Effects of Late-Cenozoic Glaciation on Habitat
Availability in Antarctic Benthic Shrimps (Crustacea:
Johannes Dambach1*, Sven Thatje2, Dennis Ro ¨dder1, Zeenatul Basher3, Michael J. Raupach4
1Zoologisches Forschungsmuseum Alexander Koenig, Bonn, Germany, 2Ocean and Earth Science, National Oceanography Centre, University of Southampton,
Southampton, United Kingdom, 3Leigh Marine Laboratory, University of Auckland, Auckland, New Zealand, 4Deutsches Zentrum fu ¨r Marine Biodiversita ¨tsforschung,
Senckenberg am Meer, Wilhelmshaven, Germany
Marine invertebrates inhabiting the high Antarctic continental shelves are challenged by disturbance of the seafloor by
grounded ice, low but stable water temperatures and variable food availability in response to seasonal sea-ice cover.
Though a high diversity of life has successfully adapted to such conditions, it is generally agreed that during the Last Glacial
Maximum (LGM) the large-scale cover of the Southern Ocean by multi-annual sea ice and the advance of the continental ice
sheets across the shelf faced life with conditions, exceeding those seen today by an order of magnitude. Conditions
prevailing at the LGM may have therefore acted as a bottleneck event to both the ecology as well as genetic diversity of
today’s fauna. Here, we use for the first time specific Species Distribution Models (SDMs) for marine arthropods of the
Southern Ocean to assess effects of habitat contraction during the LGM on the three most common benthic caridean
shrimp species that exhibit a strong depth zonation on the Antarctic continental shelf. While the shallow-water species
Chorismus antarcticus and Notocrangon antarcticus were limited to a drastically reduced habitat during the LGM, the deep-
water shrimp Nematocarcinus lanceopes found refuge in the Southern Ocean deep sea. The modeling results are in
accordance with genetic diversity patterns available for C. antarcticus and N. lanceopes and support the hypothesis that
habitat contraction at the LGM resulted in a loss of genetic diversity in shallow water benthos.
Citation: Dambach J, Thatje S, Ro ¨dder D, Basher Z, Raupach MJ (2012) Effects of Late-Cenozoic Glaciation on Habitat Availability in Antarctic Benthic Shrimps
(Crustacea: Decapoda: Caridea). PLoS ONE 7(9): e46283. doi:10.1371/journal.pone.0046283
Editor: Christopher Fulton, The Australian National University, Australia
Received June 14, 2012; Accepted August 29, 2012; Published September 27, 2012
Copyright: ? 2012 Dambach et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was funded by the German Research Foundation (DFG, RA-1688-2). The Ross Sea specimens data collected by the cruise TAN0802 (IPY-CAML
Voyage), made available through the New Zealand International Polar Year-Census of Antarctic Marine Life Project (Phase 1: So001IPY; Phase 2: IPY2007-01) was
funded by the New Zealand Government. The authors gratefully acknowledge project governance by the Ministry of Fisheries Science Team and the Ocean
Survey 20/20 CAML Advisory Group. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: firstname.lastname@example.org
With at least 350 different genera and more than 2,800
described species, caridean shrimps (Crustacea: Decapoda) repre-
sent a group of primarily marine crustaceans with a high degree of
diversity in body form and occupied habitats . Caridean
shrimps are ecologically important in near shore habitats from
tropical to high latitudes and have successfully colonized all
marine habitats from shallow waters to abyssal plains and
hydrothermal vents [1,2]. In addition to the marine species, about
650 species have also successfully invaded brackish and freshwater
habitats, particularly highly diverse in tropical and subtropical
Interestingly, only about a dozen caridean shrimp species are
known from the Southern Ocean [4–7], with only three shrimp
species left on the high-Antarctic continental shelves, where
temperatures are below zero all year round (for review see ).
Although they are low in species number, in terms of abundance
these three shrimp species represent a major component of the
mobile benthic fauna on the continental shelf [8–10]. Chorismus
antarcticus Pfeffer, 1887  (Hippolytidae) and Notocrangon
antarcticus Pfeffer, 1887  (Crangonidae) are the most abundant
shelf inhabiting Antarctic shrimps [5,10] and distributed around
the Antarctic continent [9,10,12]. Abundance values confirm a
preference for depths #400 m by Chorismus antarcticus (up to four
specimens per m2) and 200–600 m by Notocrangon antarcticus (up to
three specimens per m2) . Chorismus antarcticus may occasionally
be found in the Magellan region, but Notocrangon antarcticus has
been recorded north of the Antarctic convergence only once .
While both of these species represent typical and abundant
Antarctic shelf or slope species, the deep-sea shrimp Nematocarcinus
lanceopes Bate, 1888  is known from the deep sea around
Antarctica to approximately 4,000 m water depth, sub-Antarctic
islands as well as other adjacent deep-sea basins off Chile and
South Africa [5,14–20]. As a part of extensive studies of the
benthic fauna of the Weddell Sea, up to nine specimens per m2
were recorded between 500 and 1200 m depth, revealing a broad
bathymetric distribution and high densities of specimens on the
Antarctic shelf [9,10]. Nevertheless, beside fragmented informa-
tion of their biogeographic distribution we have only poor
knowledge of the biology of Antarctic Caridea. So far, most
studies analysed aspects of reproductive biology and larval
development [12,21–26], biochemical or metabolic characteristics
[27–34], the digestive system , as well as their infestation by
PLOS ONE | www.plosone.org1September 2012 | Volume 7 | Issue 9 | e46283
ectoparasites . A first pioneering phylogeographic study
analysing various populations of Chorismus antarcticus and Nemato-
carcinus lanceopes gave evidence for a postglacial expansion of the
shelf-inhabiting species Chorismus antarcticus , though a few
potential refugial areas may have remained on the shelf [38,39]. In
contrast, populations of the deep-water shrimp Nematocarcinus
lanceopes were less affected in their genetic diversity, supporting a
scenario that recent and recurrent glaciations of the continental
shelf are very likely to have affected benthic shallow-water shelf
species generally far more than pelagic species or primarily deep-
sea distributed species .
