SC/D06/J25

Relating the distribution patterns of Antarctic minke whales
with abiotic and biotic enviromental factors in the Ross Sea in2005 using Kaiyo Maru-JARPA joint survey data.
HIROTO MURASE1, TOSHIHIDE KITAKADO2, KOJI MATSUOKA1, SHIGETHOSHI NISHIWAKI1 AND MIKIONAGANOBU3
1

The Institute of Cetacean Research, 4-5 Toyomi-cho, Chuo-ku, Tokyo, 104-0055, JapanTokyo University of Marine Science and Technology, 4-5-7 Konan, Minato-ku, Tokyo, 108-8477,
Japan
3
National Research Institute of Far Seas Fisheries, 2-14-4 Fukuura, Kanazawa, Yokohama,
Kanagawa, 236-8648, JAPAN
2

ABSTRACT
Influential environmental factors on distribution patterns of Antarctic minke whales at small
scale (30n.miles segment) in the Ross Sea in austral summer were examined based on Kaiyo
Maru-JARPA joint survey data using the generalized additive model (GAM). The joint
survey was designed as a multi-disciplinary study combing surveys on cetacean, krill andoceanography. Relationship between distribution patterns of krill and the environmental
factors was also studied. Three abiotic factors, distance from physical boundary(combination of coast, ice edge and shelf ice lines), integrated temperature and salinity meanfrom surface to 200m (ITEM-200 and ISAM-200) as well as latitude and longitude wereused as covariates in the study. Distribution pattern of krill was described by ITEM-200 and
ISAM-200 as well as latitude and longitude. Distributions of mean krill density increased at
the salinity values higher than 34.5. The results indicated that krill distribution could be
related to the presence of Modified Circumpolar Deep Water (MCDW). Distributions of
Antarctic minke whale schools were explained by density of krill and ISAM-200 as well as
latitude and longitude. In contrast to krill, the distributions of Antarctic minke whales
increased at the salinity values lower than 34.4. The presence of Antarctic minke whales
could be related to Antarctic Surface Water (AASW). The peak of the number of schools of
Antarctic minke whales was observed around the 1g/m2 of krill density and it levelled off
toward both lower and higher densities of krill. It can be hypothesized, at the scale of thisstudy, that Antarctic minke whales could be not directly searching krill aggregations but
following the retreating sea ice (low salinity water) where the ooccurrence of moderatedensity of krill could be found in high probability. It is promising that distribution patternsof baleen whales in relation to environmental factors can be related if good data set from amulti-disciplinary study like Kaiyo Maru-JARPA joint survey is available. Howevermodelling techniques applied to this study was preliminary and much improvement isnecessary.
INTRODUCTION

Dismantling the mechanism of distribution patterns of baleen whales in the highly changing marine
environment is interesting and important study from the pure and applied ecological science
perspective. Interests from the applied ecology perspective are three folds. First, it is important toknow how distribution pattern and abundance of baleen whales at the proximate level (e.g. snap
shot type sighting survey) are affected by the abiotic and biotic environmental factors and themagnitudes of the effect should be quantified. Secondary, how the change in distribution patterns
would affect the stock structure of baleen whales. Finally, how cumulating environmental changes
at proximate level would ultimately affect the population dynamics of baleen whales at long time1

