contour) with distances offshore being positive and those
towards land being negative. Preliminary indications were that the latter layer may be an
important factor affecting biological distributions of apex predators, such as pack ice
pinnipeds (Colin Southwell, Australian Antarctic Division, pers. com.).
Classification Method
The Ross Sea region pelagic and benthic bioregionalisations were produced utilising the
same overall approach as was used in previous CCAMLR bioregionalisation workshops at
the circumpolar scale. The bioregionalisation process can be summarized as follows (from
Grant et al. 2006):
1.
2.
3.
4.
5.
6.
7.
Identify the ecological patterns and processes that have relevance to the end-use application of the
regionalisation
Identify the major environmental drivers or properties that control these patterns and processes, and extract
relevant parameters describing those properties
Pre-process the data (e.g. normalise, transform, smooth)
Compile a data matrix of individual sites (rows) by properties (columns)
Apply a clustering procedure to group locations with similar properties
Post-process the clusters to meet any application-specific constraints on the regions (e.g. minimum size)
Expert review of the resulting classes to ensure suitability for the application.
Note that this is an iterative sequence; steps 2-5 are repeated, continually adjusting
classification inputs and re-running the classification algorithm until such time as output
classifications are consistent with existing knowledge in areas where patterns are known.
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The RSR workshop methodology was similar to that of the circumpolar CCAMLR
bioregionalisation in the following ways:
i)
ii)
iii)
Only environmental data layers were used as inputs to the classification.
Biological data were not used directly by the classification, due to the paucity of
suitable data.
Inputs to the classification were selected by experts to best represent the known
environmental drivers of relevant biological patterns, from available literature
and ecological first principles.
Workshop experts subjectively assessed the resulting output classification with
reference to areas where spatial patterns were known, and iteratively adjusted
classification inputs until the most plausible classification was achieved.
The Ross Sea workshop methodology differed from that of previous CCAMLR
bioregionalisations as follows:
i)
ii)
iii)
iv)
v)
data layers were custom-generated in advance of the workshop to capture and
emphasize environmental variables thought to be most relevant for the particular
habitat of interest. For example both classifications benefited from the
preparation of modelled layers representing water body properties at userdefined depths, and the pelagic classification benefited from the availability of
multiple representations of ice dynamics, so that layers could be chosen to
represent the most ecologically important aspect of the dynamic ice regime.
Workshop experts spent considerable additional time during the workshop
combining or modifying available datasets to most accurately capture
environmental drivers or properties of known importance. For example an ice
scour disturbance layer was generated to approximate the likelihood and
intensity of natural disturbance of benthic communities (see below).
Workshop experts had at their disposal some relevant biological datasets with
which to examine/ validate the interim environmental classifications during the
iterative process of input variable selection and adjustment
Workshop experts used preliminary classification results to spatially constrain
subsequent classifications within particular areas (e.g. continental shelf only),
i.e. subjectively imposing a hierarchical classification structure. This allowed
the clustering algorithm to better perceive relevant patterns of variation within
the habitat of interest without the results being skewed by comparison with
habitats with extremely different environmental characteristics (e.g. continental
shelf vs. deep abyssal plain).
Where the best possible environmental classification still failed to reflect
contrasts between areas with known biological differences (e.g. biogeographic
contrasts between seamounts at different latitude; see below), workshop experts
imposed additional constraints to ensure that these contrasts were adequately
captured.
Results
Output 1: Benthic Bioregionalisation
Step 1. Identification of relevant ecological drivers
The assembled workshop experts compiled the following list of environmental drivers or
ecological processes known to affect the distribution and abundance of benthic marine
organisms in the Ross Sea region. This list was compiled after extensive discussion with
reference to published literature from the Ross Sea region and to ecological first principles
derived from studies conducted elsewhere. Care was taken to complete the list before
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considering the actual availability of suitable spatial datasets, in an attempt to ensure an
unbiased list and to highlight the existence of knowledge gaps for those drivers for which
proxy data were unavailable.
1.
2.
3.
4.
5.
6.
7.
Temperature
Depth
Substrate
Primary productivity
Benthic disturbance – iceberg scour
Organic input
Dissolved nutrients
Step 2. Selection of spatial data layers to represent relevant drivers
Data layers were selected to approximate spatial patterns of variation in these seven
environmental drivers or processes.
Two of the seven drivers – temperature and depth – were easily represented by the existing
data layers available without manipulation. The importance of temperature as a determinant
of benthic invertebrate and demersal fish is well known (e.g. Lockhart and Jones 2008).
Modelled sea bottom temperature (T-HIGEM_bottom) values are available from the HIGEM
hydrographic model (Shaffrey et al. 2009). Depth is available from the GEBCO (2003)
bathymetry (bathy), which effectively represents the difference between major large-scale
habitat distinctions (e.g. shelf vs. slope vs. abyss); at smaller scales it is likely that depth is
actually a proxy for the combined effect of a number of other complex environmental factors
drivers, e.g. productivity, current speed, temperature, and salinity, for which the actual effect
on biological distributions is likely more complex.
Data representing substrate is unavailable at the scale of the Ross Sea region. Workshop
experts chose to capture likely patterns of benthic substrate variation by the use of two proxy
variables thought to reflect and/or influence substrate, i.e. rugosity, derived from GEBCO
(2003) bathymetric data, and bottom current speed (speed_HIGEM_bottom) from the
HIGEM model (Shaffrey et al. 2009). It was recognized that these are an imperfect proxy,
and that using two layers implicitly doubles the weight of this driver in the subsequent
classification -- a more rigorous solution may be the generation of a single custom layer in
which bottom roughness was scaled up proportional to current speed. However the
importance of current speed as an influence in its own right on benthic community
composition (e.g. Barry et al. 2003, Smith et al. 2006), and as an influence on organic input
(below) justified the inclusion of both layers each at equal weight with those approximating
other environmental drivers. Note also that the calculation of rugosity layer values is
dependent on the scale of resolution at which the bathymetry data is summarized. It is likely
that benthic community composition responds to smaller-scale patterns than can be
accurately discerned in the GEBCO (2003) bathymetry data (bathy); the use of finer-scale
bathymetric data, when it becomes available, can be expected to greatly improve the utility
of the resulting classification at high resolution.
After considerable discussion Mean annual ice cover (ice15mean) was selected as a
straightforward proxy for primary productivity, i.e. as the best approximation of available
light (Schwarz et al. 2003; Dayton et al. 1969, 1970; Thrush et al. 2006; Cummings et al. in
prep). Direct use of the satellite-derived Chl A data layers (inferred from ocean colour) was
rejected because these data are confounded by cloud cover, such that pixel values in different
locations represent different numbers of cloud-free observation days. Furthermore the Chl A
layers approximate phytoplankton concentrations (a static property) whereas the biologically
relevant variable is phytoplankton production, a dynamic property which may not correlate
will with instantaneous Chl A concentration in locations where secondary productivity is
also high and phytoplankton are rapidly consumed by herbivores.
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To approximate benthic disturbance, a custom-generated Ice scour layer was assembled from
other available datasets. The layer was defined such that the effects of disturbance by
icebergs are a product of the interaction between proximity to ice tongues (i.e. land or ice
shelf edges) and depth. In depths of 0-400 m disturbance magnitude is directly proportional
to proximity; in depths of 400-600 m proximity values are adjusted downward with
increasing depth such that at depths greater than 600 m disturbance is assumed to be
negligible regardless of proximity (see Lenihan and Oliver 1995; Keys 1983; Thrush et al.
2006; Gutt et al. 1996).
Workshop experts determined that reliable data were unavailable to approximate spatial
patterns of the remaining two environmental drivers, organic input and dissolved nutrients.
Organic input from detrital rain can be expected to be affected by primary productivity and
modified by depth and ocean currents; variables serving as effective proxies for these three
factors are already selected, above, and smaller-scale patterns will be strongly driven by
horizontal advection by directional currents, and by local factors affecting sink rates (e.g.
Thrush et al. 2006, Cummings et al. 2006) which are too complex to be adequately
represented by available models. Similarly dissolved nutrients, e.g. as affected by water
body properties affecting the carbon saturation horizon, were thought to be an important
driver of benthic distribution patterns for which a suitable proxy variable was unavailable.