In order to understand the fragmented information of
biogeography and spatial distribution of these three shrimp
species, we developed Species Distribution Models (SDMs) based
on a most comprehensive set of species records and current
environmental conditions. SDMs are based on the theoretical
concept that every species occupies a characteristic fundamental
niche, wherein it’s realized distribution is commonly restricted by
biotic interactions and dispersal limitations . Climatic condi-
tions have a major impact on continental scales , as they affect
not only the species directly but also its biotic environment 
(see BIOCLIM); . The coherency between observation of
species ecological properities and their distribution is known in the
terrestrial and aquatic environment [45,46] and recent develop-
ment of new algorithms enabled to assess the coherences between
environmental conditions and species distribution patterns [42,47–
During the last few years, SDMs have been successfully applied
in the terrestrial environment [47,48,51] and recently also used in
studying distribution of marine species [52–57]. Possible applica-
tions comprise e.g. studies of likely future climate change effects on
global fish biodiversity [52,53], distribution of whales in the
mediterranean  and Antarctic waters  or assessment of
possible glacial refugia and population fragmentation of the
Atlantic cod .
Herein, we use SDMs to assess the potential distributions of
three Antarctic shrimps for a current and a last glacial maximum
(LGM) scenario around the Antarctic continent for the first time.
This approach allows us to examine their current potential
distribution patterns and gain information about possible glacial
refugia during times with unfavorable conditions on the Antarctic
Materials and Methods
Species records and environmental data
Species data points were compiled through various sources, e.g.
the Global Biodiversity Information Facility (GBIF, www.gbif.org),
Ocean Biogeographic Information System (OBIS, www.iobis.org),
SCAR-MarBIN (http://www.scarmarbin.be), and a comprehen-
sive literature review as well as Antarctic Expedition cruise reports
(Supp.Tab1.). All data were checked for redundancy or errors, e.g.
erroneous GPS coordinates. Species records were located all
around Antarctica with regard to different sampling effort of the
expeditions in some regions. Therefore, our final data sets
comprised of 93 records for N. lanceopes, 100 for C. antarcticus and
151 for N. antarcticus.
Marine Environmental data with a spatial resolution of 5
arcmin were obtained from Bio-ORACLE (www.oracle.ugent.be)
and interpolated from AquaMaps (http://www.aquamaps.org/
download/main.php). Ocean depth information was obtained
from ETOPO1 (www.ngdc.noaa.gov) and re-sampled to the same
resolution of 5 arcmin using ESRI ArcGIS 10.0 To develop paleo-
climatic scenarios we obtained respective environmental informa-
tion from Glacial Ocean Atlas , which was also re-sampled to
the same resolution. Glacial ocean bottom temperature based on
the findings of core analyses  (http://pmip2.lsce.ipsl.fr/).
We tested the inter-correlation structure among all predictor
variables as high inter-correlations may negatively affect SDM
performance and its transferability through space and time
[63,64]. Herein, we chose five environmental variables with
R2,0.75 based on pair-wise correlation analyses using squared
Pearson’s correlation. Variables used in our models were sea ice
coverage (icecov), depth (depth), annual mean sea surface
temperature (SSTmean), annual mean salinity (salinity), and
annual mean bottom temperature (sbt). All of them were suggested
to be putatively suitable for large-scale species distribution models
and hind casting projections [54,60]. Environmental profiles were
generated in R  with the sm.density.compare function from
the sm package .
Species distribution models
SDMs based on the species records and the five environmental
variables were computed for the three species using Maxent
version 3.3.3e applying the default settings [49,67,68]. Maxent per
default requires random background data points, which are ideally
situated in potentially colonizable areas for the target species [69–
71]. In this context, the selection of appropriate background data
represents an important step in model building and can affect the
SDM performance [69,70,72]. Here, we included as background a
smoothed buffer of 1000 km around species records plus adjacent
areas, which are likely to be reached by ocean currents due to the
fact that the exact range of all analyzed species is unknown.
Although a restriction of the environmental space used for model
training is pivotal for a good discrimination ability of the SDM,
projections beyond the training range in space or time may be
associated with an increased uncertainty. Therefore, we quantified
the spatial distribution of non-analogous environmental conditions
via multivariate environmental similarity surfaces (MESS, ).
MESS maps were computed for current and paleo scenarios,
which highlight those areas where at least one predictor exceeds
the conditions available within the training range of the SDM.
For model testing, we randomly omitted 25% of the species
records from model training and performed 100 Bootstrap
replicates. As a test for predictive performance of the SDMs,
Maxent automatically calculates two different versions of the so-
called ‘Area Under the receiver operation characteristic Curve’
(AUC). Generally, AUC scores represent the ability of the model
to distinguish presence data from background and range from 0.5
(random distribution, model without predictive ability) to 1.0
(model gives perfect predictions) [74,75]. Test AUC scores
quantify the model’s ability to capture the randomly omitted
records. In this study we used a logistic Maxent output format
giving a continuous range from 0 (unsuitable environmental
conditions) to 1 (optimal conditions) , and a minimum training
presence logistic threshold as a non-fixed threshold as proposed by
Liu et al. .
Environmental profiles in Fig. 1 illustrate the tolerances of the
species in different environmental dimensions. Here, the most
apparent differences between N. lanceopes and the other species are
the lower tolerance for annual sea ice coverage and bottom
temperature as well as a strong preference for deeper waters.
Glacial Habitat Availability in Antarctic Shrimp
PLOS ONE | www.plosone.org2 September 2012 | Volume 7 | Issue 9 | e46283
Performance of SDMs and current potential distribution
Our SDMs received excellent AUC values for all three species.
Mean test AUC for 100 computed SDMs was 0.96 for
Nematocarcinus lanceopes. For this species, ‘depth’ had the highest
explanative power (42.8%), followed by ‘icecov’ (42.1%), ‘sbt’
(8.0%) and ‘SSTmean’ (6.1%), while salinity had a relatively low
contribution value (1.0%). Average minimum training presence is
0.02 and 10 percentile training presence is 0.13. According to our
SDM, the current potential distribution of Nematocarcinus lanceopes
comprises the shelf areas and slopes of Antarctica with the
Antarctic Peninsula, South Georgia ridge, South Orkney and
South Sandwich Islands, the Kerguelen Plateau, the Pacific-
Antarctic Ridge, the western Ross Sea near Balleny islands as well
as parts of the Chilean west coast (see Fig. 2 A).