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scales (e.g. Leaper et al., 2006). Classically, such environment-whale relationship in the Antarctic
were studied using descriptive technique such as overlay mapping (Ichii, 1990; Matsuoka et al.,
2003; Murase et al., 2002) and correlation analysis (Kasamatsu et al., 2000). Recently, Generalized
Additive Model (GAM) based environment-whale modeling technique was developed for datacollected in line transect survey (Hedley et al, 1999; 2004). The technique was applied to a multidsciplinal ecological study in the Western Antarctic Peninsula Region (Friedlaender et al., 2006).
Applications of such modeling techniques have been wide spreading worldwide at various
spatiotemporal scales but it is still in developing stage (Redfern et al., 2006 for review).
The Japanese Whale Research Program under Special Permit in the Antarctic (JARPA) has
been conducted during the austral summer every year since the 1987/1988 season. One of the
primary objectives of the JARPA is “Elucidation of the role of whales in the Antarctic marine
ecosystem through the study of whale feeding ecology”. The JARPA interim review meeting took
place in May, 1997. In the meeting, it was pointed out that concurrent studies on the distribution
and abundance of prey species was required to achieve the objective. In addition, it was also pointed
out that process oriented studies would be useful which integrated information from physical andbiological oceanography with zooplankton and predator studies at meso-scale. In response to thosesuggestions, a multi-disciplinary study combining surveys on cetacean, krill and oceanography was
conducted in the Ross Sea in 2005 involving six research vessels. There was no expectation toinvestigate environment-whale relationship at small spatial scale using statistical modelling at thetime of the JARPA interim meeting but newly developed modelling techniques allow us to test
scaling effect on cetacean ecology using data collected in line transect survey. In this paper, effectof oceanographic conditions and presence of krill on distribution patterns of Antarctic minke whales(Balaenoptera bonaerensis) was examined at small spatial scale (approximately 3 by 30n.miles
grid) using data from Kaiyo Maru-JARPA joint survey conduced in the Ross Sea in austral summer
in 2005.
MATERIALS AND METHODS
Cetacean survey data
A sighting survey vessel, Kyoshin Maru No. 2 (KS2, 368 GT) was engaged in the sighting survey ofcetaceans in the Ross Sea (south of 69°S and approximately between 165°E and 155°W). Zigzag
tracklines were constructed within the survey area. The starting points of tracklines were selected
randomly. The sighting survey was conducted during daylight hours. The nominal steaming speedof SV on the tracklines was 10 knot. Three and two primary observers were in the top barrel andupper bridge, respectively. Principally, KS2 conducted the survey for 8 hours per day by Passing
Mode and for 4 hours per day by Closing Mode in alternative manner. When the sightings during
Closing Mode (primary sightings) were thought to be Antarctic minke whales, KS2 approaching toprimary sightings to confirm species as well as the number of individuals in the schools. Sightings
during approaching to primary sightings were secondary sightings and those were not included inthis analysis. All sightings were passed in Passing Mode and treated as primary sightings. Sightings
with estimated perpendicular distances more than 1.5n.miles were excluded in the analysis as in
Branch and Butterworth (2001). The effective search half widths (esw) were estimated using a
hazard-rate-model with no adjustment term. It was assumed that probability of detection of whaleson the trackline is 1 (g(0)=1). Confirmed school size sighted during Closing Mode was used toestimate the mean school size (E(s)) according to the method described in Branch and Butterworth(2001). Details of estimation methods of esw and E(s) were descried in Hakamada et al. (2006).
Estimated esw for Closing and Passing Mode was 0.609 and 0.766, respectively. Estimated E(s)was 1.673.

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Krill density data
Details of the survey and the analysis methods for the estimation of krill biomass density were
described in Murase et al. (2006). A quantitative echo sounder (Simrad EK500, Norway) with
software version 5.30 on board the KS2 was used to collect data for the analysis. Data collected at
120 kHz were used. The data were not used in the analysis when the vessels deviated from thetrackline such as during cetacean species confirmation. Distribution patterns and length frequency
information for two species were collected from RMT and stomach contents of Antarctic minke
whale. All data were analyzed using Echoview version 3.00 (SonarData, Australia). Echo fromeuphausiid was discriminated from other backscattering by taking the difference between the mean
volume backscattering strength (ΔMVBS) of 120 and 38 kHz. ΔMVBS falling between 2 and 16 dB
was classified as krill (Hewitt et al., 2004). Mean biomass density (ρ, g/m2) for every 30 n.mile ofsurvey transect from 10 to 250m was used in this study.
Oceanographic observations
Oceanographic observations were conducted in the survey area by Kaiyo Maru (KM, 2630 GT) andKS2 to calculate Integrated TEMperature Mean water depth from surface to 200m (ITEM-200).
Initial idea of ITEM-200 was derived from Naganobu and Hirano (1982, 1986). They suggested thatan integrated water temperature from 0 to 200m ( Q 200) could be used as an index of distribution
patterns of Antarctic krill. The index expresses not only absolute value but also gradient of
temperature reflecting seasonal change in surface layer. Recently, ITEM-200 was used as anindicator of macorzooplankton community in the Antarctic (Hosie et al., 2000). In addition to
ITEM-200, Integrated SAlinity Mean water depth from surface to 200m (ISAM-200) was
calucuated as an indicator of water mass in the Ross Sea. ITEM-200 and ISAM-200 wereextrapolated horizontally using kriging methods using a software, Surfer 8 (Golden Software Inc.
2002). Gird resolution for kriging was set as 30 by 30 n.miles. To get equidistance grid, geologicalreference data were converted to the equidistant cylindrical projection assuming that the equator
was at 77oS and 180o. KM and KS2 recorded water temperature and salinity profiles using
Conductivity-Temperature-Depth profiler (CTD, SBE-9-Plus (KM) and SBE-19 (KS2), Seabird,USA) and expendable CTD (XCTD, Tsurumi Seiki Co., Japan). KM conducted oceanographic
observations south of 60°S so that overall oceanographic conditions surrounding the Ross Sea could
be observed. CTD casts were conducted at 15 and 34 stations (total=49 stations) by KS2 and KM,
respectively. XCTD casts were conducted at 21 and 71 stations (total=92 stations) by KS2 and KM,
respectively.
Other covariates
Distance from the physical boundary (a combination of coast, ice shelf and sea ice edge lines) wasused as a covariate for the analysis. In addition, latitude and longitude were used as covariates.
Spatial modeling
Two spatial models were considered in this study. First, the effect of environmental factors on the
distribution patterns of krill was modeled using GAM with a normal error distribution. Mean krill
density in each 30 n.miles segment was estimated as;ln( ρi + 0.01) = ϑ0 + ∑ f k ( zik ) + ε i
k