Step 3. Data pre-processing
The following transforms were assigned to the six selected and retained environmental data
layers on the basis of the known or expected shape of the relationship between
environmental variation and spatial distribution patterns.
Benthic classification variables (all weights equal)
1) T-HIGEM_bottom:
untransformed
2) bathy:
exponential transform
3) rugosity:
untransformed
4) speed_bottom:
untransformed
5) ice15mean:
untransformed
6) ice scour:
untransformed; spatial extent limited by
depth and proximity to coastal ice
These data layers are shown in Figure 1.
Steps 4-5. Spatial clustering
Clustering was an iterative process in which the retention of different proxy variables (with
alternate transforms, weights, and spatial masks) was assessed and adjusted using expert
knowledge to select the combination of variables that resulted in the most plausible spatial
patterns in areas where benthic and community patterns are better known. Workshop
experts examined multiple rounds of prospective classifications over several days, comparing
output maps with available spatial biological datasets, published and unpublished research
results, and expert knowledge from a range of taxonomic specialists. The six retained proxy
variables (above) represent the endpoint consensus of this iterative process.
The clustering procedure used Interactive Data Language (IDL) CLUSTER_WTS() and
CLUSTER() routines within MatLab software, which perform a ‘flat’ (non-hierarchical)
clustering using a (transparent) neural network-based optimization engine. The routines
accept a standardized array of multiparameter case descriptors and yield an assignment of
each case into one of x discovered clusters.
The level of classification strength (i.e. number of clusters) was chosen subjectively by
iterative adjustment and expert consensus, with the aim of representing spatial patterns on
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the finest scale possible without generating ‘false resolution’ i.e. boundaries that are unlikely
to correspond to real biological gradients.
One inherent limitation of the clustering algorithm is that it necessarily applies a ‘flat’
classification in which the same variables are applied everywhere, using the same decision
rules, and the clustering procedure assigns each location (pixel) to a class on the basis of its
multivariate similarity or dissimilarity to all other pixels simultaneously. One consequence
of this approach is that the inclusion of highly dissimilar environments (e.g. shelf vs. abyss)
in the same classification will result in classifications that emphasize the gradient between
the two dissimilar environments and conceal more subtle patterns of variation within those
environments. But because the shape of the environment-biology relationship may be highly
non-linear, these more subtle environmental contrasts within broader habitat classes may be
of equal importance to the dominant gradient in determining actual biological patterns.
Benthic Group experts discovered that due to the limitations of a flat classification approach,
they were unable to generate acceptable levels of resolution on the continental shelf without
generating false resolution (i.e. too many and/or implausible classes) on the slope and to the
north in deeper water. To overcome this problem, they generated spatial masks to examine
the consequences of running spatial classifications restricted to particular environments. The
Benthic Group achieved an objective split by first running a classification using the retained
variables (above) over the whole of the RSR at the 14-class level, and then defining a spatial
mask at the boundary of the first major split; this resulted in a ‘shelf+slope’ area to the south,
with 8 classes, and a ‘northern’ area including the deeper water and seamounts, with 6
classes. The cluster algorithm was then re-run in each area separately (using the same six
variables), retaining the original level of resolution in each area (8 and 6 classes). The
resulting cluster results were judged to be both plausible and useful.
Step 6. Classification post-processing
The Benthic Group found that across a range of examined classification strengths the
‘northern’ area classification assigned locations in moderate and shallower depths to the
same groups regardless of latitude/ distance from the shelf break. This result was
inconsistent with known biological contrasts and suspected patterns of endemism on
seamounts and ridges at different latitudes in the RSR (A. Stewart, Te Papa, Wellington,
pers. comm.). It is possible that these patterns arise from variable stochastic colonisation
histories or relative exposure to ocean currents carrying dispersal propagules; biogeographic
influences such as these are invisible to environmental classification approaches (including
bioregionalisation). The Benthic Group therefore chose to modify the ‘northern’
classification to more accurately reflect known contrasts on seamounts and ridges,
subjectively defining shallower classes in on the northern ridges (groups 16 and 17) distinct
from comparable depths at the latitude of the Balleny Islands (groups 11 and 12).
The final benthic bioregionalisation output agreed by the assembled experts of the Benthic
Group is shown in Figure 2. Summary statistics for the groups are shown in Table 2.
Step 7: Expert review/ validation: Validation of the benthic bioregionalisation was achieved
as an iterative process with reference to available biological datasets for benthic invertebrate
and benthic/demersal fish distributions (e.g. Barry et al. 2003, Smith et al. 2006; Thrush et al.
2006; Cummings et al. 2006; US ROAVERRS and New Zealand IPY survey results; VME
bycatch data collected under CM22-07).
Output 2: Pelagic Bioregionalisation
Step 1. Identification of relevant ecological drivers
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Workshop experts identified the following three primary drivers of ecosystem processes and
corresponding biological patterns at the scale of the Ross Sea region:
1) water body properties
2) ice dynamics
3) depth
This list is unsurprising, corresponding with the consensus view of environmental drivers
affecting ecosystem dynamics in the Southern Ocean generally (Knox 2007, Marchant &
Murphy 1994), and consistent with the conclusions of the circumpolar-scale
bioregionalisation of the Southern Ocean (Grant 2006), in which water body properties and
depth were the primary drivers and ice dynamics were recognized as an important (but in the
circumpolar classification, inadequately-represented) determinant of regional-scale patterns.
Definition of split hierarchical classification: ‘continental shelf’ vs. ‘off-shelf’
Early in the bioregionalisation process workshop experts perceived the limitations inherent in
attempting to model a scale-dependent and hierarchically constrained system using a ‘flat’
classification method (i.e. clustering algorithm) in which the same decision rules are applied
at all scales and in all locations. It was also recognized that it may be difficult to represent
the most ecologically relevant aspects of the three primary environmental drivers using a
single suite of variables uniformly over the entire Ross Sea region, especially with respect to
ice dynamics, where biological patterns in different areas are driven by dynamic ice
behaviour at different times of year. To address these difficulties pelagic group experts
chose ipso facto to impose a higher-order split on the classification in accordance with the
dominant environmental contrast. The group defined a split between ‘continental shelf’ and
‘off-shelf’ areas; subsequent classifications were then completed for each area separately.
Subjectively imposing this 2-level hierarchy allowed workshop experts to select variables of
particular relevance to the area in question, and allowed the clustering algorithm to capture
biological patterns driven by environmental contrasts within each area without being
dominated by comparisons with locations in the other area with extremely different
environmental properties.
After considerable discussion the 800 m depth contour on the Ross Sea continental slope was
selected as the boundary between the ‘shelf’ vs. ‘off-shelf’ areas. This boundary was
selected with reference to biological survey transects revealing consistent discontinuities in
pelagic community composition across this boundary (e.g. O’Driscoll et al. 2008, Azzalli et
al. 2008, Phil Lyver, LandCare New Zealand, pers. comm..). In practice the specific choice
of depth contour is not of critical importance, because the true boundary is somewhat
dynamic, and the continental slope is sufficiently steep that selecting a slightly different
depth contour results in only a minor horizontal shift to the corresponding boundary.
Step 2. Selection of spatial data layers to represent relevant drivers
Considerable discussion was devoted to the selection of appropriate environmental data
layers to adequately capture spatial patterns within the continental shelf and off-shelf areas
associated with the identified ecological drivers.
1) Water body properties
The HIGEM model (Shaffrey et al. 2009) was thought to most adequately capture biological
patterns associated with water body properties, in continental shelf locations where these
patterns are known. Pelagic group experts chose to use both the salinity and temperature
data layers, extracted and averaged over the summer (Dec-Jan) months, at 200 m depth (i.e.
T-HIGEM_200_summer and S-HIGEM_200_summer). See Figures 3-4 a and b. These
specifications were chosen to represent those aspects of water body properties most
indicative of the important pelagic ecosystem processes driving biological patterns (e.g.
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during the summer phytoplankton bloom). In particular, the model was shown to accurately
depict the contrast between cold, heavy, high-salinity Shelf Water (Jacobs et al, 1970; Jacobs
et al, 2002) associated with increased sea ice production in the western Ross Sea vs.
relatively fresher, warmer water in the east affected by intrusion onto the shelf of waters
originating in the Amundsen Sea (Keys et al. 1990, Assman et al. 2005, Orsi and
Wiederwohl 2009), as well as north-south contrasts between subsurface Circumpolar Deep
Water in the north vs. colder and more saline Modified Circumpolar Deep Water inshore.