The SDMs computed for Chorismus antarcticus had a mean test
AUC of 0.98. Here, average ‘icecov’ had the highest explanative
power (51.4%), followed by ‘depth’ (42.4%), ‘sbt’ (3.7%), ‘salinity’
(1.4%) and ‘SSTmean’ (0.9%). Thresholds (minimum training and
10 percentile training) were 0.10 and 0.29. The current potential
distribution of Chorismus antarcticus comprises the lower shelf areas
of Antarctica, the Scotia Arc and South Georgia, the shelf areas of
sub-Antarctic islands, Ross Sea shelf and lower parts of the
Kerguelen Plateau (see Fig. 2 C).
Finally, SDMs computed for Notocrangon antarcticus had a test
AUC of 0.98. The variable with highest explanative power was
‘depth’ (66.6%), followed by ‘icecov’ (21.8%), ‘salinity’ (7.6%), ‘sbt’
(2.5%) and ‘SSTmean’ (1.8%). Thresholds (minimum training and
10 percentile training) were 0.04 and 0.38. The SDM for
Notocrangon antarcticus showed a potential distribution similar to
Chorismus antarcticus but with a little shift to the deeper shelf areas
(see Fig. 2 B).
Projections for a Last Glacial Maximum scenario
Our SDM projections for the Last Glacial scenario (21 ky BP)
suggest a partial shift of the potential distributions to lower
latitudes for all three analyzed species. In Fig. 2 D–F, unsuitable
shelf areas covered by grounded ice  are blue shaded.
The LGM projection for Chorismus antarcticus indicate suitable
areas in those parts of the Antarctic shelf which were probably not
completely covered by ground ice (Anderson et al. 2002). Further
Figure 1. Environmental profiles. Environmental conditions at sample localities for C. antarcticus, N. lanceopes and N. antarcticus.
Glacial Habitat Availability in Antarctic Shrimp
PLOS ONE | www.plosone.org3 September 2012 | Volume 7 | Issue 9 | e46283
Glacial Habitat Availability in Antarctic Shrimp
PLOS ONE | www.plosone.org
September 2012 | Volume 7 | Issue 9 | e46283
areas with high suitability were located around South Georgia and
the sub-Antarctic islands as well as small patches on the tip of
South America (Fig. 2 F). The projection of the potential
distribution of Notocrangon antarcticus suggested suitable areas
around South Georgia, the South Sandwich Islands, Falkland
Islands and the southern tip of South America as well as parts of
the Kerguelen Islands (Fig. 2 E). In contrast to both shallow-water
species, our projection for the deep-sea shrimp Nematocarcinus
lanceopes gave evidence for a lower suitability on the Antarctic shelf
but also revealed areas with higher suitability on a circle alongside
the area of the LGM ice extent, connecting the sub-Antarctic
islands as well as ocean ridges and plateaus between the 59th and
45th latitude. Here, areas downward to depths of 4000 meters
around South Georgia and Bouvet Island, northern parts of the
Kerguelen Plateau, the Tasmania and Campbell Plateau were
indicated as environmentally suitable areas during the LGM (Fig. 2
D). For times of the LGM the Weddell Sea exhibits non-analogous
environmental conditions exceeding those of the present training
range of C. antarcticus and N. antarcticus. Here, salinity was identified
as the most dissimilar variable.
A closer look on the current habitat suitability in the Weddell
Sea and Antarctic Peninsula between 84u west and 3u east is
provided in Fig. 3. Here, the early summer near-surface currents
were indicated to assess the direction and accessibility of larval
drifted distribution by currents when spawned in these areas
[78,79]. Currently known occurrences and suggested habitats for
N. antarcticus and C. antarcticus were located south off the Polar
Front (except samples of C. antarcticus from Prince Edward Island).
For N. lanceopes, model suggestion and sample localities were also
found north of the Polar front from ‘‘Tierra del Fuego’’ and the
western Chilean coast. Nevertheless, the habitat suitability is much
This study is the first approach to model the biogeographic
distribution patterns of benthic shallow-water and deep-sea
arthropods in the Southern Ocean covering their current
distribution and a hind casting projection. Although first
molecular studies already provided clear evidence of homogenous
genetic identity in circum-Antarctic distribution for both N.
lanceopes and C. antarcticus , a detailed assessment of their
distribution patterns was not given. Our SDMs complete the so far
only fragmented information about the potential distributions of N.
antarcticus, C. antarcticus and N. lanceopes around the Antarctic
continent. Modeling projections for the LGM give evidence for a
population reduction affecting genetic diversity in shallow water
shrimp species (c.f. ), and a northward shift but less restricted
range for the deep-sea shrimp species.
Models and data
Species records of all three shrimp species were included,
comprising various regions on the Antarctic shelf, sub-Antarctic
islands and also on southern parts of South America. Our models
are based on an adequate number of species records and display
the complete width of the environmental range of the species
across the currently realized distribution on a broad scale.
However, some areas in the Antarctic Ocean, the Amundsen
Sea or eastern Ross Sea are overall not well explored in terms for
decapods and benthic communities. Therefore, the information on
suitable habitat for Antarctic shrimps provided here may serve as a
useful baseline for future studies of those regions.
The choice of reasonable parameters for a SDM approach is
crucial and depends on the general question of the study, the
examined taxa and the availability of parameters for different
projections in time and their spatial extent [47,48,54,63,80,81]. In
our study we used a set of parameters that were suggested to be
suitable for large-scale geographic models and available for a
current and a paleoclimatic scenario. In this context, bathymetry
plays an important role in directly or indirectly affecting the
environmental conditions for marine organisms, such as pressure,
availability of primary production, temperature, and others
[82,83]. Beside bathymetry, sea ice coverage and sea surface
temperature are an important predictor and influence the primary
production and therewith the food availability for all pelagic and
benthic communities in the deeper water zones [55,84,85]. For N.
lanceopes, the mean annual sea-ice coverage is the most important
predictor. Salinity demonstrated a relatively low explanative
power as a predictive variable. However, for N. antarcticus it
seemed to have a higher contribution (7.6%) than for N. lanceopes or
C. antarcticus (1–1.4%). N. antarcticus had a higher tolerance for
salinity (see Fig. 1).