where

ρi : mean krill biomass density (g/m2) in the i-th segment with 30 n.mile lengthθ0 : an intercept.fk : nonparametric smooths of explanatory variables
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zik : the value of the k-th covariate in the i-th segment

ε i : an error term normally distributed
Here, a correction factor, 0.01, was used for the mean krill density because some of density took
value of 0. Environmental covariates, original latitude, longitude (Original.Long, Original.Lat),distance from physical boundary (DistIce), ITEM-200 (Water.temp.Aveg.200m) and ISAM-200(PSU.aveg.200m) were used for the initial model for krill distribution. A total of 5 terms in theparentheses were names used in the model. Same segments as sighting data were used in the model.
Methods of transect segmentation were described below. Segments with no survey effort distancewere omitted in the analysis.
Secondary, the effect of environmental factors on the distribution patterns of Antarctic
minke whales schools was modeled. We used counts of schools in a segment as objective variables
rather than abundance estimates themselves. In the GAM-based spatial model, shorter sighting
effort distance in a segment is problematic for the model fitting because it could result in higher
abundance estimates in the segment. In this analysis, each transects was divided into 30n.mile
equidistance segments and the sighting effort data were pooled in each segment. By this way,
Closing Mode data could be used in analysis instead of using only Passing Mode data because it
could avoid short effort distance in a segment. Segments with no sighting effort distance wereomitted in the analysis. The number of school of Antarctic minke whales in each segment wasestimated as;log E (ni ) = ln(ai ) + θ 0 + ∑ f k ( zik )
k