The accuracy of these patterns were verified by comparison with comparable temperature
and salinity data extracted from the Southern Ocean Atlas (Olbers et al, 1992).
The selection of these specific water body properties to approximate ecologically important
biological patterns was verified with reference to known patterns of phytoplankton
assemblages and the seasonal timing of primary productivity associated with the opening of
the Ross Sea polynya, as evidenced by satellite-derived chlorophyll distributions (Smith and
Comiso, 2008) and modelled regional climatology (Smith et al. 2003). The resulting
patterns accurately corresponded to known contrasts between diatom-dominated areas in the
ice-influenced eastern and western regions of the Ross Sea shelf and central areas dominated
by Phaeocystis antarctica (Arrigo et al., 1998; Smith and Asper, 2001; Smith et al., in press).
The accuracy of the HIGEM model outputs in approximating these biologically important
patterns on the continental shelf lent confidence to the use of the same data layers to model
patterns in other areas where direct evidence of biological pattern is lacking. The layers also
depict ocean front patterns of known importance in deeper water; validation of
corresponding biological contrasts was available with reference to continuously sampled
zooplankton and mesopelagic community composition (Hosie et al. 2003; Atkinson et al.
2008; O’Driscoll et al. 2008) and top predator foraging distributions (e.g. Tynan 1998,
Ainley et al. 2006). The pelagic group therefore chose to use these same two HIGEM data
layers to approximate water body properties in both the shelf and off-shelf classifications.
2) Depth
In the relatively shallow waters of the shelf the influence of depth on the pelagic
environment is fairly straightforward, i.e. influencing hydrodynamics and micronutrient
availability and directly affecting pelagic organisms capable of vertical migration or vertical
foraging behaviour.
3) Ice dynamics
Pelagic group experts selected from a large number of available data layers representing
different aspects of dynamic ice behaviour in the Ross Sea region. The group actively
sought an ice representation that would most accurately capture the pattern and sequence of
ice retreat in early summer. In particular it was important that the continental shelf
classification accurately represent the shape and sequence of ice edge retreat in the formation
of the Ross Sea Polynya, and that the off-shelf classification depict the early summer
marginal ice zone and mid-summer polynya breakout zone. The ecological importance of
polynya formation and of the marginal ice zone during the period of rapid ice retreat in early
summer -- as a location of heightened primary productivity, essential habitat for ecological
keystone species such as krill, and rapid trophic assimilation to higher trophic levels
including top predators – is well documented (Ainley et al. 2006).
The ice layer judged to most effectively capture these dynamic patterns was the layer
depicting the average change in ice concentration from November-December (i.e.
icechange_nov-dec; Figure 3-4d). Note however that because this layer exclusively
emphasizes change, it was necessary in the off-shelf classification to add the November ice
(ice_nov) layer to effectively distinguish between areas with persistently high vs. persistently
low early summer ice concentrations.
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Step 3. Data pre-processing
The temperature and salinity layers from the HIGEM model were retained untransformed.
The layers were each down-weighted by half in the cluster algorithm in order to maintain
equal weight applied to water body property variables relative to the depth and ice dynamics.
For the shelf classification, the bathy layer was log transformed to reduce the emphasis on
depth contrasts in deeper water expected to have less influence on the pelagic environment.
For the off-shelf classification, the influence of depth was truncated at depths greater than
1500 (i.e. all depths greater than 1500 m treated as equal).
The summer ice change layer was retained untransformed. In the off-shelf classification, for
which a second ice layer was necessary, both ice layers were down-weighted by half to
maintain an equal overall weighting applied to ice dynamics relative to water body properties
and depth.
The final list of retained variables for both pelagic classifications is as follows:
Continental shelf pelagic classification variables
1. T-HIGEM_200_summer:
untransformed;
2. S-HIGEM_200_summer:
untransformed;
3. bathy:
log transformed;
4. icechange_Nov-Dec:
untransformed;
weight = 0.5
weight = 0.5
weight = 1
weight = 1
Off-shelf pelagic classification variables
1. T-HIGEM_200_summer:
untransformed;
weight = 0.5
2. S-HIGEM_200_summer:
untransformed;
weight = 0.5
3. bathy:
effect truncated < 1500 m;
weight = 1
4. icechange_Nov-Dec:
untransformed;
weight = 0.5
5. ice_nov:
untransformed;
weight = 0.5
Steps 4-5. Spatial clustering
Clustering for the continental shelf and off-shelf pelagic classifications followed the same
procedure outlined for the benthic classification, above. The shelf classification was
performed first, reflecting greater available data and workshop expertise concentrated on the
shelf against which the resulting clusters could be assessed and validated. Input
environmental data layers, weights, and transforms were iteratively adjusted until an
optimum shelf classification was agreed (corresponding to the variable list above) at the 10group level. Retained variables for the shelf classification provided a starting point from
which the off-shelf classification proceeded. A range of alternate variable configurations
were examined, but ultimately the agreed optimal classification retained a similar list of
variables as in the shelf classification, again at the 10-group level.
Step 6. Classification post-processing
The Pelagic Group subjectively modified the results of the shelf and off-shelf classifications
to better correspond to known biological or ecological patterns, or to better accommodate
management concerns, as follows:
- Within the shelf classification, two extremely small coastal/shallow-water groups
were thought to be too small to be practically useful in a pelagic management
context; consisting mainly of isolated pixels within coastal embayments; the groups
were eliminated by merging the pixels into adjacent groups, yielding eight
continental shelf groups.
- In the off-shelf classification, three groups within the Antarctic Circumpolar Current
(ACC) were merged into a single group. The internal stratification of the
ACC was driven by extreme values in the salinity and temperature variables relative
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-
to southern waters; the resulting patterns were judged by workshop experts to be
unrealistic, due to the dynamic nature of the ACC.
A single group encompassing the shallowest waters of the off-shelf group (800-1500
m) was divided into three groups, i.e. ‘summer ice-free slope’, ‘summer ice-covered
slope’, and ‘Balleny Islands seamounts’. This division corresponds to known
differences in the ecosystem functional role of these areas (see below).
The shelf and off-shelf pelagic classification were then combined into a single pelagic
classification with 18 groups. The Ross Sea region pelagic bioregionalisation is shown in
Figure 5. Summary descriptions of the groups are shown in Table 3.
Step 7. Validation
Throughout the bioregionalisation process the Pelagic Group referred to available published
and unpublished sources of spatial biological data and compared relevant biological
distributions with patterns arising from the clustering procedure, to inform the selection and
retention of alternate spatial data inputs until an optimum pelagic classification had been
achieved. It was acknowledged that not all biological distributions give rise to the same
pattern, and thus the choice of which species distribution or other biological pattern to
emphasize involves subjective decisions. With considerable discussion, Pelagic Group
experts chose to emphasize those environmental patterns or species distributions thought to
most accurately correspond to ecosystem processes crucial to the function and health of the
Ross Sea regional ecosystem. For example species patterns indicative of important trophic
processes such as primary production, secondary trophic assimilation supporting highly
productive species (e.g. krill and Pleuragramma) and top predator foraging were sought.
Patterns indicative of high biomass or functionally important species were favoured over rare
species or spatially restricted distributions.
Completion of the pelagic bioregionalisation benefited from comparison of interim cluster
procedure outputs with a large number of published and unpublished representations of
pelagic species distributions or ecological patterns during the bioregionalisation (see Output
3, below, and Table 4 for references).
Output 3: Ecosystem Processes
Workshop experts identified a list of spatially predictable ecosystem processes known to be
of particular importance to ecosystem function in the Ross Sea region. Wherever possible
workshop experts also attempted to locate the identified processes on a map. Some
identified processes, for example some biological distributions and predator foraging areas,
are known only approximately or not at all and would benefit from further literature or field
investigations. Other processes, e.g. those associated with fronts and polynyas, can be
inferred from ecological first principles and located with the aid of the same spatial
environmental datasets considered as inputs to the bioregionalisation process.
Workshop experts from the Benthic and Pelagic Groups compiled separate lists of ecosystem
processes with relevance for these separate environments, but subsequently came together to
compare lists and to discuss ecosystem processes that have relevance for both environments
or that involve benthic-pelagic coupling. In particular all discussions involving the
ecological roles of fish or fish-eating predators were held involving experts from both groups
together. See Table 4. Descriptions of areas included in the combined list are as follows.