Identified areas of non-analogous environmental conditions for
C. antarcticus and N. antarcticus are likely to base on the higher
salinity in LGM environmental data for the Weddell Sea.
However, this topic is still under debate and there are different
scenarios and anomaly models for the LGM salinity of the
Weddell Sea .
For N. lanceopes our modeling suggests highly suitable areas on
the Antarctic slopes and around the sub-Antarctic islands.
Although areas with highest suitability were suggested between
1500 and 3000 m, areas with a lower suitability score were found
downward to 4500 meters. We found similar patterns for C.
antarcticus and N. antarcticus, with various suitable areas on the
Antarctic shelf connected by small corridors and around the sub-
Antarctic islands. This pattern of closely linked suitable areas is
concordant with the comprehensive molecular data that revealed
genetic homogeneity based on mtDNA and no evidence for a
geographical substructure around Antarctica and the sub-Antarc-
tic islands for N. lanceopes as well as C. antarcticus .
Potential refuges during the Last Glacial Maximum
Sea ice is an important factor affecting the distribution of
numerous marine species in Antarctica. Extensive sea ice coverage
reduces the photosynthetically driven primary production  and
therewith the survival probability of planktotrophic larvae,
although sea ice coverage does not necessarily preclude all life
under the ice. For example, larvae of the Antarctic krill (Euphausia
superba) for example are known to feed on sea ice algae under and
on the edge of sea ice  and ice drilling on the Shelf 100 km
from the coastline revealed a so far unexpected benthic suspension
feeder community ; for discussion see also .
However, in times characterized by extreme climatic conditions
like the LGM, a thick multiannual sea ice layer and additional
snow cover throughout the year was likely to restrict benthic life in
higher latitudes or at least force it to retreat to a few areas with
Figure 2. Present and paleo potential distribution maps. The potential distribution of the Antarctic decapod shrimps N. lanceopes, N.
antarcticus and C. antarcticus computed with Maxent 3.3.3e derived from current conditions (A–C) and projected onto a Last Glacial Maximum
scenario (D–F). Habitat suitability ranges from low (blue) to high (red). Also shown are the summer and winter sea-ice extent and the Polar Front.
Shaded areas (MESS) indicate climate conditions out of the species range.
Glacial Habitat Availability in Antarctic Shrimp
PLOS ONE | www.plosone.org5 September 2012 | Volume 7 | Issue 9 | e46283
Glacial Habitat Availability in Antarctic Shrimp
PLOS ONE | www.plosone.org6September 2012 | Volume 7 | Issue 9 | e46283
favorable conditions like we know from present day coastal or
open-ocean polynyas [39,44,84]. It has also been suggested that
some possible open ocean polynyas could have nourished marine
organisms in regions with multiannual sea ice coverage, acting as
‘‘glacial refugia’’ for shelf-inhabiting communities . Regarding
the potential distribution of C. antarcticus in the LGM, our models
suggested the presence of refugial areas around the southern tip of
South America, South Georgia and the Kerguelen plateau. It
should be noted however, that both C. antarcticus as well as N.
lanceopes may have faced ecological competition with congeners e.g.
in the South Atlantic, C. tuberculatus and N. longirostris, respectively.
Areas on the Antarctic shelf, which are suggested to be suitable for
C. antarcticus during the LGM, should be regarded with caution
because effects of scouring icebergs or lack of food due to extreme
distances to the sea ice front were not considered in the models yet.
Furthermore, large parts of the shelf habitats that are currently
inhabited by C. antarcticus and N. antarcticus were occupied by
grounding ice masses at the LGM [38,77,89]. Evidence for a
survival of species on the shelf during the LGM has also been
suggested by molecular genetic data on benthic direct-developing
invertebrates [90,91]. While the pelagic larvae of decapods have a
higher motility than the offspring from brooding species and can
be easily distributed by ocean currents, a scenario of a relatively
fast re-colonization of ice freed shelf areas during interglacial
periods from a few refugial areas seems more plausible .
Evidence from molecular data also indicates a late Pleistocene
bottleneck and a recent population expansion for C. antarcticus
In contrast to the more restricted habitat of the two shelf species
the predicted LGM habitat of N. lanceopes reaches down to the
abyssal plains of the Southern Ocean on a circle alongside the ice
margins. Though low in suitability, this habitat distribution pattern
along the ice margin may have allowed feeding and successful
development of pelagic larvae during the LGM . Here, a
higher primary productivity and upwelling processes could have
provided nutrient-rich waters, supporting feeding and reproduc-
tion, and advection processes may have supported biological
activity in parts of the adjacent multi-annual sea-ice zone (for
discussion see [39,88]. Furthermore, these advection processes
may have reached beyond the ice margins and may have enabled
suitable conditions. However, the precise LGM sea-ice extent is
unknown and subject to discussion, and various scenarios based on
different core analyses do exist. In this context, various data
indicate a lower LGM summer sea-ice extent around eastern
Antarctica. . If the aforementioned areas around the Antarctic
Peninsula and sub-Antarctic islands were the main refugial areas
for C. antarcticus and N. antarcticus, we would expect a higher genetic
diversity in these areas (e.g. in terms of haplotype diversity)
compared to the populations found on the shelf . Contrarily, a
specific pattern of genetically more diverse refugial areas may be
blurred and mixed up again by gene flow when larvae distribution
is fast and extensive. However, the genetic pattern of populations
from suggested refugial areas around South American could not
be tested in the present study due to the lack of suitable preserved
specimens for molecular studies .
Ocean currents play an important role for transporting larvae
from source areas to others and therefore can support a constant
dispersal of larvae even between distant populations . Few
studies showed attempts to calculate larval dispersal of pelagic fish
and invertebrate species [53,95,96]. Dispersal models typically
assume a passive dispersal and diffusion and incorporate the
strength and direction of ocean currents as well as pelagic larval
duration. Although a few studies gained insight in Antarctic
decapod larval biology [7,12], a detailed knowledge of spawned
numbers and distribution areas is still unknown.