where

ni = the number of school in the ith segment
ai = the offset variable (the area of the i-th segment calculated as 2 times esw times sighing effort
distance)
θ0 = an intercept
fk = nonparametric smooths of explanatory variables
zik = the value of the k-th covariate for the i-th segment
Environmental covariates, original latitude, longitude (Original.Long, Original.Lat), mean krill
density (mean.dens.kr.ln.001), distance from physical boundary (DistIce), ITEM-200 and ISAM200 were used for the initial model. Terms in the parentheses were names used in the model. We
assumed a Poisson distribution with overdispersion for the distribution of counts.
For both models, smoothness parameters were estimated with the generalized crossvalidation (GCV). Model selection was also conducted using GCV scores. For this analyses,“mgcv” package (version 1.30-21) of R software (R Development Core Team, 2006) was employed.
RESULTS
GAM-based spatial models considered in this study and their GCV scores were summarize in Table
1. Initial model including latitude, longitude, distance from physical boundary, ITEM-200 and
ISAM-200 had lowest GCV value (3.566) to predict mean krill density. Other two models either
considering only latitude and longitude or environmental factors had higher GCV scores (4.3412
and 4.7648). The results suggested that combination of geological reference and environmental
factors well described the distribution pattern of krill in the Ross Sea. General shapes of thefunctional forms for covariates were same for three models to predict mean krill density (Figs. 1-3).
Two peaks in latitude (76-78°S and 70-72°S) suggested habitat segregation of two krill species, ice(Euphausia crytallorophia) and Antarctic krill (E. superba). Southern peak was related to the4SC/D06/J25
distribution of ice krill while northern peak was related to the distribution of Antarctic krill.
Longitudinal distribution of krill was centered between 180° and 170°W. Mean krill density
increased as ITEM-200 decreased. Mean krill density increased as ISAM-200 increased. Abrupt
increase of mean krill density was observed at the salinity values higher than 34.5.
The model including interaction term of latitude and longitude, mean krill density and
ISAM-200 had lowest GCV value (1.2765). General shapes of the functional forms for covariates
were same between the selected model and the initial model (Figs. 4-5). Relationship between
distribution of krill and Antarctic minke whales was not constant manner. Peak of the distributionof Antarctic minke whales were observed around the mean krill density of 1.0g/m2 and it levelled
off toward both low and high mean krill density sides. Then the distribution of Antarctic minke
whales increased toward mean krill density 0g/m2. In contrast to krill, the distributions of Antarctic
minke whales increased at the salinity values lower than 34.4. The distributions of Antarctic minke
whales were high in north central part of the Ross Sea.
DISCUSSION
Though the distribution pattern of Antarctic minke whales was partially explained by the presenceof krill, the response was not monotonic increase with the increase of krill. Non liner relationshipbetween the distribution of Antarctic minke and humpback whales, and acoustic volume
backscattering levels as an index of presence of krill were reported in the Western Antarctic
Peninsula Region in austral autumn (Friendlaender et al., 2006). In the Western Antarctic Peninsula
Region in austral autumn, the distribution of Antarctic minke and humpback whales, and krill wereincreased and then stabilizing at Plateau. In contrast, the distribution of Antarctic minke whales wasleveled off beyond the certain level of mean krill density in this study. Results from Friendlaender
et al., (2006) and this study suggested that certain type of thresholding foraging behavior was
existing as reported by Piatt and Methven (1992). Piatt and Methven (1992) reported that no minke
whales were observed until capelin school density reached certain level in Witless Bay, Canada.
The shape of the functional form for mean krill density in this study was different from the result in
the Western Antarctic Peninsula Region. Few high mean krill densities were observed in the Ross
Sea in 2005. Because of low encounter rate of high mean krill density, the distributions of Antarctic
minke whales could decrease beyond the certain level of mean krill density in this study.
Though ISAM-200 was selected as an explanatory variable to predict krill and Antarctic
minke whale distribution, responses were different between them. Distributions of krill increased at
the salinity values higher than 34.5 whereas distribution of Antarctic minke whales increased at thesalinity values lower than 34.4. There are 5 major water masses in the Ross Sea according to Jacobs
and Giulivi (1999) and Russo (2000) and their characteristics were summarized in Table 2. The
salinity values higher than 34.5 could be related to presence of Modified Circumpolar Deep Water(MCDW). It was reported that distribution of krill was associated with the presence of MCDW in
the Ross Sea (Sala et al., 2002) and in the Western Antarctic Peninsula Region (Lawson et al, 2004).
Although few clear relationship between environmental factors and krill have been demonstrated(Sigel, 2005), association between MCDW and krill in embayed area of the Antarctic could beexisted. The salinity values lower than 34.4 could be related to the presence of Antarctic Surface
Water (AASW). AASW is associated with presence of melting water of sea ice. The results
suggested that Antarctic minke whales could be not searching high density area of krill but simply
follow the retreating ice edge where probability of encounter rate to optimal krill density could behigh. Such hypothesis should be tested using moving behavior of Antarctic minke whales. Satellite
tag was attached to common minke whales (B. acutorostrata) in the North Atlantic to monitor their
movement (Heide-Jorgensen et al, 2001). Study of movement of Antarctic minke whales should be
conducted using satellite tag in the future study. The results of this study indicated that ISAM-200
could be used as an index of presence of certain types of water masses but actual relationshipbetween ISAM-200 and water masses should be investigated in the future study.
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This preliminarily modelling work revealed that spatial modelling can be applied to study ofenvironment-baleen whale relationship as well as environment-krill relationship. Several points,
however, should be improved in the future study. In this study, only the number of schools of
Antarctic minke whales was considered. More individuals of Antarctic whales may concentre