Pelagic processes/ areas
The following areas were identified by the Pelagic Group.
1. shelf front intersection with seasonal ice edge
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The Ross Sea Shelf Front in combination with early summer marginal ice forms a zone of
elevated productivity, possibly fuelled by a supply of micronutrients (e.g. iron) associated
with shelf break upwelling, particularly between 160-180W (e.g. Sweeney et al, 2003).
Similar regional distinctions and processes have been described for the western Antarctic
Peninsula shelf break (Smith et al, 2008), e.g. Siegel & Loeb (1995) and Loeb et al (1997)
demonstrate that in the Antarctic Peninsula region, production and survivorship of krill is
enhanced on the overlap of ice edge with the shelf- break.
The Ross Sea shelf front in association with marginal ice is actively targeted by top predators
including seabirds (e.g. Adelie penguins – Ainley et al. 1984), pinnipeds (Crab-eater seals Gilbert and Erikson 1977) and cetaceans (Minke and Type-C – Ichii et al. 1998; Karnovsky
et al. 2007; Ainley in press), and corresponds with preferred feeding habitat for adult
toothfish (Hanchet et al. 2009). Predators forced to forage elsewhere suffer reduced fitness.
For example Wilson et al (2001) demonstrated a negative correlation between Adelie
penguin population growth and maximum winter/spring sea-ice extent in the Ross Sea
Region, and hypothesized that further increases in regional ice extent and a corresponding
northerly shift in their winter habitat would move Adelie penguins (and their predators) north
of the food rich waters that occur south of the Antarctic Circumpolar Current.
2. early summer northern Marginal Ice Zone/ Ross Sea polynya edge:
The northern marginal ice zone from about 65 to 70°S is the preferred summer foraging
zones of the populations of Great Whales and Orca types A and B.
On the Ross Sea shelf top predators (e.g. Minke whale, Balaenoptera bonaerensis; OrcaType C, Orcinus orca; Weddell seals (Leptonychotes weddellii), Emperor penguins,
Aptenodytes forsteri; and Adelie penguins, P. adeliae; and petrel spp.) concentrate in the
waters that coincide with the marginal ice zone that rings the Ross Sea polynya during spring
and summer (location as indicated by the Nov-Dec marginal ice data layer – See Figure 3d;
see also Figure 6, from Ainley et al. 2006). In general, the top predators appear to resist
foraging in the central and southern portion of the Ross Sea polynya area because of the
reduced prey abundance associated with bloom patterns of the phytoplankton Phaeocystis
antarctica (Smith et al. 2007). The physical properties (colonial growth and gelatinous
structure) of Phaeocystis and short early spring blooms lead to a food-web less suited to the
needs of top-predators (Walker Smith pers. comm. 2009).
3. Balleny Islands and proximity
Elevated densities of Euphausia superba have been recorded near the Balleny Islands and in
the northern parts of the Ross Sea (Timonin 1987; Voronina & Maslennikov 1993; Azzali &
Kalinowski 2000). In the vicinity of the Balleny Islands, these aggregations are possibly
associated with one or more of the intersecting water types (the Antarctic Circumpolar
Current, Offshore Antarctic Current, and the mixed waters from both in the Ross Gyre)
present in the area. Voronina (1995) notes that on a transect along 67 oS, early developmental
stages of E. superba were only found north of the western Ross Sea near the Balleny Islands.
This suggests that successful breeding and survival of larvae had occurred only in this
region.
Short-lived polynyas formed down-wind or down-current of islands such as the Balleny
Archipelago probably boost local marine productivity by permitting greater light availability
to the water column, and provide access to prey resources for top predators. The dimensions
of these polynyas and their effects on local trophic function may extend far beyond the
island group, similar to the case for giant icebergs in Ross Sea where zones of open water or
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reduced pack ice often exceeded 35 km and sometimes extended greater than 150 km
downwind (Keys et al. 1990, Keys pers comm.).
See Sharp (2005) and references therein for a comprehensive summary of ecosystem and
conservation values associated with the Balleny Islands.
4. Polar Front
The Polar Front is an oceanic transitional zone marked by enhanced productivity and active
seabird foraging, e.g. see Ainley and Jacobs (1981) and Weimerskierch & Cherel (1998).
5. Multi-year ice zone in eastern Ross Sea
The eastern Ross Sea region is characterised by large stable ice floes utilised by Emperor and
Adelie penguins as platforms on which to moult (Ainley et al. 2006) and perhaps to a lesser
extent by crabeater seals.
6. Northwest Ross Sea Antarctic krill aggregation
In the Ross Sea E. superba (Antarctic krill) have been mainly encountered in association
with the continental slope (Azzali & Kalinowski 2000; Sala et al. 2002; Azzali et al. 2006;
Murase et al. 2008) and north of 74°S in the eastern Ross Sea (Ackley et al. 2003) coinciding
with elevated productivity associated with the shelf break (area 1) and early summer
marginal ice zones (area 2). These krill support large populations of top predators. In
particular their proximity to the Northern Victoria Land coast (i.e. Cape Adare to the
Possession Islands) results in high concentrations of Adelie penguins, approximately 50% of
the Ross Sea population (area 11, see Figure 7). Penguin diet samples obtained by
Landcare Research at Hallett Station, near the shelf break in the Ross Sea, suggests that
major densities of E. superba occur in the study region. Makarov et al. (1991) shows high
larval densities close to 2000 m contour between 180° and 165°W.
7. Small coastal polynyas
Spatially restricted early-opening coastal polynyas on the Victoria Coast (see Tremblay &
Smith 2007; Jacobs & Clomiso 1989; Romanov 1994; Zwally et al. 2002; Jacobs and Giulivi
1998) are important sites of increased productivity supporting higher trophic levels,
including large breeding colonies of penguins and seals (see below).
8. Increased primary production in southern shelf polynya
Satellite-derived estimates of chl-a concentration reveal a distinct region south of 74ºS that is
characterized by high phytoplankton biomass; furthermore, this is on a seasonal scale driven
by the availability of irradiance for growth. Variations within that region also occur, but the
longitudinal differences are more difficult to address using a spatial composite. These
variations have also been observed in surveys of biomass and productivity (e.g., Tremblay
and Smith, 2007) and in estimates derived from satellite-based models (Arrigo and van
Djiken, 2004; Smith and Comiso, 2008).
9. Crystal krill concentrations on Ross Sea shelf
In the western Ross Sea the krill species distribution is characterised by a broad latitudinal
separation with E. superba occupying the water column to the north of approx. 70-72º lat and
E. crystallorophias to the south (Murase et al. 2008; O’Driscoll et al. 2009; Lyver et al. in
review). The variable spatial overlap between the two krill species is related to ice cover and
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perhaps age structure (Azzali et al. 2006). This krill boundary tracks south-east along the
Ross Sea Shelf Break and is likely to be dynamic according to year (Murase et al. 2008;
O’Driscoll et al. 2009).
10. Silverfish concentrations on Ross Sea shelf
After krill, the most important prey species for top predators on the Ross Sea Shelf is
Antarctic silverfish (Pleuragramma antarcticum). Despite the importance of their trophic
role, spatial distributions for silverfish are not known with great certainty; proper delineation
of boundaries for this area would benefit from additional data or analysis. Available
observations suggest that both adult and juvenile silverfish occurr widely over the western
Ross Sea shelf from 72º to 77º S and 167º 30’E to 180º. Highest acoustic densities of adults
have been observed at about 73º 30 S and 178º 30’ E, southwest of the Iselin Bank. Juvenile
silverfish appear to be more abundant in the east, but survey coverage in the area to date has
been inadequate to define distributions from direct observations.
In the absence of spatially comprehensive observations, distributions in unobserved areas
may be inferred from habitat associations. Midwater trawls targeting mesopelagic fauna
during the the 2008 New Zealand IPY-CAML survey indicate a clear transition between
‘Ross Sea’ mesopelagic fauna over the shelf – i.e. silverfish, icefish, and crystal krill -- vs
‘Oceanic’ fauna consisting of jellyfish, myctophids, deepsea smelt, and squid north of the
Ross Sea slope. Preliminary analyses indicate that the boundary between these groups
corresponds most closely to the 800 m depth contour .