In the case of Antarctic krill (Euphausia superba), a recent study
revealed a homogeneous genetic pattern and suggests an active
role of the Antarctic Circumpolar Current (ACC) to disperse and
mix up populations around the Antarctic continent . Shared
mitochondrial haplotypes for N. lanceopes and C. antarcticus in
locations on the Antarctic shelf and several sub-Antarctic islands
also support a scenario of population connectivity and panmixia
driven by ocean currents . Larvae of all three species are
planktotrophic and require food availability over several months
for a successfully complete development [12,38].
For N. lanceopes, there is evidence for a larval development
connected to opening of early summer polynyas where sufficient
food resources are available . Adult females carry relatively big
eggs and larvae are large and advanced at hatching. One
suggestion of a possible transport from larvae hatching in deep
waters to the shallower euphotic zone is by upwelling currents
. Once in the upper water levels, larvae are likely to be
transported with the predominant currents.
Our models suggest connected patches of highly suitable areas
for N. lanceopes (Fig. 3 A) ranging from the tip of the Antarctic
Peninsula and South Shetland Island via the South Orkney Islands
up to the Scotia Arc. Here, predominantly the near surface
currents run along these habitat patches in eastern direction and
are likely to support a transport of larvae from western to eastern
Genetic evidence for long distance dispersal and a ‘‘Sub-
Antarctic islands hopping’’ from west to eastern direction was also
found for the isopod Septemserolis septemcarinata , indicating the
importance of the ACC even for organisms with no pelagic stages.
On the other hand, strong currents such as the ACC in the Drake
Passage can function as an effective boundary between populations
or species and connectivity especially for benthic organisms
without pelagic larvae can be even more reduced when no
suitable corridors are available, e.g. temperature or depth is
unsuitable . Species with genetically distinct clades between
South America and Antarctica for example were found for
ophiuroids , ribbon worms  and bivalves . In the
case of N. lanceopes and C. antarcticus molecular data from South
America are currently missing, but strong ocean currents through
the Drake Passage at times when larvae are spawned may act as a
barrier and restrict direct gene flow between Antarctic and sub-
Antarctic populations compare to those in and South America on
the other side of the ACC. The existence of congeners of both
species in the South Atlantic (C. tuberculatus, N. longirostris) ,
however, may be also a strong hint of restricted gene flow across
Antarctic Expeditions and cruise reports.
Figure 3. Present potential distribution maps. The potential distribution of N. lanceopes, N. antarcticus and C. antarcticus (A–C) computed with
Maxent 3.3.3e derived from current conditions. Display window for the area Weddell Sea and Antarctic Peninsula. Indicated the early summer near-
surface currents [78,79], which are likely to affect the drift of larval stages. Shaded areas (MESS) indicate climate conditions out of the species range.
Glacial Habitat Availability in Antarctic Shrimp
PLOS ONE | www.plosone.org7 September 2012 | Volume 7 | Issue 9 | e46283
Distribution data are based on many sources, and in particular the
following expeditions. We thank Dieter K. Fu ¨tterer for managing the
expeditions ANT-XIX/3-4 (ANDEEP I, II), Wolf E. Arntz for managing
ANT XXI/2 and Eberhard Fahrbach for managing the ANDEEP III
(ANT-XXII/3) expedition. We are grateful to Ricardo Cattaneo-Vietti for
running the 19th expedition of the Programma Nazionale di Ricerche in
Antartide (S.C.r.I.). Beside this we are grateful to the crews of ‘‘Polarstern’’
and ‘‘Italica’’ for their professional help and advice during expeditions.
Thanks are due to Paul Tyler and Alex Rogers for organising and running
JC42 with RRS James Cook. (Ross Sea records collected from RV
Tangaroa, New Zealand IPY-CAML Voyage (Cruise TAN0802, 12Feb–
And finally we thank C. Fulton, A. Beu, J. Engler and two anonymous
Reviewers for helpful comments on the manuscript.
Conceived and designed the experiments: JD. Performed the experiments:
JD DR. Analyzed the data: JD ST MJR. Contributed reagents/materials/
analysis tools: JD DR ST ZB MJR. Wrote the paper: JD DR ST ZB MJR.
1. Bauer RT (2004) Remarkable shrimps: adaptations and natural history of the
carideans: University of Oklahoma Press.
2. Van Dover C (2000) The ecology of deep-sea hydrothermal vents: Princeton
3. Grave S, Cai Y, Anker A (2008) Global diversity of shrimps (Crustacea:
Decapoda: Caridea) in freshwater. Freshwater Animal Diversity Assessment
4. Yaldwyn JC (1965) Antarctic and subantarctic decapod Crustacea. In: van
Mieghem J, van Oye P, eds. Biogeography and ecology in the Antarctic. Junk,
The Hague pp. 324–332.
5. Kirkwood JM (1984) A guide to the Decapoda of the Southern Ocean.
ANARE Research Notes 11: 1–47.
6. Tiefenbacher L (1990) Eualus kinzeri, a new hippolytid shrimp from the Weddell
Sea (Antarctica) (Crustacea: Decapoda: Natantia). Spixiana 13: 117–120.
7. Thatje S, Fuentes V (2003) First record of anomuran and brachyuran larvae
(Crustacea: Decapoda) from Antarctic waters. Polar Biology 26: 279–282.
8. Thatje S, Arntz WE (2004) Antarctic reptant decapods: more than a myth?
Polar Biology 27: 195–201.
9. Arntz W, Gorny M (1991) Shrimp (Decapoda, Natantia) occurrence and
distribution in the eastern Weddell Sea, Antarctica. Polar Biology 11: 169–177.
10. Gutt J, Gorny M, Arntz W (1991) Spatial distribution of Antarctic shrimps
(Crustacea: Decapoda) by underwater photography. Antarct Science 3: 363–
11. Pfeffer G (1886) Die Krebse von Su ¨d-Georgien nach der Ausbeute der
deuschen Stationen, 1882–83. Jahrbu ¨cher der Hamburgischen Wissenschaftli-
chen Anstalten 4: 44–150.