around the certain level of mean krill density. Therefore, direct analysis for the number ofindividuals or two-stage analysis (spatial distribution of whales school and school size) would be
employed in the future analysis. Such a two-stage distribution can also be applied to the analysis
for krill density. Mean krill density of 0 g/m2 was recorded in some segments. To handle this, weused an ad-hoc way that a small value was added to the density. However, two-stage models such asdelta-lognormal may be promising and therefore be considered in the future. Because geologicalfeature was complex in the Ross Sea, handling of data around edge of the study area should beconsidered in the future study.
The results of this study indicated distribution patterns of baleen whales in relation toenvironmental factors can be related if good data set from a multi-disciplinary study like Kaiyo
Maru-JARPA joint survey is available. Spatial modelling at global scale to map the distribution ofmarine mammals was attempted in recent year (Kaschner et al, 2006) but the model overlooked thesmall local scale processes. More local specific study of environment-baleen whale relationshipshould be conducted to construct accurate large scale spatial model.
ACKNOWLEDGMENTS
Authors express thank you to the crews and the researchers who dedicated to collect data in harshenvironmental condition in the Antarctic. Special thanks were given to Ms. Tomoko Hasegawa who
supported the handling of data set used in this study. We thank Dr. Kiyoshi Ito, Ms. Naomi Takezoe
and Mr. Yoshinobu Odaira at Environmental Simulation Laboratory Co. LTD for the developmentof the data preparation program. We also thank Dr. Hiroshi Hatanaka and other colleagues at theInstitute of Cetacean Research who made critical comments to improve this manuscript.
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abundance estimates from the 1978/79 to 1997/98 IDCR-SOWER surveys. J. Cetacean Rea
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A. J. 2006. Whale distribution in relation to prey abundance and oceanographic processes in
shelf waters of the Western Antarctic Peninsula. Mar. Ecol. Prog. Ser. 317:297-310.
Hakamada, T., Matsuoka, K. and Nishiwaki, S. 2006. An update of Antarctic minke whales
abundance estimate based on JARPA data. Paper SC/D06/J6 presented to the JARPA
Review Meeting, December 2006 (unpublished).
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Cetacean Rea and Manage. 1:255-264.
Hedley, S. L., Buckland, S. T. and Borchers, D. L. 2004. Spatial distance models. In Buckland, S.
T., Anderson, D. R., Burnham, K. P., Laake, J. L., Borchers, D. L. and Thomas, L. (Eds.),
Advanced distance sampling. Oxford University Press, Oxford, pp.48-70.
Heide-Jorgensen, M. P., Nordoy, E. S., Oien, N., Folkow, L. P., Kleivane, L., Blix, A . S., Jensen,
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Hewitt, R. P., Watkins, J., Naganobu, M., Sushin, V., Brierley, A. S., Demer, D., Kasatkina, S.,
Takao, Y., Goss, C., Malyshko, A., Brandon, M., Kawaguchi, S., Siegel, V., Trathan, P.,
Emery, J., Everson, I., Miller, D. 2004. Biomass of Antarctic krill in the Scotia Sea inJanuary/February 2000 and its use in revising an estimate of precautionary yield. Deep Sea
Res. II 51:1215-1236.
Hosie, G. W., Schultz, M. B. Kitchener, J. A., Cochran, T. G. and Richards. 2000.
Macrozooplankton community structure off East Antarctica (80-150°E) during the Australsummer of 1995/1996. Deep Sea Res. II 47:2437-2463.
Ichii, T. 1990. Distribution of Antarctic krill concentrations exploited by Japanese krill trawlers andminke whales. Proceedings of the NIPR Symposium on Polar Biology 3; 36-35.
Jacobs S.S. and Giulivi C.F. 1999. Thermohaline data and ocean circulation on the Ross Sea
continental shelf. In: Spezie, G. and Manzella, G. M. R. (eds). Oceanography of the Ross
Sea, Antarctic. Springer Verlag, Berlin, Heidelberg pp.3-16.
Kasamatsu, F., Ensor, P., Joyce, G. G. and Kimura, N. 2000. Distribution of minke whales in the
Bellingshausen and Amundsen Seas (60°W-120°W), with special reference toenvironmental/physiographic variables. Fish. Oceanogr. 9:214-223.
Kaschner, K., Watson, R., Trites, A. W. and Pauly, D. 2006. Mapping world-wide distributions ofmarine mammals species using relative envriomental suitability (RES) model. Mar. Ecol.
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Acoustically-inferred zooplankton distribution in relation to hydrography west of the
Antarctic Peninsula. Deep Sea Res. II. 51:2041-2072.
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drives southern right whale (Eubalaena australis) population dynamics. Biol. Lett. 2:289292.
Matsuoka, K., Watanabe, T., Ichii, T., Shimada, H. and Nishiwaki, S. 2003. Large whale
distributions (south of 60S, 35E-130E) in relation to the southern boundary of the ACC. In
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Schrno, R.M.L., van der Vies, S.M. and Wolff, W.J. Backhuys Publishers, Leiden, The
Netherlands: 26-30.
Murase, H., Matsuoka, K., Ichii, T. and Nishiwaki, S. 2002. Relationship between the distributionof euphausiids and baleen whales in the Antarctic (35°E-145°W）. Polar Biol., 2002. 25:
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Murase, H., Tamura, T., Matsuoka, K., Nishiwaki, S., Yasuma, H., Matsukura, R., Yabuki, T.,
Takao, Y., Taki, T., Hayashi, T. and Naganobu, M. 2006. Distribution patterns andbiomasses of Antarctic krill (Euphausia superba) and ice krill (E. crystallorophias) withreferece to Antarctic minke whales in the Ross Sea in 2004/05 using Kaiyo Maru-JARPA
joint survey data. SC/D06/J24 presented to the JARPA Review Meeting, December 2006
(unpublished).
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superba DANA, and its environmental structure (extended abstract). Mem. Natl. Inst. PolarRes. Spec. Issue. 1982. 23:1–4.
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Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL
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Piatt, J. F. and Methven, D. A. 1992. Threshold foraging behavior of baleen whales. Mar. Ecol.
Prog. Ser. 84:205-210.
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K., Baumgartner, M. F., Forney, K. A., Balance, L.T., Fauchald, P., Halpin, P., Hamazaki,T., Pershing, A. J., Qian, S. S., Read, A., Reilly, S. B., Torres. L. and Werne, F. 2006.
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Ross Sea ecology. Springer Verlag, Berlin, Heidelberg. pp.83-93.
Sala, A., Azzali, M., and Russo, A.,2002. Krill of the Ross Sea: distribution, abundance anddemography of Euphausia superba and Euphausia crystallorophias during the Italian
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Polar Biol. 29:1-22.