Predator colonies and foraging areas
The following areas were identified by the Pelagic Group
11. Penguin colonies and foraging distribution [colony locations known; extended
foraging distributions only partly resolved]
The locations of Adelie and emperor penguin colonies on the Victoria Coast are shown in
Figures 7-8.
Key foraging habitats of the seals and penguins from the colonies and haul-outs in the
western Ross Sea occur within a 50-150 km zone along the Victoria Land coast and Ross
Island (Ballance et al. 2009; Lyver et al. in review). For Adelie penguin colonies in
particular, foraging areas are often characterised by proximity to coastal polynyas (area 7,
above). Weddell seals breeding in the south-western Ross Sea (McMurdo Sound) also
confine foraging to the marginal ice zone along the western boundary of the Ross Sea
polynya (area 2; see Ainley et al. 2006). There is also some evidence of seal foraging in the
troughs between shallow banks during middle and late winter. Emperor penguins confine
foraging to waters in the western third of the Ross Sea Shelf during the chick-feeding season
and pre-molt stages. Foraging appears to be associated with shallow banks to some degree.
12. Weddell seal colonies and foraging distribution [colony locations known, extended
foraging distributions only partly resolved]
The known distribution of Weddell seals is described by Ackley et al. (2003) and shown in
Figure 8c along with data indicative of foraging distributions. Because Weddell seals are
known to feed on Antarctic toothfish (Pinkerton et al. 2008), resolving their spatial foraging
distribution is potentially of particular importance for spatial management as a means of
avoiding potential trophic overlap with the toothfish fishery.
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13. Type C orca foraging locations [location currently unresolved]
Type C orcas are a distinct variant (presumed species) of specialist fish-feeder orca known to
prey on Antarctic toothfish (Pitman & Ensor 2003). Their numbers and spatial distributions
are largely unknown because available data does not resolve between type C orcas and the
more commonly observed orcas that feed primarily on whales (Type A) or on seals and
penguins (Type B). Because of the potential for trophic overlap with the toothfish fishery,
data indicative of preferred feeding distributions for Type C orcas would be a high priority
for input into spatial management planning.
Fish processes/ areas
The following areas were identified in combined discussions of both the Benthic and Pelagic
Groups.
14. Victoria coast/ ice tongues and proximity
Platelet ice formation in waters adjacent to the Victoria Coast and ice tongues provides
important habitat for fish and invertebrate larvae. The freezing water (-2.3 o C) formed at
the underside of the Ross Ice Shelf when advected towards the surface becomes supercooled
and leads to nucleation in the water column beneath the surface. Small crystals adhere to the
substrate (rocks and sessile invertebrates) and to the underside of the solid ice and grow into
large platelet like crystals. These formations occur on the substrate down to approximately
30 m and form large mats of anchor ice. Anchor ice prevents the settlement of sessile
invertebrates but is a habitat for several species of small Trematomus fishes. The same
platelets form at the underside of the solid ice where they are inhabited by ice amphipods, the
adult Pagothenia borchgrevinki and several species of larval and juvenile notothenioid
fishes.
The conditions for forming anchor ice and the subplatelet layer have been observed directly
in both the eastern and western McMurdo Sound. On the west side of the Sound and
northwards the floating glacial ice tongues and small perennial ice shelves also generate
supercooled water and associated anchor ice in the embayments of Granite Harbor, Tethys
Bay near Terra Nova Bay, and between the ice tongues north of Cape Washington.
Presumably the factors that lead to anchor ice formation are also present north of Cape Hallet
up to Cape Adare.
15. Terra Nova Bay
The northern part of Terra Nova Bay is thought to be a nursery ground for silverfish
Pleuragramma antarcticum (Guglielmo et al. 1998; Vacchi et al. 2004). High densities of
post-larvae and juvenile silverfish have been observed associated with the westward flowing
current of the limb of the Antarctic coastal current and southern limb of the Ross Sea Gyre
(Guglielmo et al. 1998); the permanent polynya located there may provide favourable food
conditions for the development of early silverfish life stages. These observations suggest a
tight relationship between reproduction events for Antarctic silverfish and the seasonal
dynamics of the pack-ice zone (Vacchi et al. 2004).
Antarctic toothfish areas
Antarctic toothfish is a major component of the Ross Sea ecosystem, as the dominant
benthic and demersal fish predator as well as a potential prey species for other top predators
such as orcas, Weddell seals, and sperm whales. Consequently the RSR workshop sought to
identify areas of particular functional importance in the life cycle of toothfish, which may be
amenable to spatial protection or management to protect areas deemed particularly important
on ecosystem grounds.
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The presumed life cycle of Antarctic toothfish in the Ross Sea stock (Hanchet et al. 2009)
holds that juvenile toothfish settle in shallow southeastern continental shelf or continental
margin areas after a pelagic larval stage, and move progressively westward and northward to
deeper waters as they mature. Sub-adult (i.e. 60-100 cm) fish have been observed in three
deeper ‘holes’ on the extreme southern and western Ross Sea shelf. As fish mature they
move gradually northward and are found in deeper water; adult Antarctic toothfish
(Dissostichus mawsoni) are broadly associated with the neritic waters along the Ross Sea
Shelf slope, Terra Nova Deep, southern McMurdo Sound, and Ross Ice Shelf edge.
Toothfish probably spawn on the underwater topographic features in the north of the Ross
Sea (north of about 70ºS) outside the polar summer season (Fenaughty 2006; Hanchet et al.
2009). Limited tag returns and spatial modelling results support this hypothesis. On this
basis the following areas were identified in combined discussions of both the Benthic and
Pelagic Groups.
16. Deeper areas of southern and western Ross Sea shelf
Three historical fishing grounds in deeper ‘holes’ within the Ross Sea shelf may constitute
important interim habitats between initial settlement of juvenile fish on the shelf and
subsequent migration of mature fish to the continental slope. The Terra Nova Bay ground is
a deep trough running northeast from the mouth of Terra Nova Bay and extending from
under the Drygalski Ice Tongue and at a maximum depth of ca. 1200 m. The Ross Island
ground lies in the area of deep water immediately to the north and east of Ross Island and
adjacent to the Ross Ice Shelf, and likely extends to ca. 900 m. The third main ground is
adjacent to the ice shelf and extends to greater than 800 m.
17. Terra Nova trench
The Terra Nova trench is a continuous channel of deeper water connecting the Terra Nova
Bay deep ground to the continental slope, and as such may be an important dispersal corridor
for maturing Antarctic toothfish (Hanchet et al. 2009).
18. 18. Ice-free continental slope
The ice-free continental slope adjacent to the Ross Sea supports the highest known
concentrations of adult toothfish in the region, and is thought to be the main feeding ground
where adult fish gain weight in preparation for spawning in the north (Hanchet et al. 2009;
Fenaughty et al. 2008).
19. Northern seamounts
Antarctic toothfish are known to spawn on seamounts to the north of the Ross Sea, where
current patterns associated with the Ross Gyre are thought to retain the eggs and deposit the
larvae back to the slope and continental margin in the southeast Ross Sea. It is thought that
fish do not spawn every year, requiring more than a year to regain condition after migrating
to spawn in what is likely to be a food-poor environment. At least a portion of the spawning
stock migrates back to the continental slope after spawning, presumably to regain condition
in preferred feeding areas (Hanchet et al. 2009; Fenaughty et al. 2008).
20. Iselin Bank
Rattails (Macrourus spp) are the dominant prey item for Antarctic toothfish (Fenaughty et al.
2003; Stevens 2004), and as such account for a substantial portion of trophic energy flow to
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higher trophic levels in demersal food chains (Pinkerton et al. 2009). The highest
concentration of rattails caught in the Ross Sea region are on the eastern Iselin Bank.
Benthic processes/ areas
The following areas were identified by the Benthic Group
21. Pacific-Antarctic Ridge
The Pacific-Antarctic Ridge lies on the boundary of the Polar Front separating different
oceanic water masses as well as on the boundary between tectonic plates. It is an overlap
zone for fish at both the northern and southern extremes of their distributions, as well as an
area of significant endemism for benthic and demersal species. For example the following
possible endemics have been observed: Giant cod, Lepidion cf. schmidti (new species
record); Blobfish, Epinania sp. (new family record); Eelpouts: Seleniolycus pectoralis & S.
robertsi (currently endemic to the PAR).