12. Thatje S, Bacardit R, Arntz WE (2005) Larvae of the deep-sea Nematocarci-
nidae (Crustacea: Decapoda: Caridea) from the Southern Ocean. Polar Biology
13. Zarenkov N (1968) Crustacea Decapoda collected in the Antarctic and
Antoboreal regions by the Soviet Antarctic expeditions. Biological Report of
the Soviet Antarctic Expedition (1955–1958) 4: 153–201.
14. Bate CS (1888) Report on the Crustacea Macrura dredged by HMS
Challenger during the years 1873–1876. Reports of the Scientific Results of
the Voyage of HMS Challenger during the years 1873–76 24: 1–942.
15. Barnard KH (1950) Descriptive catalogue of South African decapod Crustacea:
(crabs and shrimps). Annals of the South African Museum 38: 1–824.
16. Ledoyer M (1989) Mysidacea et Caridea (Crustacea) de la Campagne MD 42/
SIBEX du Marion-Dufresne entre les Iles Kerguelen et la Baie Prydz
(Antarctique). Bulletin du Muse ´um d’Histoire Naturelle de Marseilles 49: 67–
70 [in French].
17. Tiefenbacher L (1990) Beitra ¨ge zur Taxonomie von Nematocarcinus longirostris
Bate, 1888 und Nematocarcinus lanceopes Bate, 1888, neu fu ¨r die westliche
Antarktis (Crustacea, Decapoda, Natantia). Spixiana 13: 229–235.
18. Komai T, Takeuchi I, Takeda M. Deep-sea shrimp (Crustacea: Decapoda:
Caridea) from the Antarctic Sea collected during the JARE-35 cruise; 1996.
National Institute of Polar Research. pp. 179–206.
19. Guzma ´n G, Quiroga E (2005) New records of shrimps (Decapoda: Caridea and
Dendrobranchiata in deep waters off Chile. Gayana 69: 285–290.
20. Arntz WE, Thatje S, Linse K, Avila C, Ballesteros M, et al. (2006) Missing link
in the Southern Ocean: sampling the marine benthic fauna of remote Bouvet
Island. Polar Biology 29: 83–96.
21. Gorny M, Arntz W, Clarke A, Gore D (1992) Reproductive biology of caridean
decapods from the Weddell Sea. Polar Biology 12: 111–120.
22. Clarke A (1993) Egg size and egg composition in polar shrimps (Caridea;
Decapoda). Journal of Experimental Marine Biology and Ecology 168: 189–
23. Gorny M, George MR (1997) Oocyte development and gonad production of
Nematocarcinus lanceopes (Decapoda: Caridea) in the eastern Weddell Sea,
Antarctica. Polar Biology 17: 191–198.
24. Mascetti P, de la Reguera RF, Albornoz L, Oyarzu ´n S, Gorny M, et al. (1997)
Gonopore development and sex change in the Antarctic shrimp Chorismus
antarcticus (Caridea: Hippolytidae). Polar Biology 17: 384–388.
25. Lovrich GA, Romero MC, Tapella F, Thatje S (2005) Distribution,
reproductive and energetic conditions of decapod crustaceans along the Scotia
Arc (Southern Ocean). Scientia Marina: 69: 183–193.
26. Lardies MA, Wehrtmann IS (2011) Gonadal development in males of
Notocrangon antarcticus (Decapoda: Caridea) from the Weddell Sea, Antarctica.
Polar Biology 34: 707–713.
27. Clarke A (1977) Seasonal variations in the total lipid content of Chorismus
antarcticus (Pfeffer) (Crustacea: Decapoda) at South Georgia. Journal of
Experimental Marine Biology and Ecology 27: 93–106.
28. Clarke A (1979) On living in cold water: K-strategies in Antarctic benthos.
Marine Biology 55: 111–119.
29. Clarke A (1982) Temperature and embryonic development in polar marine
invertebrates. International Journal of Invertebrate Reproduction 5: 71–82.
30. Maxwell J, Ralph R (1985) Non-cold-adapted metabolism in the decapod
Chorismus antarcticus and other sub-Antarctic marine crustaceans. In: Siegfried
W, PR C, Laws R, eds. Antarctic nutrient cycles and food webs: Springer,
Berlin Heidelberg New York. pp. 397–406.
31. Dittrich B (1990) Temperature dependence of the activities of trypsin-like
proteases in decapod crustaceans from different habitats. Naturwissenschaften
32. Bluhm B, Brey T (2001) Age determination in the Antarctic shrimp Notocrangon
antarcticus (Crustacea: Decapoda), using the autofluorescent pigment lipofuscin.
Marine Biology 138: 247–257.
33. Bluhm BA, Beyer K, Niehoff B (2002) Brain structure and histological features
of lipofuscin in two Antarctic Caridea (Decapoda). Crustaceana 75: 61–76.
34. Wittmann AC, Held C, Po ¨rtner HO, Sartoris FJ (2010) Ion regulatory capacity
and the biogeography of Crustacea at high southern latitudes. Polar Biology 33:
35. Storch V, Bluhm BA, Arntz WE (2001) Microscopic anatomy and
ultrastructure of the digestive system of three Antarctic shrimps (Crustacea:
Decapoda: Caridea). Polar Biology 24: 604–614.
36. Raupach MJ, Thatje S (2006) New records of the rare shrimp parasite
Zonophryxus quinquedens Barnard, 1913 (Crustacea, Isopoda, Dajidae): ecological
and phylogenetic implications. Polar Biology 29: 439–443.
37. Raupach MJ, Thatje S, Dambach J, Rehm P, Misof B, et al. (2010) Genetic
homogeneity and circum-Antarctic distribution of two benthic shrimp species
of the Southern Ocean, Chorismus antarcticus and Nematocarcinus lanceopes. Marine
Biology 157: 1783–1797.
38. Thatje S, Hillenbrand CD, Larter R (2005) On the origin of Antarctic marine
benthic community structure. Trends in Ecology & Evolution 20: 534–540.
39. Thatje S, Hillenbrand CD, Mackensen A, Larter R (2008) Life hung by a
thread: endurance of Antarctic fauna in glacial periods. Ecology 89: 682–692.
40. Janko K, Lecointre G, DeVries A, Couloux A, Cruaud C, et al. (2007) Did
glacial advances during the Pleistocene influence differently the demographic
histories of benthic and pelagic Antarctic shelf fishes? —Inferences from
intraspecific mitochondrial and nuclear DNA sequence diversity. BMC
Evolutionary Biology 7: 220.