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Table 1. GAM based models considered in this study: (a) models for mean krill density and (b) models for number of school of Antarctic minke whale.
Initial model was marked with “*”.
(a)
Model
* mean.dens.kr.ln.001~s(Original.Lat)+s(Original.Long)+s(DistIce)+s(Water.temp.Aveg.200m)+s(PSU.aveg.200m)
mean.dens.kr.ln.001~s(Original.Lat)+s(Original.Long)
mean.dens.kr.ln.001~s(DistIce)+s(Water.temp.Aveg.200m)+s(PSU.aveg.200m)

GCV
Score
3.566
4.34124.7648(b)Model* SumAllSchool~offset(log(2*esw*Effort.dist))+s(Original.Lat)+s(Original.Long)+s(mean.dens.kr.ln.001)+s(DistIce)+s(Water.temp.Aveg.200m)+s(PSU.aveg.200m)
SumAllSchool~offset(log(2*esw*Effort.dist))+s(Original.Long,Original.Lat)+s(mean.dens.kr.ln.001)+s(PSU.aveg.200m)

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GCV
Score
1.2842
1.2765

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Table 2. Characteristics of major water masses in the Ross Sea based on Jacobs and Giulivi (1999)
and Russo (2000). Names in parenthesis are defined by Russo (2000).
Potential
Acronym Temprature(C°)

Salinity
(PSU)

Depth
(m)

High Salinity Shelf Water

HSSW<-1.91

34.6<

100<

Low Salinity Shelf Water

LSSW-1.8~-1.5

34.4~34.6

100<

Ice Shelf Water

ISW<-1.9234.6<

50<Modified Circumpolar Deep Water(Warm Core)
Antarctic Surface Water(Ross Sea Surface Water)

MCDW(WMCO)AASW(RSSW)

Water Mass

-1.5~1.0-1.9~2.0

1034.4~34.6 100~400
<34.5

<50

Main location in
the Ross Sea
Subsurface
western sector
Subsruface
eastern sector
West-central andSouthwestern sector
Near thecontinental shelf
Whole area

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Fig. 1. Shapes of the functional forms for covariates to model the distribution pattern of krill. All
covariates considered in this study were shown.

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Fig.2. Shapes of the functional forms for covariates to model the distribution pattern of krill. The
result of model only including latitude and longitude was shown.

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Fig.3. Shapes of the functional forms for covariates to model the distribution pattern of krill. The
result of model only including environmental factors was shown.

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Fig. 4. Shapes of the functional forms for covariates to model the distribution pattern of Antarctic
minke whales. All covariates considered in this study were shown.

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Fig. 5. Shapes of the functional forms for covariates selected to predict the distribution pattern of
Antarctic minke whales.

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