22. Balleny Islands and associated complex topography
As the only significant islands in the region, the Balleny Islands include isolated shallow
environments affected by complex ice and current regimes. The benthic bioregionalisation
for this area (Figure 2) include habitats that occur nowhere else in the region. See BradfordGrieve and Fenwick (2002) and references therein.
Unique, isolated, and/or potentially endemic benthic and demersal fauna have been observed
in the vicinity of the Balleny Islands, as follows: Lepidonotothen squamifrons (Günther
1880); Nototheniops loesha (Balushkin 1976); Lepidonotothen larseni Lönnberg 1905;
Gymnodraco acuticeps (Boulenger, 1902) – northern limit; Macrourus whitsoni – northern
limit; Amblyraja skate species – outer limit; Notothenia corriceps – northern limit.
23. Admiralty seamount
US ROAVERRS research voyages (Barry et al. 2003) and the NZ IPY-CAML survey
(unpubl) and earlier research voyages have observed communities of potentially vulnerable
benthic taxa. Extreme isolation suggests the potential for endemism. The following
demersal distributions have been observed: Lepidonotothen squamifrons (Günther 1880);
Macrourus whitsoni – northern limit; Amblyraja skate species – outer limit
24. Cape Adare slope
US ROAVERRS research voyages (Barry et al. 2003) and the New Zealand IPY-CAML
survey (unpubl) have observed communities of potentially vulnerable benthic taxa in this
area. The thin continental shelf and the coastal configuration relative to prevailing currents
presumably provides refuge from ice scour.
25. Lower slope Mawson bank
There are indications of high biodiversity from US ROAVERRS research voyages (Barry et
al. 2003) and the NZ IPY-CAML survey (unpubl) in this area.
26. southeast Ross Sea slope
Complex oceanographic and underwater topographic confluences and the benthic
bioregionalisation (Figure 2) suggest a highly heterogeneous (and potentially unique for the
region) benthic setting in this area.
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Discussion
Taken together, the outputs of the Ross Sea Region Bioregionalisation and Spatial
Ecosystem Processes workshop seek to provide a comprehensive spatially explicit
representation of the Ross Sea region, depicting those environmental patterns and ecosystem
processes thought to be most important for informing spatial management planning. It is
New Zealand’s intention to progress spatial management planning, perhaps using a
Systematic Conservation Planning (SCP) approach to address multiple conservation
objectives simultaneously in balance with sustainable rational use of marine resources (as in
Margules and Pressey 2000, Lombard et al. 2007, SC-CCAMLR XXVIII/14), consistent
with the CAMLR Convention and commitments of the Antarctic Treaty.
Spatial planning in this framework is a 2-phase process. Phase 1, of which the RSR
workshop is a part, is a process of assembling and analysing relevant information about the
Ross Sea regional ecosystem itself, i.e. natural environmental or biological features without
reference to management by people. Phase 2 is a process of making decisions about spatial
management following the SCP approach, i.e. putting lines on maps to delineate areas that
will be managed differently for conservation purposes. As such the outputs of the Phase 1
RSR workshop will serve as necessary inputs to the Phase 2 SCP approach. These outputs
will also help to identify data gaps, and to define a work plan for data acquisition.
The critical step in the SCP approach linking Phase 1 and Phase 2 of the spatial management
process is the clear identification of likely conservation goals, targets, and constraints
guiding Phase 2 of the process. In the language of Systematic Conservation Planning, a
‘goal’ is an agreed conservation objective that the spatial management network will seek to
achieve, e.g. ‘protect from physical disturbance a representative example of every benthic
habitat’ or ‘protect core foraging areas in the immediate vicinity of breeding colony locations
for land-based top predators’. A ‘target’ is a spatially quantitative management standard
assigned to each goal, e.g. ‘X% of each benthic habitat type closed to bottom fishing
methods’ or ‘Y% of areas within 10 nm of emperor penguin breeding colonies closed to all
fishing’. A ‘constraint’ is an additional rule or restriction imposed to optimize possible
spatial management solutions to meet conservation targets while minimising costs imposed
on other uses, e.g. ‘minimise the area required to meet targets’ or ‘minimise disruption to
historical or anticipated fishing patterns without compromising conservation targets’. The
optimal spatial management solution is the one that meets all of its objectives as defined by
the conservation targets while minimising ‘costs’ as defined by the constraints.
Identification of an optimal solution may be aided by the use of spatial conservation planning
software, modified by discussions with managers and stakeholders to negotiate tradeoffs in
the achievement of competing management objectives (as in Lombard et al. 2007, SCCCAMLR XXVIII/14).
New Zealand is moving ahead with plans for Phase 2 of a spatial management planning
process for the Ross Sea region. The identification of conservation goals will arise logically
from the outputs of the RSR workshop described in this paper (i.e. Figures 2 and 5, and
Table 4 with associated maps, currently under revision) guided by the overall objectives for
spatial management as mandated by CCAMLR and the ATCM (as described in the
Background, above). Note however that not every functionally important habitat or
ecosystem process identified in Table 4 may be amenable to protection using spatial
management tools; some processes may require protection using other management tools.
Identifying constraints on spatial management design will proceed in consultation with
managers and relevant stakeholders. Defining and adjusting quantitative targets will
consider tradeoffs between multiple objectives and competing stakeholder interests. Once
these are defined, application of the Systematic Conservation Planning process can be
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expected to yield a comprehensive and effective spatial management solution to achieve
important conservation objectives in the Ross Sea region (as in Lombard et al. 2007, SCCCAMLR XXVIII/14)
New Zealand is eager to collaborate with other CCAMLR Members and AT Consultative
Parties with an interest in the conservation and sustainable management of the Ross Sea
region as we proceed with spatial management planning. It is hoped that this document will
stimulate dialog among interested Members to progress that goal.
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Table 1. Environmental data layers compiled or prepared for use by the Ross Sea Region
Bioregionalisation workshop. Layers cover the area 150°E–150°W, and 60°S–80°S
(excluding areas under permanent ice) on a 4 km grid.
Variable
Layer name
Description
Bathymetric data layers
Average depth, from GEBCO (2003)
Gradient over bathymetric grid
Difference between bathymetry and smoothed 2 ox2o
bathymetric grids
Upper ocean chlorophyll
Mean annual chl-a concentration, 1997-2007
Maximum chl-a concentration (+ standard deviation)
Depth
Slope
Bathymetric anomaly
bathy
bathyslope
bathyanom
Chlorophyll-a
Maximum
chlorophyll-a
chlamean
chlamax,
chlamaxsd
Insolation (photosynthetially active radiation)
Solar radiation, neglecting the effect of ice and cloud
cover (i.e. symmetrical with latitude)
insolation
Sea surface temperature
sst
Mean annual sea surface temperature, 1991-2006
sstsum
Mean annual sea surface temperature, Decembersstsumsd
February, 1991-2006 (+ standard deviation)
Satellite-derived indicators of ocean frontal features
sstgrad
Gradient of mean annual SST (proxy for SSH)
Dynamic sea ice behaviour
ice15mean,
Mean number of ice-free (<15% ice cover) days per year,
ice15sd
1980-2007 (+ standard deviation)
ice_jan, ice_feb, Average daily ice concentration by month, 1980-2007
etc
Average proportion of days per month (December and
ice_pack_jan,
January) with daily ice concentrations of 40-70%
ice_pack_dec
ice_marginal_jan, Average proportion of days per month (December and
ice_marginal_dec
January) with daily ice concentrations of 15-40%
icechange_novAverage change in monthly ice concentrations from
dec
previous month (Nov-Dec and Dec-Jan)
icechange_dec-jan
Dissolved nutrients
N200,
Ph200, Dissolved nutrient concentrations – Nitrate, Phosphate,
Si200
and Silicate -- at 200 m depth
Ocean alkalinity and CO2
alk_surface,
Modelled ocean alkalinity at the surface, at the ocean
alk_bottom,
floor, and at user-defined intermediate depths (in
alk_xxxx
meters)
tCO2_surface,
Modelled CO2 concentration at the surface, at the ocean
tCO2_bottom,
floor, and at user-defined intermediate depths (in
tCO2_xxxx
meters)
palk_surface,
Potential alkalinity (i.e. corrected for mixing and
palk_bottom
decomposition) at the surface, at the ocean floor, and
palk_xxxx
at user-defined intermediate depths
Circulation models and water column structure
speed_surface,
Ocean current speed -- at the surface, at the ocean floor,
speed_bottom,
and at user-defined intermediate depths (in meters)
speed_xxxx
Photosynthetically
active
radiation
(PAR)
SST
Summer SST
SST gradient
Annual ice duration
Ice cover (by month)
Summer
unconsolidated pack
ice (by month)
Summer marginal ice
(by month)
Summer ice retreat
Nutrients (Ni, Ph, Si)
Alkalinity
Total
concentration
CO2
Potential alkalinity
Current speed
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Temperature
TWater temperature -- at the surface, at the ocean floor, and
HIGEM_surface,
at user-defined intermediate depths (in meters)
THIGEM_bottom,
T-HIGEM_xxxx
Salinity
SWater salinity -- at the surface, at the ocean floor, and at
HIGEM_surface,
user-defined intermediate depths (in meters)
S-HIGEM_bottom,
S-HIGEM_xxxx
Proximity variables approximating biogeographic and behavioural effects
Land proximity
landprox
Distance to nearest land
Shelf break proximity shelfbreak
Distance to continental shelf break (1000 m depth contour)
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Table 2. Benthic bioregionalisation -- group descriptions and references.