41. Hutchinson G (1957) Cold Spring Harbor Symposium on Quantitative
Biology. Concluding Remarks 22: 415–427.
42. Thuiller W, Araujo MB, Lavorel S (2004) Do we need land-cover data to
model species distributions in Europe? Journal of Biogeography 31: 353–361.
43. Busby J (1991) BIOCLIM - a bioclimate analysis and prediction system. Plant
Protection Quarterly 6: 8–9.
44. Fraser CI, Nikula R, Ruzzante DE, Waters JM (2012) Poleward bound:
biological impacts of Southern Hemisphere glaciation. Trends in Ecology &
Evolution 1543: 10.
45. Grinnell J (1917) The niche-relationships of the California Thrasher. The Auk
46. Macpherson E (2002) Large-scale species-richness gradients in the Atlantic
Ocean. Proceedings of the Royal Society of London Series B: Biological
Sciences 269: 1715–1720.
47. Elith J, Leathwick JR (2009) Species distribution models: ecological explanation
and prediction across space and time. Annual Review of Ecology, Evolution
and Systematics 40: 677–697.
48. Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in
ecology. Ecological Modelling 135: 147–186.
49. Phillips SJ, Dudı ´k M, Schapire RE. A maximum entropy approach to species
distribution modeling. Proceedings of the 21st International Conference on
Machine Learning; 2004; Banff, Canada. pp. 83.
50. Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of
species geographic distributions. Ecological modelling 190: 231–259.
Glacial Habitat Availability in Antarctic Shrimp
PLOS ONE | www.plosone.org8September 2012 | Volume 7 | Issue 9 | e46283
51. Guisan A, Thuiller W (2005) Predicting species distribution: offering more than Download full-text
simple habitat models. Ecology Letters 8: 993–1009.
52. Cheung WWL, Lam VWY, Pauly D (2008) Modelling present and climate-
shifted distribution of marine fishes and invertebrates. The Fisheries Centre
Research Report, University of British Columbia 16(3).
53. Cheung WWL, Lam VWY, Sarmiento JL, Kearney K, Watson R, et al. (2009)
Projecting global marine biodiversity impacts under climate change scenarios.
Fish and Fisheries 10: 235–251.
54. Dambach J, Ro ¨dder D (2011) Applications and future challenges in marine
species distribution modeling. Aquatic Conservation: Marine and Freshwater
Ecosystems 21: 92–100.
55. Gutt J, Zurell D, Bracegridle TJ, Cheung W, Clark MS, et al. (2012)
Correlative and dynamic species distribution modelling for ecological
predictions in the Antarctic: a cross-disciplinary concept. Polar Research 31:
56. Robinson L, Elith J, Hobday A, Pearson R, Kendall B, et al. (2011) Pushing the
limits in marine species distribution modelling: lessons from the land present
challenges and opportunities. Global Ecology and Biogeography 20: 789–802.
57. Tittensor DP, Baco AR, Brewin PE, Clark MR, Consalvey M, et al. (2009)
Predicting global habitat suitability for stony corals on seamounts. Journal of
Biogeography 36: 1111–1128.
58. Praca E, Gannier A (2008) Ecological niches of three teuthophageous
odontocetes in the northwestern Mediterranean Sea. Ocean Science 4: 49–59.
59. Ainley DG, Jongsomjit D, Ballard G, Thiele D, Fraser WR, et al. (2012)
Modeling the relationship of Antarctic minke whales to major ocean
boundaries. Polar Biology: 1–10.
60. Bigg GR, Cunningham CW, Ottersen G, Pogson GH, Wadley MR, et al.
(2008) Ice-age survival of Atlantic cod: agreement between palaeoecology
models and genetics. Proceedings of the Royal Society B: Biological Sciences
61. Paul A, Scha ¨fer-Neth C (2003) Modeling the water masses of the Atlantic
Ocean at the Last Glacial Maximum. Paleoceanography 18: 1058.
62. Adkins JF, McIntyre K, Schrag DP (2002) The salinity, temperature, and d18O
of the glacial deep ocean. Science 298: 1769–1773.
63. Heikkinen RK, Luoto M, Arau ´jo MB, Virkkala R, Thuiller W, et al. (2006)
Methods and uncertainties in bioclimatic envelope modelling under climate
change. Progress in Physical Geography 30: 751–777.
64. Jime ´nez-Valverde A, Nakazawa Y, Lira-Noriega A, Peterson AT (2009)
Environmental correlation structure and ecological niche model projections.
Biodiversity Informatics 6: 28–35.
65. Team R (2010) R: A language and environment for statistical computing. R
Foundation for Statistical Computing Vienna Austria.
66. Bowman A, Azzalini A (2005) sm: Smoothing methods for nonparametric
regression and density estimation. R package version 22-4.
67. Elith J, Phillips SJ, Hastie T, Dudı ´k M, Chee YE, et al. (2011) A statistical
explanation of MaxEnt for ecologists. Diversity and Distributions 11: 43–57.
68. Phillips SJ, Dudı ´k M (2008) Modeling of species distributions with Maxent: new
extensions and a comprehensive evaluation. Ecography 31: 161–175.
69. Anderson RP, Raza A (2010) The effect of the extent of the study region on
GIS models of species geographic distributions and estimates of niche
evolution: preliminary tests with montane rodents (genus Nephelomys) in
Venezuela. Journal of Biogeography 37: 1378–1393.
70. Phillips SJ, Dudı ´k M, Elith J, Graham CH, Lehmann A, et al. (2009) Sample
selection bias and presence-only distribution models: implications for
background and pseudo-absence data. Ecological Applications 19: 181–197.
71. VanDerWal J, Shoo LP, Graham C, Williams SE (2009) Selecting pseudo-
absence data for presence-only distribution modeling: How far should you stray
from what you know? Ecological Modelling 220: 589–594.
72. Barve N, Barve V, Jime ´nez-Valverde A, Lira-Noriega A, Maher SP, et al.
(2011) The crucial role of the accessible area in ecological niche modeling and
species distribution modeling. Ecological Modelling: 1810–1819.