Group
1) Deep
blue)
2)
3)
4)
5)
6)
7)
8)
9)
10)
11)
12)
13)
14)
15)
16)
17)
Geographic location and description
(dark Most of shelf, except western coast, esp mid to east, a little bit of the north
eastern offshore area round from Adare;
Deepest shelf, includes troughs.
Rough lower slope, Follows slope, gaps in middle
very seldom ice free
(orangey yellow)
Shallow, high scour North-west Adare coast (Coulman Is north and round corner to ?), plus midshelf (green)
shelf banks and a small bit north of Drygalski
Smooth lower slope, Lower northern slope edge
seldom ice free (pink)
Ice covered coast s-w coast and shelf banks, far east shelf, north west coast
(pale blue)
Smooth shelf edge Small patches, thin strip on shelf break, hole on north western edge
(purple-blue)
Shallow ice covered Eastern and north-western shelf edges
shelf edge (orange)
Rough, warm, ice Tiny area on far eastern shelf
covered eastern slope
(lime yellow)
Northern
abyss Northern region;
(darker pale blue)
pretty much everywhere
Islands/seamounts/ P- Northern region;
A ridge, deep (red)
surrounding islands, seamounts and Pacific Antarctic ridge
Ice
influenced Northern region;
islands/seamounts
south of ACC; Around flanks of Balleny & Scott islands, Admiralty sea
(yellow)
mount and southern PA ridge;
Northern
ice Northern Balleny Islands, tiny spot just north of Scott Island; very small
influenced
islands portion of overall cluster (0.036%)
(pastel green)
(white)
Northern region; Two tiny spots north-west and east of Ballenys; very small
portion of overall cluster (0.021%)
(grey)
Northern region;
Tops of islands and seamounts, plus a couple of spots north of ACC; very
small portion of overall cluster (0.027%)
(purple)
Northern region; microscopic portion of overall cluster (0.0016%)
(lime green)
Northern region; P-A ridge
(orange)
Northern region; Two ovals on mid P-A ridge
shelf
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Table 3. Pelagic bioregionalisation -- group descriptions and references.
Group
1) Victoria Land coastal zone
2) Southern [Early] Ross Sea
polynya zone
Physical and biological attributes
Ice persists through January partly due to
convergence; strong stratification; influence of
continent; diatom-dominated
Ice concentration reduces as polynya opens from
south due to divergence and melting of ice. Ice-free
for long period; Phaeocystis dominated; hi
productivity; southern region significant for Adelie
and Emperor penguin breeding and foraging
References
Smith &
1985;
Nelson,
Zwally et al 1985,
Sturman & Anderson
1986,
Smith
&
Gordon, 1997; Arrigo
et
al.,
1999;
Kooyman et al. 2007;
Ballance et al. 2009
Zwally et al 1985,
Arrigo et al., 2004
3) December Ross Sea
polynya zone
Ice free for summer period; moderate productivity;
4) Northeast Ross Sea shelf
warmer water incursion
zone
Warmer water at depth; influence of CDW
Jacobs et al 1985, ,
Orsi & Wiederwohl
2008
5) Southeast Ross Sea late
polynya zone
Opening of polyna usually after December, E.
superba present
6) Colbeck-Sultzberger
dynamic zone
Dynamic zone of divergence in Antarctic Coastal
Current , AASW and some MCDW, late ice melting
and advection of multiyear sea ice from east, possible
molting area
7) Oates Land coastal zone
Persistent convergent ice cover but influenced locally
by katabatic winds
Persistent convergent ice cover influenced by
grounded icebergs and ice tongues in west
APIS study; Ackley
et al. 2003, Zwally et
al 1985
Zwally et al 1985,
Sturman & Anderson
1986, Assmann et al
2005,
Orsi
&
Wiederwohl
2008,
Comiso et al., 1993
Zwally et al 1985,
8) Hudson Bank and shelf
extension
9) Ross Sea continental slope
Normally relatively ice-free from January, high adult
toothfish abundance; vigorous shelf flow and front
interactions, feeding area
10) Oates-George V Land icecovered continental slope
Persistent convergent ice cover
11) Balleny Islands and
associated seamounts
Steep topography disrupting strong ocean currents,
with variable effects on ice regime; transient
polynyas
12) Persistent pack ice zone
Older ice including advected multiyear ice
contribution; productivity extremely variable among
years. Significant area for ice-dependent species
during late summer.
13) Ross Sea near-slope zone
Location immediately north of shelf front and
southeast of Iselin Bank; ice retreat during late
December on average, E. superba present; historical
foraging zone for great whales (Blue whale)
Area of large amounts of marginal ice (bordering
pack ice zone #13) and high productivity, foraging
14) Mid summer ice retreat
zone
15) Early summer marginal ice
Ice retreat during December, subsequent southern
feeding zone of Great Whales
Zwally et al 1985, R
Massom pers comm
Zwally et al 1985,
Ainley & Jacobs,
1981; Hanchet et al.
2009
Zwally et al 1985
Bradford-Greive &
Fenwick 2002; Sharp
2005, and refernces
therein
Zwally et al 1985,
Comiso et al., 1993,
Sturman & Anderson
1986; Ainley et al.
2006
Zwally et al 1985,
Ainley & Jacobs,
1981; Ainley 2009
Zwally et al 1985
Zwally et al 1985,
Comiso et al. 1993,
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16) Pack ice maximum extent
17) Polar Front
18) ACC
Northern pack ice zone (May-November) with
maximum northern extent reached in September
Strong transition zone; multiple fronts; highly
seasonal production; energetic flow
Strong transition zone; multiple fronts; highly
seasonal production; energetic flow; positon of
maximum sea ice extent in relation to ACC
significant for Adelie penguin sub-adult survival
Laws 1977
Zwally et al 1985,
Comiso et al. 1993 ;
Ainley & Jacobs,
1981
Orsi et al 1995;
Wilson et al. 2001
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Table 4. Areas containing functionally important ecosystem processes or habitats.
Area number
Physical and biological attributes
Pelagic environment processes/ areas
References
1. shelf front intersection
with seasonal ice edge
Ross Sea Shelf Front in combination with
marginal ice forms a zone of elevated
productivity targeted by top predators including
seabirds, pinnipeds (Crab-eater seals) and
cetaceans (Minke and Orca Type-C) as well as
adult toothfish.
2. early summer Marginal
Ice Zone/ polynya edge
3. Balleny Islands and
proximity
High primary and secondary productivity; top
predator foraging
Islands disrupt circumpolar currents; with altered
ice regime and short-lived polynyas; top
predator foraging; land-based colonies; Antarctic
krill; humpback whale migration destination
Focus area for seabird foraging; transition zone
between distinct distributions
Siegel & Loeb
1995; Loeb et al.
1997;
Gilbert & Erickson
1977; Ainley et al.
1984; Ichii et al.
1998; Karnovsky et
al. 2007; Ainley in
press
Ainley et al. 2006;
Ainley in press
Bradford-Grieve &
Fenwick
2002;
Sharp 2005 and
references therein
Ainley & Jacobs
1985;
Weimerskirch
&
Cherel. 1998.
Ainley et al. 2006;
4. Polar Front
5. multi-year ice zone,
eastern Ross Sea
6. northwest Ross Sea
Antarctic krill
aggregation
Moulting habitat area for Adelie and emperor
penguins; crabeater seal habitat
Focus area for krill predator foraging, especially
at overlap with ice edge/ continental shelf
7. coastal polynyas
Primary and secondary productivity; foraging
areas for land-based predator colonies
8. increased primary
production in southern
shelf polynya
9. crystal krill
10. Pleuragramma
11. penguin colonies and
foraging distributions
12. Weddell seal colonies
and foraging
distribution
Important conduit for energy transfer to higher
trophic levels on the shelf; Western Ross Sea
shelf; precise location poorly sampled
Important conduit for energy transfer to higher
trophic levels on the shelf; location undefined –
whole of Ross Sea shelf?
Predator foraging areas
Foraging areas during breeding season
Foraging distributions
Sala et al. 2002;
Azzali et al. 2006;
Azzali
&
Kalinowski 2000;
Murase et al. 2008;
Noganubu et al.
2007; Taki et al.
2008
Tremblay & Smith
2007; Jacobs &
Clomiso
1989;
Romanov
1994;
Zwally et al. 2002;
Jacobs and Giulivi
1998
Arrigo
&
van
Djiken 2004; Smith
& Comiso, 2008.
Murase et al. 2008;
O’Driscoll et al.
2009
Murase et al. 2008;
O’Driscoll et al.
2009
Balance et al. 2009;
Lyver et al. in
review
Ainley et al. 2006;
Gilbert & Erickson
1977
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13. type C orca foraging
locations
14. Victoria coast/ ice
tongues and proximity
Fish predators; possible top down control;
possible trophic overlap with fishery; location
undefined
Fish processes/ areas
Platelet ice formation: important habitat for fish
and invertebrate larvae on the underside of ice,
and for other ice-associated fauna
15. Terra Nova Bay
Pleuragramma spawning area
16. Deeper holes in
southern continental
shelf
17. Terra Nova trench
Antarctic toothfish: likely preferred recruitment
habitat for juveniles
18. Ice-free continental
slope
19. northern seamounts
20. Iselin Bank
21. Pacific- Antarctic Ridge
22. Balleny Islands Region
(note multiple clusters
in classification esp. in
shallow water)
23. Admiralty seamount
24. Cape Adare Slope
25. Lower slope Mawson
bank
26. SE Ross Sea slope
27. Southern McMurdo
sound, New Harbour
Antarctic toothfish:
Likely ontogenetic
migration corridor between preferred juvenile
and adult habitats
Antarctic toothfish: preferred adult habitat/
feeding area, for weight gain between spawning
migrations
Antarctic toothfish: spawning location
Rattails: highest concentration of macrourus
spp. – major prey spp for Antarctic toothfish
Benthic processes/ areas
Plate boundaries as areas of composition change,
endemism.
Only significant offshore Islands, many
examples of unique ecology and scientific
interest; known black coral habitat
Ainley in press;
Pitman & Ensor
2003
Lewis & Perkins
1985; Vacchi et al.
unpubl; Cattaneo et
al. unpubl
La Mesa et al.
2004; Vacchi et al.
2004
Hanchet et al. 2009
Hanchet et al. 2008
Hanchet et al. 2009
Fenaughty
2006;
Hanchet et al. 2009
Andrew
Stewart
unpubl.
Bradford-Grieve &
Fenwick 2002. and
references therein
Vulnerable benthic habitats; black corals (VME
taxa)
Thin shelf, steep slope, and slope as an
evolutionary refuge from ice; VME taxa;
Indication of high biodiversity
Barry et al. 2003;
IPY unpubl
Bioregionalisation suggests many distinct
regions here
Existence of deep water type communities but in
relatively shallow water; large hexactnellid
sponges
Bioregionalisation
output
Dayton et al. 1996;
Thrush et al. 2006
41
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Figure 1. Environmental data layers selected and ultimately retained for use in clustering for
the benthic bioregionalisation. All layers were weighted equally.
T-HIGEM_bottom
bathy
rugosity
speed_bottom
ice15mean
ice scour
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Figure 2. Benthic bioregionalisation of the Ross Sea region. This classification was
generated by cluster analysis of the spatial environmental data layers shown in Figure 1, with
modifications as described in the text.
Shelf
Balleny Islands
Far North
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Figure 3. Environmental data layers selected and ultimately retained for use in clustering for
the continental shelf (i.e. depth <800 m) pelagic bioregionalisation.
a) T-HIGEM_200_summer (weight = 0.5)
b) S-HIGEM_200_summer (weight = 0.5)
c) bathy (weight = 1)
d) icechange_nov-dec (weight = 1)
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Figure 4. Environmental data layers selected and ultimately retained for use in clustering for
the off-shelf (i.e. depth > 800 m) pelagic classification
a) T-HIGEM_200_summer (weight = 0.5)
b) S-HIGEM_200_summer (weight = 0.5)
c) bathy (weight = 1)
d) icechange_nov-dec (weight = 0.5)
e) ice_nov (weight = 0.5)
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Figure 5. Pelagic bioregionalisation of the Ross Sea region. This classification was generated
by cluster analysis of the spatial environmental data layers shown in Figures 3 and 4, with
modifications as described in the text. The final classification below combines the outputs of
separate classifications for the continental shelf and non-shelf areas, joined at the 800 m
contour.
Shel
f
Slop
e
- Balleny Islands
Off-shelf/
deep
- ACC
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Figure 6: The Ross Sea in early December showing sea-ice cover and location of polynyas,
overlain (left panel) by the distribution of marine birds (about 9 million individuals) and
(right panel) cetaceans (about 14 thousand minke whales and orcas). The pattern for Weddell
seals is similar. From Ainley et al. (2006).
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Figure 7: Adelie penguin colony locations and abundance (breeding pairs) and approximated
foraging utilisation distributions for December (pink shading) and January (light blue
shading) in the Ross Sea region. Data from Phil Lyver (unpubl.)
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Figure 8: Land-based top predator colony/ haul-out locations, approximate populations, and
available tracking data for colonies on the Victoria Coast. About 3 million penguins and 57
thousand Weddell seals breed along the Victoria Land coast, the highest concentrations of
these species anywhere in the high latitude Antarctic. From Ainley et al. (2006).
a) Adelie pengins
b) Emperor penguins
c) Weddell seals
54
Appendix 1: Expert participant list for the Ross Sea Region Bioregionalisation and Spatial Ecosystem
Processes Expert Workshop.
Expert name
Affiliation
Expertise
Neil Gilbert
Antarctica New Zealand, NZ
(chair)
Ben Sharp
Ministry of Fisheries, NZ
(technical co-chair)
Susie Grant
British Antarctic Survey, UK
Walker Smith
Virginia Institute of Marine Science, USA
National
Oceanic
and
Atmospheric
Administration, USA
(technical co-chair)
pelagic
ecology;
ecosystem function
Susanne Lockhart
Sharon Stammerjohn
trophic
ice dynamics, pelagic ecology
benthic ecology
Art DeVries
University of California, Santa Cruz, USA
University of Illinois, Urbana Champaign,
USA
Barbara Breen
Auckland University of Technology, NZ
GIS, MPA network design
Matt Pinkerton
NIWA Research, NZ
ecosystem modeling
Steve Parker
NIWA Research, NZ
benthic ecology
Stuart Hanchet
NIWA Research, NZ
Alastair Dunn
NIWA Research, NZ
Antarctic fisheries, fish ecology
Antarctic fisheries, toothfish
modeling
Mike Williams
NIWA Research, NZ
physical oceanography
Richard O'Driscoll
NIWA Research, NZ
pelagic ecology
Vonda Cummings
NIWA Research, NZ
benthic ecology
Peter Wilson
Landcare Research, NZ (retired)
top predator ecology
Phil Lyver
Landcare Research, NZ
top predator ecology
Harry Keys
Department of Conservation, NZ
top predator ecology
Andrew Stewart
Museum of New Zealand, Te Papa, NZ
fish biology, taxonomy
Barry Weeber
ASOC/ ECO of Aotearoa, NZ
top predator ecology
John Bennett
Sanford Ltd, NZ
Antarctic fisheries
fish biology, taxonomy