73. Elith J, Kearney M, Phillips S (2010) The art of modelling range-shifting
species. Methods in Ecology and Evolution 1: 330–342.
74. Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240:
75. Wiley E, McNyset KM, Peterson AT, Robins CR, Stewart AM (2003) Niche
modeling and geographic range predictions in the marine environment using a
machine-learning algorithm. Oceanography 16: 120–127.
76. Liu C, Berry PM, Dawson TP, Pearson RG (2005) Selecting thresholds of
occurrence in the prediction of species distributions. Ecography 28: 385–393.
77. Anderson JB, Shipp SS, Lowe AL, Wellner JS, Mosola AB (2002) The
Antarctic Ice Sheet during the Last Glacial Maximum and its subsequent
retreat history: a review. Quaternary Science Reviews 21: 49–70.
78. Lumpkin R, Garraffo Z (2005) Evaluating the decomposition of tropical
Atlantic drifter observations. Journal of Atmospheric and Oceanic Technology
79. Lumpkin R, Garzoli SL (2005) Near-surface circulation in the tropical Atlantic
Ocean. Deep Sea Research Part I: Oceanographic Research Papers 52: 495–
80. Austin MP, Van Niel KP (2011) Improving species distribution models for
climate change studies: variable selection and scale. Journal of Biogeography
81. Ro ¨dder D, Schmidtlein S, Veith M, Lo ¨tters S (2009) Alien invasive slider turtle
in unpredicted habitat: a matter of niche shift or of predictors studied? PLoS
ONE 4: e7843.
82. Brey T, Dahm C, Gorny M, Klages M, Stiller M, et al. (1996) Do Antarctic
benthic invertebrates show an extended level of eurybathy? Antarctic Science 8:
83. Carney RS (2005) Zonation of deep biota on continental margins. In: Gibson
R, Atkinson R, Gordon J, eds. Oceanography and Marine Biology: An Annual
Review. pp. 211–278.
84. Arrigo KR, Worthen D, Schnell A, Lizotte MP (1998) Primary production in
Southern Ocean waters. Journal of Geophysical Research 103: 15587–15600.
85. Smith Jr WO, Comiso JC (2008) Influence of sea ice on primary production in
the Southern Ocean: A satellite perspective. Journal of Geophysical Research
86. Brierley AS, Fernandes PG, Brandon MA, Armstrong F, Millard NW, et al.
(2002) Antarctic krill under sea ice: elevated abundance in a narrow band just
south of ice edge. Science 295: 1890–1892.
87. Riddle M, Craven M, Goldsworthy P, Carsey F (2007) A diverse benthic
assemblage 100 km from open water under the Amery Ice Shelf, Antarctica.
Paleoceanography 22: PA1204.
88. Dayton PK, Oliver JS (1977) Antarctic soft-bottom benthos in oligotrophic and
eutrophic environments. Science 197: 55–58.
89. Huybrechts P (2002) Sea-level changes at the LGM from ice-dynamic
reconstructions of the Greenland and Antarctic ice sheets during the glacial
cycles. Quaternary Science Reviews 21: 203–231.
90. Leese F, Kop A, Wa ¨gele JW, Held C (2008) Cryptic speciation in a benthic
isopod from Patagonian and Falkland Island waters and the impact of
glaciations on its population structure. Frontiers in Zoology 5: 19.
91. Wilson NG, Schro ¨dl M, Halanych KM (2009) Ocean barriers and glaciation:
evidence for explosive radiation of mitochondrial lineages in the Antarctic sea
slug Doris kerguelenensis (Mollusca, Nudibranchia). Molecular Ecology 18: 965–
92. Gersonde R, Crosta X, Abelmann A, Armand L (2005) Sea-surface
temperature and sea ice distribution of the Southern Ocean at the EPILOG
Last Glacial Maximum—a circum-Antarctic view based on siliceous microfossil
records. Quaternary Science Reviews 24: 869–896.
93. Maggs CA, Castilho R, Foltz D, Henzler C, Jolly MT, et al. (2008) Evaluating
signatures of glacial refugia for North Atlantic benthic marine taxa. Ecology 89:
94. Scheltema RS (1971) Larval dispersal as a means of genetic exchange between
geographically separated populations of shallow-water benthic marine
gastropods. The Biological Bulletin 140: 284–322.
95. Gaines SD, Gaylord B, Largier JL (2003) Avoiding current oversights in marine
reserve design. Ecological Applications 13: S32–S46.
96. Possingham HP, Roughgarden J (1990) Spatial Population-Dynamics of a
Marine Organism with a Complex Life-Cycle. Ecology 71: 973–985.
97. Bortolotto E, Bucklin A, Mezzavilla M, Zane L, Patarnello T (2011) Gone with
the currents: lack of genetic differentiation at the circum-continental scale in
the Antarctic krill Euphausia superba. BMC Genetics 12: 32.
98. Leese F, Agrawal S, Held C (2010) Long-distance island hopping without
dispersal stages: transportation across major zoogeographic barriers in a
Southern Ocean isopod. Naturwissenschaften 97: 583–594.
99. Clarke A, Barnes DKA, Hodgson DA (2005) How isolated is Antarctica?
Trends in Ecology & Evolution 20: 1–3.
100. Hunter RL, Halanych KM (2008) Evaluating connectivity in the brooding
brittle star Astrotoma agassizii across the Drake Passage in the Southern Ocean.
Journal of Heredity 99: 137–148.
101. Thornhill DJ, Mahon AR, Norenburg JL, Halanych KM (2008) Open-ocean
barriers to dispersal: a test case with the Antarctic Polar Front and the ribbon
worm Parborlasia corrugatus (Nemertea: Lineidae). Molecular Ecology 17: 5104–
102. Page TJ, Linse K (2002) More evidence of speciation and dispersal across the
Antarctic Polar Front through molecular systematics of Southern Ocean
Limatula (Bivalvia: Limidae). Polar Biology 25: 818–826.
103. Gorny M (1999) On the biogeography and ecology of the Southern Ocean
decapod fauna. Scientia Marina 63: 367–382.
Glacial Habitat Availability in Antarctic Shrimp
PLOS ONE | www.plosone.org9 September 2012 | Volume 7 | Issue 9 | e46283